0:\\n while True: #validacion para la fila ingresada por el usuario\\n try:\\n elegir_fila = int(input(\\\"Ingresa una fila: \\\"))\\n if elegir_fila < 1 or (elegir_fila >10 and elegir_fila != 24):\\n raise ValueError\\n break\\n except ValueError:\\n print(\\\"{}No existe dicha fila{}\\\".format(Fore.LIGHTRED_EX, Fore.RESET))\\n while True: #validacion para la columna ingresada por el usuario\\n try:\\n elegir_columna = int(input(\\\"Ingresa una columna: \\\"))\\n if elegir_columna < 1 or (elegir_columna >10):\\n raise ValueError\\n break\\n except ValueError:\\n print(\\\"{}No existe dicha columna{}\\\".format(Fore.LIGHTRED_EX, Fore.RESET))\\n tiro_elegido = (elegir_fila, elegir_columna) #cada tiro se almacena en una lista\\n if tiro_elegido[0] == 24:\\n print(\\\"Has accedido a un cheat code, los barcos estan en: \\\",lista_ubicacion_barco)\\n elif tiro_elegido in disparos_elegidos: #si la coordenada ingresada por el usuario ya la ingreso anteriormente, quedo guardada en la lista y no le va a contar como disparo efectuadao\\n print(\\\"Este disparo ya lo has hecho antes :|\\\")\\n disparos_repetidos += 1 \\n elif tiro_elegido in lista_ubicacion_barco:\\n disparos_elegidos.append(tiro_elegido)\\n print(\\\"Has acertado\\\\n\\\")\\n tablero[elegir_fila - 1][elegir_columna - 1] = \\\"{}F{}\\\".format(Fore.RED, Fore.RESET) #se remplaza la coordenada acertada por una F roja\\n lista_ubicacion_barco.remove(tiro_elegido) #se quita la posicion del barco de la lista general donde estan todas las ubicaciones\\n disparos_efectuados += 1\\n disparos_acertados += 1\\n puntaje += 10\\n #se verifica si la coordenada ingresada pertenece a alguna coordenada almacenada en las listas de cada barco y se elimina\\n #cuando una lista este vacia muestra un mensaje que ha hundido a la nave especificaa\\n if tiro_elegido in coordenadas_portaviones: \\n coordenadas_portaviones.remove(tiro_elegido) \\n if len(coordenadas_portaviones) == 0:\\n print(\\\"Felicitaciones has hundido el portaviones, su ataque aereo quedo neutralizado\\\\n\\\")\\n elif tiro_elegido in coordenadas_fragata:\\n coordenadas_fragata.remove(tiro_elegido)\\n if len(coordenadas_fragata) == 0:\\n print(\\\"Felicitaciones has hundido la Fragata, su comunicacion con tierra ha sido detenida\\\\n\\\")\\n elif tiro_elegido in coordenadas_submarinos:\\n coordenadas_submarinos.remove(tiro_elegido)\\n if len(coordenadas_submarinos) == 0:\\n print(\\\"Felicitaciones has hundido los submarinos\\\\n\\\")\\n else:\\n disparos_elegidos.append(tiro_elegido)\\n print(\\\"Has fallado\\\\n\\\")\\n tablero[elegir_fila - 1][elegir_columna - 1] = \\\"{}X{}\\\".format(Fore.BLUE, Fore.RESET) #se remplaza la coordenada errada por una X azul\\n disparos_efectuados += 1\\n disparos_fallidos += 1\\n puntaje -= 2\\n mostrar_tablero(tablero)\\n disparos_elegidos.clear()\\n print(\\\"{}Ese disparo me ha dolido.{} Has logrado hundir toda mi flota :(\\\".format(Fore.RED, Fore.RESET))\\n sleep(1)\\n if disparos_efectuados == 9:\\n print(\\\"¿Eres un robot? lo que acabas de hacer es poco probable... ¿viste los cheat codes verdad?\\\")\\n elif disparos_efectuados < 45:\\n print(\\\"Excelente estrategia\\\")\\n elif disparos_efectuados >= 45 and disparos_efectuados <= 70:\\n print(\\\"Buena estrategia, pero hay que mejorar(o buscar los cheat codes)\\\")\\n elif disparos_efectuados > 70:\\n print(\\\"{}Considérese Perdedor, tiene que mejorar{}\\\".format(Fore.RED, Fore.RESET))\\n print(\\\"\\\\nCargando tus estadisticas :| .......... pssss deberias probar el numero de kobe en fila\\\\n\\\")\\n sleep(2.5)\\n for x in usuario_partida: #usuario_partida almacena el usuario de cada partida, se borra la lista cuando se ingresa otro jugador\\n print(\\\"{}{} tus estadisticas fueron las siguientes:{}\\\".format(Fore.YELLOW,x, Fore.RESET))\\n print(\\\"Disparos realizados = {}\\\".format(disparos_efectuados))\\n print(\\\"Puntaje total = {}\\\".format(puntaje))\\n print(\\\"Disparos repetidos = {}\\\".format(disparos_repetidos))\\n print(\\\"Tu tablero quedo asi:\\\")\\n mostrar_tablero(tablero)\\n #se agregaran los puntajes y disparos del usuario a la base de datos en el txt\\n for x in usuario_partida:\\n y = x\\n with open(\\\"Basedatos.txt\\\", \\\"r\\\") as bd:\\n punto = []\\n datos = bd.readlines()\\n nuevo_valor = \\\"\\\"\\n for x in datos:\\n if y in x:\\n index = datos.index(x)\\n puntos = x[:-1].split(\\\",\\\")\\n if int(puntos[4]) < puntaje: #si el usuario ya tiene un puntaje se va a almacenar en el txt el que sea mayor (puede ser el viejo o el que acaba de obtener)\\n puntos[4] = \\\" {}\\\".format(puntaje)\\n if int(puntos[5]) > disparos_efectuados: #si el usuario ya ha jugado se va a almacenar en el txt la menor cantidad de disparos que haya obtenido \\n puntos[5] = \\\" {}\\\".format(disparos_efectuados)\\n for x in range(len(puntos)):\\n if x != len(puntos) - 1:\\n nuevo_valor += puntos[x] + \\\",\\\"\\n else:\\n nuevo_valor += puntos[x] + \\\"\\\\n\\\"\\n datos[index] = nuevo_valor\\n with open(\\\"Basedatos.txt\\\", \\\"w\\\") as bd: #se reescribira el txt con los datos del usuario que jugo la partida actualizados\\n bd.writelines(datos)\",\n \"def cobroNC(self):\\n if self.total_a_pagar == 0:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"El saldo restante a pagar es cero\\\")\\n else:\\n self.rbtnNC.setChecked(True)\\n totalFactura = self.total_a_pagar\\n numero,ok = QtGui.QInputDialog.getText(self,\\\"Cobro c/Nota de Crédito\\\",\\\"Ingrese número de Nota de Crédito\\\")\\n if ok:\\n notaCredito = NotaCreditoModel.getNotaCredito(self.padre.sesion,int(numero))\\n if notaCredito == None:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"La Nota de Crédito ingresada no existe\\\")\\n elif notaCredito.getTotal(self.padre.sesion) < totalFactura:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"El monto de la Nota de Credito es insuficiente\\\")\\n elif notaCredito.getTotal(self.padre.sesion) - CobroClienteModel.getTotalNC(self.padre.sesion,notaCredito.numero) < totalFactura:\\n dif = notaCredito.getTotal(self.padre.sesion) - CobroClienteModel.getTotalNC(self.padre.sesion,notaCredito.numero)\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"La Nota solo posee $\\\" + str(dif))\\n else:\\n temp = [\\\"Nota de Crédito\\\",self.total_a_pagar,notaCredito.numero]\\n self.detalles_cobro[self.tablePagos.rowCount()] = temp\\n self.total_a_pagar = 0\\n self.actualizar_total()\\n self.actualizar_tabla()\",\n \"def procesa_parametros_factura():\\n form = FormParametrosFactura()\\n print(form.num_fac_ini.data, form.factura_actual.data)\\n\\n if request.method == 'POST' and form.validate_on_submit():\\n cursor_insert = connectiondb.cursor()\\n cursor_update = connectiondb.cursor()\\n cursor_select = connectiondb.cursor()\\n # verifica si existen datos en la tabla\\n sql_select = \\\"\\\"\\\"SELECT num_fac_ini FROM ParametrosFactura\\\"\\\"\\\"\\n cursor_select.execute(sql_select)\\n resultado = cursor_select.fetchone()\\n cursor_select.close()\\n\\n if resultado is None:\\n sql_insert = \\\"\\\"\\\"INSERT INTO ParametrosFactura (num_fac_ini, \\\\\\n num_fac_fin, num_resolucion, fecha_resolucion, \\\\\\n fecha_venc_resolucion, factura_actual) VALUES \\\\\\n ('{}', '{}', '{}', '{}', '{}', '{}');\\n \\\"\\\"\\\".format(form.num_fac_ini.data, form.num_fac_fin.data,\\n form.num_resolucion.data, form.fecha_resolucion.data,\\n form.fecha_venc_resolucion.data, form.factura_actual.data)\\n cursor_insert.execute(sql_insert)\\n connectiondb.commit()\\n cursor_insert.close()\\n # connectiondb.close()\\n return \\\"Los datos se guardaron correctamente\\\"\\n else:\\n sql_update = \\\"\\\"\\\"UPDATE ParametrosFactura \\\\\\n SET num_fac_ini = '{}', \\\\\\n num_fac_fin = '{}', \\\\\\n num_resolucion = '{}', \\\\\\n fecha_resolucion = '{}',\\n fecha_venc_resolucion = '{}', \\\\\\n factura_actual = '{}' \\\\\\n WHERE num_fac_ini = '{}';\\n \\\"\\\"\\\".format(form.num_fac_ini.data,\\n form.num_fac_fin.data,\\n form.num_resolucion.data,\\n form.fecha_resolucion.data,\\n form.fecha_venc_resolucion.data,\\n form.factura_actual.data,\\n int(resultado['num_fac_ini']))\\n\\n cursor_update.execute(sql_update)\\n connectiondb.commit()\\n cursor_update.close()\\n return \\\"Los datos se guardaron correctamente\\\"\",\n \"def buscarOs(self):\\n\\n if self.lineRazon.isEnabled():\\n self.filtrarObra()\\n\\n elif not self.lineRazon.isEnabled() and (self.tableNC.rowCount() != 0 or self.tableFactura.rowCount() != 0):\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"Imposible cambiar de Obra Social. Ya se ha seleccionado\\\\\\n una\\\")\\n else:\\n self.gbNotaCredito.setEnabled(False)\\n self.gbFactura.setEnabled(False)\\n self.lineRazon.clear()\\n self.lineRazon.setEnabled(True)\\n self.lineCuit.clear()\\n self.lineCuit.setEnabled(True)\\n self.tableOs.setEnabled(True)\",\n \"def main():\\n\\n # Create the console\\n console = Console()\\n\\n # Should we show off the good code that's been written\\n show_off_mode = True\\n\\n # Create the console for making fancy graphics\\n console = Console()\\n\\n # Creds\\n username = lambda: os.environ.get(\\\"AGTERRAUSER\\\", \\\"\\\")\\n password = lambda: os.environ.get(\\\"AGTERRAPASS\\\", \\\"\\\")\\n\\n duplicate_record_counter_dict = dict()\\n\\n # Pickle Path\\n pickle_path_strider = Path(\\\"/tmp/agterra_strider_form.pickle.py\\\")\\n\\n if pickle_path_strider.exists():\\n console.log(f\\\"Pickle found at {pickle_path_strider}\\\")\\n raw_forms_resp = pickle.load(open(pickle_path_strider, \\\"rb\\\"))\\n else:\\n # Get projects and save to pickle\\n with requests.Session() as sess:\\n sess.auth = (username, password)\\n console.log(f\\\"Requesting Forms From Server to Populate Pickle\\\")\\n if show_off_mode:\\n sleep(0.7)\\n raw_forms_resp = get_forms(console=console, sess=sess)\\n if show_off_mode:\\n sleep(0.5)\\n console.log(f\\\"Pickle dumped to {pickle_path_strider}\\\")\\n pickle.dump(raw_forms_resp, open(pickle_path_strider, \\\"wb\\\"))\\n\\n\\n # Set up ProgressBar\\n with Progress(\\\"[progress.description]{task.description}\\\", BarColumn(), \\\"{task.completed} of {task.total}\\\",\\n console=console, transient=False) as progress:\\n\\n # BEGIN DEDUPE PROCESS #\\n for form_name, form_data_list in raw_forms_resp.items():\\n overall_task = progress.add_task(f\\\"[green]DeDupe Progress\\\", total=len(form_data_list))\\n # Start with the general form, for sanity\\n if form_name == \\\"General_Record_2020v2.0\\\":\\n progress.update(overall_task, advance=1)\\n for golden_form_submitted in form_data_list:\\n check_against_all_others(progress_bar=progress, golden_form=golden_form_submitted,\\n form_data_list=form_data_list, prog_bar_task=overall_task)\\n # break\\n # break\",\n \"def clone(self):\\n\\n # recrea la instancia del form\\n form_clone = FormularioDinamico(\\n lang=self.lang,\\n name=\\\"clon de %s\\\" % self.name,\\n is_active=self.is_active,\\n form_title=self.form_title,\\n form_template=self.form_template,\\n send_email=self.send_email,\\n from_email=self.from_email,\\n recipient_list=self.recipient_list,\\n subject_template=self.subject_template,\\n body_template=self.body_template,\\n error_class=self.error_class,\\n required_css_class=self.required_css_class,\\n autorespond=self.autorespond,\\n autorespond_subject_template=self.autorespond_subject_template,\\n autorespond_body_template=self.autorespond_body_template,\\n autorespond_email_field=self.autorespond_email_field\\n )\\n\\n form_clone.save()\\n\\n content_type = ContentType.objects.get_for_model(form_clone)\\n\\n # recrea todos los fields\\n for field in self.get_fields():\\n field_clone = DynaFormField(\\n content_type=content_type,\\n object_pk=form_clone.pk,\\n field_name=field.field_name,\\n field_label=field.field_label,\\n field_type=field.field_type,\\n field_widget=field.field_widget,\\n field_help=field.field_help,\\n is_required=field.is_required,\\n is_hidden=field.is_hidden,\\n default_value=field.default_value,\\n choices=field.choices,\\n choices_delimiter=field.choices_delimiter,\\n choices_queryset=field.choices_queryset,\\n choices_queryset_filter=field.choices_queryset_filter,\\n choices_queryset_empty_label=field.choices_queryset_empty_label,\\n choices_queryset_label=field.choices_queryset_label,\\n choices_related_field=field.choices_related_field,\\n field_order=field.field_order\\n )\\n\\n field_clone.save()\",\n \"def cambiar_libro_bcompartida(request, id_biblioteca_compartida, id_libro_compartido=None):\\n template = \\\"libros/cambiar_libro_bcompartida.html\\\"\\n\\n bcompartida = BibliotecaCompartida.objects.get(id=id_biblioteca_compartida)\\n\\n libro_disponible = None\\n if id_libro_compartido:\\n libro_disponible = LibrosBibliotecaCompartida.objects.get(id=id_libro_compartido)\\n\\n if request.method == \\\"POST\\\":\\n form = FormCambiarLibroBCompartida(request.POST)\\n if form.is_valid():\\n id_libro_cambiado = form.cleaned_data.get(\\\"id_libro_cambiado\\\")\\n titulo_nuevo_libro = form.cleaned_data.get(\\\"titulo_recibido\\\")\\n autor_nuevo_libro = form.cleaned_data.get(\\\"autor_recibido\\\")\\n # = form.cleaned_data.get(\\\"usuario_cambiar\\\", None)\\n\\n libro_cambiado = LibrosBibliotecaCompartida.objects.get(id=id_libro_cambiado)\\n libro_cambiado.disponible = False\\n libro_cambiado.eliminado = True\\n libro_cambiado.save()\\n\\n nuevo_libro = Libro.objects.create(titulo=titulo_nuevo_libro, autor=autor_nuevo_libro)\\n LibrosBibliotecaCompartida.objects.create(libro=nuevo_libro, biblioteca_compartida=bcompartida)\\n\\n # Crear notificacion\\n # if username_cambiar:\\n # if Perfil.objects.filter(usuario__username=username_cambiar).exists():\\n # usuario_cambiar = Perfil.objects.get(usuario__username=username_cambiar)\\n # Notificacion.objects.bcompartida_cambio(biblioteca_compartida, libro_cambiado.libro, nuevo_libro,\\n # usuario_cambiar)\\n\\n Notificacion.objects.bcompartida_cambio(bcompartida, libro_cambiado.libro, nuevo_libro)\\n\\n return redirect('libros:editar_libros_bcompartida', slug_biblioteca_compartida=bcompartida.slug)\\n\\n else:\\n if id_libro_compartido:\\n form = FormCambiarLibroBCompartida(initial={\\n 'titulo_inicial': libro_disponible.libro.titulo,\\n 'id_libro_cambiado': libro_disponible.id\\n })\\n else:\\n form = FormCambiarLibroBCompartida()\\n\\n libros_bcompartida = LibrosBibliotecaCompartida.objects.filter(biblioteca_compartida=bcompartida,\\n disponible=True).select_related(\\\"libro\\\")\\n titulos_autocomplete = {}\\n for libro_bc in libros_bcompartida:\\n titulos_autocomplete[libro_bc.libro.titulo] = libro_bc.id\\n\\n usuarios_autocomplete_obj = Perfil.objects.filter(ciudad=bcompartida.ciudad).select_related(\\\"usuario\\\")\\n usernames_autocomplete = [perfil.usuario.username for perfil in usuarios_autocomplete_obj]\\n\\n context = {'form': form, 'libro_disponible': libro_disponible,\\n 'titulos_autocomplete': json.dumps(titulos_autocomplete),\\n 'biblioteca_compartida': bcompartida,\\n 'usernames_autocomplete': json.dumps(usernames_autocomplete)}\\n\\n return render(request, template, context)\",\n \"def on_btnReservatool_clicked(self, widget):\\n try:\\n panelactual = variables.panel.get_current_page()\\n if panelactual != 1:\\n variables.panel.set_current_page(1)\\n funcioneshab.listadonumhab(self)\\n else:\\n pass\\n except:\\n print(\\\"error botón cliente barra herramientas\\\")\",\n \"def complete_form_data():\\n\\n missing_fields = {\\n 'link' : 'http://bvsalud.org',\\n 'originator' : 'BIREME',\\n 'source_type': 1,\\n 'source_language': 1,\\n 'originator_location' : 1,\\n\\n 'main-descriptor-content_type-object_id-TOTAL_FORMS' : '1',\\n\\n 'main-descriptor-content_type-object_id-0-id' : '',\\n 'main-descriptor-content_type-object_id-0-text' : 'malaria',\\n 'main-descriptor-content_type-object_id-0-code' : '^d8462',\\n 'main-descriptor-content_type-object_id-0-status' : '0',\\n\\n 'main-resourcethematic-content_type-object_id-TOTAL_FORMS' : '1',\\n 'main-resourcethematic-content_type-object_id-0-thematic_area' : '1',\\n 'main-resourcethematic-content_type-object_id-0-status' : '0',\\n }\\n\\n complete_form_data = minimal_form_data()\\n complete_form_data.update(missing_fields)\\n\\n return complete_form_data\",\n \"def DeleteComite(request,pk):#esta enlazado con la clase FaseForm del archivo getion/forms\\n\\n proyecto = User_Proyecto.objects.filter(proyecto_id=pk)\\n gerente = User.objects.get(id=proyecto[0].user_id)\\n print(gerente.username)\\n\\n proyecto_validar=Proyecto.objects.get(id_proyecto=pk)\\n\\n if validar_permiso(request.user, \\\"is_gerente\\\",proyecto_validar)==False: # primero se valida si es gerente en el proyecto actual)\\n messages.error(request, 'No eres gerente de proyecto, por lo tanto no puedes eliminar el comite de cambio')\\n return redirect('gestion:comite', pk)\\n\\n comite = Comite.objects.all()\\n form = Usuario.objects.all()\\n proyectos=Proyecto.objects.get(id_proyecto=pk)\\n\\n if request.method == 'POST': #preguntamos primero si la petición Http es POST ||| revienta todo con este\\n some_var=request.POST.getlist('checkbox')\\n\\n if ((len(some_var)+1)%2==0 or (len(some_var)+1)==1):# SE VALIDA QUE DEBE DE SER IMPAR Y MAYOR A 1\\n messages.error(request,'EL NUMERO DE USUARIOS EN EL COMITE DEBE DE SER IMPAR Y MAYOR A UNO')\\n return redirect('gestion:DeleteComite',pk)\\n\\n for id in some_var:\\n id_user =id\\n usuario = User.objects.get(id=id_user)\\n registrarAuditoriaProyecto(request.user, \\\"Desvinculo del comite de cambio al usuario: \\\" + str(usuario.username),\\n proyectos.id_proyecto, proyectos.nombre, \\\"\\\")\\n\\n desvinculacionComite(request,pk,id_user)\\n\\n\\n return redirect('gestion:comite',pk)\\n else:\\n list=[]\\n if(comite != None):\\n for i in range(form.count()):\\n ok = False\\n if form[i].esta_aprobado == True:\\n for x in comite:\\n if x.id_user == form[i].user.id and x.id_proyecto == pk:\\n ok=True\\n if ok:\\n list.append(form[i].user.id)\\n print(list)\\n return render(request, 'proyectos/delete_comite.html', {'form': form,'list':list,'pk':pk,'proyectos':proyectos,'idGerente':gerente.id})\",\n \"def cria_receita(request):\\n if request.user.is_authenticated:\\n if request.method == 'POST':\\n nome_receita = request.POST['nome_receita']\\n ingredientes = request.POST['ingredientes']\\n modo_preparo = request.POST['modo_preparo']\\n tempo_preparo = request.POST['tempo_preparo']\\n rendimento = request.POST['rendimento']\\n categoria = request.POST['categoria']\\n foto_receita = request.FILES['foto_receita']#por se tratar de um dado do tipo file\\n user = get_object_or_404(User, pk=request.user.id)\\n\\n if campo_vazio(nome_receita):\\n messages.error(request, 'Nome da receita é obrigatório')\\n return redirect('cria_receita')\\n\\n if campo_vazio(ingredientes):\\n messages.error(request, 'ingredientes são obrigatórios')\\n return redirect('cria_receita')\\n\\n if campo_vazio(modo_preparo):\\n messages.error(request, 'Modo de preparo é obrigatório')\\n return redirect('cria_receita')\\n\\n if campo_vazio(tempo_preparo):\\n messages.error(request, 'tempo de preparo é obrigatório')\\n return redirect('cria_receita')\\n\\n if campo_vazio(rendimento):\\n messages.error(request, 'rendimento é obrigatórios')\\n return redirect('cria_receita')\\n\\n if campo_vazio(categoria):\\n messages.error(request, 'categoria é obrigatório')\\n return redirect('cria_receita')\\n\\n receita = Receita.objects.create(\\n pessoa=user,\\n nome_receita=nome_receita,\\n ingredientes=ingredientes,\\n modo_preparo=modo_preparo,\\n tempo_preparo=tempo_preparo,\\n rendimento=rendimento,\\n categoria=categoria,\\n publicado=False,\\n foto_receita=foto_receita\\n )\\n receita.save()\\n return redirect('dashboard')\\n else:\\n return render(request, 'cria_receita.html')\\n else:\\n return redirect('index')\",\n \"def iniciar_cadastro_dos_registros():\\r\\n # quarto\\r\\n result = False\\r\\n try:\\r\\n fc.inserir_registros(_browser)\\r\\n result = True\\r\\n except:\\r\\n result = False\\r\\n\\r\\n assert result\",\n \"def estadoProyecto(request,pk):\\n\\n proyecto_validar=Proyecto.objects.get(id_proyecto=pk)\\n\\n if validar_permiso(request.user, \\\"is_gerente\\\",proyecto_validar)==False: # primero se valida si es gerente en el proyecto actual)\\n context = {\\n \\\"mensaje\\\": \\\"No eres gerente de proyecto, por lo tanto no puedes cambiar el estado\\\" ,\\n \\\"titulo\\\": \\\"Conflicto de Permiso \\\",\\n \\\"titulo_b2\\\": \\\"Salir\\\",\\n \\\"boton2\\\": \\\"/proyectos/\\\",\\n }\\n return render(request, \\\"Error.html\\\", context)\\n\\n\\n form=FormProyectoEstados(request.POST)\\n p = Proyecto.objects.get(id_proyecto=pk) ##### BUSCA EL PROYECTO CON ID\\n if form.is_valid():\\n x=form.cleaned_data\\n z=x.get(\\\"estado\\\")#### ESTADO SELECCIONADO\\n #print(z)\\n #print(pk)\\n\\n if(z==\\\"FINALIZADO\\\"):\\n\\n #registrarAuditoriaProyecto(request.user,\\\" cambio el estado a finalizado \\\",p.id_proyecto,p.nombre,\\\"\\\")\\n return redirect('gestion:listar_proyectos')### VUELVE A LISTAR LOS PROYECTOS DEL USUARIO\\n elif(z==\\\"INICIADO\\\"):\\n ok=False\\n fase= Fase.objects.all()\\n IdFase=0\\n\\n cantidad=0\\n try:\\n comite = Comite.objects.filter(id_proyecto=pk)\\n cantidad = comite.count()\\n except:\\n comite = None\\n cantidad =0\\n\\n if(cantidad < 3):\\n comite=None\\n\\n if (comite == None):\\n context = {\\n \\\"mensaje\\\": \\\"CREE EL COMITE DE CAMBIO PARA CONTINUAR\\\",\\n \\\"titulo\\\": \\\"ERROR NO POSEE COMITE\\\",\\n \\\"titulo_b1\\\": \\\"CREAR COMITE\\\",\\n \\\"boton1\\\": \\\"/AggComite/\\\" + str(pk),\\n \\\"titulo_b2\\\": \\\"CANCELAR\\\",\\n \\\"boton2\\\": \\\"/detallesProyecto/\\\" + str(pk),\\n }\\n return render(request, 'Error.html', context)\\n\\n for i in range(fase.count()):\\n if(fase[i].id_Proyecto.id_proyecto==p.id_proyecto):\\n ti = TipoItem.objects.all()\\n IdFase = fase[i].id_Fase\\n for x in range(ti.count()):\\n if(ti[x].fase.id_Fase==fase[i].id_Fase):\\n ok=True\\n print(IdFase)\\n if(ok==True):\\n registrarAuditoria(request.user,\\\"cambio el estado del proyecto : \\\"+str(p.nombre)+ \\\" a Iniciado\\\")\\n p.estado=z####### SE ASIGNA ESTADO\\n p.save()##### SE GUARDA\\n registrarAuditoriaProyecto(request.user, \\\" cambio el estado a iniciado \\\", p.id_proyecto, p.nombre, \\\"\\\")\\n return redirect('gestion:detalles_Proyecto',pk)### VUELVE A LISTAR LOS PROYECTOS DEL USUARIO\\n\\n context = {\\n \\\"mensaje\\\":\\\"NO POSEE TIPOS DE ITEM CREE AL MENOS UNO PAARA INICIAR EL PROYECTO\\\",\\n \\\"titulo\\\":\\\"FALTA TI\\\",\\n \\\"titulo_b1\\\": \\\"Crear TI\\\",\\n \\\"boton1\\\":\\\"/crear/TipoItem/\\\"+str(IdFase),\\n \\\"titulo_b2\\\":\\\"Volver a proyectos\\\",\\n \\\"boton2\\\":\\\"/proyectos/\\\"\\n }\\n return render(request, 'Error.html', context)\\n\\n\\n elif(z==\\\"CANCELADO\\\"):\\n if(p.estado != 'FINALIZADO'):\\n registrarAuditoriaProyecto(request.user, \\\" cambio el estado a cancelado \\\", p.id_proyecto, p.nombre, \\\"\\\")\\n p.estado=z####### SE ASIGNA ESTADO\\n p.save()##### SE GUARDA\\n return redirect('gestion:listar_proyectos')### VUELVE A LISTAR LOS PROYECTOS DEL USUARIO\\n else:\\n context = {\\n \\\"mensaje\\\": \\\"EL PROYECTO SE ENCUENTRA FINALIZADO POR ENDE NO SE PUEDE CANCELAR\\\",\\n \\\"titulo\\\": \\\"PROYECTO YA SE FINALIZO\\\",\\n \\\"titulo_b1\\\": \\\"SALIR\\\",\\n \\\"boton1\\\": \\\"/proyectos/\\\" ,\\n \\\"titulo_b2\\\": \\\"\\\",\\n \\\"boton2\\\": \\\"\\\"\\n }\\n return render(request, 'Error.html', context)\\n\\n context={\\n \\\"form\\\":form,\\n \\\"estado\\\": p.estado,\\n 'proyecto':p,\\n 'proyectos': p\\n }\\n return render(request, 'Menu/estado_proyecto.html',context)\",\n \"def cargarObra(self):\\n rowActual=self.tableObra.currentItem().row()\\n self.lineObra.setText(str(self.tableObra.item(rowActual,0).text()))\\n self.lineCuit.setText(str(self.tableObra.item(rowActual,1).text()))\\n self.tableObra.hide()\\n self.lineObra.setEnabled(False)\\n self.lineCuit.setEnabled(False)\\n self.obraSocialSeleccionada = str(self.lineObra.text())\\n self.cargar_productos(self.obraSocialSeleccionada)\\n self.gbProducto.setVisible(True)\",\n \"def aggAtributos(request,idTI):\\n atributos= Atributo.objects.filter(ti_id=idTI)\\n\\n if validar_permiso(request.user,\\\"is_gerente\\\",atributos[0].ti.fase.id_Proyecto) or request.user.has_perm('crear_item',atributos[0].ti.fase.id_Proyecto) and validar_rol_fase('crear_item',atributos[0].ti.fase,request.user):\\n print('tiene el permiso de crear_item')\\n else:\\n messages.error(request,\\\"NO SE POSEE EL PERMISO: crear_item\\\" + \\\" SOLICITE EL PERMISO CORRESPONDINTE PARA REALIZAR LA ACCION\\\")\\n return redirect('gestion:detallesFase',atributos.ti.fase.id_Proyecto.id_proyecto)\\n\\n if(atributos.count() == 0):\\n return HttpResponse(request,\\\"id de TI invalida\\\",status=400)\\n\\n if request.method == 'POST':\\n itemID = Item.objects.last()\\n ti = TipoItem.objects.get(id_ti=idTI)\\n contador=0\\n\\n for c in atributos:\\n print(c.id_atributo)\\n contador=contador+1\\n\\n item=Item.objects.last()#SE OBTIENE EL ITEM CREADO RECIENTEMENTE\\n list=[]\\n for atributos in atributos:#SE RECORRE POR VALOR INGRESADO CONSULTANDO SI ES OBLIGARORIO Y ESTA VACIO-->MUESTRA ERROR\\n ok = False\\n if(atributos.tipo_dato == 'Boolean'):\\n x = request.POST.getlist(atributos.tipo_dato)\\n tiposAtributo = Atributo.objects.filter(ti_id=idTI, tipo_dato=atributos.tipo_dato)\\n print(x)\\n for ini in range(len(x)):\\n if(x[ini] == '' and tiposAtributo[ini].es_obligatorio == True):\\n ok=True\\n nombre=tiposAtributo[ini].nombre\\n if( ok == True ):\\n messages.error(request, \\\"EL ATRIBUTO ES OBLIGATRIO FAVOR INGRESE UN VALOR PARA EL ATRIBUTO: \\\"+nombre)\\n return redirect('gestion:aggAtributos', idTI)\\n\\n list=[\\\"Decimal\\\",\\\"Boolean\\\",\\\"File\\\",\\\"String\\\",\\\"Date\\\"]\\n for ini in range(len(list)): #SI INGRESO VALORES CORRECTAMENTE LOS GUARDA RELACIONANDO CON EL ITEM CORRESPONDIENTE\\n print('este es el valor de ini'+str(ini))\\n print(list[ini])\\n try:\\n tiposAtributo = Atributo.objects.filter(ti_id=idTI, tipo_dato=list[ini])\\n x=request.POST.getlist(list[ini])\\n except:\\n tiposAtributo=None\\n\\n if (tiposAtributo!=None):\\n for valor in range(tiposAtributo.count()):\\n if(list[ini]==\\\"File\\\"):\\n list2 = []\\n for atr in tiposAtributo:\\n DOC = request.FILES.getlist(str(atr.id_atributo))\\n if (DOC != list2):\\n print(\\\"no vacio\\\",DOC[0])\\n ruta = str(ti.fase.id_Proyecto.id_proyecto) + \\\"/\\\" + str(itemID.id_item)\\n PATH = f'/{ruta}/{DOC[0]}'\\n #SubirArchivo(DOC[0], PATH)\\n print(\\\"--\\\",PATH)\\n\\n\\n ##se sube archivo a dropbox en segundo plano\\n t2 = Thread(\\n target=SubirArchivo,\\n args=(DOC[0],PATH),\\n )\\n t2.start()\\n\\n p = Atributo_Item(idAtributoTI=atr, id_item=item, valor=str(PATH))\\n p.save()\\n else:\\n print(\\\"vacio\\\",DOC)\\n p = Atributo_Item(idAtributoTI=atr, id_item=item, valor=\\\"Sin archivos adjuntos\\\")\\n p.save()\\n break\\n else:\\n p = Atributo_Item(idAtributoTI=tiposAtributo[valor],id_item=item,valor=str(x[valor]))\\n p.save()\\n\\n itemID=Item.objects.last()\\n ti=TipoItem.objects.get(id_ti=idTI)\\n return redirect('gestion:relacionarItem',ti.fase.id_Proyecto.id_proyecto,itemID.id_item)\\n item_id = Item.objects.last()\\n contexto={\\n 'atributos':atributos,\\n 'true':True,\\n 'false':False,\\n 'id_item':item_id.id_item\\n }\\n return render (request,'items/aggAtributos.html',contexto)\",\n \"def agregar_cuota(request, pk):\\n socio = get_object_or_404(Socio, pk=pk)\\n if request.method == \\\"POST\\\":\\n form = CuotaForm(request.POST, instance = socio)\\n if form.is_valid():\\n form.save()\\n return redirect(detalle_cuota,pk=cuota.pk)\\n else:\\n nombre = Socio.objects.get(pk=pk)\\n form = CuotaForm(instance = socio)\\n return render(request,'sociedad/agregar_cuota.html',{'form':form})\",\n \"def regLA(): # Esta sección fue hecha por Ángel\\n print(\\\"A continuación se muestra la lista de productos en el inventario: \\\")\\n creaLprod(creaLI(\\\"Inventario\\\"))\\n resp = 1\\n while resp == 1:\\n fecha = input(\\\"Fecha de llegada del articulo(dd/mm/aa): \\\")\\n producto = int(input(\\\"Introduce el número del producto reabastecido: \\\"))\\n cantiProd = int(input(\\\"Ingresa la cantidad que fue reabastecida: \\\")) \\n geneRegLleg(fecha, producto, cantiProd)\\n listaaIM = crearLBI()\\n geneALlegA(crearLIA(actualizarLInv(producto, listaaIM, cantiProd)))\\n resp = int(input(\\\"Llegaron más artículos 1. Sí 2. No: \\\"))\\n if resp == \\\"2\\\":\\n menu(listaUCE)\",\n \"def acquisizioneParametri(self):\\n\\n messaggio =''\\n\\n try: \\n self.__rete = slugify(self.ui.nomeRete.text())\\n # controllo se la lunghezza del nome inserito sia > di 5 caratteri\\n if(len(self.__rete) < 5 or len(self.__rete) > 30):\\n\\n messaggio = 'err: inserimento Nome'\\n raise NameError\\n \\n # controllo che il nome scelto sia univoco\\n isPresent = self.__NNNameCheck()\\n if(isPresent):\\n messaggio = 'err: nome già utilizzato'\\n raise NameError\\n\\n # controlli su numero layer e numero nodi che siano >= 1\\n # e che siano rispettivamente <= 20 e <= 50\\n self.__layer = int(self.ui.nLayer.text())\\n if(self.__layer < 1):\\n messaggio = 'err: numero layer < 1'\\n raise ValueError\\n elif(self.__layer >= 20):\\n messaggio = 'err: numero layer > 20'\\n raise ValueError\\n\\n self.__nodi = int(self.ui.nNodi.text())\\n if(self.__nodi < 1):\\n messaggio = 'err: numero nodi < 1'\\n raise ValueError\\n if(self.__nodi >= 50):\\n messaggio = 'err: numero nodi > 50'\\n raise ValueError\\n\\n # salvataggio della funzione scelta\\n self.__funzione = self.ui.funzione.currentText()\\n \\n # controllo che la percentuale di Vs sia < 25%\\n # e che la percentuale di Ts sia > 75%\\n if(self.__percentuale < 25):\\n messaggio = 'err: suddivisione'\\n raise ValueError\\n if (self.__percentuale > 75):\\n messaggio = 'err: suddivisione'\\n raise ValueError\\n\\n # controllo che sia stato scelto effettivamente un dataset\\n if(len(self.__dataSet) == 0):\\n messaggio = 'err: dataSet errato'\\n raise NameError\\n\\n # setto il tasto caricamento di una rete non cliccabile\\n self.ui.but_caricaRete.setEnabled(False)\\n\\n # cambio nome del tasto convalida\\n self.ui.but_convalida.setText('confermato')\\n self.ui.comunicazione.setText('')\\n #abilito salvataggio\\n self.ui.but_salva.setEnabled(True)\\n\\n # settandola a True permetto che il training venga effettuato\\n # dato che i dati inseriti sono validi\\n self.__convalida = True\\n return True\\n except:\\n # in caso di eccezzioni faccio comparire il messaggio\\n self.ui.comunicazione.setText(messaggio)\\n return False\",\n \"def add_annonce(request):\\n ImageFormSet = modelformset_factory(ImageAnnonce, form=ImageForm, extra=4, max_num=4, validate_max=True)\\n if request.method == \\\"POST\\\":\\n a_form = AnnonceFrom(request.POST)\\n formset = ImageFormSet(\\n request.POST,\\n request.FILES,\\n queryset=ImageAnnonce.objects.none()\\n )\\n if a_form.is_valid() and formset.is_valid():\\n user = request.user.profile\\n annonceForm = a_form.save(commit=False)\\n annonceForm.owner = user\\n annonceForm.save()\\n for form in formset.cleaned_data:\\n if form:\\n image = form['image']\\n photo = ImageAnnonce(annonce_images=annonceForm, image=image)\\n photo.save()\\n # capture_message(f\\\"add annonce format valide utiliser par {request.user.username}\\\", level=\\\"info\\\") \\n messages.add_message(\\n request, messages.SUCCESS, 'Annonce ajouter avec succès'\\n )\\n return redirect('home')\\n else:\\n print(a_form.errors)\\n print('Is not valid')\\n # capture_message(f\\\"Annonce format non valide utiliser par {request.user.username}\\\", level=\\\"error\\\") \\n else:\\n a_form = AnnonceFrom()\\n formset = ImageFormSet(queryset=ImageAnnonce.objects.none())\\n a_form = AnnonceFrom()\\n formset = ImageFormSet(queryset=ImageAnnonce.objects.none())\\n # capture_message(f\\\"{request.user.username} try to update annonce\\\", level=\\\"info\\\")\\n\\n context = {\\n \\\"a_form\\\": a_form,\\n \\\"formset\\\": formset,\\n }\\n return render(request, 'Annonce/add.html', context)\",\n \"def _process(self):\\n disable_form(self.kwargs[\\\"collect\\\"].ona_scan_form_pk)\",\n \"def synchronizeSPIPForm(request, template='contacts/person/synchronize.html'):\\n\\n if not request.user.is_authenticated():\\n return HttpResponseRedirect('/login/?next=%s' % request.path)\\n\\n registres = 0\\n user = request.user\\n\\n if request.method == 'POST':\\n form = SynchronizeSPIPForm(request.POST)\\n if form.is_valid():\\n if form.cleaned_data['confirma'] == True:\\n lastperson = Person.objects.latest('external_id')\\n\\n req = urllib2.Request(settings.SPIP_SYNC_URL + '?action=get&last_id=' + str(lastperson.external_id) , None, {'user-agent':settings.SPIP_SYNC_AGENT})\\n opener = urllib2.build_opener()\\n json_data = simplejson.load(opener.open(req))\\n\\n person = Person()\\n person.external_id = 0\\n laboral_levels = []\\n courses = []\\n\\n for row in json_data['list']:\\n if row['id_donnee'] != str(person.external_id) and person.external_id > 0:\\n # donam d'alta la persona anterior\\n person.slug = calculaSlugPersona(person)\\n person.user_add = user\\n person.user_modify = user\\n person.save()\\n # alta relacio persona cursos\\n for course_id in courses:\\n person.courses.add(Course.objects.get(id=course_id))\\n\\n registres = registres + 1\\n # nova persona\\n person = Person()\\n laboral_levels = []\\n courses = []\\n\\n person.external_id = row['id_donnee']\\n person.date_registration = row['date']\\n\\n\\n if row['champ'] == 'ligne_1':\\n person.first_name = row['valeur'] #.decode(\\\"utf8\\\", \\\"ignore\\\")\\n elif row['champ'] == 'ligne_2':\\n person.last_name = row['valeur'] #.decode(\\\"utf8\\\", \\\"ignore\\\")\\n elif row['champ'] == 'ligne_3':\\n person.id_card = row['valeur']\\n elif row['champ'] == 'ligne_4':\\n person.home_address = row['valeur'] #.decode(\\\"utf8\\\", \\\"ignore\\\")\\n elif row['champ'] == 'ligne_5':\\n person.home_postalcode = row['valeur']\\n elif row['champ'] == 'ligne_6':\\n person.home_town = row['valeur'] #.decode(\\\"utf8\\\", \\\"ignore\\\")\\n elif row['champ'] == 'select_5':\\n person.home_province = row['titre'] #.decode(\\\"utf8\\\", \\\"ignore\\\")\\n elif row['champ'] == 'email_1':\\n person.email_address = row['valeur']\\n elif row['champ'] == 'ligne_8':\\n person.phone_number = row['valeur']\\n elif row['champ'] == 'ligne_9':\\n person.mobile_number = row['valeur']\\n elif row['champ'] == 'ligne_19':\\n person.twitter = row['valeur'] #.decode(\\\"utf8\\\", \\\"ignore\\\")\\n elif row['champ'] == 'select_1':\\n person.laboral_category = row['rang']\\n elif row['champ'] == 'multiple_1':\\n laboral_levels.append(row['rang'])\\n person.laboral_levels = \\\",\\\".join(\\\"'%s'\\\" % str(level) for level in laboral_levels)\\n # print >> sys.stderr, 'Laboral levels = %s' % person.laboral_levels\\n elif row['champ'] == 'ligne_10':\\n person.laboral_nrp = row['valeur']\\n elif row['champ'] == 'num_1':\\n person.laboral_years = float(row['valeur']) if '.' in row['valeur'] else int(row['valeur'])\\n elif row['champ'] == 'select_2':\\n person.laboral_cuerpo = row['rang']\\n elif row['champ'] == 'ligne_11':\\n person.laboral_degree = row['valeur'] #.decode(\\\"utf8\\\", \\\"ignore\\\")\\n elif row['champ'] == 'ligne_12':\\n person.laboral_centername = row['valeur'] #.decode(\\\"utf8\\\", \\\"ignore\\\")\\n elif row['champ'] == 'ligne_13':\\n person.laboral_centercode = row['valeur']\\n elif row['champ'] == 'ligne_16':\\n person.laboral_centerpostalcode = row['valeur']\\n elif row['champ'] == 'ligne_14':\\n person.laboral_centertown = row['valeur'] #.decode(\\\"utf8\\\", \\\"ignore\\\")\\n elif row['champ'] == 'select_4':\\n person.laboral_centerprovince = row['titre'] #.decode(\\\"utf8\\\", \\\"ignore\\\")\\n elif row['champ'] == 'select_3':\\n person.math_society = row['rang']\\n elif row['champ'] == 'texte_1':\\n person.remarks = row['valeur'] #.decode(\\\"utf8\\\", \\\"ignore\\\")\\n elif row['champ'] == 'select_6':\\n person.lang = row['rang']\\n # nou, cursos\\n elif row['champ'] == 'multiple_2':\\n courses.append(row['rang'])\\n\\n\\n # Hem de donar d'alta la darrera persona\\n if person.external_id > 0:\\n person.slug = calculaSlugPersona(person)\\n person.user_add = user\\n person.user_modify = user\\n person.save()\\n # alta relacio persona cursos\\n for course_id in courses:\\n person.courses.add(Course.objects.get(id=course_id))\\n registres = registres + 1\\n\\n\\n # Posam a 0 els registres no sincronitzats\\n if not request.session:\\n request.session={}\\n request.session['regs_not_sync'] = 0\\n\\n else:\\n form = SynchronizeSPIPForm()\\n registres = -1\\n\\n return render_to_response(template, {'registres': registres, 'form': form}, RequestContext(request))\",\n \"def on_btnBajaServicio_clicked(self, widget):\\n try:\\n codigoReservaServicio = variables.lblCodigoReservaServicio.get_text()\\n if(variables.codigoServicio!= \\\"\\\"):\\n funciones_servicios.eliminarServicio(variables.codigoServicio)\\n funciones_servicios.listadoServicio(variables.listServicios, codigoReservaServicio)\\n funciones_factura.listadoServicios(variables.listFactura,variables.codr,variables.lblHabitacionServicio.get_text())\\n funciones_factura.calcularPreciosServicios()\\n else:\\n variables.mensajeError = \\\"Debes seleccionar un servicio\\\"\\n variables.vError.show()\\n\\n\\n except:\\n print (\\\"error baja servicio\\\")\",\n \"def relacionarItem(request,id_proyecto,id_item):\\n try:\\n proyecto=Proyecto.objects.get(id_proyecto=id_proyecto)#se obtiene el proyecto\\n fases=Fase.objects.filter(id_Proyecto=proyecto).order_by('id_Fase')#se obtienen las fases del proyecto\\n itemActual=Item.objects.get(id_item=id_item)\\n list=[]\\n list=lista_items_relacion(itemActual,fases,id_proyecto,id_item)\\n items = Item.objects.filter(actual=True)\\n except:\\n\\n return HttpResponse(request, \\\"id de TI invalida\\\",status=400)\\n\\n\\n if validar_permiso(request.user,\\\"is_gerente\\\",proyecto) or request.user.has_perm('crear_item',proyecto) and validar_rol_fase('crear_item',itemActual.fase,request.user):\\n print('tiene el permiso de crear_item')\\n else:\\n messages.error(request,\\\"NO SE POSEE EL PERMISO: crear_item\\\" + \\\" SOLICITE EL PERMISO CORRESPONDINTE PARA REALIZAR LA ACCION\\\")\\n return redirect('gestion:detallesFase', atributos.ti.fase.id_Fase)\\n\\n if request.method == 'POST': #preguntamos primero si la petición Http es POST ||| revienta todo con este\\n some_var=request.POST.getlist('checkbox')\\n #print(some_var)\\n lis=[]\\n proyecto = Proyecto.objects.get(id_proyecto=id_proyecto)\\n fases = Fase.objects.filter(id_Proyecto=proyecto).order_by('id_Fase')\\n item = Item.objects.filter(id_item=id_item)\\n # hacer una funcion\\n # [1][2][3]\\n # [3][2][1]\\n #VERIFICAR SI ES DE LA PRIMERA FASE SIN RELACIONES, SINO MOSTRAR ERROR\\n if(lis==some_var):\\n print(fases[0].id_Fase)\\n print(item[0].fase.id_Fase)\\n if fases[0].id_Fase!= item[0].fase.id_Fase:#sino es igual a la primera fase muestra error\\n context = {\\n \\\"mensaje\\\": \\\"EL ITEM NO ES DE LA PRIMERA FASE, POR ENDE DEBE DE CONTAR CON RELACION Y TENER DE FORMA DIRECTA O INDIRECTA RELACION CON LA PRIMERA FASE DEL PROYECTO \\\",\\n \\\"titulo\\\": \\\"ITEM SIN RELACION\\\",\\n \\\"titulo_b1\\\": \\\"AÑADE RELACION\\\",\\n \\\"boton1\\\": \\\"/relacionarItem/\\\" + str(id_proyecto)+\\\"/\\\"+str(id_item),\\n \\\"titulo_b2\\\": \\\"CANCELAR ITEM\\\",\\n \\\"boton2\\\": \\\"/itemCancelado/\\\",\\n }\\n return render(request, 'Error.html', context)\\n\\n if fases[0].id_Fase!= item[0].fase.id_Fase:#sino es igual a la primera fase muestra error\\n #VERIFICAR SI TIENE RELACION CON LA F1\\n if(primeraFase(id_proyecto, id_item, some_var)==True):\\n context = {\\n \\\"mensaje\\\": \\\"EL ITEM NO TIENE RELACION CON LA PRIMERA FASE POR ENDE NO ES VALIDO, FAVOR VOLVER A REALIZAR RELACIONES Y VOLVER CONSISTENTE EL ITEM \\\",\\n \\\"titulo\\\": \\\"ITEM SIN RELACION CON LA FASE 1\\\",\\n \\\"titulo_b1\\\": \\\"VOLVER A AÑADIR RELACION\\\",\\n \\\"boton1\\\": \\\"/relacionarItem/\\\" + str(id_proyecto)+\\\"/\\\" + str(id_item),\\n \\\"titulo_b2\\\": \\\"CANCELAR ITEM\\\",\\n \\\"boton2\\\": \\\"/itemCancelado/\\\",\\n }\\n return render(request, 'Error.html', context)\\n\\n #VERIFICAR SI SE GENERAN CICLOS--------- INCONSISTENCIAS\\n\\n registrarAuditoriaProyecto(request.user,'creo el item: '+str(item[0].nombre),id_proyecto,proyecto.nombre,item[0].fase.nombre)\\n\\n for id in some_var:###### SE GUARDAN LAS RELACIONES\\n itemSeleccionado=Item.objects.get(id_item=id)\\n if(itemSeleccionado.fase.id_Fase > itemActual.fase.id_Fase):#si el item es sucesor, sera apuntado por el item creado\\n p = Relacion(fin_item=id,inicio_item=id_item)\\n p.save()\\n print(itemActual.nombre,\\\" --> \\\",itemSeleccionado.nombre)\\n else:# sino es el sucesor, seguira siendo apuntado por los seleccionados\\n p = Relacion(fin_item=id_item,inicio_item=id)\\n p.save()\\n print(itemActual.nombre,\\\" <-- \\\",itemSeleccionado.nombre)\\n\\n #----------------------------------------------------------#\\n ## se puede volver generico si se restringe preguntando si el item es igual al ultimo\\n version=Versiones(id_Version=1,id_item=id_item,id_padre=id_item)#SE GUARDA LA VERSION\\n version.save()\\n #----------------------------------------------------------#\\n\\n return redirect('gestion:detallesFase',item[0].fase.id_Fase)\\n else:\\n return render(request, 'items/relacionarItem.html', {'form': items,'list':list,'itemActual':itemActual})\",\n \"def form_valid(self, form):\\n self.object = form.save(commit=False)\\n self.object.fecha_fin = self.object.fecha_inicio + datetime.timedelta(days=self.object.duracion_sprint)\\n self.object.sumarHoras()\\n self.object.registrarHoras()\\n self.object.save()\\n formsetb = self.UserStoryFormset(self.request.POST)\\n if formsetb.is_valid():\\n proccessed_forms = []\\n for subform in formsetb:\\n if subform.has_changed() and 'userStory' in subform.cleaned_data:\\n new_userStory = subform.cleaned_data['userStory']\\n if subform in formsetb.deleted_forms and not new_userStory in proccessed_forms:\\n # desaciamos los user story que se eliminaron del form\\n new_userStory.desarrollador = None\\n new_userStory.sprint = None\\n new_userStory.actividad = None\\n\\n else:\\n new_flujo = subform.cleaned_data['flujo']\\n self.flujo = new_flujo\\n new_desarrollador = subform.cleaned_data['desarrollador']\\n if new_userStory.estado != 3 and new_userStory.estado != 4: # si el user story no ha finalizado\\n new_userStory.desarrollador = new_desarrollador\\n new_userStory.sprint = self.object\\n new_userStory.actividad = self.flujo.actividad_set.first()\\n new_userStory.estado = 1\\n new_userStory.estado_actividad = 0\\n\\n self.notify(new_userStory)\\n new_userStory.save()\\n proccessed_forms.append(new_userStory)\\n return HttpResponseRedirect(self.get_success_url())\\n\\n self.__filtrar_formset__(formsetb)\\n\\n return render(self.request, self.get_template_names(), {'form': form, 'formset': formsetb},\\n context_instance=RequestContext(self.request))\",\n \"def cobroEfectivo(self):\\n if self.total_a_pagar == 0:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"El saldo restante a pagar es cero\\\")\\n else:\\n self.rbtnEfectivo.setChecked(True)\\n monto_a_pagar, ok = QtGui.QInputDialog.getDouble(self,\\\"Cobro Efectivo\\\",\\\"Ingrese monto a pagar\\\",0,0,2000,2)\\n\\n if ok:\\n if monto_a_pagar >= self.total_a_pagar:\\n QtGui.QMessageBox.information(self,\\\"Cobro Efectivo\\\",\\\"Su vuelto es:%.2f\\\" % (monto_a_pagar - self.total_a_pagar))\\n temp = [\\\"Efectivo\\\",monto_a_pagar]\\n self.detalles_cobro[self.tablePagos.rowCount()] = temp\\n self.total_a_pagar = 0\\n elif monto_a_pagar == 0:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"El monto ingresado no puede ser cero\\\")\\n else:\\n temp = [\\\"Efectivo\\\",monto_a_pagar]\\n self.detalles_cobro[self.tablePagos.rowCount()] = temp\\n self.total_a_pagar -= monto_a_pagar\\n\\n self.actualizar_total()\\n self.actualizar_tabla()\",\n \"def form_valid(self, form):\\n self.instance = form.save()[0].prueba\\n self.instance.evaluar()\\n self.instance.save()\\n return super(PresentarView, self).form_valid(form)\",\n \"def fetchDataToForm(self, row, column, fields=\\\"Recent\\\"):\\n columns = self.piggybank.columnCount()\\n\\n for columnCounter in range(columns):\\n\\n self.columnHeaderLabel = str(self.piggybank.horizontalHeaderItem(columnCounter).text())\\n self.cellValue = str(self.piggybank.item(row, columnCounter).text())\\n\\n if self.columnHeaderLabel == \\\"Description Type\\\":\\n self.typeIndex = self.comboBoxType.findText(self.cellValue)\\n self.comboBoxType.setCurrentIndex(self.typeIndex)\\n\\n elif self.columnHeaderLabel == \\\"Priority\\\":\\n self.priorityIndex = self.comboBoxPriority.findText(self.cellValue)\\n self.comboBoxPriority.setCurrentIndex(self.priorityIndex)\\n\\n elif self.columnHeaderLabel == \\\"Source\\\":\\n self.sourceIndex = self.comboBoxSource.findText(self.cellValue)\\n self.comboBoxSource.setCurrentIndex(self.sourceIndex)\\n\\n elif self.columnHeaderLabel == \\\"BU\\\":\\n self.BUIndex = self.comboBoxBU.findText(self.cellValue)\\n self.comboBoxBU.setCurrentIndex(self.BUIndex)\\n self.populateSuperCategory()\\n\\n elif self.columnHeaderLabel == \\\"Super-Category\\\":\\n self.superIndex = self.comboBoxSuperCategory.findText(self.cellValue)\\n self.comboBoxSuperCategory.setCurrentIndex(self.superIndex)\\n self.populateCategory()\\n\\n elif self.columnHeaderLabel == \\\"Category\\\":\\n self.categoryIndex = self.comboBoxCategory.findText(self.cellValue)\\n self.comboBoxCategory.setCurrentIndex(self.categoryIndex)\\n self.populateSubCategory()\\n\\n elif self.columnHeaderLabel == \\\"Sub-Category\\\":\\n self.subCatIndex = self.comboBoxSubCategory.findText(self.cellValue)\\n self.comboBoxSubCategory.setCurrentIndex(self.subCatIndex)\\n self.populateBrandVertical()\\n\\n elif self.columnHeaderLabel == \\\"Vertical\\\":\\n self.verticalIndex = self.comboBoxVertical.findText(self.cellValue)\\n self.comboBoxVertical.setCurrentIndex(self.verticalIndex)\\n\\n elif self.columnHeaderLabel == \\\"Brand\\\":\\n self.lineEditBrand.setText(self.cellValue)\\n\\n elif fields == \\\"All\\\":\\n if self.columnHeaderLabel == \\\"FSN\\\":\\n self.lineEditFSN.setText(self.cellValue)\\n elif self.columnHeaderLabel == \\\"Upload Link\\\":\\n self.lineEditUploadLink.setText(self.cellValue)\\n elif self.columnHeaderLabel == \\\"Reference Link\\\":\\n self.lineEditRefLink.setText(self.cellValue)\\n elif self.columnHeaderLabel == \\\"Word Count\\\":\\n self.spinBoxWordCount.setValue(int(self.cellValue))\",\n \"def actualizar(self):\\n if self.obraSocialSeleccionada!=None:\\n self.cargar_productos(self.obraSocialSeleccionada)\\n else:\\n self.cargarProductosSinObra()\",\n \"def form_valid(self, form):\\n self.object= form.save(commit=False)\\n self.object.fecha_fin= self.object.fecha_inicio + datetime.timedelta(days=self.object.duracion_sprint)\\n self.object.sumarHoras()\\n self.object.registrarHoras()\\n self.object.save()\\n formsetb= self.UserStoryFormset(self.request.POST)\\n if formsetb.is_valid():\\n proccessed_forms = []\\n for subform in formsetb:\\n if subform.has_changed() and 'userStory' in subform.cleaned_data:\\n new_userStory = subform.cleaned_data['userStory']\\n if subform in formsetb.deleted_forms and not new_userStory in proccessed_forms:\\n # desaciamos los user story que se eliminaron del form\\n new_userStory.desarrollador = None\\n new_userStory.sprint = None\\n new_userStory.actividad = None\\n\\n else:\\n new_flujo = subform.cleaned_data['flujo']\\n self.flujo = new_flujo\\n new_desarrollador = subform.cleaned_data['desarrollador']\\n if new_userStory.estado != 3 and new_userStory.estado != 4: # si el user story no ha finalizado\\n new_userStory.desarrollador = new_desarrollador\\n new_userStory.sprint = self.object\\n new_userStory.actividad = self.flujo.actividad_set.first()\\n new_userStory.estado = 1\\n new_userStory.estado_actividad = 0\\n\\n\\n new_userStory.save()\\n proccessed_forms.append(new_userStory)\\n return HttpResponseRedirect(self.get_success_url())\\n\\n self.__filtrar_formset__(formsetb)\\n\\n return render(self.request, self.get_template_names(), {'form': form, 'formset': formsetb},\\n context_instance=RequestContext(self.request))\",\n \"def manipular_carga_general(self, camion):\\r\\n operaciones = self.operaciones[\\\"Operaciones manipuleo\\\"]\\r\\n\\r\\n # Manipuleo de camiones por cargar/descargar\\r\\n # Espera camion para realizar transbordo o interrumpe la espera de otro\\r\\n ejecucion_espera_o_interrumpe = yield self.process(\\r\\n camion.espera_transbordo_o_interrumpe(self))\\r\\n\\r\\n # Si el camion espera procede con un tranbordo\\r\\n if ejecucion_espera_o_interrumpe[\\\"Resultado\\\"] != \\\"Interrumpio espera\\\":\\r\\n transbordo = operaciones[\\\"Transbordo con grua\\\"]\\r\\n\\r\\n yield self.process(self.transbordar_o_cargar_descargar(\\r\\n camion, ejecucion_espera_o_interrumpe,\\r\\n transbordo, 500))\",\n \"def asignar_roles_sistema(request, usuario_id):\\n user = User.objects.get(username=request.user.username)\\n permisos = get_permisos_sistema(user)\\n usuario = get_object_or_404(User, id=usuario_id)\\n lista_roles = UsuarioRolSistema.objects.filter(usuario = usuario)\\n lista_permisos = RolPermiso.objects.filter()\\n lista_rolusuario = RolUsuario.objects.filter(usuario=usuario)\\n print lista_permisos\\n tam=len(lista_permisos)\\n relac=ProductOwner()\\n print tam\\n if request.method == 'POST':\\n form = AsignarRolesForm(1, request.POST)\\n if form.is_valid():\\n lista_nueva = form.cleaned_data['roles']\\n for i in lista_roles:\\n i.delete()\\n lista_rolusuario.delete()\\n\\n for i in lista_nueva:\\n nuevo = UsuarioRolSistema()\\n rel = RolUsuario()\\n nuevo.usuario = usuario\\n nuevo.rol = i\\n nuevo.save()\\n if i.id == 2:\\n rel.usuario = usuario\\n rel.save()\\n if i.id == 5:\\n relac.usuario = usuario\\n relac.save()\\n return HttpResponseRedirect(\\\"/usuarios\\\")\\n else:\\n if usuario.id == 1:\\n error = \\\"No se puede editar roles sobre el superusuario.\\\"\\n return render_to_response(\\\"admin/usuarios/asignar_roles.html\\\", {'mensaje': error,\\n 'usuario':usuario,\\n 'user': user,\\n 'asignar_roles': 'Asignar rol' in permisos},context_instance=RequestContext(request))\\n dict = {}\\n for i in lista_roles:\\n print i.rol\\n dict[i.rol.id] = False\\n form = AsignarRolesForm(1,initial = {'roles': dict})\\n return render_to_response(\\\"admin/usuarios/asignar_roles.html\\\", {'form':form, 'usuario':usuario, 'user':user, 'asignar_roles': 'Asignar rol' in permisos},context_instance=RequestContext(request))\",\n \"def _process(self):\\n self.output[\\\"data\\\"] = get_form_data(self.kwargs[\\\"collect\\\"].ona_scan_form_pk)\",\n \"def agregarProducto(self):\\n itemActual=self.tableProductos.currentItem()\\n cantidad, ok = QtGui.QInputDialog.getInt(self,\\\"Cantidad\\\",\\\"Ingrese cantidad del producto\\\",1,1,2000,5)\\n if not ok:\\n self.showMsjEstado(\\\"No se ha seleccionado cantidad del producto\\\")\\n else:\\n cantidadProducto=int(self.tableProductos.item(itemActual.row(),6).text())\\n if cantidad>cantidadProducto:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"La cantidad ingresada es mayor que la del stock\\\")\\n else:\\n if self.productosAgregados == 0 and self.factura == None:\\n self.factura=FacturaModel(FacturaModel.generarNumero(self.sesion))\\n self.factura.guardar(self.sesion)\\n self.productosAgregados+=1\\n rowItemActual=itemActual.row()\\n rows=self.tableFactura.rowCount()\\n self.tableFactura.insertRow(rows)\\n\\n #--Carga de items en la tabla--*\\n producto = int(self.tableProductos.item(rowItemActual,0).text())\\n importeActual=float(self.tableProductos.item(rowItemActual,5).text())\\n descuentoActual=float(self.tableProductos.item(rowItemActual,4).text())\\n subtotal=importeActual*(1-descuentoActual)\\n ####-------------------------#####\\n detalleFactura=DetalleFacturaModel(self.factura.numero,producto,cantidad,\\n subtotal*cantidad,descuentoActual,self.productosAgregados\\n )\\n self.descontarCantidad(detalleFactura,producto,cantidad)\\n self.tableFactura.setItem(rows,0,QtGui.QTableWidgetItem(str(detalleFactura.producto)))\\n self.tableFactura.setItem(rows,1,QtGui.QTableWidgetItem(str(detalleFactura.cantidad)))\\n self.tableFactura.setItem(rows, 2, QtGui.QTableWidgetItem(str(\\\"%.2f\\\"%(subtotal*cantidad))))\\n\\n detalleFactura.guardar(self.sesion)\\n self.detallesTabla[rows] = detalleFactura\\n\\n self.data[rows] = [\\n producto, cantidad, subtotal*cantidad, descuentoActual\\n ]\\n\\n self.actualizar()\\n self.objectModified.emit()\",\n \"def preguntar_partida_nueva():\\n layout = [\\n [sg.T(\\\"Tiene una partida guardada, si continua, se borrará\\\", justification=\\\"center\\\",\\n font=(\\\"Georgia\\\", 14))],\\n [sg.Button('Continuar'), sg.Button('Regresar')]\\n ]\\n window_preguntar = sg.Window(\\\"ScrabbleAR - Partida Guardada\\\").Layout(layout)\\n while True:\\n event, _ = window_preguntar.Read()\\n if event is None:\\n condicion = False\\n break\\n elif event in 'Continuar':\\n condicion = True\\n break\\n elif event in 'Regresar':\\n condicion = False\\n break\\n\\n window_preguntar.Close()\\n return condicion\",\n \"def submit_init_noname1(self):\\n # AutoGen method submit form: init\\n self.click_element(self.ID_SUBMIT)\",\n \"def prepare(self, form):\\n \\n return form\",\n \"def form_valid(self, form):\\n self.object= form.save(commit=False)\\n self.object.fecha_fin= self.object.fecha_inicio + datetime.timedelta(days=self.object.duracion_sprint)\\n self.object.sumarHoras()\\n self.object.registrarHoras()\\n self.object.save()\\n formsetb= self.UserStoryFormset(self.request.POST)\\n if formsetb.is_valid():\\n proccessed_forms = []\\n for subform in formsetb:\\n if subform.has_changed() and 'userStory' in subform.cleaned_data:\\n new_userStory = subform.cleaned_data['userStory']\\n if subform in formsetb.deleted_forms and not new_userStory in proccessed_forms:\\n # desaciamos los user story que se eliminaron del form\\n new_userStory.desarrollador = None\\n new_userStory.sprint = None\\n new_userStory.actividad = None\\n\\n else:\\n new_flujo = subform.cleaned_data['flujo']\\n self.flujo = new_flujo\\n new_desarrollador = subform.cleaned_data['desarrollador']\\n if new_userStory.estado != 3 and new_userStory.estado != 4: # si el user story no ha finalizado\\n new_userStory.desarrollador = new_desarrollador\\n new_userStory.sprint = self.object\\n new_userStory.actividad = self.flujo.actividad_set.first()\\n new_userStory.estado = 1\\n new_userStory.estado_actividad = 0\\n\\n self.notify(new_userStory)\\n new_userStory.save()\\n proccessed_forms.append(new_userStory)\\n return HttpResponseRedirect(self.get_success_url())\\n\\n self.__filtrar_formset__(formsetb)\\n\\n return render(self.request, self.get_template_names(), {'form': form, 'formset': formsetb},\\n context_instance=RequestContext(self.request))\",\n \"def post(self):\\r\\n piso=self.request.get('piso')\\r\\n numext=self.request.get('numext')\\r\\n numint=self.request.get('numint')\\r\\n piso=self.validonumero(piso)\\r\\n numext=self.validonumero(numext)\\r\\n numint=self.validonumero(numint)\\r\\n \\r\\n empresa=empresas()\\r\\n empresa.nombre=self.request.get('desc')\\r\\n empresa.calle=self.request.get('calle')\\r\\n empresa.numeroExterior=int(numext)\\r\\n empresa.numeroInterior=int(numint)\\r\\n empresa.colonia=self.request.get('colonia')\\r\\n empresa.piso=int(piso)\\r\\n empresa.andador=self.request.get('andador')\\r\\n empresa.codigo_postal=int(self.request.get('cp'))\\r\\n empresa.sitioweb=self.request.get('web')\\r\\n empresa.correo=self.request.get('mail')\\r\\n empresa.nombreContacto=\\\"\\\"\\r\\n empresa.paternoContacto=\\\"\\\"\\r\\n empresa.maternoContacto=\\\"\\\"\\r\\n #### \\r\\n ciudad=self.request.get('ciudad')\\r\\n query=\\\"where ciudad='%s'\\\"%ciudad\\r\\n cd=ciudades.gql(query)\\r\\n city=cd.fetch(1)\\r\\n for lstcd in city:\\r\\n empresa.id_Ciudad=lstcd.key().id()\\r\\n empresa.put()\\r\\n jsondic={}\\r\\n jsondata=[]\\r\\n jsondata+=[self.addKey(jsondic,\\\"Dato\\\", empresa.key().id())]\\r\\n self.response.out.write(simplejson.dumps(jsondata))\\r\\n return False\",\n \"def iniciarEntradas(self):\\n tituloEntry = self.crearEntrada(self.master, self.tituloString, 30, 1, 1)\\n descripcionEntry = self.crearEntrada(self.master, self.descripcionString, 30, 2, 1)\",\n \"def minimal_form_data():\\n\\n form_data = { \\n 'status': '0',\\n 'title': 'Recurso de teste',\\n 'description': 'Recurso para testes',\\n 'abstract': 'Resumo',\\n \\n 'main-descriptor-content_type-object_id-TOTAL_FORMS': '0', \\n 'main-descriptor-content_type-object_id-INITIAL_FORMS': '0',\\n\\n 'main-keyword-content_type-object_id-TOTAL_FORMS': '0', \\n 'main-keyword-content_type-object_id-INITIAL_FORMS': '0',\\n\\n 'main-resourcethematic-content_type-object_id-TOTAL_FORMS': '0',\\n 'main-resourcethematic-content_type-object_id-INITIAL_FORMS': '0',\\n }\\n\\n return form_data\",\n \"def addProductFields(form, forCreation=False, restWriter=None, hasOptions=False):\\n form.addField('code', formal.String(required=True, strip=True))\\n form.addField('title', formal.String(required=True, strip=True))\\n\\n images = formal.Group('images')\\n form.add( images )\\n images.add( formal.Field('mainImage', formal.File(required=forCreation), \\n widgetFactory=formal.widgetFactory( formal.FileUploadWidget,\\n convertibleFactory=contenttypeutil.KeyToFileConverter,\\n originalKeyIsURL=True),description='click to change') )\\n images.add( formal.Field('ndgrad', formal.File(), \\n widgetFactory=formal.widgetFactory( formal.FileUploadWidget,\\n convertibleFactory=contenttypeutil.KeyToFileConverter,\\n originalKeyIsURL=True),description='click to change') )\\n\\n\\n availability = formal.Group('availability')\\n form.add( availability )\\n\\n availability.add( formal.Field('show', formal.Boolean()))\\n availability.add( formal.Field('available', formal.Boolean()) )\\n availability.add( formal.Field('availabilityDescription', formal.String()) )\\n\\n metadata = formal.Group('metadata')\\n form.add( metadata )\\n\\n metadata.add( formal.Field('date', formal.Date(), formal.widgetFactory(formal.DatePartsInput, dayFirst=True)))\\n metadata.add( formal.Field('location', formal.String()) )\\n \\n lensOptions = [\\n \\\"80mm Schneider Super Symmar XL f/4.5\\\",\\n \\\"110mm Schneider Super Symmar XL f/5.6\\\",\\n \\\"150mm Rodenstock Sironar S f/5.6\\\",\\n \\\"240mm Fujinon A f/9\\\",\\n \\\"360mm Nikkor T*ED f/8\\\",\\n \\\"360mm Nikkor T*ED f/11\\\",\\n ]\\n metadata.add( formal.Field('lens', formal.String(),formal.widgetFactory(formal.SelectOtherChoice, options=lensOptions) ) )\\n \\n # this is a redundant field... need to remove if possible\\n metadata.add( formal.Field('speedaperture', formal.String()) )\\n \\n speedOptions = ['1/500', '1/250','1/125','1/60','1/30','1/15','1/8','1/4','1/2','1s','2s','4s','8s','15s','30s','1m','2m']\\n metadata.add( formal.Field('speed', formal.String(),formal.widgetFactory(formal.SelectOtherChoice, options=speedOptions),description='If you enter a text value please use the same format as the existing values e.g. 6s, 1/3, 2m' ) )\\n \\n \\n apertureOptions = ['f/5.6','f/6.3','f/8','f/8⅓','f/8½','f/8⅔','f/16','f/16⅓','f/16½','f/16⅔','f/22','f/22⅓','f/22½','f/22⅔','f/32','f/32⅓','f/32½','f/32⅔','f/45','f/45⅓','f/45½','f/45⅔']\\n metadata.add( formal.Field('aperture', formal.String(),formal.widgetFactory(formal.SelectOtherChoice, options=apertureOptions) ) ) \\n metadata.add( formal.Field('tiltswing', formal.String()) )\\n metadata.add( formal.Field('fronttilt', formal.Integer()) )\\n metadata.add( formal.Field('reartilt', formal.Integer()) )\\n metadata.add( formal.Field('risefall', formal.String()) )\\n ndfilters = ['0.3S','0.45S','0.6S','0.75S','0.9S','0.3H','0.45H','0.6H','0.75H','0.9H']\\n metadata.add( formal.Field('ndfilters', formal.String(),formal.widgetFactory(formal.SelectOtherChoice, options=ndfilters)) )\\n otherfilters=['81A','81B','81C','Polariser']\\n metadata.add( formal.Field('otherfilters', formal.String(), formal.widgetFactory(formal.SelectOtherChoice, options=otherfilters)) )\\n\\n \\n \\n \\n data_strings = [\\n (0, '-'),\\n (1, '*'),\\n (2, '**'),\\n (3, '***'),\\n (4, '****'),\\n (5, '*****'),\\n ] \\n \\n metadata.add( formal.Field('rating', formal.Integer(), formal.widgetFactory(formal.SelectChoice, options=data_strings)) )\\n\\n\\n description = formal.Group('description')\\n form.add( description )\\n parsers = [('markdown','MarkDown'),('xhtml','XHTML'),('plain','Plain Text')]\\n description.add( formal.Field('summary', formal.RichTextType(required=True),\\n widgetFactory=formal.widgetFactory(richtextarea.RichTextArea, parsers=parsers),\\n cssClass=' '.join(['imagepicker','preview','itemselector']) ) )\\n description.add( formal.Field('description', formal.RichTextType(required=True),\\n widgetFactory=formal.widgetFactory(richtextarea.RichTextArea, parsers=parsers),\\n cssClass=' '.join(['imagepicker','preview','itemselector']) ) )\\n description.add( formal.Field('categories', formal.Sequence(formal.String()), \\n widgetFactory=categorieswidget.FormalCheckboxTreeMultichoice ) )\\n\\n\\n\\n if not hasOptions:\\n pricing = formal.Group('pricing')\\n form.add( pricing )\\n pricing.add( formal.Field('price', formal.Decimal(required=True)) )\\n\\n\\n seo = formal.Group('seo')\\n form.add( seo )\\n seo.add( formal.Field('titleTag', formal.String()) )\\n seo.add( formal.Field('metaDescription', formal.String()) )\\n seo.add( formal.Field('metaKeywords', formal.String()) )\",\n \"def get_user_input():\\n st.sidebar.header('Parámetros de entrada') \\n acti2 = st.sidebar.selectbox('Código de Actividad Económica', ['ACABADO DE PRODUCTOS TEXTILES',\\n 'ACTIVIDADES COMBINADAS DE SERVICIOS ADMINISTRATIVOS DE OFICINA', \\n 'ACTIVIDADES CREATIVAS, ARTÍSTICAS Y DE ENTRETENIMIENTO', \\n 'ACTIVIDADES DE AGENCIAS DE COBRO Y AGENCIAS DE CALIFICACIÓN CREDITICIA', \\n 'ACTIVIDADES DE AGENCIAS DE EMPLEO', \\n 'ACTIVIDADES DE AGENCIAS DE VIAJES', \\n 'ACTIVIDADES DE AGENTES Y CORREDORES DE SEGUROS', \\n 'ACTIVIDADES DE ALOJAMIENTO PARA ESTANCIAS CORTAS', \\n 'ACTIVIDADES DE APOYO A LA ENSEÑANZA', \\n 'ACTIVIDADES DE APOYO PARA LA EXTRACCIÓN DE PETRÓLEO Y GAS NATURAL', \\n 'ACTIVIDADES DE APOYO PARA OTRAS ACTIVIDADES DE EXPLOTACIÓN DE MINAS Y CANTERAS', \\n 'ACTIVIDADES DE ARQUITECTURA E INGENIERÍA Y ACTIVIDADES CONEXAS DE CONSULTORÍA TÉCNICA', \\n 'ACTIVIDADES DE ASOCIACIONES EMPRESARIALES Y DE EMPLEADORES', \\n 'ACTIVIDADES DE ASOCIACIONES PROFESIONALES', \\n 'ACTIVIDADES DE ATENCIÓN DE ENFERMERÍA EN INSTITUCIONES', \\n 'ACTIVIDADES DE BIBLIOTECAS Y ARCHIVOS', \\n 'ACTIVIDADES DE CENTROS DE LLAMADAS', \\n 'ACTIVIDADES DE CLUBES DEPORTIVOS', \\n 'ACTIVIDADES DE CONSULTORÍA DE GESTIÓN', \\n 'ACTIVIDADES DE CONTABILIDAD, TENEDURÍA DE LIBROS Y AUDITORÍA; CONSULTORÍA FISCAL', \\n 'ACTIVIDADES DE DESCONTAMINACIÓN Y OTROS SERVICIOS DE GESTIÓN DE DESECHOS', \\n 'ACTIVIDADES DE DISTRIBUCIÓN DE PELÍCULAS CINEMATOGRÁFICAS, VÍDEOS Y PROGRAMAS DE TELEVISIÓN', \\n 'ACTIVIDADES DE ENVASADO Y EMPAQUETADO', \\n 'ACTIVIDADES DE EXHIBICIÓN DE PELÍCULAS CINEMATOGRÁFICAS Y CINTAS DE VÍDEO', \\n 'ACTIVIDADES DE FOTOGRAFÍA', \\n 'ACTIVIDADES DE GESTIÓN DE FONDOS', \\n 'ACTIVIDADES DE HOSPITALES', \\n 'ACTIVIDADES DE INVESTIGACIÓN', \\n 'ACTIVIDADES DE JARDINES BOTÁNICOS Y ZOOLÓGICOS Y RESERVAS NATURALES', \\n 'ACTIVIDADES DE JUEGOS DE AZAR Y APUESTAS', \\n 'ACTIVIDADES DE MENSAJERÍA', \\n 'ACTIVIDADES DE MUSEOS Y GESTIÓN DE LUGARES Y EDIFICIOS HISTÓRICOS', \\n 'ACTIVIDADES DE MÉDICOS Y ODONTÓLOGOS', \\n 'ACTIVIDADES DE OFICINAS CENTRALES', \\n 'ACTIVIDADES DE OPERADORES TURÍSTICOS', \\n 'ACTIVIDADES DE ORGANIZACIONES RELIGIOSAS', \\n 'ACTIVIDADES DE OTRAS ASOCIACIONES N.C.P.', \\n 'ACTIVIDADES DE PARQUES DE ATRACCIONES Y PARQUES TEMÁTICOS', \\n 'ACTIVIDADES DE PRODUCCIÓN DE PELÍCULAS CINEMATOGRÁFICAS, VÍDEOS Y PROGRAMAS DE TELEVISIÓN', \\n 'ACTIVIDADES DE RESTAURANTES Y DE SERVICIO MÓVIL DE COMIDAS', \\n 'ACTIVIDADES DE SEGURIDAD PRIVADA', \\n 'ACTIVIDADES DE SERVICIO DE BEBIDAS', \\n 'ACTIVIDADES DE SERVICIO DE SISTEMAS DE SEGURIDAD', \\n 'ACTIVIDADES DE SERVICIOS RELACIONADAS CON LA IMPRESIÓN', \\n 'ACTIVIDADES DE SERVICIOS VINCULADAS AL TRANSPORTE ACUÁTICO', \\n 'ACTIVIDADES DE SERVICIOS VINCULADAS AL TRANSPORTE AÉREO', \\n 'ACTIVIDADES DE SERVICIOS VINCULADAS AL TRANSPORTE TERRESTRE', \\n 'ACTIVIDADES DE TELECOMUNICACIONES ALÁMBRICAS', \\n 'ACTIVIDADES DE TELECOMUNICACIONES INALÁMBRICAS', \\n 'ACTIVIDADES DE TELECOMUNICACIONES POR SATÉLITE.', \\n 'ACTIVIDADES ESPECIALIZADAS DE DISEÑO', \\n 'ACTIVIDADES INMOBILIARIAS REALIZADAS A CAMBIO DE UNA RETRIBUCIÓN O POR CONTRATA', \\n 'ACTIVIDADES INMOBILIARIAS REALIZADAS CON BIENES PROPIOS O ARRENDADOS', \\n 'ACTIVIDADES JURÍDICAS', \\n 'ACTIVIDADES POSTALES', \\n 'ACTIVIDADES VETERINARIAS', \\n 'ACUICULTURA DE AGUA DULCE', \\n 'ACUICULTURA MARÍTIMA', \\n 'ADMINISTRACIÓN DE MERCADOS FINANCIEROS', \\n 'ALMACENAMIENTO Y DEPÓSITO', \\n 'ALQUILER Y ARRENDAMIENTO DE OTROS EFECTOS PERSONALES Y ENSERES DOMÉSTICOS', \\n 'ALQUILER Y ARRENDAMIENTO DE OTROS TIPOS DE MAQUINARIA, EQUIPO Y BIENES TANGIBLES', \\n 'ALQUILER Y ARRENDAMIENTO DE VEHÍCULOS AUTOMOTORES', \\n 'ARRENDAMIENTO DE PROPIEDAD INTELECTUAL Y PRODUCTOS SIMILARES, EXCEPTO OBRAS PROTEGIDAS POR DERECHOS DE AUTOR', \\n 'ARRENDAMIENTO FINANCIERO', \\n 'ASERRADOS Y ACEPILLADURA DE MADERA', \\n 'CAPTACIÓN, TRATAMIENTO Y DISTRIBUCIÓN DE AGUA', \\n 'CONSTRUCCIÓN DE BUQUES Y ESTRUCTURAS FLOTANTES', \\n 'CONSTRUCCIÓN DE CARRETERAS Y LÍNEAS DE FERROCARRIL', \\n 'CONSTRUCCIÓN DE EDIFICIOS', \\n 'CONSTRUCCIÓN DE OTRAS OBRAS DE INGENIERÍA CIVIL', \\n 'CONSTRUCCIÓN DE PROYECTOS DE SERVICIO PÚBLICO', \\n 'CONSULTORÍA DE INFORMÁTICA Y DE GESTIÓN DE INSTALACIONES INFORMÁTICAS', \\n 'CORRETAJE DE VALORES Y DE CONTRATOS DE PRODUCTOS BÁSICOS', \\n 'CORTE, TALLA Y ACABADO DE LA PIEDRA', \\n 'CURTIDO Y ADOBO DE CUEROS', \\n 'DESTILACIÓN, RECTIFICACIÓN Y MEZCLA DE BEBIDAS ALCOHÓLICAS', \\n 'EDICIÓN DE LIBROS', \\n 'EDICIÓN DE PERIÓDICOS, REVISTAS Y OTRAS PUBLICACIONES PERIÓDICAS', \\n 'EDUCACIÓN DEPORTIVA Y RECREATIVA', \\n 'ELABORACIÒN Y CONSERVACIÓN DE CARNE', \\n 'ELABORACIÒN Y CONSERVACIÓN DE FRUTAS,LEGUMBRES Y HORTALIZAS', \\n 'ELABORACIÒN Y CONSERVACIÓN DE PESCADOS, CRUSTÁCEOS Y MOLUSCOS', \\n 'ELABORACIÓN DE ACEITES Y GRASAS DE ORIGEN VEGETAL Y ANIMAL', \\n 'ELABORACIÓN DE AZÚCAR', \\n 'ELABORACIÓN DE BEBIDAS MALTEADAS Y DE MALTA', \\n 'ELABORACIÓN DE BEBIDAS NO ALCOHÓLICAS', \\n 'ELABORACIÓN DE CACAO Y CHOCOLATE Y DE PRODUCTOS DE CONFITERÍA', \\n 'ELABORACIÓN DE COMIDAS Y PLATOS PREPARADOS', \\n 'ELABORACIÓN DE MACARRONES, FIDEOS, ALCUZCUS Y PRODUCTOS FARINÁCEOS SIMILARES', \\n 'ELABORACIÓN DE OTROS PRODUCTOS ALIMENTICIOS N.C.P.', \\n 'ELABORACIÓN DE PIENSOS PREPARADOS PARA ANIMALES', \\n 'ELABORACIÓN DE PRODUCTOS DE MOLINERÍA.', \\n 'ELABORACIÓN DE PRODUCTOS DE PANADERÍA', \\n 'ELABORACIÓN DE PRODUCTOS LÁCTEOS', \\n 'ELABORACIÓN DE VINOS', \\n 'ENSAYOS Y ANÁLISIS TÉCNICOS', \\n 'ENSEÑANZA CULTURAL', \\n 'ENSEÑANZA PREESCOLAR Y PRIMARIA', \\n 'ENSEÑANZA SECUNDARIA DE FORMACIÓN GENERAL', \\n 'ENSEÑANZA SECUNDARIA DE FORMACIÓN TÉCNICA Y PROFESIONAL', \\n 'ENSEÑANZA SUPERIOR', \\n 'ESTUDIOS DE MERCADO Y ENCUESTAS DE OPINIÓN PÚBLICA', \\n 'EVACUACIÓN DE AGUAS RESIDUALES', \\n 'EXPLOTACIÓN DE OTRAS MINAS Y CANTERAS N.C.P.', \\n 'EXTRACCIÓN DE CARBÓN DE PIEDRA', \\n 'EXTRACCIÓN DE GAS NATURAL', \\n 'EXTRACCIÓN DE MINERALES DE HIERRO', \\n 'EXTRACCIÓN DE MINERALES PARA LA FABRICACIÓN DE ABONOS Y PRODUCTOS QUÍMICOS', \\n 'EXTRACCIÓN DE OTROS MINERALES METALÍFEROS NO FERROSOS', \\n 'EXTRACCIÓN DE PETRÓLEO CRUDO', \\n 'EXTRACCIÓN DE PIEDRA, ARENA Y ARCILLA', \\n 'EXTRACCIÓN DE SAL', \\n 'FABRICACIÓN ABONOS Y COMPUESTOS DE NITRÓGENO', \\n 'FABRICACIÓN DE APARATOS DE USO DOMÉSTICO', \\n 'FABRICACIÓN DE ARTICULOS DE PUNTO Y GANCHILLO', \\n 'FABRICACIÓN DE ARTÍCULOS CONFECCIONADOS DE MATERIALES TEXTILES, EXCEPTO PRENDAS DE VESTIR', \\n 'FABRICACIÓN DE ARTÍCULOS DE CUCHILLERÍA, HERRAMIENTAS DE MANO Y ARTÍCULOS DE FERRETERÍA', \\n 'FABRICACIÓN DE ARTÍCULOS DE DEPORTE', \\n 'FABRICACIÓN DE ARTÍCULOS DE HORMIGÓN, DE CEMENTO Y DE YESO', \\n 'FABRICACIÓN DE ARTÍCULOS DE PIEL', \\n 'FABRICACIÓN DE BICICLETAS Y DE SILLONES DE RUEDAS PARA INVÁLIDOS', \\n 'FABRICACIÓN DE BISUTERÍA Y ARTÍCULOS CONEXOS', \\n 'FABRICACIÓN DE BOMBAS, COMPRESORES, GRIFOS Y VÁLVULAS', \\n 'FABRICACIÓN DE CALZADO', \\n 'FABRICACIÓN DE CARROCERÍAS PARA VEHÍCULOS AUTOMOTORES', \\n 'FABRICACIÓN DE CEMENTO, CAL Y YESO', \\n 'FABRICACIÓN DE COMPONENTES Y TABLEROS ELECTRÓNICOS', \\n 'FABRICACIÓN DE CUBIERTAS Y CÁMARAS DE CAUCHO', \\n 'FABRICACIÓN DE CUERDAS, CORDELES, BRAMANTES Y REDES', \\n 'FABRICACIÓN DE EQUIPO DE ELEVACIÓN Y MANIPULACIÓN', \\n 'FABRICACIÓN DE EQUIPO DE IRRADIACIÓN Y EQUIPO ELECTRÓNICO DE USO MÉDICO Y TERAPÉUTICO', \\n 'FABRICACIÓN DE EQUIPO ELÉCTRICO DE ILUMINACIÓN', \\n 'FABRICACIÓN DE FIBRAS ARTIFICIALES', \\n 'FABRICACIÓN DE HERRAMIENTAS DE MANO MOTORIZADAS', \\n 'FABRICACIÓN DE HOJAS DE MADERA PARA ENCHAPADO Y TABLEROS A BASE DE MADERA', \\n 'FABRICACIÓN DE INSTRUMENTOS Y MATERIALES MÉDICOS Y ODONTOLÓGICOS', \\n 'FABRICACIÓN DE INSTRUMENTOS ÓPTICOS Y EQUIPO FOTOGRÁFICO', \\n 'FABRICACIÓN DE JABONES Y DETERGENTES, PREPARADOS PARA LIMPIAR Y PULIR, PERFUMES Y PREPARADOS DE TOCADOR.', \\n 'FABRICACIÓN DE JOYAS Y ARTÍCULOS CONEXOS', \\n 'FABRICACIÓN DE JUEGOS Y JUGUETES', \\n 'FABRICACIÓN DE MALETAS, BOLSOS DE MANO, Y ARTÍCULOS SIMILARES,Y DE ARTICULOS DE TALABARTERÍA Y GUARNICIONERÍA', \\n 'FABRICACIÓN DE MAQUINARIA AGROPECUARIA Y FORESTAL', \\n 'FABRICACIÓN DE MAQUINARIA METALÚRGICA', \\n 'FABRICACIÓN DE MAQUINARIA PARA EXPLOTACIÓN DE MINAS Y CANTERAS Y PARA OBRAS DE CONSTRUCCIÓN', \\n 'FABRICACIÓN DE MAQUINARIA PARA LA ELABORACIÓN DE ALIMENTOS, BEBIDAS Y TABACO', \\n 'FABRICACIÓN DE MATERIALES DE CONSTRUCCIÓN DE ARCILLA', \\n 'FABRICACIÓN DE MOTOCICLETAS', \\n 'FABRICACIÓN DE MOTORES Y TURBINAS, EXCEPTO MOTORES PARA AERONAVES, VEHÍCULOS AUTOMOTORES Y MOTOCICLETAS', \\n 'FABRICACIÓN DE MOTORES, GENERADORES Y TRANSFORMADORES ELÉCTRICOS Y APARATOS DE DISTRIBUCIÓN Y CONTROL DE LA ENERGÍA ELÉCTRICA', \\n 'FABRICACIÓN DE MUEBLES', \\n 'FABRICACIÓN DE OTROS ARTÍCULOS DEL PAPEL Y CARTÓN', \\n 'FABRICACIÓN DE OTROS HILOS Y CABLES ELÉCTRICOS', \\n 'FABRICACIÓN DE OTROS PRODUCTOS DE CAUCHO', \\n 'FABRICACIÓN DE OTROS PRODUCTOS DE MADERA; FABRICACIÓN DE ARTÍCULOS DE CORCHO, PAJA Y MATERIALES TRENZABLES.', \\n 'FABRICACIÓN DE OTROS PRODUCTOS DE PORCELANA Y DE CERÁMICA', \\n 'FABRICACIÓN DE OTROS PRODUCTOS ELABORADOS DE METAL N.C.P.', \\n 'FABRICACIÓN DE OTROS PRODUCTOS MINERALES NO METÁLICOS N.C.P.', \\n 'FABRICACIÓN DE OTROS PRODUCTOS QUÍMICOS N.C.P.', \\n 'FABRICACIÓN DE OTROS PRODUCTOS TEXTILES N.C.P.', \\n 'FABRICACIÓN DE OTROS TIPOS DE EQUIPO DE TRANSPORTE N.C.P.', \\n 'FABRICACIÓN DE OTROS TIPOS DE EQUIPO ELÉCTRICO', \\n 'FABRICACIÓN DE OTROS TIPOS DE MAQUINARIA DE USO ESPECIAL', \\n 'FABRICACIÓN DE OTROS TIPOS DE MAQUINARIA DE USO GENERAL', \\n 'FABRICACIÓN DE PARTES Y PIEZAS DE CARPINTERÍA PARA EDIFICIOS Y CONSTRUCCIONES', \\n 'FABRICACIÓN DE PARTES, PIEZAS Y ACCESORIOS PARA VEHÍCULOS DE AUTOMOTORES', \\n 'FABRICACIÓN DE PASTA DE MADERA, PAPEL Y CARTÓN', \\n 'FABRICACIÓN DE PILAS, BATERÍAS Y ACUMULADORES', \\n 'FABRICACIÓN DE PINTURAS, BARNICES Y PRODUCTOS DE REVESTIMIENTO SIMILARES, TINTAS DE IMPRENTA Y MASILLAS', \\n 'FABRICACIÓN DE PLAGUICIDAS Y OTROS PRODUCTOS QUÍMICOS DE USO AGROPECUARIO', \\n 'FABRICACIÓN DE PLÁSTICOS Y DE CAUCHO SINTÉTICO EN FORMAS PRIMARIAS', \\n 'FABRICACIÓN DE PRENDAS DE VESTIR, EXCEPTO PRENDAS DE PIEL', \\n 'FABRICACIÓN DE PRODUCTOS DE LA REFINACIÓN DEL PETRÓLEO', \\n 'FABRICACIÓN DE PRODUCTOS DE PLÁSTICO', \\n 'FABRICACIÓN DE PRODUCTOS FARMACÉUTICOS, SUSTANCIAS QUÍMICAS MEDICINALES Y PRODUCTOS BOTÁNICOS DE USO FARMACÉUTICO', \\n 'FABRICACIÓN DE PRODUCTOS METÁLICOS PARA USO ESTRUCTURAL', \\n 'FABRICACIÓN DE PRODUCTOS PRIMARIOS DE METALES PRECIOSOS Y OTROS METALES NO FERROSOS', \\n 'FABRICACIÓN DE PRODUCTOS REFRACTARIOS', \\n 'FABRICACIÓN DE RECIPIENTES DE MADERA', \\n 'FABRICACIÓN DE SUSTANCIAS QUÍMICAS BÁSICAS', \\n 'FABRICACIÓN DE TANQUES, DEPÓSITOS Y RECIPIENTES DE METAL', \\n 'FABRICACIÓN DE TAPICES Y ALFOMBRAS', \\n 'FABRICACIÓN DE TEJIDOS DE PUNTO Y GANCHILLO', \\n 'FABRICACIÓN DE VEHÍCULOS AUTOMOTORES', \\n 'FABRICACIÓN DE VIDRIO Y DE PRODUCTOS DE VIDRIO', \\n 'FABRICACIÓN DEL GAS', \\n 'FABRICACIÓN DEL PAPEL Y CARTÓN ONDULADO Y DE ENVASES DE PAPEL Y CARTÓN', \\n 'FONDOS DE PENSIONES', \\n 'FONDOS Y SOCIEDADES DE INVERSIÓN Y ENTIDADES FINANCIERAS SIMILARES', \\n 'FORJA, PRENSADO, ESTAMPADO Y LAMINADO DE METALES; PULVIMETALURGIA', \\n 'FOTOCOPIADO, PREPARACIÓN DE DOCUMENTOS Y OTRAS ACTIVIDADES ESPECIALIZADAS DE APOYO DE OFICINA', \\n 'FUNDICIÓN DE HIERRO Y ACERO', \\n 'FUNDICIÓN DE METALES NO FERROSOS', \\n 'GENERACIÓN, TRANSMISIÓN Y DISTRIBUCIÓN DE ENERGÍA ELÉCTRICA', \\n 'GESTIÓN DE INSTALACIONES DEPORTIVAS', \\n 'IMPRESIÓN', \\n 'INDUSTRIAS BÁSICAS DE HIERRO Y ACERO', \\n 'INSTALACIONES DE FONTANERÍA, CALEFACCIÓN Y AIRE ACONDICIONADO', \\n 'INSTALACIONES ELÉCTRICAS', \\n 'INSTALACIÓN DE MAQUINARIA Y EQUIPO INDUSTRIALES', \\n 'INVESTIGACIÓN Y DESARROLLO EXPERIMENTAL EN EL CAMPO DE LAS CIENCIAS NATURALES Y LA INGENIERÍA', \\n 'INVESTIGACIÓN Y DESARROLLO EXPERIMENTAL EN EL CAMPO DE LAS CIENCIAS SOCIALES Y LAS HUMANIDADES', \\n 'LAVADO Y LIMPIEZA, INCLUIDA LA LIMPIEZA EN SECO, DE PRODUCTOS TEXTILES Y DE PIEL', \\n 'LIMPIEZA GENERAL DE EDIFICIOS', \\n 'MANIPULACIÓN DE CARGA', \\n 'MANTENIMIENTO Y REPARACIÓN DE VEHÍCULOS AUTOMOTORES', \\n 'ORGANIZACIÓN DE CONVENCIONES Y EXPOSICIONES COMERCIALES', \\n 'OTRAS ACTIVIDADES AUXILIARES DE LAS ACTIVIDADES DE SEGUROS Y FONDOS DE PENSIONES', \\n 'OTRAS ACTIVIDADES AUXILIARES DE LAS ACTIVIDADES DE SERVICIOS FINANCIEROS', \\n 'OTRAS ACTIVIDADES DE ALOJAMIENTO', \\n 'OTRAS ACTIVIDADES DE APOYO AL TRANSPORTE', \\n 'OTRAS ACTIVIDADES DE ASISTENCIA SOCIAL SIN ALOJAMIENTO', \\n 'OTRAS ACTIVIDADES DE ATENCIÓN DE LA SALUD HUMANA', \\n 'OTRAS ACTIVIDADES DE ATENCIÓN EN INSTITUCIONES', \\n 'OTRAS ACTIVIDADES DE CONCESIÓN DE CRÉDITO', \\n 'OTRAS ACTIVIDADES DE DOTACIÓN DE RECURSOS HUMANOS', \\n 'OTRAS ACTIVIDADES DE EDICIÓN', \\n 'OTRAS ACTIVIDADES DE ESPARCIMIENTO Y RECREATIVAS N.C.P.', \\n 'OTRAS ACTIVIDADES DE LIMPIEZA DE EDIFICIOS E INSTALACIONES INDUSTRIALES', \\n 'OTRAS ACTIVIDADES DE SERVICIO DE COMIDAS', \\n 'OTRAS ACTIVIDADES DE SERVICIOS DE APOYO A LAS EMPRESAS N.C.P', \\n 'OTRAS ACTIVIDADES DE SERVICIOS DE INFORMACIÓN N.C.P.', \\n 'OTRAS ACTIVIDADES DE SERVICIOS FINANCIEROS, EXCEPTO LAS DE SEGUROS Y FONDOS DE PENSIONES, N.C.P.', \\n 'OTRAS ACTIVIDADES DE SERVICIOS PERSONALES N.C.P.', \\n 'OTRAS ACTIVIDADES DE TECNOLOGÍA DE LA INFORMACIÓN Y DE SERVICIOS INFORMÁTICOS', \\n 'OTRAS ACTIVIDADES DE TELECOMUNICACIÓN.', \\n 'OTRAS ACTIVIDADES DE TRANSPORTE POR VÍA TERRESTRE', \\n 'OTRAS ACTIVIDADES DE VENTA AL POR MENOR EN COMERCIOS NO ESPECIALIZADOS', \\n 'OTRAS ACTIVIDADES DE VENTA AL POR MENOR NO REALIZADAS EN COMERCIOS, PUESTOS DE VENTA O MERCADOS', \\n 'OTRAS ACTIVIDADES DEPORTIVAS', \\n 'OTRAS ACTIVIDADES ESPECIALIZADAS DE LA CONSTRUCCIÓN', \\n 'OTRAS ACTIVIDADES PROFESIONALES, CIENTÍFICAS Y TÉCNICAS N.C.P.', \\n 'OTRAS INDUSTRIAS MANUFACTURERAS N.C.P.', \\n 'OTRAS INSTALACIONES PARA OBRAS DE CONSTRUCCIÓN', \\n 'OTROS SERVICIOS DE RESERVAS Y ACTIVIDADES CONEXAS', \\n 'OTROS TIPOS DE ENSEÑANZA N.C.P.', \\n 'OTROS TIPOS DE INTERMEDIACIÓN MONETARIA.', \\n 'PELUQUERÍA Y OTROS TRATAMIENTOS DE BELLEZA', \\n 'PESCA DE AGUA DULCE', \\n 'PESCA MARÍTIMA', \\n 'POMPAS FÚNEBRES Y ACTIVIDADES CONEXAS', \\n 'PORTALES WEB', \\n 'PREPARACIÓN DEL TERRENO', \\n 'PREPARACIÓN E HILATURA DE FIBRAS TEXTILES', \\n 'PROCESAMIENTO DE DATOS, HOSPEDAJE Y ACTIVIDADES CONEXAS', \\n 'PROGRAMACIÓN INFORMÁTICA', \\n 'PROGRAMACIÓN Y TRANSMISIONES DE TELEVISIÓN', \\n 'PUBLICIDAD', \\n 'RECOGIDA DE DESECHOS NO PELIGROSOS', \\n 'RECOGIDA DE DESECHOS PELIGROSOS', \\n 'RECUPERACIÓN DE MATERIALES', \\n 'REPARACIÓN DE APARATOS DE USO DOMÉSTICO Y EQUIPO DOMÉSTICO Y DE JARDINERÍA', \\n 'REPARACIÓN DE APARATOS ELECTRÓNICOS DE CONSUMO', \\n 'REPARACIÓN DE EQUIPO DE TRANSPORTE, EXCEPTO VEHÍCULOS AUTOMOTORES', \\n 'REPARACIÓN DE EQUIPO ELÉCTRICO', \\n 'REPARACIÓN DE EQUIPOS COMUNICACIONALES', \\n 'REPARACIÓN DE MAQUINARIA', \\n 'REPARACIÓN DE ORDENADORES Y EQUIPO PERIFÉRICO', \\n 'REPARACIÓN DE OTROS TIPOS DE EQUIPO', \\n 'REPARACIÓN DE PRODUCTOS ELABORADOS DE METAL', \\n 'SEGUROS DE VIDA', \\n 'SEGUROS GENERALES', \\n 'SUMINISTRO DE COMIDAS POR ENCARGO', \\n 'SUMINISTRO DE VAPOR Y AIRE ACONDICIONADO', \\n 'TEJEDURA DE PRODUCTOS TEXTILES', \\n 'TERMINACIÓN Y ACABADO DE EDIFICIOS', \\n 'TRANSMISIONES DE RADIO', \\n 'TRANSPORTE DE CARGA MARÍTIMO Y DE CABOTAJE', \\n 'TRANSPORTE DE CARGA POR CARRETERA', \\n 'TRANSPORTE DE CARGA POR FERROCARRIL', \\n 'TRANSPORTE DE CARGA POR VÍA AÉREA', \\n 'TRANSPORTE DE CARGA, POR VÍAS DE NAVEGACIÓN INTERIORES', \\n 'TRANSPORTE DE PASAJEROS MARÍTIMO Y DE CABOTAJE', \\n 'TRANSPORTE DE PASAJEROS POR VÍA AÉREA', \\n 'TRANSPORTE DE PASAJEROS POR VÍAS DE NAVEGACIÓN INTERIORES', \\n 'TRANSPORTE INTERURBANO DE PASAJEROS POR FERROCARRIL', \\n 'TRANSPORTE URBANO Y SUBURBANO DE PASAJEROS POR VÍA TERRESTRE', \\n 'TRATAMIENTO Y ELIMINACIÓN DE DESECHOS NO PELIGROSOS', \\n 'TRATAMIENTO Y ELIMINACIÓN DE DESECHOS PELIGROSOS', \\n 'TRATAMIENTO Y REVESTIMIENTO DE METALES', \\n 'VENTA AL POR MAYOR A CAMBIO DE UNA RETRIBUCIÓN O POR CONTRATA', \\n 'VENTA AL POR MAYOR DE ALIMENTOS, BEBIDAS Y TABACO.', \\n 'VENTA AL POR MAYOR DE COMBUSTIBLES SÓLIDOS, LÍQUIDOS Y GASEOSOS Y PRODUCTOS CONEXOS', \\n 'VENTA AL POR MAYOR DE DESPERDICIOS, DESECHOS, CHATARRA Y OTROS PRODUCTOS N.C.P', \\n 'VENTA AL POR MAYOR DE EQUIPO, PARTES Y PIEZAS ELECTRÓNICOS Y DE TELECOMUNICACIONES', \\n 'VENTA AL POR MAYOR DE MAQUINARIA, EQUIPO Y MATERIALES AGROPECUARIOS', \\n 'VENTA AL POR MAYOR DE MATERIALES DE CONSTRUCCIÓN, ARTÍCULOS DE FERRETERÍA Y EQUIPO Y MATERIALES DE FONTANERÍA Y CALEFACCIÓN.', \\n 'VENTA AL POR MAYOR DE MATERIAS PRIMAS AGROPECUARIAS Y ANIMALES VIVOS.', \\n 'VENTA AL POR MAYOR DE METALES Y MINERALES METALÍFEROS', \\n 'VENTA AL POR MAYOR DE ORDENADORES, EQUIPO PERIFÉRICO Y PROGRAMAS DE INFORMÁTICA', \\n 'VENTA AL POR MAYOR DE OTROS ENSERES DOMÉSTICOS', \\n 'VENTA AL POR MAYOR DE OTROS TIPOS DE MAQUINARIA Y EQUIPO', \\n 'VENTA AL POR MAYOR DE PRODUCTOS TEXTILES, PRENDAS DE VESTIR Y CALZADO', \\n 'VENTA AL POR MAYOR NO ESPECIALIZADA', \\n 'VENTA AL POR MENOR DE ALIMENTOS EN COMERCIOS ESPECIALIZADOS', \\n 'VENTA AL POR MENOR DE ALIMENTOS, BEBIDAS Y TABACO EN PUESTOS DE VENTA Y MERCADOS', \\n 'VENTA AL POR MENOR DE APARATOS ELÉCTRICOS DE USO DOMÉSTICO, MUEBLES, EQUIPO DE ILUMINACIÓN Y OTROS ENSERES DOMÉSTICOS EN COMERCIOS ESPECIALIZADOS', \\n 'VENTA AL POR MENOR DE ARTÍCULOS DE FERRETERÍA, PINTURAS Y PRODUCTOS DE VIDRIO EN COMERCIOS ESPECIALIZADOS', \\n 'VENTA AL POR MENOR DE BEBIDAS EN COMERCIOS ESPECIALIZADOS', \\n 'VENTA AL POR MENOR DE COMBUSTIBLES PARA VEHÍCULOS AUTOMOTORES EN COMERCIOS ESPECIALIZADOS', \\n 'VENTA AL POR MENOR DE EQUIPO DE DEPORTE EN COMERCIOS ESPECIALIZADOS', \\n 'VENTA AL POR MENOR DE EQUIPO DE SONIDO Y DE VÍDEO EN COMERCIOS ESPECIALIZADOS', \\n 'VENTA AL POR MENOR DE LIBROS, PERIÓDICOS Y ARTÍCULOS DE PAPELERÍA EN COMERCIOS ESPECIALIZADOS', \\n 'VENTA AL POR MENOR DE ORDENADORES, EQUIPO PERIFÉRICO, PROGRAMAS INFORMÁTICOS Y EQUIPO DE TELECOMUNICACIONES EN COMERCIOS ESPECIALIZADOS', \\n 'VENTA AL POR MENOR DE OTROS PRODUCTOS EN PUESTOS DE VENTA Y MERCADOS', \\n 'VENTA AL POR MENOR DE OTROS PRODUCTOS NUEVOS EN COMERCIOS ESPECIALIZADOS', \\n 'VENTA AL POR MENOR DE PRENDAS DE VESTIR, CALZADO Y ARTÍCULOS DE CUERO EN COMERCIOS ESPECIALIZADOS', \\n 'VENTA AL POR MENOR DE PRODUCTOS FARMACÉUTICOS Y MEDICINALES, COSMÉTICOS Y ARTÍCULOS DE TOCADOR EN COMERCIOS ESPECIALIZADOS', \\n 'VENTA AL POR MENOR DE PRODUCTOS TEXTILES EN COMERCIOS ESPECIALIZADOS', \\n 'VENTA AL POR MENOR DE PRODUCTOS TEXTILES, PRENDAS DE VESTIR Y CALZADO EN PUESTOS DE VENTA Y MERCADOS', \\n 'VENTA AL POR MENOR EN COMERCIOS NO ESPECIALIZADOS CON PREDOMINIO DE LA VENTA DE ALIMENTOS, BEBIDAS O TABACO', \\n 'VENTA AL POR MENOR POR CORREO Y POR INTERNET', \\n #'VENTA DE VEHÍCULOS AUTOMOTORES' 128\\n #'VENTA, MANTENIMIENTO Y REPARACIÓN DE MOTOCICLETAS Y DE SUS PARTES, PIEZAS Y ACCESORIOS.', \\n #'VENTAS DE PARTES, PIEZAS Y ACCESORIOS PARA VEHÍCULOS AUTOMOTORES'\\n ])\\n Departament = st.sidebar.selectbox('Nombre del Departamento', ['AMAZONAS','AREQUIPA','ÁNCASH','APURÍMAC','AYACUCHO','HUANCAVELICA','HUÁNUCO','JUNÍN','MADRE DE DIOS','MOQUEGUA','PASCO','SAN MARTÍN','TACNA','TUMBES','UCAYALI','PUNO','LIMA','CALLAO','CUSCO','LA LIBERTAD','JUNÍN','CAJAMARCA','LAMBAYEQUE','LORETO'])\\n Tama = st.sidebar.selectbox('Tamaño de Empresa', ['MICRO', 'PEQUEÑA','MEDIANA','GRANDE'])\\n st.sidebar.header('Seguridad: No(0), Si(1))') \\n F1 = st.sidebar.slider('Infraestructura física (alambrado, muros, etc.?', 0,1)\\n F2 = st.sidebar.slider('Sistema de video y captura de imágenes?', 0,1)\\n F3 = st.sidebar.slider('Sistema de control de acceso de personal?', 0,1)\\n F4 = st.sidebar.slider('Sistema de alarma de seguridad electrónica?', 0,1)\\n F5 = st.sidebar.slider('Seguridad para el traslado de valores?', 0,1)\\n F6 = st.sidebar.slider('Seguridad para el traslado de bienes?', 0,1)\\n F7 = st.sidebar.slider('Personal para resguardo (guardaespaldas)?',0,1)\\n F8 = st.sidebar.slider('Personal de seguridad de bienes e inmuebles?', 0,1)\\n \\n features = {'acti2': acti2\\t,\\n 'Departament': Departament,\\n 'Tama': Tama,\\n 'F1': F1,\\n 'F2': F2,\\n 'F3': F3,\\n 'F4': F4,\\n 'F5': F5,\\n 'F6': F6,\\n 'F7': F7,\\n 'F8': F8}\\n data = pd.DataFrame(features,index=[0])\\n\\n return data\",\n \"def __busca_notas(self, tipo_busca, intervalo_inicial, intervalo_final, serie, \\r\\n\\t\\t chave_acesso):\\r\\n info_consulta = \\\"11\\\" # padrao 1\\r\\n\\tresposta = \\\" \\\"*231 # padrao 230\\r\\n\\t#resposta = None \\r\\n\\r\\n status = self.dll.rRetornarInformacao_NFCe_Daruma(tipo_busca, \\r\\n intervalo_inicial, intervalo_final, serie, chave_acesso, \\r\\n\\t info_consulta, resposta) \\r\\n\\tif status !=1:\\r\\n\\t if status == -1:\\r\\n\\t\\traise Exception(\\\"-1: Erro encontrado na execucao do metodo\\\")\\r\\n elif status == -2:\\r\\n\\t\\traise Exception(\\\"-2: Chave Invalida\\\")\\r\\n\\t elif status == -3:\\r\\n\\t\\traise Exception(\\\"-3: Falha no schema XML.\\\")\\r\\n\\t elif status == -4:\\r\\n\\t\\traise Exception(\\\"-4: XML fora do padrao\\\")\\r\\n\\t elif status == -5:\\r\\n\\t\\traise Exception(\\\"-5: Erro generico\\\")\\r\\n\\t elif status == -8:\\r\\n\\t\\traise Exception(\\\"-8: Usuario nao Autorizado\\\")\\r\\n elif status == -9:\\r\\n\\t\\traise Exception(\\\"-9: Usuario nao Licenciado\\\")\\r\\n\\t elif status == -10:\\r\\n\\t\\traise Exception(\\\"-10: Documento e Ambiente nao identificados\\\")\\r\\n\\t elif status == -13:\\r\\n\\t\\traise Exception(\\\"-13: Tipo de Documento nao identificado\\\")\\r\\n elif status == -14:\\r\\n\\t\\traise Exception(\\\"-14: Erro retornado pelo WebService.\\\")\\r\\n elif status == -52:\\r\\n\\t\\traise Exception(\\\"-52: Erro ao gravar em arquivo temporario\\\")\\r\\n elif status == -99:\\r\\n\\t\\traise Exception(\\\"-99: Parametros invalidos ou ponteiro nulo de pametros\\\")\\r\\n elif status == -99:\\r\\n\\t\\traise Exception(\\\"-103: Nao foram encontradas as DLLs auxiliaes\\\")\\r\\n\\t else:\\r\\n\\t\\traise Exception(\\\"Erro ao executar o metodo Retornar Informacao.\\\")\",\n \"def fit():\\n form = MedForm(request.form)\\n if request.method == 'POST' and form.validate():\\n\\n zipcode = form.zipcode.data\\n # Check the zipcode\\n\\n plan = form.plan.data\\n medication = form.medication.data\\n\\n ip = str(request.environ.get('HTTP_X_REAL_IP', request.remote_addr))\\n rq = Requests(**dict(user=current_user.id, ip = ip, zipcode = zipcode, plan = plan, drug = medication))\\n rq.save()\\n\\n # Process either medicare or medicaid\\n plan_type = form.plan_type.data\\n try:\\n if plan_type == 'medicare':\\n table = get_medicare_plan(medication, plan, zipcode)\\n else:\\n table = get_medicaid_plan(medication, plan, zipcode, plan_type)\\n\\n except tools.BadPlanName as e:\\n form.errors['plan_name'] = str(e)\\n context = {'form': form}\\n html = 'fit.html'\\n\\n except tools.BadLocation as e:\\n form.errors['zipcode'] = str(e)\\n context = {'form': form}\\n html = 'fit.html'\\n else:\\n # You have to order the data in a list or it won't show right\\n data = []\\n for item in table['data']:\\n row = [item[h] for h in table['heading']]\\n data.append(row)\\n\\n context = {'data':data,\\n 'head':table['heading'],\\n 'drug':medication,\\n 'pa': table['pa'],\\n 'zipcode':zipcode,\\n 'plan':plan,\\n 'plan_type':form.plan_type.data,\\n }\\n html = 'table.html'\\n\\n # If its a GET see if parameters were passed\\n else:\\n if request.method == 'GET':\\n form.zipcode.data = request.args.get('zipcode', \\\"\\\")\\n form.plan.data = request.args.get('plan', \\\"\\\")\\n form.medication.data = request.args.get('drug', \\\"\\\")\\n form.plan_type.data = request.args.get('plan_type', \\\"medicare\\\")\\n\\n # a POST with errors\\n elif form.errors:\\n if 'plan_type' in form.errors:\\n form.errors['plan_type'] = \\\"Please pick a Medicare, Medicaid, or Private plan\\\"\\n\\n context = {'form': form}\\n html = 'fit.html'\\n\\n content = render_template(html, **context)\\n return content\",\n \"def crearItem(request,Faseid):\\n try:\\n fase = Fase.objects.get(id_Fase=Faseid)\\n except:\\n return HttpResponse(request,\\\"id de fase invalida\\\",status=400)\\n\\n proyecto=Proyecto.objects.get(id_proyecto=fase.id_Proyecto.id_proyecto)\\n fases=Fase.objects.filter(id_Proyecto=proyecto)\\n\\n if validar_permiso(request.user,\\\"is_gerente\\\",fase.id_Proyecto) or request.user.has_perm('crear_item',proyecto) and validar_rol_fase('crear_item',fase,request.user):\\n print('tiene el permiso de crear_item')\\n else:\\n messages.error(request,\\\"NO SE POSEE EL PERMISO: crear_item\\\" + \\\" SOLICITE EL PERMISO CORRESPONDINTE PARA REALIZAR LA ACCION\\\")\\n return redirect('gestion:detallesFase',fase.id_Proyecto.id_proyecto)\\n\\n #if(proyecto.estado != \\\"INICIADO\\\"):\\n # messages.error(request,\\\"EL PROYECTO NO SE ENCUENTRA INICIADO POR ENDE NO SE PUEDE CREAR ITEMS AUN, FAVOR CAMBIE SU ESTADO A INICIADO SI DESEA REALIZAR ESTA ACCION, ESTADO ACTUAL DEL PROYECTO: \\\"+str(proyecto.estado))\\n # return redirect('gestion:detallesFase',fase.id_Proyecto.id_proyecto)\\n \\\"\\\"\\\"\\n if(fase1SinItems(fases,fase)==True):# si no es de la primera fase y la F1 no tiene items muestra error\\n messages.error(request,\\\"LA FASE ANTERIOR NO CONTIENE ITEMS POR ENDE NO PODRA RELACIONAR CON LA PRIMERA FASE, CREE ITEM EN LA FASE ANTERIOR A ESTA Y LUEGO INTENTE NUEVAMENTE\\\")\\n return redirect('gestion:detallesFase',fase.id_Proyecto.id_proyecto)\\n \\\"\\\"\\\" \\n if (hayTiFase(fase)): # muestra mensaje de error si no hay TI no se puede crear item\\n messages.error(request,\\\"LA FASE NO CONTIENE NINGUN TI Y UN ITEM NECESARIAMENTE REQUIERE UNA, ASI QUE CREELA E INTENTE NUEVAMENTE\\\")\\n return redirect('gestion:detallesFase',fase.id_Fase)\\n\\n form= FormItem(request.POST)\\n if form.is_valid():\\n form.save(commit=False)\\n\\n datosFormulario= form.cleaned_data\\n fase= Fase.objects.get(id_Fase=Faseid)\\n item=Item(nombre=datosFormulario.get('nombre'),descripcion=datosFormulario.get('descripcion'),costo=datosFormulario.get('costo'),fase=fase)\\n\\n item.save()\\n\\n #varsion_item = Versiones(id_item=item.id_item, id_padre=item.id_item,\\n # id_Version=1)\\n #varsion_item.save()\\n return redirect('gestion:agg_listar_tipo_item',Faseid)\\n contexto={\\n \\\"form\\\":form\\n }\\n return render (request,'items/Item.html',contexto)\",\n \"def visionActivada(\\n self,\\n ):\\n Ventana_Formulario(self.root_controler)\",\n \"def ejecutarproceso(self):\\n self.generarConsultasLibres()\\n self.aplicarConsultasLibres()\\n self.generarCombinaciones()\\n self.generarConsultasConexion()\\n self.archivoSalida()\",\n \"def listar_atributos(request,idAtributoTI,id_item,ver = None):\\n print('el id del item es :',id_item)\\n atributos = Atributo_Item.objects.filter(id_item=id_item) #obtiene los valores del atributo del item\\n TI=TipoItem.objects.get(id_ti=idAtributoTI) #obtiene el tipo de item\\n atributo= Atributo.objects.filter(ti=TI) #lista los atributos del tipo de item\\n itemActual=Item.objects.get(id_item=id_item) #el item a ser modificado\\n if(request.method=='POST'):\\n if request.method == 'POST':\\n ''' obtengo el item viejo (itemActual) y actualizo sus valores de boolean a false.'''\\n itemActual.actual=False\\n itemActual.save()\\n '''Ahora en estas variables voy a obtener los valores editados de mi ITEM '''\\n nombre_item_editado = request.POST.get('item_nombre')\\n descripcion_item_editado = request.POST.get('item_descripcion')\\n estado_item_editado = request.POST.get('item_estado')\\n costo_item_editado = request.POST.get('item_costo')\\n print('nombre es:',nombre_item_editado)\\n ''' crear un nuevo item, con un nuevo id con los valores recibidos del form'''\\n item_editado = Item(nombre=nombre_item_editado,descripcion=descripcion_item_editado,costo=costo_item_editado,\\n actual=True,estado=itemActual.estado,fase_id=TI.fase_id,ti_id=TI.id_ti)\\n item_editado.save()\\n\\n ''' Crear un nuevo registro en la tabla versiones con los valores correspondientes del id_padre y version'''\\n version_item_actual= Versiones.objects.get(id_item=itemActual.id_item) #obtengo el registro del item actual\\n varsion_item_editado = Versiones(id_item=item_editado.id_item, id_padre= version_item_actual.id_padre,\\n id_Version=version_item_actual.id_Version+1)\\n varsion_item_editado.save()\\n\\n diccionario_data = verificar_datos_form_atributo_item(request,TI.id_ti,atributo,request.POST,request.FILES)\\n print('el diccionario data es: ',diccionario_data)\\n for data in diccionario_data:\\n if(data['AtributoTI'].tipo_dato=='File'): #si es de tipo File ,entonces armo la ruta para subir el archivo\\n if(data['valor_atributo_item'] != 'Sin archivos adjuntos' and data['valor_atributo_item'] != 'Archivo Elminado' ): # si se recibe algo es porque el edito el archivo, por lo tanto creamos una nueva ruta en la nube\\n ruta = str(TI.fase.id_Proyecto.id_proyecto) + \\\"/\\\" + str(item_editado.id_item)\\n doc = data['valor_atributo_item']\\n PATH = f'/{ruta}/{doc[0]}'\\n ''' proceso de subir archivo a dropbox'''\\n SubirArchivo(doc[0], PATH)\\n print('-----',PATH)\\n ##se sube archivo a dropbox en segundo plano\\n t2 = Thread(\\n target=SubirArchivo,\\n args=(doc[0], PATH),\\n )\\n t2.start()\\n atributo_item_editado = Atributo_Item(idAtributoTI=data['AtributoTI'], id_item=item_editado,\\n valor=PATH)\\n atributo_item_editado.save()\\n elif(data['valor_atributo_item'] == 'Archivo Elminado'):\\n atributo_item_editado = Atributo_Item(idAtributoTI=data['AtributoTI'],\\n id_item=item_editado,\\n valor='Sin archivos adjuntos')\\n atributo_item_editado.save()\\n else:\\n atributo_item_editado = Atributo_Item(idAtributoTI=data['AtributoTI'], id_item=item_editado,\\n valor=Atributo_Item.objects.get(id_item=itemActual,idAtributoTI=data['AtributoTI']).valor)\\n print('el editado sin modificar',atributo_item_editado.valor)\\n atributo_item_editado.save()\\n else:\\n print('atributo_id:',data['AtributoTI'].id_atributo,' valor:',data['valor_atributo_item'])\\n atributo_item_editado=Atributo_Item(idAtributoTI=data['AtributoTI'], id_item=item_editado,\\n valor=data['valor_atributo_item'])\\n atributo_item_editado.save()\\n '''aca se actulizan las relaciones del item'''\\n itemActual_inicio=Relacion.objects.filter(inicio_item=itemActual.id_item) #obtengo todos los items en donde el es el origen\\n for relacion in itemActual_inicio:\\n nueva_relacion=Relacion(inicio_item=item_editado.id_item,fin_item=relacion.fin_item)\\n nueva_relacion.save()\\n\\n itemActual_fin = Relacion.objects.filter(fin_item=itemActual.id_item) # obtengo todos los items en donde el es el fin\\n for relacion in itemActual_fin:\\n nueva_relacion = Relacion(inicio_item=relacion.inicio_item, fin_item=item_editado.id_item)\\n nueva_relacion.save()\\n\\n return redirect('gestion:detallesFase',TI.fase_id)\\n\\n\\n context = {\\n \\\"atributos\\\":atributos,\\n \\\"atributo\\\":atributo,\\n 'proyectos': itemActual.fase.id_Proyecto,\\n 'item':itemActual,\\n 'true':True,\\n 'false':False,\\n 'ver':ver\\n }\\n return render(request, 'items/listar_atributos.html', context)\",\n \"def crear():\\n\\n return dict(form=form)\",\n \"def form_valid(self, form):\\n with create_revision():\\n # form_data = form.save(commit=False)\\n bom_name = form.cleaned_data['bom_name']\\n parts = get_bom_parts(bom_name)\\n total_price = cal_total_price(bom_name)\\n qty = form.cleaned_data['quantity']\\n product_name = form.cleaned_data['product_name']\\n warehouse_name = form.cleaned_data['warehouse_name']\\n asset_state = form.cleaned_data['asset_state']\\n mfg_date = form.cleaned_data['mfg_date']\\n remarks = form.cleaned_data['remarks']\\n # multipule_assets = []\\n # Outwards for asset creation\\n\\n # Outward all parts in an asset\\n # multiply it with assets `qty` to limit number\\n # queries for creation of Outwards\\n for part in parts:\\n part_obj = Parts.objects.get(part_number=part['PartNumber'])\\n authorised_by = \\\"asset_auto_creation\\\"\\n verified_by = \\\"asset_auto_creation\\\"\\n mode_of_transport = \\\"asset_auto_creation\\\"\\n remarks = \\\"This outward was created due to asset creation.\\\"\\n parts_assets_qty = float(part['Qty']) * qty\\n Outward.objects.create(\\n part_type=Type.objects.get(type_name=\\\"ASSETAUTOOUTWARD\\\"),\\n part_number=part_obj,\\n part_name=part_obj.part_name,\\n quantity=parts_assets_qty,\\n from_warehouse_name=warehouse_name,\\n to_warehouse_name=warehouse_name,\\n authorised_by=authorised_by,\\n verified_by=verified_by,\\n mode_of_transport=mode_of_transport,\\n unit_of_measure=part_obj.unit_of_measure,\\n remarks=remarks,\\n )\\n\\n for i in range(qty):\\n single_asset = Asset(\\n product_name=product_name,\\n bom_name=bom_name,\\n parts=parts,\\n total_price=total_price,\\n warehouse_name=warehouse_name,\\n asset_state=asset_state,\\n remarks=remarks,\\n mfg_date=mfg_date,\\n )\\n single_asset.save()\\n revisions.set_user(self.request.user)\\n revisions.set_comment(\\\"Created from web form.\\\")\\n return_value = super().form_valid(single_asset)\\n return return_value\",\n \"def load_drug_forms():\\n\\n print(\\\"Forms\\\")\\n\\n Form.query.delete()\\n\\n with open(\\\"seed_data/forms_seed.psv\\\") as forms:\\n for row in forms:\\n name = row.strip()\\n\\n form = Form(form_name=name)\\n\\n db.session.add(form)\\n\\n db.session.commit()\",\n \"def initcomponentform():\\n form = ComponentsForm()\\n form.name.value = []\\n form.id.value = []\\n form.currentstock.value = []\\n form.reorderlevel.value = []\\n form.unitprice.value = []\\n form.supplier.value = []\\n form.location.value = []\\n form.datasheet.value = []\\n return form\",\n \"def buscarpermiso():\\n if not current_user.is_authenticated():\\n flash('Debe loguearse primeramente!!!!', 'loggin')\\n return render_template('index.html')\\n \\n permission = UserRol('ADMINISTRADOR')\\n if permission.can():\\n valor = request.args['patron']\\n parametro = request.args['parametro']\\n idrol =request.form.get('rol')\\n #init_db(db_session)\\n if valor=='' or valor == None:\\n return administrarpermiso()\\n else:\\n if parametro == 'id_recurso':\\n p = db_session.query(Permiso).from_statement(\\\"SELECT * FROM permiso where to_char(\\\"+parametro+\\\", '99999') ilike '%\\\"+valor+\\\"%'\\\").all()\\n #p = db_session.query(Permiso).from_statement(\\\"SELECT * FROM permiso where \\\"+parametro+\\\" = CAST(\\\"+valor+\\\" AS Int)\\\").all()\\n else:\\n p = db_session.query(Permiso).from_statement(\\\"SELECT * FROM permiso where \\\"+parametro+\\\" ilike '%\\\"+valor+\\\"%'\\\").all()\\n #p = db_session.query(Permiso).filter(Permiso.codigo.like('%'+valor+'%'))\\n if idrol != '' and idrol != None:\\n yourPermiso=getPermisosByRol(idrol)\\n lista = []\\n for per in yourPermiso:\\n lista.append(per)\\n return render_template('permiso/administrarpermiso.html', permisos = p, isAdministrar = False, idrol = idrol, asignados = lista)\\n return render_template('permiso/administrarpermiso.html', permisos = p, isAdministrar = True)\\n else:\\n flash('Sin permisos para buscar permisos', 'permiso')\\n return render_template('index.html')\",\n \"def new(self, *args, **kw):\\n \\\"\\\"\\\"filtramos solo los Tipos de Items asociados al Proyecto\\\"\\\"\\\"\\n id_fase = args[0]\\n #traemos la fase de la BD\\n fase = DBSession.query(Fase).filter_by(id_fase = id_fase).one()\\n #obtenemos los Tipos de Items asignados a la fase..\\n tipo_items = fase.tipo_items\\n #obtenemos lo id y los nombres de los tipos de items del Proyecto para enviarlos como las opciones \\n #disponibles\\n id_tipo_items = []\\n for tipo_item in tipo_items:\\n id_tipo_items.append((tipo_item.id_tipo_item,tipo_item.nombre))\\n\\n if len(args) > 0:\\n kw['id_fase_fk'] = args[0]\\n\\n tmpl_context.widget = self.new_form\\n retorno = dict(value=kw, model=self.model.__name__)\\n retorno[\\\"id_tipo_items\\\"] = id_tipo_items\\n\\n return retorno\",\n \"def comenzar_nuevo_juego():\\n escena_uno.cargarEscena1(screen, display_width, display_height)#Se pone a correr la escena\\n #escena_uno.cargarEscena2(screen, display_width, display_height)\",\n \"def dispone_producto_espacio_sistema(self, sistema):\\r\\n camiones_comparables_a_cargar = 0\\r\\n camiones_comparables_a_descargar = 0\\r\\n\\r\\n if self.tipo == \\\"Carga\\\":\\r\\n camiones_comparables_a_cargar += \\\\\\r\\n sum(1 for c in sistema.camiones_en_sistema\\r\\n if c.carga == self.carga and c.tipo == \\\"Carga\\\" and not c.manipulado.triggered)\\r\\n camiones_comparables_a_descargar += \\\\\\r\\n sum(1 for c in sistema.camiones_en_sistema\\r\\n if c.carga == self.carga and c.tipo == \\\"Descarga\\\" and not c.manipulado.triggered)\\r\\n if not self.manipulado.triggered and self in sistema.camiones_en_sistema:\\r\\n camiones_comparables_a_cargar -= 1\\r\\n else:\\r\\n camiones_comparables_a_cargar += \\\\\\r\\n sum(1 for c in sistema.camiones_en_sistema\\r\\n if self.medios_almacenamiento_destino(sistema) == c.medios_almacenamiento_destino(sistema) and\\r\\n c.tipo == \\\"Carga\\\" and not c.manipulado.triggered)\\r\\n camiones_comparables_a_descargar += \\\\\\r\\n sum(1 for c in sistema.camiones_en_sistema\\r\\n if self.medios_almacenamiento_destino(sistema) == c.medios_almacenamiento_destino(sistema) and\\r\\n c.tipo == \\\"Descarga\\\" and not c.manipulado.triggered)\\r\\n if not self.manipulado.triggered and self in sistema.camiones_en_sistema:\\r\\n camiones_comparables_a_descargar -= 1\\r\\n\\r\\n producto_espacio_disponible = 0\\r\\n\\r\\n for almacen in self.medios_almacenamiento_destino(sistema):\\r\\n\\r\\n if self.tipo == \\\"Carga\\\":\\r\\n producto_espacio_disponible += almacen.niveles[self.carga]\\r\\n else:\\r\\n producto_espacio_disponible += almacen.espacio\\r\\n\\r\\n if self.tipo == \\\"Carga\\\":\\r\\n\\r\\n if producto_espacio_disponible + 28 * (camiones_comparables_a_descargar - camiones_comparables_a_cargar) \\\\\\r\\n >= 28:\\r\\n return True\\r\\n else:\\r\\n return False\\r\\n else:\\r\\n if self.carga == \\\"Fierro\\\":\\r\\n return True\\r\\n elif producto_espacio_disponible + 28 * (camiones_comparables_a_cargar - camiones_comparables_a_descargar) \\\\\\r\\n >= 28:\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def enviar_novos():\\n\\n template = 'produtos/form-enviar-produtos.html'\\n form = EnviarProdutosNovosForm()\\n imagens = read_images().keys()\\n\\n task = current_app.enviar_novos_task\\n clear_task = request.args.get('clear_task', None)\\n if clear_task and clear_task == 'yes':\\n if task and task.state == 'SUCCESS':\\n current_app.enviar_novos_task = task = None\\n return redirect(url_for('produtos.enviar_novos'))\\n\\n produtos = None\\n if not task:\\n produtos = buscar_produtos_novos()\\n\\n if not produtos and not task:\\n warning('Não existem produtos para serem enviados para o site')\\n\\n categorias = buscar_categorias_produtos()\\n if not categorias:\\n warning('Não existem categorias do site cadastradas')\\n\\n if form.validate_on_submit() and produtos and categorias:\\n produtos_task = {}\\n\\n for p in produtos:\\n secao = int(request.form.get(f'secao-{p.idsubproduto}', 0))\\n grupo = int(request.form.get(f'grupo-{p.idsubproduto}', 0))\\n subgrupo = int(request.form.get(f'subgrupo-{p.idsubproduto}', 0))\\n\\n if secao and grupo and subgrupo:\\n categorias = [secao, grupo, subgrupo]\\n produtos_task[p.idsubproduto] = categorias\\n\\n task = enviar_novos_task.apply_async(args=(produtos_task,))\\n current_app.enviar_novos_task = task\\n\\n success(f'Tarefa iniciada com sucesso')\\n return redirect(url_for('produtos.enviar_novos'))\\n\\n result = {\\n 'title': 'Produtos',\\n 'subtitle': 'Enviar Novos',\\n 'form': form,\\n 'imagens': imagens,\\n 'produtos': produtos,\\n 'categorias': categorias,\\n 'task': task\\n }\\n return render_template(template, **result)\",\n \"def agg_listar_tipo_item(request,id_fase):\\n\\n fase_proyecto=Fase.objects.get(id_Fase=id_fase)\\n proyecto=Proyecto.objects.get(id_proyecto=fase_proyecto.id_Proyecto.id_proyecto)\\n\\n if validar_permiso(request.user,\\\"is_gerente\\\",fase_proyecto.id_Proyecto) or request.user.has_perm('crear_item',proyecto) and validar_rol_fase('crear_item',fase_proyecto,request.user):\\n print('tiene el permiso de crear_item')\\n else:\\n messages.error(request,\\\"NO SE POSEE EL PERMISO: crear_item\\\" + \\\" SOLICITE EL PERMISO CORRESPONDINTE PARA REALIZAR LA ACCION\\\")\\n return redirect('gestion:detallesFase',fase_proyecto.id_Proyecto.id_proyecto)\\n\\n item_id=Item.objects.last()\\n tipoItem = TipoItem.objects.filter(fase_id=id_fase)\\n if(tipoItem.count() == 0):\\n return HttpResponse(request,\\\"id de fase invalida\\\",status=400)\\n\\n if request.method == 'POST':\\n x=request.POST.get('ti')\\n item=Item.objects.last()\\n tipoItem2 = TipoItem.objects.filter(nombre=x,fase_id=id_fase)\\n print(tipoItem2)\\n item.ti =tipoItem2[0]\\n item.save()\\n\\n return redirect('gestion:aggAtributos',tipoItem2[0].id_ti)\\n\\n contexto={\\n 'TipoItem':tipoItem,\\n 'id_item':item_id.id_item\\n }\\n return render (request,'items/aggTI.html',contexto)\",\n \"def ingresar_fasores(self):\\n print('\\\\n\\\\n')\\n print('Ingresar 2 Fasores\\\\nDebe respetar el formato adecuado, sinusoidales con frecuencias iguales\\\\n')\\n print('Para el formato Seno y conseno debe escribir: (a,b,c)\\\\nDonde a(amplitud), b(frecuencia) y c(fase inicial) representa al fasor\\\\n')\\n \\n formato = True\\n while formato:\\n \\n input_tipo_funcion=input('Elegir el tipo de la primer funcion o escriba r para regresar a las operaciones\\\\n1. Seno\\\\n2. Coseno\\\\n')\\n \\n if input_tipo_funcion == 'r':\\n return True\\n break\\n \\n if not self.validar_tipo_funcion(input_tipo_funcion):\\n continue\\n \\n input_fasor1 = input('Ingresar el primer Fasor o escriba r para regresar a las operaciones\\\\n')\\n \\n if input_fasor1 == 'r':\\n return True\\n break\\n \\n formato_valido_f1 = True\\n if input_fasor1[0] == '(':\\n formato_valido_f1 = self.controlador.validar_entrada_fasor(input_fasor1)\\n \\n input_tipo_funcion2=input('Elegir el tipo de la segunda funcion o escriba r para regresar a las operaciones \\\\n1. Seno\\\\n2. Coseno\\\\n')\\n \\n if input_tipo_funcion2 == 'r':\\n return True\\n break\\n \\n if not self.validar_tipo_funcion(input_tipo_funcion2):\\n continue \\n \\n input_fasor2 = input('Ingresar el segundo Fasor o escriba r para regresar a las operaciones\\\\n')\\n \\n if input_fasor2 == 'r':\\n return True\\n break\\n \\n formato_valido_f2 = True\\n if input_fasor2[0] == '(':\\n formato_valido_f2 = self.controlador.validar_entrada_fasor(input_fasor2) \\n \\n if not self.controlador.validar_igualdad_frecuencias(input_fasor1,input_fasor2):\\n print('Las funciones no tiene la mismas frecuencias')\\n continue\\n \\n if formato_valido_f1 == False and formato_valido_f2 == False: # salida del usuario para regresar\\n self.fasor1 = input_fasor1\\n self.fasor2 = input_fasor2\\n self.tipo_funcion1 = input_tipo_funcion\\n self.tipo_funcion2 = input_tipo_funcion2\\n return False\\n \\n print('Escriba nuevamente el formato correcto o presione la tecla r para salir\\\\n')\",\n \"def updatePeliculas():\\n\\txml_ciudades = \\\"http://api2.cinemex.com/rsvr.php?Action=GetFiltrados&IdDev=1\\\"\\n\\txml_peliculas = \\\"http://api2.cinemex.com/rsvr.php?Action=GetFiltrados&IdDev=1&ciudad=%s&byciudad=1\\\" #id_ciudad\\n \\n\\tciudades = parse_ciudades( urlopen(url_movil) ) \\n\\t\\n\\t\\n\\tbase_url_pelicula = \\\"http://www.cinemex.com/cartelera/pelicula.php?vcode=%s\\\" #mex_vc\\n\\tpeliculas = {}\\n \\n\\tpelis_obj = [] #Contiene toda la info de las peliculas, titulo, sinopsis, etc\\n \\n\\t#Crea un diccionario con el vc y el objeto de la pelicula\\n\\t#De esta forma no hay peliculas repetidas\\n\\tfor ciudad_id in ciudades:\\n\\t\\txml_url = xml_peliculas % ciudad_id\\n\\t\\ttry:\\n\\t\\t\\txml = urlopen(xml_url)\\n\\t\\texcept:\\n\\t\\t\\tlogger.debug( 'error cargando pagina %s' % xml_url)\\n\\t\\t\\tcontinue\\n\\t\\tpelis_actual = parse_peliculas(xml)\\n\\t\\t#Agregar las peliculas q no estan todavia\\n\\t\\tfor peli in pelis_actual:\\n\\t\\t\\tkey = peli.get('mex_vc', '')\\n\\t\\t\\tif key not in peliculas: peliculas[key] = peli\\n \\n\\tfor k, v in peliculas.items():\\n\\t\\turl = base_url_pelicula % k\\n\\t\\thtml = urlopen(url)\\n\\t\\tpelis_obj.append(scrape_pelicula(html, v))\\n \\n\\tfor peli in pelis_obj:\\n\\t\\tcreatePelicula(peli)\",\n \"def writerep_general(contact_link, i):\\n\\n b = browser.Browser()\\n print \\\"In writerep_general, opening contact_link\\\", contact_link\\n b.open(contact_link)\\n\\n def get_challenge():\\n ''' find captchas'''\\n labels = b.find_nodes('label', lambda x: x.get('for') == 'HIP_response')\\n if labels: return labels[0].string\\n \\n def fill_inhofe_lgraham(f):\\n \\\"\\\"\\\"special function to fill in forms for inhofe and lgraham\\\"\\\"\\\"\\n if DEBUG: print \\\"Filling special inhofe or lgraham form\\\"\\n f.fill_all(A01=i.prefix, B01=i.fname, C01=i.lname, D01=i.addr1, E01=i.addr2, F01=i.city,\\n G01=i.state, H01=i.zip5, H02=i.phone, H03=i.phone, I01=i.email, J01=\\\"Communications\\\", K01=i.full_msg)\\n f.fill(type='textarea', value=i.full_msg)\\n if DEBUG: print \\\"f filled and ready to submit: \\\", f\\n \\n def fill_form(f):\\n ''' f is a form '''\\n\\n f.fill_name(i.prefix, i.fname, i.lname)\\n if DEBUG: print \\\"in fill_form, filling addr\\\"\\n f.fill_address(i.addr1, i.addr2)\\n if DEBUG: print \\\"in fill_form, filling phone\\\"\\n f.fill_phone(i.phone)\\n if DEBUG: print \\\"in fill_form, filling textarea\\\"\\n textareacontrol = f.fill(type='textarea', value=i.full_msg)\\n if DEBUG: print 'filled textareacontrol' , textareacontrol\\n if DEBUG: print \\\"in fill_form, filling all\\\"\\n\\n if DEBUG: print \\\"Printing all controls\\\"\\n for c in f.controls:\\n if DEBUG: print \\\"control: \\\", c.name, \\\" type: \\\", c.type\\n \\n f.fill_all(city=i.city, zipcode=i.zip5, zip4=i.zip4, state=i.state.upper(),\\n email=i.email,\\n issue=['TECH', 'GEN', 'OTH'],\\n subject=i.subject, reply='yes',\\n Re='issue', #for billnelson\\n newsletter='noAction', aff1='Unsubscribe',\\n MessageType=\\\"Express an opinion or share your views with me\\\")\\n\\n # page has one required control that has no name. so we need to fill it in\\n if (i.dist == 'SD-00' or 'coburn' in b.url):\\n empty_controls = [c for c in f.controls if not c.value]\\n for c in empty_controls:\\n if DEBUG: print f.fill('OTH', control=c)\\n\\n \\n\\n\\n # Solve captchas. I included this here because it was placed here by Aaron,\\n # but I haven't found a captcha that it works on. -NKF\\n challenge = get_challenge()\\n if challenge:\\n print \\\"Found challenge!\\\"\\n try:\\n solution = captchasolver.solve(challenge)\\n except Exception, detail:\\n print >> sys.stderr, 'Exception in CaptchaSolve', detail\\n print >> sys.stderr, 'Could not solve:\\\"%s\\\"' % challenge,\\n \\n if DEBUG: print \\\"f filled and ready to submit to \\\", b.url, \\\"\\\\n\\\", f\\n #return b.open(f.click())\\n \\n \\n\\n # max loops\\n k = 6\\n\\n # needed this from some weird error that I forgot to document.\\n # we only want to do the WYR form once,\\n # so it's a flag so we don't choose this one again. \\n completedWyrForm = False\\n for cnt in range(1,k):\\n # todo, place newurl into cache\\n if DEBUG: print \\\"Loop \\\", cnt, \\\":\\\\n\\\", b.url, \\\"\\\\n\\\" #, b.page, \\\"\\\\n Done with page \\\", cnt, \\\"\\\\n\\\\n\\\"\\n\\n # check if this is a refresh page\\n # to do: see if we can get javascript window.location refreshes\\n # (would require some smart parsing or using a javascript interpreter module)\\n if 'http-equiv=\\\"refresh\\\"' in b.page:\\n if DEBUG: print \\\"Redirect to a new page:\\\"\\n newurl = r_refresh.findall(b.page)[0]\\n newurl = newurl.replace(' ', '%20')\\n newurl = newurl.replace('&', '&')\\n if DEBUG: print \\\"\\\\nNewurl:\\\", newurl\\n try:\\n b.open(newurl)\\n continue #next loop\\n except:\\n print \\\"Failed to open url \\\", newurl, \\\" error: \\\", traceback.print_exc()\\n\\n # some pages have multiple forms on them.\\n # For example, there may be a search tool in the sidebar.\\n # or there may be forms which are hidden by not displayed by the css.\\n # try to see what we can grab out the page, then we'll decide which one's the best to try\\n textareaform = get_form(b, lambda f: f.find_control_by_type('textarea'))\\n zipform = get_form(b, lambda f: f.has(name='zip'))\\n verificationform = get_form(b, lambda f: 'formproc' in f.action)\\n nameform = get_form(b, lambda f: 'wrep_const' in f.action) #see AL-06 for an example, has zip form in page too\\n wyrform = get_form(b, lambda f: f.find_control_by_id('state') and f.find_control_by_name('zip') and f.find_control_by_name('zip4')) #get_form(b, not_signup_or_search)\\n indexform = get_form(b, lambda f: f.has(name='Re')) # see billnelson for example\\n\\n #choose which form we want to use\\n form = None\\n if textareaform:\\n if DEBUG: print \\\"textareaform\\\"\\n form = textareaform\\n elif wyrform and not completedWyrForm:\\n if DEBUG: print \\\"wyrform\\\"\\n form = wyrform\\n completedWyrForm = True\\n elif nameform:\\n if DEBUG: print \\\"step2 contact form with name\\\"\\n form = nameform\\n elif zipform:\\n if DEBUG: print \\\"zipform\\\"\\n form = zipform\\n elif verificationform:\\n if DEBUG: print \\\"verification form\\\"\\n form = verificationform\\n elif indexform:\\n if DEBUG: print \\\"index form\\\"\\n form = indexform\\n\\n #if no redirect and no form was found, just return. can go no further\\n if not form:\\n return b.page\\n \\n \\n #to do, add back in captcha solver\\n if form.find_control_by_name('captcha') or form.find_control_by_name('validation'):\\n if DEBUG: print \\\"captcha found\\\"\\n #raise Captcha\\n return b.page\\n else:\\n if DEBUG: print \\\"no captcha found\\\"\\n\\n #try:\\n if DEBUG: print \\\"going to fill_form from \\\", b.url, \\\" now \\\\n\\\", form, \\\"\\\\n End form\\\", cnt, \\\"\\\\n\\\"\\n if \\\"inhofe\\\" in contact_link or \\\"lgraham\\\" in contact_link:\\n fill_inhofe_lgraham(form)\\n else:\\n fill_form(form) #, aggressive=True)\\n\\n try:\\n nextpage = b.open(form.click())\\n except:\\n print \\\"caught an http error\\\"\\n print \\\"Failed to submit form for url \\\", b.url, \\\" error: \\\", traceback.print_exc()\\n return \\\"Failed to submit form for url \\\"+ b.url+ \\\" error: \\\"+ traceback.format_exc()\\n\\n \\n # Now, look for common errors or confirmations.\\n foundError = False\\n thanked = False\\n if DEBUG: print \\\"Looking for errors in page \\\" #, b.page\\n \\n errorStr = getError(b.page)\\n if errorStr:\\n if DEBUG: print \\\"Found error: \\\", errorStr, \\\" done with \\\", contact_link\\n foundError = True\\n\\n if DEBUG: print \\\"Looking for thank you in page: \\\"# , nextpage.lower()\\n confirmations=[cstr for cstr in confirmationStrings if cstr in nextpage.lower()]\\n\\n if len(confirmations) > 0:\\n print 'thanked, done with ', contact_link\\n thanked = True\\n\\n successUrls = ['https://mulvaneyforms.house.gov/submit-contact.aspx']\\n if b.url in successUrls:\\n thanked = True\\n\\n if thanked or foundError:\\n return nextpage\\n\\n if DEBUG: print \\\"Tried \\\", k, \\\"times, unsuccessfully, to fill form\\\"\\n return b.page\\n #raise UnsuccessfulAfter5Attempts(b.page) \",\n \"def render_form():\",\n \"def SolicitarCambio(request, id_item):\\n #tipoItemExistente = TipoItem.objects.get(id=id_tipoitem)\\n #tipoItemNuevo = TipoItem.objects.get(id=id_tipoitem)\\n item = Item.objects.get(id=id_item)\\n\\n #tipoItemNuevo.id = None\\n #tipoItemNuevo.fase = fase\\n try:\\n item.calcular_impacto()\\n except IntegrityError as e:\\n return render(request, \\\"keyduplicate_tipoitem.html\\\",\\n {'item': item, \\\"message\\\": e.message},\\n context_instance=RequestContext(request))\\n if item.estado == 'A':\\n item.estado = 'V'\\n super(Item, item).save()\\n solicitud = SolicitudItem()\\n solicitud.item = item\\n solicitud.complejidad = item.complejidadtotal\\n solicitud.costo = item.costototal\\n solicitud.votos = 0\\n solicitud.votossi = 0\\n solicitud.votosno = 0\\n solicitud.completo = False\\n solicitud.solicitante = request.user\\n solicitud.save()\\n messages.info(request, \\\"Se realizo correctamente la solicitud del cambio del item %s .\\\" % item)\\n return HttpResponseRedirect('/admin/todo/solicituditem/')\\n else:\\n messages.error(request, \\\"Se realizo correctamente la solicitud del cambio del item %s .\\\" % item)\\n return HttpResponseRedirect('/admin/todo/solicituditem/')\",\n \"def __init__(self,mdi):\\n MdiWidget.__init__(self, mdi)\\n self.sesion = self.mdi().window().getSesionBD()\\n self.validadores()\\n self.cargar_obras()\\n self.lineMedicamento.returnPressed.connect(self.buscarProd)\\n self.lineMonodroga.returnPressed.connect(self.buscarProd)\\n self.lineCuit.returnPressed.connect(self.buscarObra)\\n self.lineObra.returnPressed.connect(self.buscarObra)\\n self.tableObra.itemDoubleClicked.connect(self.cargarObra)\\n self.tableProductos.itemDoubleClicked.connect(self.agregarProducto)\\n self.btnBuscar.pressed.connect(self.limpiarObra)\\n self.btnAceptar.pressed.connect(self.confirmarOperacion)\\n self.btnCancelar.pressed.connect(self.cancelarOperacion)\\n self.btnEliminar.pressed.connect(self.eliminarDetalle)\\n self.rbtnObra.pressed.connect(self.habilitarObras)\\n self.btnBuscar.setEnabled(False)\\n self.tableObra.setVisible(False)\\n self.lineCuit.setEnabled(False)\\n self.lineObra.setEnabled(False)\\n self.cargarProductosSinObra()\\n self.productosAgregados=0\\n self.lotesVentas={}\\n self.facturaCobrada=False\\n self.obraSocialSeleccionada=None\\n self.formapago = None\\n self.factura = None\\n self.data = {}\\n self.detallesTabla = {}\",\n \"def new(self,fid=None, *args, **kw):\\n tmpl_context.widget = self.new_form\\n #return dict(value=kw, model=self.model.__name__)\\n \\\"\\\"\\\"obtiene los id y nombre de los items, de la fase actual \\\"\\\"\\\"\\n #items = [x for x in (DBSession.query(Item.id, Item.nombre).filter_by(idFase=fid))]\\n itemfaseactual= [x for x in DBSession.query(Item.id, Item.nombre).filter_by(idFase=fid, idLineaBase=None, estado='aprobado', ultimaversion=1).all()]\\n #return dict(value={'idFase':fid}, model=self.model.__name__, item_options=items)\\n longitud=len(itemfaseactual)\\n \\n if (longitud == 0):\\n flash(\\\"No existen items aprobados en la fase actual\\\", \\\"error\\\")\\n redirect('../?fid='+fid)\\n else:\\n return dict(value={'idFase':fid}, model=self.model.__name__, items_options=itemfaseactual)\",\n \"def filluserform(form):\\n\\n## //ACCOUNT TYPE\\n## //IF Client account = Client\\n## //Show AUTH TYPE\\n## //IF Passphrase auth = Passphrase\\n## //SHOW NAME, EMAIL, PASS, GROUP, EXPIRY\\n## //IF Email auth = Email\\n## //SHOW NAME, EMAIL, GROUP, EXPIRY\\n## //IF None auth = None\\n## //SHOW NAME, GROUP, EXPIRY\\n## //IF ADMIN account = Admin\\n## //SHOW NAME, EMAIL, PASS\\n\\n groups = hl.getAllGroups()\\n nodes = hl.getAllNodes()\\n\\n if request.method == 'POST':\\n if form == \\\"AC\\\":\\n #Store Account Type in session variable \\n if request.form['accountType1'] == \\\"Client\\\":\\n session['accountType'] = \\\"Client\\\"\\n return render_template(\\\"userform_create_user.html\\\", postback = 1, account = \\\"Client\\\", auth = \\\"NULL\\\", groups = groups, nodes = nodes)\\n elif request.form['accountType1'] == \\\"Admin\\\":\\n session['accountType'] = \\\"Admin\\\"\\n session['authType'] = \\\"Passphrase\\\"\\n return render_template(\\\"userform_create_user.html\\\", postback = 1, account = \\\"Admin\\\", auth = \\\"Passphrase\\\", groups = groups, nodes = nodes)\\n else:\\n abort(404)\\n \\n elif form == \\\"AU\\\":\\n #Store Auth Type in session variable\\n if request.form['authType1'] == \\\"Passphrase\\\":\\n session['authType'] = \\\"Passphrase\\\"\\n return render_template(\\\"userform_create_user.html\\\", postback = 1, account = \\\"Client\\\", auth = \\\"Passphrase\\\", groups = groups, nodes = nodes)\\n elif request.form['authType1'] == \\\"Email\\\":\\n session['authType'] = \\\"Email\\\"\\n return render_template(\\\"userform_create_user.html\\\", postback = 1, account = \\\"Client\\\", auth = \\\"Email\\\", groups = groups, nodes = nodes)\\n elif request.form['authType1'] == \\\"None\\\":\\n session['authType'] = \\\"None\\\"\\n return render_template(\\\"userform_create_user.html\\\", postback = 1, account = \\\"Client\\\", auth = \\\"None\\\", groups = groups, nodes = nodes)\\n else:\\n abort(404)\\n\\n elif form == \\\"ET\\\":\\n user = session['user']\\n return render_template(\\\"userform_edit_user.html\\\", postback = 1, username=user[\\\"Name\\\"], email=user[\\\"Email\\\"], authtype=user[\\\"Auth_Type\\\"], accounttype=user[\\\"Account_Type\\\"], expire=user[\\\"Expiry\\\"], grp = user[\\\"Grp\\\"], groups = groups, node = user[\\\"Node\\\"])\\n \\n elif form == \\\"DE\\\":\\n #MAKE SURE ALL VALUE THAT ARE NOT PART OF REQUEST.FORM DO NOT THROW 400 BAD REQUEST ERROR\\n name = request.form['name1']\\n\\n auth = session['authType']\\n session.pop('authType', None)\\n \\n account = session['accountType']\\n session.pop('accountType', None)\\n \\n \\n if auth == \\\"Passphrase\\\":\\n pwd = randompassword() #Default Generation or Not\\n email = request.form['email1']\\n elif auth == \\\"Email\\\":\\n pwd = \\\"\\\" \\n email = request.form['email1']\\n else:\\n pwd = \\\"\\\"\\n email = \\\"\\\"\\n\\n if account == \\\"Client\\\":\\n group = int(request.form['groupId1'])\\n expiry = request.form['expiry1']\\n if 'node1' in request.form:\\n node = int(request.form['node1'])\\n else:\\n node = -1\\n else:\\n group = -1\\n expiry = \\\"\\\"\\n node = -1 \\n \\n if createNewUser(name, account, auth, email, pwd, group, expiry, node):\\n return redirect(url_for('confirm', confirmed = 'New User Created!'))\\n else:\\n flash(\\\"User already exists\\\")\\n \\n elif form == \\\"EU\\\":\\n user = {\\\"Name\\\" : request.form['name2'], \\\"Email\\\" : \\\"\\\", \\\"Auth_Type\\\" : session[\\\"user\\\"][\\\"Auth_Type\\\"], \\\"Account_Type\\\" : session[\\\"user\\\"][\\\"Account_Type\\\"], \\\"Expiry\\\" : request.form['expiry1'], \\\"Grp\\\" : int(request.form['groupId2'])}\\n \\n if 'email2' in request.form:\\n user[\\\"Email\\\"] = request.form['email2'] \\n \\n if 'node2' in request.form:\\n user[\\\"Node\\\"] = int(request.form['node2']) \\n\\n if hl.updateUser(session[\\\"user\\\"][\\\"ID\\\"], user):\\n session.pop(\\\"user\\\", None)\\n return redirect(url_for('confirm', confirmed = 'User Information Successfully Updated'))\\n else:\\n flash(\\\"Cannot Update User Information\\\")\\n else: #Must be fake input\\n abort(404)\\n\\n if form == \\\"CU\\\":\\n return render_template(\\\"userform_create_user.html\\\", postback = -1, account = \\\"NULL\\\", auth = \\\"NULL\\\", groups = groups, nodes = nodes)\\n elif hl.getUser(\\\"ID\\\", form) != None:\\n user = hl.getUser(\\\"ID\\\", form)\\n session['user'] = user\\n return render_template(\\\"userform_edit_user.html\\\", postback = -1, authtype=user[\\\"Auth_Type\\\"], accounttype=user[\\\"Account_Type\\\"])\\n #return render_template(\\\"userform_edit_user.html\\\", postback = -1, username=user[\\\"Name\\\"], email=user[\\\"Email\\\"], authtype=user[\\\"Auth_Type\\\"], accounttype=user[\\\"Account_Type\\\"], grp = user[\\\"Grp\\\"], nde = user[\\\"Node\\\"], groups = groups, nodes = nodes)\\n else: #Must be fake input\\n abort(404)\",\n \"def add_measurement():\\n form = MeasurementForm()\\n modules = db.session.query(PvModule).all()\\n\\n current_user_data = current_user.__dict__\\n\\n user = {'students': current_user_data['student1'] + ', ' +\\n current_user_data['student2'] + ', ' +\\n current_user_data['student3'],\\n 'meas_series': current_user_data['user_name']}\\n\\n form.pv_modul.choices = []\\n\\n # Every user can only insert one measurement\\n if db.session.query(Measurement).filter(Measurement.measurement_series == user['meas_series']).first() is not None\\\\\\n and user['meas_series'] != 'admin':\\n print(db.session.query(Measurement).filter(Measurement.measurement_series == user['meas_series']).first())\\n flash('Sie haben bereits eine Messung hinzugefügt.', category='danger')\\n return redirect(url_for('measurement.measurements'))\\n\\n # populate select field with available distinct modules\\n for module in modules:\\n if (module.model, str(module.manufacturer) + ' ' + str(module.model)) not in form.pv_modul.choices:\\n form.pv_modul.choices.append((module.model, str(module.manufacturer) + ' ' + str(module.model)))\\n\\n if request.method == 'POST':\\n chosen_module = db.session.query(PvModule).filter(PvModule.model == form.pv_modul.data).first()\\n # noinspection PyArgumentList\\n new_measurement = Measurement(date=form.mess_datum.data,\\n measurement_series=user['meas_series'],\\n producer=user['students'],\\n )\\n # save file that was uploaded\\n # if form.validate_on_submit():\\n f = form.messungen.data\\n filename = secure_filename(f.filename)\\n\\n if not allowed_file(filename):\\n flash('Ungültiges Dateiformat.', category='danger')\\n return redirect(url_for('measurement.measurements'))\\n\\n f.save(os.path.join(UPLOAD_FOLDER, filename))\\n\\n chosen_module.measurements.append(new_measurement)\\n try:\\n process_data_file(filename, new_measurement)\\n except InvalidFileType:\\n flash('Messung hochladen fehlgeschlagen!', category='danger')\\n return redirect(url_for('measurement.measurements'))\\n db.session.add(chosen_module)\\n db.session.commit()\\n\\n add_timestamp()\\n flash('Messung erfolgreich hinzugefügt.', category='success')\\n return redirect(url_for('measurement.measurements'))\\n\\n # flash current user\\n flash('Angemeldet als:', )\\n flash(current_user_data['user_name'], category='primary')\\n\\n return render_template('measurement/add_measurement.html', form=form, user=user)\",\n \"def confirmarOperacion(self):\\n\\n if self.tableNC.rowCount() == 0 :\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",QtCore.QString.fromUtf8(\\\"No se han agregado productos a la Nota de Crédito\\\"))\\n\\n else:\\n ok = QtGui.QMessageBox.information(self,QtCore.QString.fromUtf8(\\\"Confirmación\\\"),\\\\\\n QtCore.QString.fromUtf8(\\\"¿Desea generar la Nota Crédito?\\\"),\\\\\\n QtGui.QMessageBox.Cancel, QtGui.QMessageBox.Accepted)\\n\\n if (ok==1):\\n notaCredito = NotaCreditoModel(NotaCredito.generarNumero(self.sesion))\\n notaCredito.guardar(self.sesion)\\n for lineaNC, data in enumerate(self.detallesReintegrables):\\n detalleNC = DetalleNCModel(notaCredito.numero, lineaNC+1, data[0], data[1])\\n detalleNC.setImporte(data[3])\\n detalleNC.setDescuento(data[2])\\n detalleNC.guardar(self.sesion)\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",QtCore.QString.fromUtf8(\\\"La Nota de Crédito ha sido generada con éxito\\\"))\\n self.facturaSeleccionada.setNC(notaCredito.numero)\\n self.facturaSeleccionada.modificar(self.sesion)\\n\\n #Se genera un diccionario con los datos necesarios para imprimir la nota de credito\\n data = {}\\n data[\\\"numero\\\"] = notaCredito.numero\\n data[\\\"fecha\\\"] = notaCredito.fecha_emision\\n data[\\\"detalles\\\"] = self.detallesImprimibles\\n generarNotaCredito(data)\\n self.limpiarVentana()\\n\\n else:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",QtCore.QString.fromUtf8(\\\"La Nota de Crédito no ha sido generada\\\"))\",\n \"def iniciarEtiquetas(self):\\n self.ingrese = Label(self.master, text=\\\"Ingrese sus datos\\\", font=\\\"Arial 12\\\", width=45)\\n self.ingrese.grid(row=0, column=0, sticky=N, columnspan=5, pady=10)\\n self.ingrese.configure(bg=\\\"#9a32cd\\\")\\n self.tituloLabel = self.crearEtiqueta(\\\"Título\\\", \\\"Arial 12\\\", 1, 0, \\\"#f2f2f2\\\")\\n self.descripcionLabel = self.crearEtiqueta(\\\"Descripción\\\", \\\"Arial 12\\\", 2, 0, \\\"#f2f2f2\\\")\\n self.mostrarString.set('Mostrando Registros Existentes en ' + str(self.base.getDbName()))\\n self.tituloTree = Label(text=self.mostrarString.get(), font=\\\"Arial 10\\\", bg=\\\"#d9d9d9\\\")\\n self.tituloTree.grid(row=3, column=0, sticky=N, columnspan=4,pady=10)\",\n \"def updateComplejos():\\n\\t\\n\\turl_base_cd = \\\"http://m.cinemex.com/estrenos.php?cveciudad=%s\\\"\\n\\t\\n\\tbase_url_complejo = \\\"http://www.cinemex.com/cinemex/complejos.php?cvecine=%s\\\"\\n\\t\\n\\tciudades = parse_ciudades( urlopen(url_movil) )\\n\\t\\n\\tfor ciudad_id in ciudades.keys():\\n\\t\\turl_cd = url_base_cd % ciudad_id\\n\\t\\tcomplejos = parse_complejos( urlopen(url_cd) )\\n\\t\\tfor comp in complejos:\\n\\t\\t\\turl = base_url_complejo % comp['id_org']\\n\\t\\t\\tcomp_html = urlopen(url)\\n\\t\\t\\tcomplejo, platino = scrape_compInfo(comp_html,comp)\\n\\t\\t\\t#logger.debug( complejo['id_org'])\\n\\t\\t\\tcomplejo['id_ciudad']= ciudad_id\\n\\t\\t\\tcreateComplejo(complejo)\\n\\t\\t\\tif platino: createComplejoPlatino(complejo)\\t\\n\\t\\n\\t#Por alguna razon hay algunos cinemex sin nombre y causan problema\\n\\t#Borra vacios\\n\\tComplejo.objects.filter(nombre='Cinemex ').delete()\",\n \"def enviar_imagem():\\n template = 'produtos/form-enviar-imagem.html'\\n\\n if current_app.config['UPLOADS_DEFAULT_URL'] is None:\\n warning('A URL Base do Sistema não está configurada. ' +\\n 'Configure-a para que as imagens possam ser salvas.')\\n return redirect(url_for('integrador.produtos_enviar_imagem'))\\n\\n if request.method == 'POST':\\n import os\\n import glob\\n\\n base_path = os.getcwd() + '/uploads/photos/'\\n imagem = request.files['file']\\n nome = imagem.filename\\n sku = int(nome.split('.')[0])\\n filepath = base_path + nome\\n\\n if nome == '':\\n return 'Nenhuma Imagem Foi Selecionada', 400\\n\\n if nome.rfind('.') == -1:\\n return 'O arquivo informado não possui extensão definida', 400\\n\\n ext = nome.split('.')[1].lower()\\n if not imageSet.extension_allowed(ext):\\n return 'A extensão informada não é válida', 400\\n\\n if not validar_nome_imagem(nome):\\n return 'O nome da imagem precisa ser um codigo válido', 400\\n\\n # verifca se ja existe alguma imagem na pasta deste item\\n # pegando o sku do item e iterando na pasta de destino, verificando\\n # se existe alguma imagem e a removendo\\n path = os.path.dirname(filepath)\\n if os.path.exists(path):\\n for im in glob.glob(os.path.join(path, f'{sku}*.*')):\\n os.remove(os.path.join(path, im))\\n\\n image = imageSet.save(imagem)\\n image = base_path + image\\n genImage(image, image)\\n\\n # busca o cadastro do produto e se existir altera para atualizar imagem\\n produto = MagProduto.by(sku=sku)\\n if produto:\\n produto.atualiza_imagem = True\\n produto.possui_imagem = True\\n produto.update()\\n\\n Log.info(f'[ENVIAR IMAGEM] Enviado Imagem do produto {nome}.')\\n\\n result = {\\n 'title': 'Produtos',\\n 'subtitle': 'Enviar Imagens'\\n }\\n return render_template(template, **result)\",\n \"def crear_userstory(request, id_proyecto):\\n band=False\\n context = RequestContext(request)\\n\\n\\n rol_en_proyecto=Equipo.objects.get(usuario_id=request.user.pk, proyecto_id=id_proyecto)\\n rol = Group.objects.get(id=rol_en_proyecto.rol.pk)\\n user_permissions_groups = list(rol.permissions.all())\\n\\n for p in user_permissions_groups:\\n if (p.codename == 'add_userstory'):\\n band = True\\n\\n if (band == True):\\n\\n #valor booleano para llamar al template cuando el registro fue correcto\\n registered = False\\n\\n if request.method == 'POST':\\n userstory_form = UserstoryForm(data=request.POST, id_proyecto=id_proyecto)\\n\\n # If the two forms are valid...\\n if userstory_form.is_valid():\\n\\n # Guarda el Usuarios en la bd\\n #us = userstory_form\\n userstory_form.clean()\\n nombre = userstory_form.cleaned_data['nombre']\\n descripcion =userstory_form.cleaned_data['descripcion']\\n tiempoestimado =userstory_form.cleaned_data['tiempoestimado']\\n usuarioasignado= userstory_form.cleaned_data['usuarioasignado']\\n prioridad =userstory_form.cleaned_data['prioridad']\\n porcentajerealizado= userstory_form.cleaned_data['porcentajerealizado']\\n sprint=userstory_form.cleaned_data['sprint']\\n\\n us = Userstory()\\n\\n if usuarioasignado and sprint:\\n us.estado = 'InPlanning'\\n\\n if userstory_form.cleaned_data['prioridad'] == 'Alta':\\n cambioDePrioridades(usuarioasignado, sprint)\\n\\n us.nombre = nombre\\n us.descripcion =descripcion\\n us.tiempoestimado =tiempoestimado\\n us.usuarioasignado =usuarioasignado\\n us.prioridad=prioridad\\n us.porcentajerealizado=porcentajerealizado\\n us.sprint = sprint\\n\\n us.save()\\n #Actualiza la variable para llamar al template cuando el registro fue correcto\\n registered = True\\n\\n # Invalid form or forms - mistakes or something else?\\n # Print problems to the terminal.\\n # They'll also be shown to the user.\\n else:\\n print userstory_form.errors\\n\\n pass\\n\\n # Not a HTTP POST, so we render our form using two ModelForm instances.\\n # These forms will be blank, ready for user input.\\n else:\\n userstory_form = UserstoryForm(id_proyecto=id_proyecto)\\n\\n\\n # Render the template depending on the context.\\n return render_to_response('./Userstories/crearUserstory.html', {'user_form': userstory_form, 'registered': registered, 'id_proyecto': id_proyecto}, context)\\n else:\\n raise Http404(\\\"No cuenta con los permisos necesarios\\\")\",\n \"def VIEJOupdateComplejos():\\n\\txml_ciudades = \\\"http://api2.cinemex.com/rsvr.php?Action=GetFiltrados&IdDev=1\\\"\\n\\txml_complejos = \\\"http://api2.cinemex.com/rsvr.php?Action=GetFiltrados&IdDev=1&ciudad=%s\\\"\\n\\t\\n\\t\\n\\tciudades = parse_ciudadesxml( urlopen(xml_ciudades) )#Diccionario de ciudades de Mexico\\n\\t\\n\\t#xml = urlopen(xml_complejos)\\n\\t#ids = parse_complejosxml(xml)\\n\\t\\n\\tbase_url = \\\"http://www.cinemex.com/cinemex/complejos.php?cvecine=%s\\\"\\n\\t\\n\\tfor ciudad_id in ciudades:\\n\\t\\txml_url = xml_complejos % ciudad_id\\n\\t\\tcomplejos_xml = urlopen(xml_url)\\n\\t\\tcomplejos = parse_complejosxml(complejos_xml)\\n\\t\\tfor comp in complejos:\\n\\t\\t\\turl = base_url % comp['id_org']\\n\\t\\t\\tcomp_html = urlopen(url)\\n\\t\\t\\tcomplejo, platino = scrape_compInfo(comp_html,comp)\\n\\t\\t\\t#logger.debug( complejo['id_org'])\\n\\t\\t\\tcomplejo['id_ciudad']= ciudad_id\\n\\t\\t\\t#createComplejo(complejo)\\n\\t\\t\\tlogger.debug( complejo)\\n\\t\\t\\tlogger.debug( platino)\\n\\t\\t\\t#if platino: createComplejoPlatino(complejo)\",\n \"def confirmarOperacion(self):\\n\\n if self.productosSeleccionados != 0:\\n\\n nc = NotaCreditoModel(NotaCreditoModel.generarNumero(self.sesion))\\n nc.guardar(self.sesion)\\n for nro_lnc, nro_lfactura in enumerate(self.detallesDevueltos):\\n detalle_nc = DetalleNCModel(nc.numero,nro_lnc+1,self.facturaSeleccionada.numero,nro_lfactura)\\n detalle_nc.setImporte(self.data[nro_lfactura][3])\\n detalle_nc.guardar(self.sesion)\\n self.detallesDevueltos[nro_lfactura].devolver(self.sesion) # Devuelve el detalle asociado de la factura\\n self.facturaSeleccionada.setNC(nc.numero)\\n self.facturaSeleccionada.modificar(self.sesion)\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"La factura ha sido devuelta\\\")\\n self.objectModified.emit()\\n\\n cobros = self.facturaSeleccionada.getCobros(self.sesion)\\n if len(cobros) == 1 and cobros[0].tipo == \\\"Efectivo\\\":\\n QtGui.QMessageBox.information(self,\\\"Devolucion\\\",\\\"El importe en efectivo a entregar es de: $%.2f\\\" % self.calcularTotal())\\n\\n #Se genera un diccionario con los datos necesarios para imprimir la nota de credito\\n data = {}\\n data[\\\"numero\\\"] = nc.numero\\n data[\\\"fecha\\\"] = nc.fecha_emision\\n data[\\\"detalles\\\"] = self.data.values()\\n generarNotaCredito(data)\\n\\n self.facturaSeleccionada=None\\n self.productosSeleccionados=0\\n self.detallesDevueltos = {}\\n self.limpiarVentana()\\n self.data = {}\\n\\n else:\\n QtGui.QMessageBox.information(self,\\\"Devolucion Cliente\\\",\\\"No se ha agregado ningun producto para devolver\\\")\",\n \"def abrir_informe(self, widget, event):\\n\\t\\tpath_array = widget.get_path_at_pos(event.x, event.y)\\n\\t\\tpath = path_array[0][0]\\n\\t\\tcol = path_array[1]\\n\\t\\tcol_title = col.get_title()\\n\\t\\tif (col_title =='Informe de Resultado'):\\n\\t\\t\\tselection = self.Tree.get_model()\\n\\t\\t\\titer_selection = selection.get_iter(path)\\n\\t\\t\\tPaciente = self.Lista.get_value(iter_selection, 0)\\n\\t\\t\\tPaciente.Progreso = 100\\n\\t\\t\\trep = Paciente.generar_Informe()\\n\\t\\t\\tself.w61a.get_buffer().set_text(rep)\\n\\t\\t\\tself.Lista.set_value(iter_selection, self.LInforme, self.botonInformeON)\\n\\t\\t\\tself.Lista.set_value(iter_selection, self.LProgreso, 100)\\n\\t\\t\\tself.w61.show_all()\",\n \"def on_Panel_select_page(self, widget):\\n try:\\n funcioneshab.listadonumhab()\\n except:\\n print(\\\"error botón cliente barra herramientas\\\")\",\n \"def manipular_contedenor(self, camion):\\r\\n operaciones = self.operaciones[\\\"Operaciones manipuleo\\\"]\\r\\n\\r\\n # Manipuleo de camiones por cargar\\r\\n if camion.tipo == \\\"Carga\\\":\\r\\n\\r\\n carga = operaciones[\\\"Carga con grua\\\"]\\r\\n yield self.process(carga.ejecutar(self, camion, 300, self.medios_almacenamiento[\\\"Patio de Contenedores\\\"]))\\r\\n\\r\\n # Manipuleo de camiones por descargar\\r\\n elif camion.tipo == \\\"Descarga\\\":\\r\\n\\r\\n descarga = operaciones[\\\"Descarga con grua\\\"]\\r\\n yield self.process(\\r\\n descarga.ejecutar(self, camion, 300, self.medios_almacenamiento[\\\"Patio de Contenedores\\\"]))\",\n \"def rellenar_atril(self):\\n while self.get_atril_espaciosVacios() > 0 and self.bolsa.cantidad_Fichas() > 0:\\n self.agregar_al_atril()\",\n \"def test_CRMA080_CT001(self):\\n self.oHelper.WaitShow(\\\"Configuração de Filtros - Funil de Vendas\\\")\\n self.oHelper.ScrollGrid(column=\\\"Processo\\\",match_value=\\\"FAT004\\\",grid_number=2)\\n self.oHelper.ClickBox(fields=\\\"Processo\\\", contents_list=\\\"FAT004\\\", select_all=False, grid_number=2)\\n self.oHelper.SetValue(\\\"Dt. Início\\\",\\\"18/07/2019\\\")\\n self.oHelper.ClickCheckBox(\\\"Todos Vendedores\\\")\\n self.oHelper.SetButton(\\\"Confirmar\\\")\\n self.oHelper.WaitShow(\\\"Consulta - Funil de Vendas\\\")\\n self.oHelper.ClickGridCell(column=\\\"Oportunidade\\\",row=2,grid_number=2)\\n self.oHelper.SetButton(\\\"Outras Ações\\\",\\\"Visualizar Oportunidade\\\")\\n self.oHelper.WaitShow(\\\"Oportunidade de Venda - Visualizar Oportunidade\\\")\\n self.oHelper.SetButton(\\\"Fechar\\\")\\n self.oHelper.WaitShow(\\\"Consulta - Funil de Vendas\\\")\\n self.oHelper.SetButton(\\\"Outras Ações\\\",\\\"Configurar Filtros\\\")\\n self.oHelper.WaitShow(\\\"Configuração de Filtros - Funil de Vendas\\\")\\n self.oHelper.SetButton(\\\"Confirmar\\\")\\n self.oHelper.WaitShow(\\\"Consulta - Funil de Vendas\\\")\\n self.oHelper.SetButton(\\\"Cancelar\\\")\\n self.oHelper.WaitHide(\\\"Consulta - Funil de Vendas\\\")\\n self.oHelper.AssertTrue()\",\n \"def agregar_bolsa(self, letra, cantidad):\",\n \"def parse_forms():\\n print('Updating forms.json')\\n with open('game_master.json', 'r') as f:\\n gm = json.load(f)\\n new_forms = {}\\n for item in gm: # iterate over items in gm\\n pokemon = dict()\\n if item['templateId'].startswith('FORMS'): # only care about forms\\n pokemon_id = str(int(item['templateId'].split('_')[1][1:])).zfill(3) # \\\"FORMS_V0001_POKEMON_BULBASAUR\\\" -> \\\"001\\\"\\n pokemon['name'] = item['formSettings']['pokemon']\\n pokemon['forms'] = dict()\\n if 'forms' in item['formSettings']: # some pokemon don't have any shadow forms\\n for form in item['formSettings']['forms']:\\n if is_unique_form(pokemon['name'], form['form']):\\n cleaned_name = form['form'].replace(f\\\"{pokemon['name']}_\\\", '')\\n if 'assetBundleValue' in form:\\n pokemon['forms'][cleaned_name] = str(form['assetBundleValue'])\\n elif 'assetBundleSuffix' in form:\\n pokemon['forms'][cleaned_name] = str(form['assetBundleSuffix']) # exclusive to armored mewtwo and hatachus?\\n else:\\n pokemon['forms'][cleaned_name] = '00'\\n else:\\n pokemon['forms'] = {'NORMAL':'00'}\\n pokemon['has_forms'] = len(pokemon['forms']) > 1\\n new_forms[pokemon_id] = pokemon\\n\\n # save to \\n with open('forms.json', 'r') as f:\\n file = json.load(f)\\n if file['pokemon'] == new_forms:\\n return\\n file['pokemon'] = new_forms\\n with open('forms.json', 'w') as f:\\n json.dump(file, f, indent=4)\",\n \"def affichage_creation_tournoi():\\n nom = \\\"\\\"\\n lieu = \\\"\\\"\\n date = \\\"\\\"\\n nb_tours = 4\\n joueurs = []\\n temps = \\\"\\\"\\n note = \\\"\\\"\\n\\n print(\\\"\\\\n---------------------------\\\")\\n while len(nom) == 0:\\n try:\\n nom = str(input(\\\"\\\\nNom : \\\"))\\n except ValueError:\\n print(\\\"\\\\nVous n'avez pas saisi un nom valide.\\\")\\n sl(2)\\n continue\\n\\n print(\\\"\\\\n---------------------------\\\")\\n while len(lieu) == 0:\\n try:\\n lieu = str(input(\\\"\\\\nLieu : \\\"))\\n except ValueError:\\n print(\\\"\\\\nVous n'avez pas saisi un lieu valide.\\\")\\n sl(2)\\n continue\\n\\n print(\\\"\\\\n---------------------------\\\")\\n while len(date) == 0:\\n try:\\n date = str(input(\\\"\\\\nDate\\\\nFormat : jj/mm/aaaa : \\\"))\\n except ValueError:\\n print(\\\"\\\\nVous n'avez pas saisi une date valide.\\\")\\n sl(2)\\n continue\\n test_date = OutilsControleurs.test_date(date)\\n if test_date == 0:\\n print(\\\"\\\\nVous avez saisi une valeur trop grande.\\\")\\n date = \\\"\\\"\\n if test_date == 1:\\n print(\\\"\\\\nVous avez saisi une valeur trop petite.\\\")\\n date = \\\"\\\"\\n if test_date == 2:\\n break\\n if test_date == 3:\\n print(\\\"\\\\nVous avez saisi un format de date incorrect.\\\")\\n date = \\\"\\\"\\n\\n print(\\\"\\\\n---------------------------\\\")\\n nb_tours_modif = \\\"\\\"\\n while nb_tours_modif != 2 or nb_tours_modif != 1:\\n try:\\n print(\\\"\\\\nNombre de tours\\\\nPar default le nombre est de 4\\\\nVoulez-vous modifier cette valeur ?\\\")\\n nb_tours_modif = int(input(\\\"\\\\n1 - Oui\\\\n2 - Non\\\\n\\\\nVotre choix: \\\"))\\n except ValueError:\\n print(\\\"\\\\nVous n'avez pas saisi un nombre valide.\\\")\\n sl(2)\\n continue\\n if nb_tours_modif == 1:\\n while nb_tours == 4:\\n try:\\n nb_tours = int(input(\\\"\\\\nNombre de tours : \\\"))\\n except ValueError:\\n print(\\\"\\\\nVous n'avez pas saisi un nombre valide.\\\")\\n sl(2)\\n continue\\n if nb_tours == 4:\\n break\\n break\\n if nb_tours_modif == 2:\\n break\\n\\n print(\\\"\\\\n---------------------------\\\\n\\\\nListe des joueurs :\\\\n\\\")\\n liste_joueurs_tournois = Joueur.joueurs_tournoi()\\n if liste_joueurs_tournois == 0:\\n print(\\\"Il n'y a pas ou pas suffisament de joueurs pour organiser un tounois.\\\")\\n print(\\\"Veuillez ajouter des joueurs via le menu.\\\")\\n input(\\\"\\\\nAppuyer sur entrer pour continuer\\\")\\n return\\n\\n for arg in liste_joueurs_tournois:\\n print(arg)\\n x = 8\\n while x != 0:\\n try:\\n joueur = int(input(\\\"Saisir encore {} indice de joueurs : \\\".format(x)))\\n except ValueError:\\n print(\\\"\\\\nVous n'avez pas saisi un indice valide.\\\")\\n sl(2)\\n continue\\n if joueur > 0 and joueur <= len(liste_joueurs_tournois):\\n if joueur not in joueurs:\\n joueurs.append(joueur)\\n else:\\n print(\\\"Vous avez deja saisi ce joueur.\\\")\\n x += 1\\n else:\\n x += 1\\n x -= 1\\n\\n y = 1\\n nom_joueurs = []\\n for arg in liste_joueurs_tournois:\\n arg = arg[:-15]\\n nom_joueurs.append(str(arg).replace(\\\"Indice joueur : {}\\\\n \\\".format(y), \\\"\\\").replace(\\\"\\\\n \\\", \\\"\\\"))\\n y += 1\\n joueurs = Joueur.get_joueurs_tournoi(joueurs, nom_joueurs)\\n\\n print(\\\"\\\\n---------------------------\\\")\\n temps_choix = 0\\n while temps_choix != 1 or temps_choix != 2 or temps_choix != 3:\\n try:\\n temps_choix = int(input(\\\"\\\\nContrôle de temps\\\\n1 - Bullet\\\\\\n \\\\n2 - Blitz\\\\n3 - Coup rapide\\\\n\\\\nVotre choix : \\\"))\\n except ValueError:\\n print(\\\"\\\\nVous n'avez pas saisi une valeur valide.\\\")\\n sl(2)\\n continue\\n if temps_choix == 1:\\n temps = \\\"Bullet\\\"\\n break\\n if temps_choix == 2:\\n temps = \\\"Blitz\\\"\\n break\\n if temps_choix == 3:\\n temps = \\\"Coup rapide\\\"\\n break\\n\\n print(\\\"\\\\n---------------------------\\\")\\n while len(note) == 0:\\n try:\\n note = str(input(\\\"\\\\nDescription : \\\"))\\n except ValueError:\\n print(\\\"\\\\nVous n'avez pas saisi une valeur valide.\\\")\\n sl(2)\\n continue\\n if len(note) == 0:\\n break\\n return nom, lieu, date, nb_tours, joueurs, temps, note\",\n \"def modificarUserstory(request,id_proyecto, id_userstory):\\n band = False\\n\\n rol_en_proyecto=Equipo.objects.get(usuario_id=request.user.pk, proyecto_id=id_proyecto)\\n rol = Group.objects.get(id=rol_en_proyecto.rol.pk)\\n user_permissions_groups = list(rol.permissions.all())\\n\\n for p in user_permissions_groups:\\n if (p.codename == 'change_userstory'):\\n band = True\\n warning = False\\n registered = False\\n warningUS = False\\n warningPorcentaje = False\\n marca = False\\n mensaje = 'ATENCION: \\\\nNo puede modificar el estado de un US en estado Comentario\\\\nDebe concluir con los US en Alta'\\n mensajeCurso = 'ATENCION: \\\\nNo puede modificar el estado de este US en estado Curso, ya posee otro US en ese estado\\\\nFinalice su otro US o coloque a Comentario'\\n mensajePorcentaje = 'ATENCION: \\\\nNo puede modificar el estado de este US a estado Resuelta, porque su porcentaje no esta en 100%\\\\n Cambie a 100% antes de realizar este cambio'\\n us = Userstory.objects.get(id=id_userstory)\\n estado_us = us.estado\\n if (band == True):\\n\\n if request.method == 'POST':\\n form = UserstoryModificadoForm(request.POST, estado_us=estado_us)\\n if form.is_valid():\\n form.clean()\\n nombre = form.cleaned_data['Nombre']\\n descripcion = form.cleaned_data['Descripcion']\\n #usuarioasignado = form.cleaned_data['usuarioasignado']\\n if estado_us == 'Resuelta':\\n estado = form.cleaned_data['Estado']\\n prioridad = form.cleaned_data['Prioridad']\\n\\n\\n\\n '''\\n Procedimiento si se modifica la prioridad del us a 'Alta'\\n '''\\n\\n # sprint = us.sprint\\n if prioridad == 'Alta':\\n cambioDePrioridades(us.usuarioasignado, us.sprint)\\n\\n '''\\n Procedimiento necesario para definir el historial\\n '''\\n modificaciones = ''\\n modificaciones = modificaciones + str(us.historial)\\n if us.nombre != nombre or us.estado != estado or us.prioridad != prioridad:\\n marca = 'True'\\n modificaciones = modificaciones + \\\"\\\\nActualizado por \\\"\\n modificaciones = modificaciones + str(us.usuarioasignado)\\n ahora = datetime.date.today()\\n modificaciones = modificaciones + \\\" el \\\" + str(ahora) + \\\"\\\\n\\\"\\n\\n if marca == 'True':\\n if us.nombre != nombre and nombre!='':\\n modificaciones = modificaciones + \\\" \\\\n \\\\t* NOMBRE -> Cambiado de \\\" + str(\\n us.nombre) + \\\" por \\\" + str(nombre)\\n us.nombre=nombre\\n\\n\\n if us.descripcion != descripcion and descripcion!='':\\n modificaciones = modificaciones + \\\" \\\\n \\\\t* DESCRIPCION -> Cambiado de \\\" + str(\\n us.descripcion) + \\\" por \\\" + str(descripcion)\\n us.descripcion = descripcion\\n\\n\\n if (us.estado != estado):\\n if estado == 'Validado':\\n us.estado = estado\\n elif estado == 'Rechazado':\\n us.estado = 'InPlanning'\\n proyecto_flujo_actividades = ProyectoFlujoActividad.objects.filter(userstory_id=us.pk)\\n for pfa in proyecto_flujo_actividades:\\n ProyectoFlujoActividad.objects.filter(id=pfa.pk).update(estado='ToDo')\\n\\n\\n if us.prioridad != prioridad:\\n modificaciones = modificaciones + \\\" \\\\n \\\\t* PRIORIDAD -> Cambiado de \\\" + str(\\n us.prioridad) + \\\" por \\\" + str(prioridad)\\n\\n\\n\\n\\n '''if (us.prioridad == 'Alta' and (estado == 'Resuelta' or estado == 'Validado')):\\n userStories = Userstory.objects.filter(sprint_id=us.sprint.pk)\\n\\n if (tieneUsuarioUSAlta(us) is not True):\\n for userStory in userStories:\\n if (userStory.usuarioasignado == us.usuarioasignado) and (userStory.estado == 'Comentario'):\\n Userstory.objects.filter(id=userStory.pk).update(estado='InPlaning')'''\\n\\n mensajePrioridadAlta=False\\n if tieneUsuarioUSAlta(us):\\n mensajePrioridadAlta = 'No puede asignar otro US con prioridad alta al usuario' + request.user.username\\n else:\\n us.prioridad = prioridad\\n\\n us.historial = us.historial + modificaciones\\n #us.sprint = sprint\\n\\n us.save()\\n '''\\n Obtener Lider, scrum master del proyecto al que se corresponde este US\\n '''\\n sprint = us.sprint\\n userstories_del_sprint = Userstory.objects.filter(sprint_id=sprint.pk)\\n userstories_del_sprint_validado = Userstory.objects.filter(sprint_id=sprint.pk, estado='Validado')\\n if len(userstories_del_sprint) == len(userstories_del_sprint_validado):\\n FlujoProyecto.objects.filter(proyecto_id=id_proyecto, sprint_id=sprint.pk).update(estado='Done')\\n Sprint.objects.filter(id=sprint.pk).update(estado='Finalizado')\\n\\n #scrum_master = Equipo.objects.get(proyecto_id=FlujoProyecto.objects.get(sprint_id=us.sprint.pk).proyecto_id, rol_id = 2).usuario\\n\\n '''\\n Enviar correo electronico al SCRUM MASTER\\n '''\\n #send_mail('Modificaciones del US', modificaciones, settings.EMAIL_HOST_USER,\\n # ['gabyvazquez92@gmail.com',Equipo.objects.get(proyecto_id=FlujoProyecto.objects.get(sprint_id=us.sprint.pk).proyecto_id, rol_id = 2).usuario.email],\\n # fail_silently=False)\\n\\n registered = True\\n template_name = './Userstories/userstory_modificado.html'\\n return render(request, template_name,\\n {'mensaje': mensaje, 'warning': warning, 'mensajeCurso': mensajeCurso,'mensajePorcentaje': mensajePorcentaje, 'warningUS': warningUS, 'warningPorcentaje': warningPorcentaje,'registered': registered,'mensajePrioridadAlta':mensajePrioridadAlta })\\n else:\\n data = {'Nombre': us.nombre, 'Estado': us.estado,\\n 'Prioridad': us.prioridad,\\n 'Descripcion': us.descripcion,\\n }\\n form = UserstoryModificadoForm(data, estado_us=estado_us)\\n template_name = './Userstories/modificar_userstory.html'\\n return render(request, template_name, {'form': form, 'id_userstory': id_userstory, 'id_proyecto': id_proyecto, 'us':us})\\n else:\\n raise Http404(\\\"No cuenta con los permisos necesarios\\\")\",\n \"def configurar():\\n try:\\n with open(f'archivos{ruta()}valores_puntajes.json',\\n encoding='UTF-8') as f:\\n dic = json.load(f)\\n except FileNotFoundError:\\n dic = crear_valores_puntajes()\\n with open(f'archivos{ruta()}valores_puntajes.json', 'w',\\n encoding='UTF-8') as f:\\n json.dump(dic, f, indent=4)\\n\\n letras = dic['Personalizada']['bolsa']\\n lista_letras= []\\n for elemento in letras:\\n lista_letras.append(elemento[0])\\n valor_letra = letras[0][1] \\n valor_punto = dic['Personalizada']['puntos_letra']['a']\\n layout=[\\n [sg.T(\\\"Perfil personalizado\\\", size=(17,1), justification = \\\"center\\\",\\n font=(\\\"Georgia\\\", 17))],\\n [sg.T(\\\"Letra\\\",font=(\\\"Georgia\\\", 12)),\\n sg.Combo(lista_letras, size=(8,1), default_value= 'a', enable_events=True),\\n sg.T(\\\"Cantidad:\\\", font=(\\\"Georgia\\\", 12),),\\n sg.Slider(range=(1, 12), orientation='h', size=(13, 20), default_value=valor_letra),\\n sg.T(\\\"Valor:\\\", font=(\\\"Georgia\\\", 12),),\\n sg.Slider(range=(1, 20), orientation='h', size=(20, 20), default_value=valor_punto)],\\n [sg.T(\\\"Dificultad de la IA: \\\"),\\n sg.DropDown(('Facil','Medio','Dificil'),\\n default_value=(dic['Personalizada']['dificultad_IA']),size=(10,1)),\\n sg.T(\\\"Dificultad del tablero: \\\"),\\n sg.DropDown(('Facil','Medio','Dificil'),\\n default_value=(dic['Personalizada']['dificultad_Tablero']),size=(10,1))],\\n [sg.Button(\\\"Guardar\\\", size=(12, 1), key=\\\"-guardar-\\\")]\\n ]\\n\\n window_configurar = sg.Window(\\\"ScrabbleAR - Configurar\\\").Layout(layout)\\n\\n while True:\\n event, values = window_configurar.Read()\\n \\\"\\\"\\\"\\n values[0] = retorna la letra elegida\\n values[1] = retorna la cantidad de letras\\n values[2] = retorna el puntaje seleccionado\\n values[3] = retorna la dificultad de la IA\\n values[4] = retorna la dificultad del tablero\\n \\\"\\\"\\\"\\n try:\\n for item in letras:\\n if item[0] == values[0]:\\n valor_letra = int(item[1])\\n window_configurar.find_element(1).Update(valor_letra)\\n valor_punto = dic['Personalizada']['puntos_letra'][values[0]]\\n window_configurar.find_element(2).Update(valor_punto)\\n except:\\n break\\n if event == None:\\n break\\n elif event == '-guardar-':\\n for item in letras:\\n if item[0] == values[0]:\\n item[1] = int(values[1])\\n break\\n dic['Personalizada']['bolsa'] = letras\\n dic['Personalizada']['puntos_letra'][values[0]] = int(values[2])\\n dic['Personalizada']['dificultad_IA'] = values[3]\\n dic['Personalizada']['dificultad_Tablero'] = values[4]\\n try:\\n with open(f'archivos{ruta()}valores_puntajes.json', 'w', encoding='UTF-8') as f:\\n json.dump(dic, f, indent=4)\\n sg.popup('Se guardaron los cambios', auto_close= True, auto_close_duration= 1)\\n except FileNotFoundError:\\n sg.popup('No se encuentra el archivo \\\"valores_puntajes.json\\\"')\\n window_configurar.Close()\",\n \"def process_step(self, form):\\n #print(form.data)\\n\\n #print(form.data)\\n #print(self)\\n \\n institution = {}\\n inst_list = []\\n if self.steps.current == '1':\\n \\n institution['institution'] = form.data['1-0-institution']\\n institution['date_from'] = form.data['1-0-date_from']\\n institution['date_to'] = form.data['1-0-date_to']\\n inst_list.append(institution)\\n inst_keys = dict(form.data.lists())\\n \\n #Create dictionary dynamically for the other institutions incase more than two institutions are entered\\n if inst_keys.get('1-NaN-institution') and type(inst_keys.get('1-NaN-institution')) is list:\\n inst_list2 = []\\n #Add institutions \\n for i,insti in enumerate(inst_keys.get('1-NaN-institution')):\\n inst_i = {}\\n #print(i)\\n date_from = inst_keys['1-NaN-date_from'][i]\\n date_to = inst_keys['1-NaN-date_to'][i]\\n course_duration = inst_keys['1-NaN-course_duration'][i]\\n inst_i['institution'] = insti\\n inst_i['date_from'] = date_from\\n inst_i['date_to'] = date_to\\n \\n inst_list2.append(inst_i)\\n #print(inst_list2)\\n inst_list.extend(inst_list2)\\n #Create dictionary dynamically for the other institutions incase more than two institutions are entered\\n if inst_keys.get('1-NaN-institution') and type(inst_keys.get('1-NaN-institution')) is not list:\\n inst_0 = {}\\n inst_0['institution'] = form.data['1-NaN-institution']\\n inst_0['date_from'] = form.data['1-NaN-date_from']\\n inst_0['date_to'] = form.data['1-NaN-date_to']\\n inst_0['course_duration'] = form.data['1-NaN-course_duration']\\n #inst_0['achievements'] = ''\\n inst_list.append(inst_0)\\n \\n #Add the entered information to a session object\\n self.request.session['institution'] = inst_list\",\n \"def config_nuevo_juego():\\n s = []\\n for i in range(1, 61):\\n s.append(str(i))\\n try:\\n with open(f'archivos{ruta()}configuraciones.json',\\n 'r', encoding='UTF-8') as f:\\n configuracion = json.load(f)\\n nombre = configuracion['nombre']\\n except FileNotFoundError:\\n configuracion = {\\n \\\"dificultad\\\": \\\"Medio\\\",\\n \\\"tiempo\\\": \\\"10\\\",\\n \\\"nombre\\\": \\\"\\\"\\n }\\n nombre = configuracion['nombre']\\n\\n layout = [\\n [sg.T(\\\"Nuevo juego\\\", size=(17,1), justification=\\\"center\\\",\\n font=(\\\"Georgia\\\", 17))],\\n [sg.T(\\\"Dificultad: \\\"),\\n sg.DropDown(('Facil','Medio','Dificil','Personalizada'),\\n default_value=(configuracion['dificultad']),size=(10,1))],\\n [sg.T(\\\"Tiempo de juego (minutos): \\\", size=(22, 1)),\\n sg.InputCombo((s),size=(5,1),default_value=(configuracion['tiempo']))],\\n [sg.T('Nombre de usuario'), sg.Input(nombre,size=(17,1))],\\n [sg.Button(\\\"Jugar\\\", size=(12, 1), key=\\\"-jugar-\\\"),\\n sg.Button(\\\"Personalizar\\\",size=(12,1), key=\\\"-personalizar-\\\")]\\n ]\\n\\n window_nuevo_juego = sg.Window(\\\"ScrabbleAR - Nuevo juego\\\").Layout(layout)\\n\\n while True:\\n event, values = window_nuevo_juego.Read()\\n if event == \\\"-jugar-\\\" and values[2] != \\\"\\\":\\n configuracion['dificultad'] = values[0]\\n configuracion['tiempo'] = values[1]\\n configuracion['nombre'] = values[2]\\n if values[0] == 'Personalizada':\\n sg.popup('¡Atencion! \\\\n En la dificultad personalizada'\\n 'no se guardan los puntajes')\\n break\\n elif event == \\\"-jugar-\\\" and values[2] == \\\"\\\":\\n sg.popup('Ingrse un nombre')\\n elif event == \\\"-personalizar-\\\":\\n configurar()\\n else:\\n break\\n # No necesita manejo de excepciones porque está en modo escritura\\n with open(f'archivos{ruta()}configuraciones.json', 'w',\\n encoding='UTF-8') as f:\\n json.dump(configuracion, f, indent=4)\\n\\n window_nuevo_juego.Close()\\n \\n if event == \\\"-jugar-\\\":\\n return True\\n else:\\n return False\",\n \"def actualizar_datos(elegir):\\n print(\\\"\\\"\\\"\\n¿Qué dato desea modificar?\\n1 - Username\\n2 - Nombre\\n3 - Edad\\n4 - Género\\nNi el puntaje ni los disparos los puedes cambiar, que pensabas?\\n \\\"\\\"\\\")\\n while True: #validacion para la opcion a escoger\\n try:\\n eleccion = int(input(\\\"Ingrese una opción: \\\"))\\n if eleccion < 1 or eleccion > 4:\\n raise ValueError\\n break\\n except ValueError:\\n print(\\\"{}La opcion ingresada no es valida{}\\\".format(Fore.LIGHTRED_EX, Fore.RESET))\\n with open(\\\"Basedatos.txt\\\", \\\"r\\\") as bd:\\n datos = bd.readlines()\\n user = datos[elegir - 1][:-1].split(\\\",\\\")\\n if eleccion == 1:\\n user[eleccion - 1] = input(\\\"Ahora ingrese su nuevo username: \\\") \\n validacion_username = user[eleccion - 1].islower()\\n while validacion_username == False or len(user[eleccion - 1]) > 30 or \\\" \\\" in user[eleccion - 1]: #validacion para el nuevo username\\n print(\\\"{}Su usuario solo puede contener minusculas y numeros sin ningun espacio{}\\\\n\\\".format(Fore.LIGHTRED_EX, Fore.RESET))\\n user[eleccion - 1] = input(\\\"Ingerese su username nuevamente: \\\")\\n validacion_username = user[eleccion - 1].islower()\\n usuario_partida.clear()\\n usuario_partida.append(user[eleccion - 1])\\n elif eleccion == 2:\\n user[eleccion - 1] = input(\\\"Ahora ingrese su nuevo nombre: \\\") #validacion para el nuevo usuario\\n validacion_nombre = bool(re.fullmatch('[A-Za-z]{2,25}( [A-Za-z]{2,25})+?', user[eleccion - 1]))\\n while validacion_nombre == False:\\n print(\\\"{}Este nombre no es valido{}\\\".format(Fore.LIGHTRED_EX, Fore.RESET))\\n user[eleccion - 1] = input(\\\"Ingrese su nombre completo: \\\")\\n validacion_nombre = bool(re.fullmatch('[A-Za-z]{2,25}( [A-Za-z]{2,25})+?', user[eleccion - 1]))\\n user[eleccion - 1] = \\\" \\\" + user[eleccion - 1].title() \\n elif eleccion == 3:\\n while True: #Validacion para la nueva edad\\n try:\\n user[eleccion - 1] = int(input(\\\"Ingresa su edad: \\\"))\\n if user[eleccion - 1] < 5 or user[eleccion - 1] > 100:\\n raise ValueError\\n break\\n except ValueError:\\n print(\\\"{}Tu edad no es adecuada para jugar{}\\\".format(Fore.LIGHTRED_EX, Fore.RESET))\\n user[eleccion - 1] = \\\" \\\" + str(user[eleccion - 1]) \\n elif eleccion == 4:\\n while True: #Validacion para el nuevo genero\\n try:\\n user[eleccion - 1] = int(input(\\\"Ingrese su genero: \\\\n1) Femenino \\\\n2) Masculino \\\\n3) Ninguno\\\\n\\\"))\\n if user[eleccion - 1] < 1 or user[eleccion - 1] > 3:\\n raise ValueError\\n if user[eleccion - 1] == 1:\\n user[eleccion - 1] = \\\" Femenino\\\"\\n elif user[eleccion - 1] == 2: \\n user[eleccion - 1] = \\\" Masculino\\\"\\n elif user[eleccion - 1] == 3:\\n user[eleccion - 1] = \\\" Ninguno\\\"\\n break\\n except ValueError:\\n print(\\\"{}El valor ingresado no es valido{}\\\".format(Fore.LIGHTRED_EX, Fore.RESET))\\n nuevo_valor = \\\"\\\"\\n for x in range(len(user)):\\n if x != len(user) - 1:\\n nuevo_valor += user[x] + ','\\n else:\\n nuevo_valor += user[x] + '\\\\n'\\n datos[elegir - 1] = nuevo_valor\\n with open(\\\"Basedatos.txt\\\", \\\"w\\\") as bd: #se reescribe el archivo txt con el valor del usuario modificado \\n bd.writelines(datos)\",\n \"def habilitarObras(self):\\n if self.productosAgregados != 0:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"Ya se han agregado productos a la factura\\\")\\n else:\\n if not self.rbtnObra.isChecked():\\n self.btnBuscar.setEnabled(True)\\n self.lineObra.setEnabled(True)\\n self.lineCuit.setEnabled(True)\\n self.tableObra.setVisible(True)\\n else:\\n self.lineObra.clear()\\n self.lineCuit.clear()\\n self.btnBuscar.setEnabled(False)\\n self.lineObra.setEnabled(False)\\n self.lineCuit.setEnabled(False)\\n self.tableObra.setVisible(False)\\n self.obraSocialSeleccionada=None\\n self.cargarProductosSinObra()\",\n \"def save_plante():\\n f = None\\n f = PlanteForm()\\n f.parterre.data = get_parterre(get_plante(f.get_id()).get_parterre())\\n a = get_plante(f.get_id())\\n if f.validate_on_submit():\\n a.set_name(f.get_name())\\n a.set_comportement(f.get_comportement())\\n a.set_taux_humidite(f.get_taux_humidite())\\n a.set_quantite(f.get_quantite())\\n p = Actions(\\n contenu = \\\"Modification de la plante \\\"+a.get_name() + \\\" du parterre \\\"+ get_parterre(a.get_parterre()).get_name(),\\n liste = 1\\n )\\n db.session.add(p)\\n db.session.commit()\\n return redirect(url_for(\\n \\\"plante_info\\\", id = f.get_id()))\\n f.next.data = \\\"save_plante\\\"\\n return render_template(\\n \\\"create-plante.html\\\",\\n title = a.get_name()+\\\" - edit\\\",\\n form = f,\\n plante = a,\\n param = \\\"modif\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6147121","0.6094942","0.6081782","0.6065424","0.605813","0.605813","0.60315925","0.5945697","0.58566546","0.576516","0.57575446","0.5739197","0.56512237","0.56397307","0.56377494","0.5607466","0.5599807","0.55913407","0.55809695","0.55348605","0.5534168","0.55033654","0.54978883","0.5487197","0.5482885","0.54682004","0.546209","0.54563624","0.54493654","0.5436564","0.5430511","0.5423618","0.54172754","0.540351","0.5403224","0.5402397","0.5401439","0.5396381","0.53923076","0.53888386","0.5377899","0.5345398","0.53436166","0.5335656","0.53236973","0.53115016","0.5310612","0.5307933","0.53016424","0.5297955","0.5289261","0.5286054","0.5285716","0.5274834","0.5271467","0.52689564","0.52669805","0.52653605","0.5263096","0.5261576","0.52600914","0.5254056","0.5240833","0.52404827","0.52388763","0.5237391","0.52349967","0.52320963","0.52316815","0.5228559","0.52275515","0.5224216","0.5222603","0.52195346","0.5209903","0.52042043","0.5189045","0.5178055","0.5176841","0.517545","0.5168291","0.5157924","0.5154207","0.51522976","0.5150211","0.5142081","0.51375914","0.5137237","0.51353604","0.5129534","0.5122312","0.51198393","0.51171345","0.51162696","0.5116007","0.51155794","0.5110639","0.51038635","0.5101621","0.5099211","0.5096722"],"string":"[\n \"0.6147121\",\n \"0.6094942\",\n \"0.6081782\",\n \"0.6065424\",\n \"0.605813\",\n \"0.605813\",\n \"0.60315925\",\n \"0.5945697\",\n \"0.58566546\",\n \"0.576516\",\n \"0.57575446\",\n \"0.5739197\",\n \"0.56512237\",\n \"0.56397307\",\n \"0.56377494\",\n \"0.5607466\",\n \"0.5599807\",\n \"0.55913407\",\n \"0.55809695\",\n \"0.55348605\",\n \"0.5534168\",\n \"0.55033654\",\n \"0.54978883\",\n \"0.5487197\",\n \"0.5482885\",\n \"0.54682004\",\n \"0.546209\",\n \"0.54563624\",\n \"0.54493654\",\n \"0.5436564\",\n \"0.5430511\",\n \"0.5423618\",\n \"0.54172754\",\n \"0.540351\",\n \"0.5403224\",\n \"0.5402397\",\n \"0.5401439\",\n \"0.5396381\",\n \"0.53923076\",\n \"0.53888386\",\n \"0.5377899\",\n \"0.5345398\",\n \"0.53436166\",\n \"0.5335656\",\n \"0.53236973\",\n \"0.53115016\",\n \"0.5310612\",\n \"0.5307933\",\n \"0.53016424\",\n \"0.5297955\",\n \"0.5289261\",\n \"0.5286054\",\n \"0.5285716\",\n \"0.5274834\",\n \"0.5271467\",\n \"0.52689564\",\n \"0.52669805\",\n \"0.52653605\",\n \"0.5263096\",\n \"0.5261576\",\n \"0.52600914\",\n \"0.5254056\",\n \"0.5240833\",\n \"0.52404827\",\n \"0.52388763\",\n \"0.5237391\",\n \"0.52349967\",\n \"0.52320963\",\n \"0.52316815\",\n \"0.5228559\",\n \"0.52275515\",\n \"0.5224216\",\n \"0.5222603\",\n \"0.52195346\",\n \"0.5209903\",\n \"0.52042043\",\n \"0.5189045\",\n \"0.5178055\",\n \"0.5176841\",\n \"0.517545\",\n \"0.5168291\",\n \"0.5157924\",\n \"0.5154207\",\n \"0.51522976\",\n \"0.5150211\",\n \"0.5142081\",\n \"0.51375914\",\n \"0.5137237\",\n \"0.51353604\",\n \"0.5129534\",\n \"0.5122312\",\n \"0.51198393\",\n \"0.51171345\",\n \"0.51162696\",\n \"0.5116007\",\n \"0.51155794\",\n \"0.5110639\",\n \"0.51038635\",\n \"0.5101621\",\n \"0.5099211\",\n \"0.5096722\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76668,"cells":{"query":{"kind":"string","value":"Borra el arbol completo para actualizar las entradas"},"document":{"kind":"string","value":"def resetTree(self):\n for fila in self.verDatos.get_children():\n self.verDatos.delete(fila)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def commit(self):","def actualizar_velocidad(self):\r\n pass","def actualizar_puntaje(self):\r\n pass","def save():","def updateAll(self):\n \tself.idToUpdate=''\n \tself.newState=''\n \tself.save()","def actualizar_valores(self,valores): \n self.__valores.update(valores)\n self.set_valores(self.__valores)","def save(self):\n # TODO (Pierre): code","def transact(self):","def transact(self):","def save (self):\n pass","def save(self):\n pass","def save(self):\n pass","def save(self):\n pass","def save(self):\n pass","def save(self):\n pass","def model_update(self, db):\n db.session.commit()","def actualizar_tabla(self):\n self.ajustar_tabla()\n sincronizaciones = Archivo_crontab.listar_sincronizaciones()\n self.tablaSincronizaciones.setRowCount(len(sincronizaciones))\n row = 0\n for i in sincronizaciones:\n (btn_borrar, btn_editar) = self.crear_botones_fila()\n ruta_archivo = QTableWidgetItem(i[0])\n servidor = QTableWidgetItem(i[2])\n tiempo = QTableWidgetItem(' ' + i[3] + ' ' + i[4] + ' ' + i[5] + ' ' + i[6])\n self.tablaSincronizaciones.setItem(row, 0, ruta_archivo)\n self.tablaSincronizaciones.setItem(row, 1, servidor)\n self.tablaSincronizaciones.setItem(row, 2, tiempo)\n self.tablaSincronizaciones.setCellWidget(row, 3, btn_borrar)\n self.tablaSincronizaciones.setCellWidget(row, 4, btn_editar)\n row += 1\n\n self.tablaSincronizaciones.setEditTriggers(QtWidgets.QTableWidget.NoEditTriggers)\n self.tablaSincronizaciones.resizeRowsToContents()","def save(self, obj):","def save():\n pass","def update(self):\n db.session.commit()","def update(self):\n db.session.commit()","def save(self):\n self.db.commit()","def updateMasivo(self, objects):\n retorno = 1\n try:\n for object in objects:\n self.session.merge(object)\n self.session.commit()\n \n except sqlalchemy.exc.DBAPIError, e:\n if self.session is not None:\n self.session.rollback()\n retorno = -1\n print(\"Error!\", e)\n finally:\n if self._DAOAlchemy__cerrarSesion:\n self.session.close()\n return retorno","def update_geonet_data():\n rs = TremorData()\n rs.update()","def update():","def update():","def update_E(self):","def save(self):\n\n pass","def commit(self):\n pass","def commit(self):\n pass","def commit(self):\n pass","def commit(self):\n pass","def commit(self):\n pass","def edit():","def save_db(self) -> None:","def modificacion(self, socio):\n\n aux = self.buscar(socio.id)\n print('El socio a modificar en capa de datos:', aux.id, aux.nombre)\n\n if aux == None:\n return False\n else:\n #persona = session.query(Socio).filter(Socio.dni == aux.id)\n aux.nombre = socio.nombre\n aux.apellido = socio.apellido\n aux.dni = socio.dni\n\n session.commit()\n\n return aux","def save_model(self, request, obj, form, change):\n obj.revise()","def save(self):\n self.__db.commit()","def test_update_virt_realm(self):\n pass","def dummy():\n\t\t\tself.save()","def post_revert(self):","def save(self):\n self.add_statements(self.triples())","def save(self):\n db.session.commit()","def revise():","def realizar_entrega(self, id_alumno, comentarios, archivos):\r\n if Entrega.objects.filter(alumno_id=id_alumno, actvidad_id=self.pk).count() == 0:\r\n self._registrar_entrega(id_alumno, comentarios, archivos)\r\n else:\r\n self._actualizar_entrega(id_alumno, comentarios, archivos)","def put(self, id):\n empleadoactualizar = EmployeeModel.query.filter_by(employee_id=id).first()\n if empleadoactualizar:\n reg = api.payload\n empleadoactualizar.employee_id = reg['employee_id']\n empleadoactualizar.name = reg['name']\n empleadoactualizar.age = reg['age']\n empleadoactualizar.position = reg['position']\n empleadoactualizar.fechaingreso = datetime.date.fromisoformat(reg['fechaingreso'])\n db.session.merge(empleadoactualizar)\n db.session.commit()\n return 201\n api.abort(404)","def save(self) -> None:\n pass","def save(self) -> None:\n pass","def save(self) -> None:\n pass","def save(self):\n self.session.commit()","def save(self, context=None):\n updates = self.obj_get_changes()\n self.dbapi.update_boar(context, self.id, updates)\n self.obj_reset_changes()","def modificarPrecios(precios):\n try:\n conexion.cur.execute('update precios set precioDesayuno = ?, precioComida= ?, precioParking = ?',\n (precios[0], precios[1], precios[2]))\n conexion.conex.commit()\n except sqlite3.OperationalError as e:\n print(e)\n conexion.conex.rollback()","def establecerSeccion(self, cuentas, unaSeccion):\n for unaCuenta in cuentas :\n unaCuenta.seccion = unaSeccion\n self.almacen.commit()","def test_update_record(self):\n pass","def update(self, id, **kw):\n orden = kw['orden']\n del kw['orden']\n del kw['tareaID']\n if kw['los_archivos_entrada'].filename:\n kw['archivos_entrada'] = kw['los_archivos_entrada'].file.read()\n del kw['los_archivos_entrada']\n if kw['los_archivos_a_comparar'].filename:\n kw['archivos_a_comparar'] = kw['los_archivos_a_comparar'].file.read()\n del kw['los_archivos_a_comparar']\n # TODO : Hacer ventanita mas amigable para cargar esto.\n try:\n kw['archivos_a_guardar'] = tuple(kw['archivos_guardar'].split(','))\n except AttributeError:\n pass\n del kw['archivos_guardar']\n r = validate_set(id, kw)\n flash(_(u'El %s fue actualizado.') % name)\n raise redirect('../list/%d' % r.tarea.id)","def save(self, db):\n pass","def actualizar(self):\n if self.obraSocialSeleccionada!=None:\n self.cargar_productos(self.obraSocialSeleccionada)\n else:\n self.cargarProductosSinObra()","def update_data():\n pass","def commit(self):\n return","def Update(self):\r\n\r\n # does nothing\r\n pass","def post_migrate_function(obj):\n obj.a = obj.a + u\"-modified\"\n return True","def update( ):\r\n pass","def _update_from_rest_data(self) -> None:","def _commit(self):\n if self._con is not None and self.__tainted:\n self._con.execute('UPDATE word SET normalized_id = rowid WHERE normalized_id is NULL')\n self._con.commit()\n self.__tainted = False","def commit(self) -> None:\n pass","def save(self):\n for t in self.ace_types:\n self.api.api_request(\"PUT\", self.url + t, data={t: self[t]})","def modificarProductosVenta(self, listaProductos):\n\t#modificar -- [idventa, codProducto, numeroitem, valorUnitario, cantidad, IVA]\n\tmodificar = listaProductos[0]\n\t#eliminar -- [idventa, idProducto, numeroitem]\n\teliminar = listaProductos[1]\n\t# primero se modifican los productos\n\tfor i in modificar:\n\t try:\n self.conexion.ejecutarSQL(\"\"\"update ProductosXVentas\n set valor_unitario=%s,\n cantidad=%s,\n IVA=%s,\n valor_total=%s*%s\n where id_Venta=%s\n and codigo_Producto = '%s'\n and numero_item=%s\"\"\"%(i[3], i[4], i[5], i[4], i[3], i[0], i[1], i[2]))\n\t except Exception, e:\n\t\tprint \"modificarProductosVenta excepcion: \", e\n\t\tself.conexion.rollback()\n\t\treturn False\n\t# Ahora se eliminan los Productos\n\tfor i in eliminar:\n\t try:\n\t\tself.conexion.ejecutarSQL(\"delete from ProductosXVentas where id_Venta=%s and codigo_Producto = '%s' and numero_item=%s\"%(i[0],i[1],i[2]))\n\t except Exception, e:\n\t\tprint \"modificarProductosVenta --> Eliminar excepcion: \", e\n\t\tself.conexion.rollback()\n\t\treturn False\n\t \n\t#Se actualiza el kardex para las ventas modificadas como para las ventas eliminadas.\n\t\n\t#Para las ventas modificadas\n\t# PENDIENTE REVISAR SI HUBO CAMBIO DE VALOR UNITARIO\n\ttry:\n\t #modificar -- [idventa,idProducto,numeroitem,cantidad,cantidad_anterior]\n\t for i in modificar:\n\t\tcantActual = float(i[3])\n\t\tcantAnterior = float(i[4])\n\t\tif cantActual == cantAnterior: #S'olo se actualiza el kardex si hubo cambio\n\t\t continue\n\t\telse:\n\t\t codigoProducto = i[1]\n\t\t info = self.getKardexProducto(codigoProducto)\n\t\t #info --> saldo_valor, saldo_cantidad, costo_unitario, valor_total, detalle\n\t\t info = info[-1]\n\t\t if cantActual < cantAnterior: # Si hizo una devoluci'on en la venta (el cliente me devolvi'o cant productos)\n\t\t\tcant = cantAnterior - cantActual\n\t\t\tdetalle = 'Compra'\n\t\t\tvalorUnitario = float(info[2])\n\t\t\tvalorTotal = valorUnitario*cant\n\t\t\tsaldoCantidad = float(info[1])+cant\n\t\t\tsaldoValor = float(info[0])+valorTotal\n\t\t else:# Si hizo una adici'on a la venta (le tengo que vender cant productos al cliente)\n\t\t\tcant = cantActual - cantAnterior\n\t\t\tdetalle = 'Venta'\n\t\t\tvalorUnitario = float(info[2])\n\t\t\tvalorTotal = valorUnitario*cant\n\t\t\tsaldoCantidad = float(info[1])-cant\n\t\t\tsaldoValor = float(info[0])-valorTotal\n\t\t costoUnitario = saldoValor/float(saldoCantidad)\n\t\t self.conexion.ejecutarSQL(\"\"\"\n\t\t insert into Kardex (codigo_Producto, fecha, hora, detalle,cantidad, valor_total,saldo_cantidad, \n\t\t saldo_valor, valor_unitario,costo_unitario) values \n\t\t ('%s',DATE('now','localtime'),TIME('now','localtime'),'%s',%s,%s,%s,%s,%s,%s )\"\"\"%(codigoProducto,\n\t\t detalle,cant,valorTotal,saldoCantidad,\n\t\t saldoValor,valorUnitario,costoUnitario))\n\t self.conexion.commit()\n\t return True\n\texcept:\n\t print \"modificarProductosVenta --> Actualizar kardex (ventas modificadas) excepcion: \", e\n\t self.conexion.rollback()\n\t return False","def update_result(result):\n invader = result['invader'] \n countryOfInvader = result['countryOfInvader'] \n defender = result['defender'] \n countryOfdefender = result['countryOfdefender'] \n invasionHistory = InvasionHistory(\n invader ,\n countryOfInvader,\n defender,\n countryOfdefender,\n datetime.datetime.now() )\n\n #update territory info\n t_record = Territory.get_by_key_name(countryOfdefender)\n t_record.occupier = invader\n t_record.put() \n\n db.put([invasionHistory])","def put(self):\n pass","def put(self):\n pass","def save(self, values):","def update_object(self, name: str) -> None:","def test_update_case(self):\n pass","def commit(self):\n self.create()\n return self.refresh()","def commit(self):\n\t\t#firstly, get all variables and values of this model\n\t\tcontent = self.__dict__.copy() \n\t\t#if '_rev' is one of the variables of this model instance,\n\t\t#it means this user is retrived from database. \n\t\t#We are actually going to update the model document in database\n\t\t#instead of creating a new user document.\n\t\tres = dbop.update_create_user_in_database(self._id, content) \n\t\tself._id = res['id']\n\t\tself._rev = res['rev']","def update_entities(self):\n raise NotImplementedError()","def Save(self) -> None:\n self.__conn.commit()","def update_obj(obj, attributes, params):\n for key in params.keys():\n if key in attributes:\n try:\n set_attribute(obj, key, params[key])\n except:\n abort(400)\n \n Session.flush()\n Session.commit()","def save_now(self):\r\n self.save()","def save_now(self):\r\n self.save()","def persist(self):\n pass","def save(self):\n self.rpc.call(MsfRpcMethod.CoreSave)","def update(self) -> None:\n ...","def update(self):","def update(self):","def update(self):","def commit(obj):\n db.session.add(obj)\n db.session.commit()\n db.session.refresh(obj)\n return obj","def save (self):\n if self.newobj:\n using_sequence = self.sequence ()\n self.keyvals['id'] = using_sequence\n self.seq = using_sequence\n else:\n using_sequence = self.seq\n for key, val in self.keyvals.items ():\n r_key = self.prepare_key (key, using_sequence)\n r.set (r_key, val)\n self.keyvals = {}\n self.newobj = False","def commit(self):\n self.getSession().commit()","def test_e_editar_atributo(self): \n print '+++ Edicion de atributo +++'\n request = self._editar_atributo(nombre, 'descrip editada','String')\n print '*-- datos de prueba ::: ' + nombre+', '+ 'descrip editada' + ', ' + str(id_tipo_atributo) +' --*'\n self.assertNotIn('Debe loguearse primeramente!!!!', request.data, 'No se ha logueado correctamente')\n self.assertNotIn('No posee los permisos suficientes para realizar la operacion', request.data, 'No tiene permisos para editar atributos')\n self.assertNotIn('Error', request.data, 'Tiene errores el form')\n self.assertIn('El atributo ha sido editado con exito', request.data, 'Error al editar la atributo')\n print '*-- request result: ' + request._status + ' --*'\n self.assertIn(nombre, request.data, 'El atributo creado no se encuentra en la tabla')\n print '*-- '+nombre+' creado correctamente, aparece en la tabla de atributos--*'\n print '*---test 5 atributo---*'","def actualizar_poderes(self, event):\r\n pass","def atualizar(self, **kwargs):\n p_id_aluno = kwargs.get('id_aluno')\n p_nome = kwargs.get('nome')\n p_cpf = kwargs.get('cpf')\n p_data_nasc = kwargs.get('data_nasc')\n p_telefone = kwargs.get('telefone')\n\n sql = f\"\"\"\n UPDATE public.Alunos\n SET nome_aluno = '{p_nome}',\n cpf_aluno = '{p_cpf}',\n data_nasc_aluno = '{p_data_nasc}',\n telefone_aluno = '{p_telefone}'\n WHERE id_aluno = '{p_id_aluno}';\n \"\"\"\n\n conn = None\n updated_rows = 0\n\n try: \n params = config(filename=\".\\database.ini\")\n conn = psycopg2.connect(**params)\n\n cur = conn.cursor()\n\n cur.execute(sql)\n\n deleted_rows = cur.rowcount\n\n conn.commit()\n\n cur.close()\n return deleted_rows\n except(Exception, psycopg2.DatabaseError) as error:\n print(error)\n finally:\n if conn is not None:\n conn.close()","def _do_commit(self):","def commit(self):\n self.DB.commit()","def actualizar(self, producto, cantidad):\n producto_id = str(producto)\n\n if producto_id in self.carro:\n self.carro[producto_id]['cantidad'] = cantidad\n \n self.guardar()","def commit_record( self ):\n\n print( \"Committing art record #{:d}.\".format( self.record[\"id\"] ) )\n\n # update the record based on what's currently visible.\n self.record[\"type\"] = self.artTypeComboBox.currentText()\n self.record[\"size\"] = self.artSizeComboBox.currentText()\n self.record[\"quality\"] = self.artQualityComboBox.currentText()\n self.record[\"date\"] = self.artDateLineEdit.text()\n self.record[\"state\"] = self.artProcessingStateComboBox.currentText()\n self.record[\"modified_time\"] = time.mktime( time.gmtime() )\n self.record[\"tags\"] = list( map( lambda x: x.strip(),\n self.artTagsLineEdit.text().split( \", \" ) ) )\n\n self.record[\"artists\"] = [artist.data() for artist in self.artArtistsListView.selectedIndexes()]\n self.record[\"associates\"] = [associate.data() for associate in self.artAssociatesListView.selectedIndexes()]\n self.record[\"vandals\"] = [vandal.data() for vandal in self.artVandalsListView.selectedIndexes()]\n\n # update the region.\n normalized_geometry = self.photoPreview.get_region_geometry( True )\n\n self.record[\"region\"] = normalized_geometry.getRect()\n\n self.db.mark_data_dirty()\n\n super().commit_record()","def save(self):\n pass","def save(self):\n pass","def guardar(self, *args):\n self.objeto = self.modelo()\n for e in self.campos:\n print e.name, ': ', e.widget.valor()\n setattr(self.objeto, e.name, e.widget.valor())\n self.objeto.save()\n self.emit('save')\n print 'save', self","def actualizarLInv(produ,datInv,cant): # Esta sección fue hecha por Valeria \n datInv[produ][2] += cant\n return datInv","def commit(self):\n self.session.commit()"],"string":"[\n \"def commit(self):\",\n \"def actualizar_velocidad(self):\\r\\n pass\",\n \"def actualizar_puntaje(self):\\r\\n pass\",\n \"def save():\",\n \"def updateAll(self):\\n \\tself.idToUpdate=''\\n \\tself.newState=''\\n \\tself.save()\",\n \"def actualizar_valores(self,valores): \\n self.__valores.update(valores)\\n self.set_valores(self.__valores)\",\n \"def save(self):\\n # TODO (Pierre): code\",\n \"def transact(self):\",\n \"def transact(self):\",\n \"def save (self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def model_update(self, db):\\n db.session.commit()\",\n \"def actualizar_tabla(self):\\n self.ajustar_tabla()\\n sincronizaciones = Archivo_crontab.listar_sincronizaciones()\\n self.tablaSincronizaciones.setRowCount(len(sincronizaciones))\\n row = 0\\n for i in sincronizaciones:\\n (btn_borrar, btn_editar) = self.crear_botones_fila()\\n ruta_archivo = QTableWidgetItem(i[0])\\n servidor = QTableWidgetItem(i[2])\\n tiempo = QTableWidgetItem(' ' + i[3] + ' ' + i[4] + ' ' + i[5] + ' ' + i[6])\\n self.tablaSincronizaciones.setItem(row, 0, ruta_archivo)\\n self.tablaSincronizaciones.setItem(row, 1, servidor)\\n self.tablaSincronizaciones.setItem(row, 2, tiempo)\\n self.tablaSincronizaciones.setCellWidget(row, 3, btn_borrar)\\n self.tablaSincronizaciones.setCellWidget(row, 4, btn_editar)\\n row += 1\\n\\n self.tablaSincronizaciones.setEditTriggers(QtWidgets.QTableWidget.NoEditTriggers)\\n self.tablaSincronizaciones.resizeRowsToContents()\",\n \"def save(self, obj):\",\n \"def save():\\n pass\",\n \"def update(self):\\n db.session.commit()\",\n \"def update(self):\\n db.session.commit()\",\n \"def save(self):\\n self.db.commit()\",\n \"def updateMasivo(self, objects):\\n retorno = 1\\n try:\\n for object in objects:\\n self.session.merge(object)\\n self.session.commit()\\n \\n except sqlalchemy.exc.DBAPIError, e:\\n if self.session is not None:\\n self.session.rollback()\\n retorno = -1\\n print(\\\"Error!\\\", e)\\n finally:\\n if self._DAOAlchemy__cerrarSesion:\\n self.session.close()\\n return retorno\",\n \"def update_geonet_data():\\n rs = TremorData()\\n rs.update()\",\n \"def update():\",\n \"def update():\",\n \"def update_E(self):\",\n \"def save(self):\\n\\n pass\",\n \"def commit(self):\\n pass\",\n \"def commit(self):\\n pass\",\n \"def commit(self):\\n pass\",\n \"def commit(self):\\n pass\",\n \"def commit(self):\\n pass\",\n \"def edit():\",\n \"def save_db(self) -> None:\",\n \"def modificacion(self, socio):\\n\\n aux = self.buscar(socio.id)\\n print('El socio a modificar en capa de datos:', aux.id, aux.nombre)\\n\\n if aux == None:\\n return False\\n else:\\n #persona = session.query(Socio).filter(Socio.dni == aux.id)\\n aux.nombre = socio.nombre\\n aux.apellido = socio.apellido\\n aux.dni = socio.dni\\n\\n session.commit()\\n\\n return aux\",\n \"def save_model(self, request, obj, form, change):\\n obj.revise()\",\n \"def save(self):\\n self.__db.commit()\",\n \"def test_update_virt_realm(self):\\n pass\",\n \"def dummy():\\n\\t\\t\\tself.save()\",\n \"def post_revert(self):\",\n \"def save(self):\\n self.add_statements(self.triples())\",\n \"def save(self):\\n db.session.commit()\",\n \"def revise():\",\n \"def realizar_entrega(self, id_alumno, comentarios, archivos):\\r\\n if Entrega.objects.filter(alumno_id=id_alumno, actvidad_id=self.pk).count() == 0:\\r\\n self._registrar_entrega(id_alumno, comentarios, archivos)\\r\\n else:\\r\\n self._actualizar_entrega(id_alumno, comentarios, archivos)\",\n \"def put(self, id):\\n empleadoactualizar = EmployeeModel.query.filter_by(employee_id=id).first()\\n if empleadoactualizar:\\n reg = api.payload\\n empleadoactualizar.employee_id = reg['employee_id']\\n empleadoactualizar.name = reg['name']\\n empleadoactualizar.age = reg['age']\\n empleadoactualizar.position = reg['position']\\n empleadoactualizar.fechaingreso = datetime.date.fromisoformat(reg['fechaingreso'])\\n db.session.merge(empleadoactualizar)\\n db.session.commit()\\n return 201\\n api.abort(404)\",\n \"def save(self) -> None:\\n pass\",\n \"def save(self) -> None:\\n pass\",\n \"def save(self) -> None:\\n pass\",\n \"def save(self):\\n self.session.commit()\",\n \"def save(self, context=None):\\n updates = self.obj_get_changes()\\n self.dbapi.update_boar(context, self.id, updates)\\n self.obj_reset_changes()\",\n \"def modificarPrecios(precios):\\n try:\\n conexion.cur.execute('update precios set precioDesayuno = ?, precioComida= ?, precioParking = ?',\\n (precios[0], precios[1], precios[2]))\\n conexion.conex.commit()\\n except sqlite3.OperationalError as e:\\n print(e)\\n conexion.conex.rollback()\",\n \"def establecerSeccion(self, cuentas, unaSeccion):\\n for unaCuenta in cuentas :\\n unaCuenta.seccion = unaSeccion\\n self.almacen.commit()\",\n \"def test_update_record(self):\\n pass\",\n \"def update(self, id, **kw):\\n orden = kw['orden']\\n del kw['orden']\\n del kw['tareaID']\\n if kw['los_archivos_entrada'].filename:\\n kw['archivos_entrada'] = kw['los_archivos_entrada'].file.read()\\n del kw['los_archivos_entrada']\\n if kw['los_archivos_a_comparar'].filename:\\n kw['archivos_a_comparar'] = kw['los_archivos_a_comparar'].file.read()\\n del kw['los_archivos_a_comparar']\\n # TODO : Hacer ventanita mas amigable para cargar esto.\\n try:\\n kw['archivos_a_guardar'] = tuple(kw['archivos_guardar'].split(','))\\n except AttributeError:\\n pass\\n del kw['archivos_guardar']\\n r = validate_set(id, kw)\\n flash(_(u'El %s fue actualizado.') % name)\\n raise redirect('../list/%d' % r.tarea.id)\",\n \"def save(self, db):\\n pass\",\n \"def actualizar(self):\\n if self.obraSocialSeleccionada!=None:\\n self.cargar_productos(self.obraSocialSeleccionada)\\n else:\\n self.cargarProductosSinObra()\",\n \"def update_data():\\n pass\",\n \"def commit(self):\\n return\",\n \"def Update(self):\\r\\n\\r\\n # does nothing\\r\\n pass\",\n \"def post_migrate_function(obj):\\n obj.a = obj.a + u\\\"-modified\\\"\\n return True\",\n \"def update( ):\\r\\n pass\",\n \"def _update_from_rest_data(self) -> None:\",\n \"def _commit(self):\\n if self._con is not None and self.__tainted:\\n self._con.execute('UPDATE word SET normalized_id = rowid WHERE normalized_id is NULL')\\n self._con.commit()\\n self.__tainted = False\",\n \"def commit(self) -> None:\\n pass\",\n \"def save(self):\\n for t in self.ace_types:\\n self.api.api_request(\\\"PUT\\\", self.url + t, data={t: self[t]})\",\n \"def modificarProductosVenta(self, listaProductos):\\n\\t#modificar -- [idventa, codProducto, numeroitem, valorUnitario, cantidad, IVA]\\n\\tmodificar = listaProductos[0]\\n\\t#eliminar -- [idventa, idProducto, numeroitem]\\n\\teliminar = listaProductos[1]\\n\\t# primero se modifican los productos\\n\\tfor i in modificar:\\n\\t try:\\n self.conexion.ejecutarSQL(\\\"\\\"\\\"update ProductosXVentas\\n set valor_unitario=%s,\\n cantidad=%s,\\n IVA=%s,\\n valor_total=%s*%s\\n where id_Venta=%s\\n and codigo_Producto = '%s'\\n and numero_item=%s\\\"\\\"\\\"%(i[3], i[4], i[5], i[4], i[3], i[0], i[1], i[2]))\\n\\t except Exception, e:\\n\\t\\tprint \\\"modificarProductosVenta excepcion: \\\", e\\n\\t\\tself.conexion.rollback()\\n\\t\\treturn False\\n\\t# Ahora se eliminan los Productos\\n\\tfor i in eliminar:\\n\\t try:\\n\\t\\tself.conexion.ejecutarSQL(\\\"delete from ProductosXVentas where id_Venta=%s and codigo_Producto = '%s' and numero_item=%s\\\"%(i[0],i[1],i[2]))\\n\\t except Exception, e:\\n\\t\\tprint \\\"modificarProductosVenta --> Eliminar excepcion: \\\", e\\n\\t\\tself.conexion.rollback()\\n\\t\\treturn False\\n\\t \\n\\t#Se actualiza el kardex para las ventas modificadas como para las ventas eliminadas.\\n\\t\\n\\t#Para las ventas modificadas\\n\\t# PENDIENTE REVISAR SI HUBO CAMBIO DE VALOR UNITARIO\\n\\ttry:\\n\\t #modificar -- [idventa,idProducto,numeroitem,cantidad,cantidad_anterior]\\n\\t for i in modificar:\\n\\t\\tcantActual = float(i[3])\\n\\t\\tcantAnterior = float(i[4])\\n\\t\\tif cantActual == cantAnterior: #S'olo se actualiza el kardex si hubo cambio\\n\\t\\t continue\\n\\t\\telse:\\n\\t\\t codigoProducto = i[1]\\n\\t\\t info = self.getKardexProducto(codigoProducto)\\n\\t\\t #info --> saldo_valor, saldo_cantidad, costo_unitario, valor_total, detalle\\n\\t\\t info = info[-1]\\n\\t\\t if cantActual < cantAnterior: # Si hizo una devoluci'on en la venta (el cliente me devolvi'o cant productos)\\n\\t\\t\\tcant = cantAnterior - cantActual\\n\\t\\t\\tdetalle = 'Compra'\\n\\t\\t\\tvalorUnitario = float(info[2])\\n\\t\\t\\tvalorTotal = valorUnitario*cant\\n\\t\\t\\tsaldoCantidad = float(info[1])+cant\\n\\t\\t\\tsaldoValor = float(info[0])+valorTotal\\n\\t\\t else:# Si hizo una adici'on a la venta (le tengo que vender cant productos al cliente)\\n\\t\\t\\tcant = cantActual - cantAnterior\\n\\t\\t\\tdetalle = 'Venta'\\n\\t\\t\\tvalorUnitario = float(info[2])\\n\\t\\t\\tvalorTotal = valorUnitario*cant\\n\\t\\t\\tsaldoCantidad = float(info[1])-cant\\n\\t\\t\\tsaldoValor = float(info[0])-valorTotal\\n\\t\\t costoUnitario = saldoValor/float(saldoCantidad)\\n\\t\\t self.conexion.ejecutarSQL(\\\"\\\"\\\"\\n\\t\\t insert into Kardex (codigo_Producto, fecha, hora, detalle,cantidad, valor_total,saldo_cantidad, \\n\\t\\t saldo_valor, valor_unitario,costo_unitario) values \\n\\t\\t ('%s',DATE('now','localtime'),TIME('now','localtime'),'%s',%s,%s,%s,%s,%s,%s )\\\"\\\"\\\"%(codigoProducto,\\n\\t\\t detalle,cant,valorTotal,saldoCantidad,\\n\\t\\t saldoValor,valorUnitario,costoUnitario))\\n\\t self.conexion.commit()\\n\\t return True\\n\\texcept:\\n\\t print \\\"modificarProductosVenta --> Actualizar kardex (ventas modificadas) excepcion: \\\", e\\n\\t self.conexion.rollback()\\n\\t return False\",\n \"def update_result(result):\\n invader = result['invader'] \\n countryOfInvader = result['countryOfInvader'] \\n defender = result['defender'] \\n countryOfdefender = result['countryOfdefender'] \\n invasionHistory = InvasionHistory(\\n invader ,\\n countryOfInvader,\\n defender,\\n countryOfdefender,\\n datetime.datetime.now() )\\n\\n #update territory info\\n t_record = Territory.get_by_key_name(countryOfdefender)\\n t_record.occupier = invader\\n t_record.put() \\n\\n db.put([invasionHistory])\",\n \"def put(self):\\n pass\",\n \"def put(self):\\n pass\",\n \"def save(self, values):\",\n \"def update_object(self, name: str) -> None:\",\n \"def test_update_case(self):\\n pass\",\n \"def commit(self):\\n self.create()\\n return self.refresh()\",\n \"def commit(self):\\n\\t\\t#firstly, get all variables and values of this model\\n\\t\\tcontent = self.__dict__.copy() \\n\\t\\t#if '_rev' is one of the variables of this model instance,\\n\\t\\t#it means this user is retrived from database. \\n\\t\\t#We are actually going to update the model document in database\\n\\t\\t#instead of creating a new user document.\\n\\t\\tres = dbop.update_create_user_in_database(self._id, content) \\n\\t\\tself._id = res['id']\\n\\t\\tself._rev = res['rev']\",\n \"def update_entities(self):\\n raise NotImplementedError()\",\n \"def Save(self) -> None:\\n self.__conn.commit()\",\n \"def update_obj(obj, attributes, params):\\n for key in params.keys():\\n if key in attributes:\\n try:\\n set_attribute(obj, key, params[key])\\n except:\\n abort(400)\\n \\n Session.flush()\\n Session.commit()\",\n \"def save_now(self):\\r\\n self.save()\",\n \"def save_now(self):\\r\\n self.save()\",\n \"def persist(self):\\n pass\",\n \"def save(self):\\n self.rpc.call(MsfRpcMethod.CoreSave)\",\n \"def update(self) -> None:\\n ...\",\n \"def update(self):\",\n \"def update(self):\",\n \"def update(self):\",\n \"def commit(obj):\\n db.session.add(obj)\\n db.session.commit()\\n db.session.refresh(obj)\\n return obj\",\n \"def save (self):\\n if self.newobj:\\n using_sequence = self.sequence ()\\n self.keyvals['id'] = using_sequence\\n self.seq = using_sequence\\n else:\\n using_sequence = self.seq\\n for key, val in self.keyvals.items ():\\n r_key = self.prepare_key (key, using_sequence)\\n r.set (r_key, val)\\n self.keyvals = {}\\n self.newobj = False\",\n \"def commit(self):\\n self.getSession().commit()\",\n \"def test_e_editar_atributo(self): \\n print '+++ Edicion de atributo +++'\\n request = self._editar_atributo(nombre, 'descrip editada','String')\\n print '*-- datos de prueba ::: ' + nombre+', '+ 'descrip editada' + ', ' + str(id_tipo_atributo) +' --*'\\n self.assertNotIn('Debe loguearse primeramente!!!!', request.data, 'No se ha logueado correctamente')\\n self.assertNotIn('No posee los permisos suficientes para realizar la operacion', request.data, 'No tiene permisos para editar atributos')\\n self.assertNotIn('Error', request.data, 'Tiene errores el form')\\n self.assertIn('El atributo ha sido editado con exito', request.data, 'Error al editar la atributo')\\n print '*-- request result: ' + request._status + ' --*'\\n self.assertIn(nombre, request.data, 'El atributo creado no se encuentra en la tabla')\\n print '*-- '+nombre+' creado correctamente, aparece en la tabla de atributos--*'\\n print '*---test 5 atributo---*'\",\n \"def actualizar_poderes(self, event):\\r\\n pass\",\n \"def atualizar(self, **kwargs):\\n p_id_aluno = kwargs.get('id_aluno')\\n p_nome = kwargs.get('nome')\\n p_cpf = kwargs.get('cpf')\\n p_data_nasc = kwargs.get('data_nasc')\\n p_telefone = kwargs.get('telefone')\\n\\n sql = f\\\"\\\"\\\"\\n UPDATE public.Alunos\\n SET nome_aluno = '{p_nome}',\\n cpf_aluno = '{p_cpf}',\\n data_nasc_aluno = '{p_data_nasc}',\\n telefone_aluno = '{p_telefone}'\\n WHERE id_aluno = '{p_id_aluno}';\\n \\\"\\\"\\\"\\n\\n conn = None\\n updated_rows = 0\\n\\n try: \\n params = config(filename=\\\".\\\\database.ini\\\")\\n conn = psycopg2.connect(**params)\\n\\n cur = conn.cursor()\\n\\n cur.execute(sql)\\n\\n deleted_rows = cur.rowcount\\n\\n conn.commit()\\n\\n cur.close()\\n return deleted_rows\\n except(Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n finally:\\n if conn is not None:\\n conn.close()\",\n \"def _do_commit(self):\",\n \"def commit(self):\\n self.DB.commit()\",\n \"def actualizar(self, producto, cantidad):\\n producto_id = str(producto)\\n\\n if producto_id in self.carro:\\n self.carro[producto_id]['cantidad'] = cantidad\\n \\n self.guardar()\",\n \"def commit_record( self ):\\n\\n print( \\\"Committing art record #{:d}.\\\".format( self.record[\\\"id\\\"] ) )\\n\\n # update the record based on what's currently visible.\\n self.record[\\\"type\\\"] = self.artTypeComboBox.currentText()\\n self.record[\\\"size\\\"] = self.artSizeComboBox.currentText()\\n self.record[\\\"quality\\\"] = self.artQualityComboBox.currentText()\\n self.record[\\\"date\\\"] = self.artDateLineEdit.text()\\n self.record[\\\"state\\\"] = self.artProcessingStateComboBox.currentText()\\n self.record[\\\"modified_time\\\"] = time.mktime( time.gmtime() )\\n self.record[\\\"tags\\\"] = list( map( lambda x: x.strip(),\\n self.artTagsLineEdit.text().split( \\\", \\\" ) ) )\\n\\n self.record[\\\"artists\\\"] = [artist.data() for artist in self.artArtistsListView.selectedIndexes()]\\n self.record[\\\"associates\\\"] = [associate.data() for associate in self.artAssociatesListView.selectedIndexes()]\\n self.record[\\\"vandals\\\"] = [vandal.data() for vandal in self.artVandalsListView.selectedIndexes()]\\n\\n # update the region.\\n normalized_geometry = self.photoPreview.get_region_geometry( True )\\n\\n self.record[\\\"region\\\"] = normalized_geometry.getRect()\\n\\n self.db.mark_data_dirty()\\n\\n super().commit_record()\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def guardar(self, *args):\\n self.objeto = self.modelo()\\n for e in self.campos:\\n print e.name, ': ', e.widget.valor()\\n setattr(self.objeto, e.name, e.widget.valor())\\n self.objeto.save()\\n self.emit('save')\\n print 'save', self\",\n \"def actualizarLInv(produ,datInv,cant): # Esta sección fue hecha por Valeria \\n datInv[produ][2] += cant\\n return datInv\",\n \"def commit(self):\\n self.session.commit()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6122221","0.6036619","0.6020295","0.59620243","0.58875334","0.5807845","0.57788527","0.5673862","0.5673862","0.56692386","0.5656522","0.5656522","0.5656522","0.5656522","0.5656522","0.5628181","0.56140774","0.56119204","0.55775714","0.55766964","0.55766964","0.55662805","0.55501413","0.55353487","0.5532447","0.5532447","0.5532316","0.5531889","0.5524365","0.5524365","0.5524365","0.5524365","0.5524365","0.5502974","0.5481492","0.547721","0.5466481","0.5458707","0.5420941","0.5411555","0.53970116","0.5375478","0.53751844","0.53705984","0.53677833","0.53386205","0.53378665","0.53378665","0.53378665","0.53270835","0.5325274","0.53197974","0.531638","0.5316156","0.5311183","0.5308009","0.53050184","0.5296485","0.52956843","0.52864796","0.5286056","0.5283819","0.5281255","0.52756035","0.5274245","0.5271634","0.5271301","0.5258694","0.5253918","0.5253918","0.52459395","0.52367896","0.5230738","0.52223974","0.5221465","0.5216958","0.52164596","0.52108085","0.5197017","0.5197017","0.5196639","0.5190634","0.51846725","0.5181097","0.5181097","0.5181097","0.5179682","0.5179636","0.51786786","0.5173574","0.5170289","0.5169295","0.51686","0.51677996","0.5160848","0.5154742","0.5147178","0.5147178","0.51347965","0.51309","0.51298726"],"string":"[\n \"0.6122221\",\n \"0.6036619\",\n \"0.6020295\",\n \"0.59620243\",\n \"0.58875334\",\n \"0.5807845\",\n \"0.57788527\",\n \"0.5673862\",\n \"0.5673862\",\n \"0.56692386\",\n \"0.5656522\",\n \"0.5656522\",\n \"0.5656522\",\n \"0.5656522\",\n \"0.5656522\",\n \"0.5628181\",\n \"0.56140774\",\n \"0.56119204\",\n \"0.55775714\",\n \"0.55766964\",\n \"0.55766964\",\n \"0.55662805\",\n \"0.55501413\",\n \"0.55353487\",\n \"0.5532447\",\n \"0.5532447\",\n \"0.5532316\",\n \"0.5531889\",\n \"0.5524365\",\n \"0.5524365\",\n \"0.5524365\",\n \"0.5524365\",\n \"0.5524365\",\n \"0.5502974\",\n \"0.5481492\",\n \"0.547721\",\n \"0.5466481\",\n \"0.5458707\",\n \"0.5420941\",\n \"0.5411555\",\n \"0.53970116\",\n \"0.5375478\",\n \"0.53751844\",\n \"0.53705984\",\n \"0.53677833\",\n \"0.53386205\",\n \"0.53378665\",\n \"0.53378665\",\n \"0.53378665\",\n \"0.53270835\",\n \"0.5325274\",\n \"0.53197974\",\n \"0.531638\",\n \"0.5316156\",\n \"0.5311183\",\n \"0.5308009\",\n \"0.53050184\",\n \"0.5296485\",\n \"0.52956843\",\n \"0.52864796\",\n \"0.5286056\",\n \"0.5283819\",\n \"0.5281255\",\n \"0.52756035\",\n \"0.5274245\",\n \"0.5271634\",\n \"0.5271301\",\n \"0.5258694\",\n \"0.5253918\",\n \"0.5253918\",\n \"0.52459395\",\n \"0.52367896\",\n \"0.5230738\",\n \"0.52223974\",\n \"0.5221465\",\n \"0.5216958\",\n \"0.52164596\",\n \"0.52108085\",\n \"0.5197017\",\n \"0.5197017\",\n \"0.5196639\",\n \"0.5190634\",\n \"0.51846725\",\n \"0.5181097\",\n \"0.5181097\",\n \"0.5181097\",\n \"0.5179682\",\n \"0.5179636\",\n \"0.51786786\",\n \"0.5173574\",\n \"0.5170289\",\n \"0.5169295\",\n \"0.51686\",\n \"0.51677996\",\n \"0.5160848\",\n \"0.5154742\",\n \"0.5147178\",\n \"0.5147178\",\n \"0.51347965\",\n \"0.51309\",\n \"0.51298726\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76669,"cells":{"query":{"kind":"string","value":"Metodo para actualizar el arbol junto al formulario y carga de nuevo con los datos actualizados"},"document":{"kind":"string","value":"def updateTree(self):\n self.reset()\n self.resetTree() \n self.read()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def actualizar(self):\n if self.obraSocialSeleccionada!=None:\n self.cargar_productos(self.obraSocialSeleccionada)\n else:\n self.cargarProductosSinObra()","def actualizar_puntaje(self):\r\n pass","def actualizar_tabla(self):\n self.ajustar_tabla()\n sincronizaciones = Archivo_crontab.listar_sincronizaciones()\n self.tablaSincronizaciones.setRowCount(len(sincronizaciones))\n row = 0\n for i in sincronizaciones:\n (btn_borrar, btn_editar) = self.crear_botones_fila()\n ruta_archivo = QTableWidgetItem(i[0])\n servidor = QTableWidgetItem(i[2])\n tiempo = QTableWidgetItem(' ' + i[3] + ' ' + i[4] + ' ' + i[5] + ' ' + i[6])\n self.tablaSincronizaciones.setItem(row, 0, ruta_archivo)\n self.tablaSincronizaciones.setItem(row, 1, servidor)\n self.tablaSincronizaciones.setItem(row, 2, tiempo)\n self.tablaSincronizaciones.setCellWidget(row, 3, btn_borrar)\n self.tablaSincronizaciones.setCellWidget(row, 4, btn_editar)\n row += 1\n\n self.tablaSincronizaciones.setEditTriggers(QtWidgets.QTableWidget.NoEditTriggers)\n self.tablaSincronizaciones.resizeRowsToContents()","def actualizar_velocidad(self):\r\n pass","def ejecutarOperacion(self):\n\n if self.option == 1:\n \"\"\"\n El usuario desea agregar un punto al mapa\n \"\"\"\n # Capturo los x, y\n k = self.elementoSeleccionado.split(\"-\")\n print(\"info\",k)\n x = int(k[0])\n y = int(k[1])\n # Ya capture los valores los agrego al arbol\n # Ojo: En modo creacion no entran\n self.arbol.ADD((x, y), \"X\")\n\n\n if self.option == 2:\n \"\"\"\n El usuario desea eliminar un punto del mapa\n \"\"\"\n k= self.elementoSeleccionado.split(\"-\")\n x = int(k[0])\n y = int(k[1])\n \n self.arbol.eliminarNodo((x, y))\n\n if self.option == 3:\n # Si no se ha seleccionado el punto A capturelo\n if self.PUNTOA == None and self.PUNTOB == None:\n k = self.elementoSeleccionado.split(\"-\")\n x = int(k[0])\n y = int(k[1])\n self.PUNTOA = (x, y)\n else:\n # YA se selecciono el punto A si es diferente de B se captura\n k = self.elementoSeleccionado.split(\"-\")\n x = int(k[0])\n y = int(k[1])\n # Creo el punto para poder comparar que A sea diferente de B\n temp = (x, y)\n if temp != self.PUNTOA:\n self.PUNTOB = temp\n # Lo modifico en el arbol\n self.arbol.modificarData(self.PUNTOA, self.PUNTOB)","def save_parterre():\n f = None\n f= ParterreForm()\n a = get_parterre(f.get_id())\n na = a.get_name()\n if f.validate_on_submit():\n a.set_name(f.get_name())\n form = request.form\n longitudes = form.getlist(\"longitudes\")\n if longitudes != []:\n latitudes = form.getlist(\"latitudes\")\n num = 0\n a.remove_coordonnees()\n for longitude,latitude in zip(longitudes, latitudes):\n c = Coordonnees(x = longitude,\n y = latitude,\n parterre = a.get_id(),\n num = num)\n num = num+1\n a.add_coordonnee(c)\n p = Actions(\n contenu = \"Modification du parterre \"+na+\" -> \"+a.get_name(),\n liste = 1\n )\n db.session.add(p)\n db.session.commit()\n return redirect(url_for(\"parterre_info\", id = a.get_id()))\n f.next.data = \"save_parterre\"\n return render_template(\"create-parterre.html\",\n title= a.get_name()+\" - edit\",\n form = f,\n parterre = a,\n param = \"modif\")","def save_plante():\n f = None\n f = PlanteForm()\n f.parterre.data = get_parterre(get_plante(f.get_id()).get_parterre())\n a = get_plante(f.get_id())\n if f.validate_on_submit():\n a.set_name(f.get_name())\n a.set_comportement(f.get_comportement())\n a.set_taux_humidite(f.get_taux_humidite())\n a.set_quantite(f.get_quantite())\n p = Actions(\n contenu = \"Modification de la plante \"+a.get_name() + \" du parterre \"+ get_parterre(a.get_parterre()).get_name(),\n liste = 1\n )\n db.session.add(p)\n db.session.commit()\n return redirect(url_for(\n \"plante_info\", id = f.get_id()))\n f.next.data = \"save_plante\"\n return render_template(\n \"create-plante.html\",\n title = a.get_name()+\" - edit\",\n form = f,\n plante = a,\n param = \"modif\")","def VIEJOupdateComplejos():\n\txml_ciudades = \"http://api2.cinemex.com/rsvr.php?Action=GetFiltrados&IdDev=1\"\n\txml_complejos = \"http://api2.cinemex.com/rsvr.php?Action=GetFiltrados&IdDev=1&ciudad=%s\"\n\t\n\t\n\tciudades = parse_ciudadesxml( urlopen(xml_ciudades) )#Diccionario de ciudades de Mexico\n\t\n\t#xml = urlopen(xml_complejos)\n\t#ids = parse_complejosxml(xml)\n\t\n\tbase_url = \"http://www.cinemex.com/cinemex/complejos.php?cvecine=%s\"\n\t\n\tfor ciudad_id in ciudades:\n\t\txml_url = xml_complejos % ciudad_id\n\t\tcomplejos_xml = urlopen(xml_url)\n\t\tcomplejos = parse_complejosxml(complejos_xml)\n\t\tfor comp in complejos:\n\t\t\turl = base_url % comp['id_org']\n\t\t\tcomp_html = urlopen(url)\n\t\t\tcomplejo, platino = scrape_compInfo(comp_html,comp)\n\t\t\t#logger.debug( complejo['id_org'])\n\t\t\tcomplejo['id_ciudad']= ciudad_id\n\t\t\t#createComplejo(complejo)\n\t\t\tlogger.debug( complejo)\n\t\t\tlogger.debug( platino)\n\t\t\t#if platino: createComplejoPlatino(complejo)","def save():","def actualizar(self, producto, cantidad):\n producto_id = str(producto)\n\n if producto_id in self.carro:\n self.carro[producto_id]['cantidad'] = cantidad\n \n self.guardar()","def post(self):\r\n piso=self.request.get('piso')\r\n numext=self.request.get('numext')\r\n numint=self.request.get('numint')\r\n piso=self.validonumero(piso)\r\n numext=self.validonumero(numext)\r\n numint=self.validonumero(numint)\r\n \r\n empresa=empresas()\r\n empresa.nombre=self.request.get('desc')\r\n empresa.calle=self.request.get('calle')\r\n empresa.numeroExterior=int(numext)\r\n empresa.numeroInterior=int(numint)\r\n empresa.colonia=self.request.get('colonia')\r\n empresa.piso=int(piso)\r\n empresa.andador=self.request.get('andador')\r\n empresa.codigo_postal=int(self.request.get('cp'))\r\n empresa.sitioweb=self.request.get('web')\r\n empresa.correo=self.request.get('mail')\r\n empresa.nombreContacto=\"\"\r\n empresa.paternoContacto=\"\"\r\n empresa.maternoContacto=\"\"\r\n #### \r\n ciudad=self.request.get('ciudad')\r\n query=\"where ciudad='%s'\"%ciudad\r\n cd=ciudades.gql(query)\r\n city=cd.fetch(1)\r\n for lstcd in city:\r\n empresa.id_Ciudad=lstcd.key().id()\r\n empresa.put()\r\n jsondic={}\r\n jsondata=[]\r\n jsondata+=[self.addKey(jsondic,\"Dato\", empresa.key().id())]\r\n self.response.out.write(simplejson.dumps(jsondata))\r\n return False","def ejecutarproceso(self):\n self.generarConsultasLibres()\n self.aplicarConsultasLibres()\n self.generarCombinaciones()\n self.generarConsultasConexion()\n self.archivoSalida()","def cargarObra(self):\n rowActual=self.tableObra.currentItem().row()\n self.lineObra.setText(str(self.tableObra.item(rowActual,0).text()))\n self.lineCuit.setText(str(self.tableObra.item(rowActual,1).text()))\n self.tableObra.hide()\n self.lineObra.setEnabled(False)\n self.lineCuit.setEnabled(False)\n self.obraSocialSeleccionada = str(self.lineObra.text())\n self.cargar_productos(self.obraSocialSeleccionada)\n self.gbProducto.setVisible(True)","def updatePeliculas():\n\txml_ciudades = \"http://api2.cinemex.com/rsvr.php?Action=GetFiltrados&IdDev=1\"\n\txml_peliculas = \"http://api2.cinemex.com/rsvr.php?Action=GetFiltrados&IdDev=1&ciudad=%s&byciudad=1\" #id_ciudad\n \n\tciudades = parse_ciudades( urlopen(url_movil) ) \n\t\n\t\n\tbase_url_pelicula = \"http://www.cinemex.com/cartelera/pelicula.php?vcode=%s\" #mex_vc\n\tpeliculas = {}\n \n\tpelis_obj = [] #Contiene toda la info de las peliculas, titulo, sinopsis, etc\n \n\t#Crea un diccionario con el vc y el objeto de la pelicula\n\t#De esta forma no hay peliculas repetidas\n\tfor ciudad_id in ciudades:\n\t\txml_url = xml_peliculas % ciudad_id\n\t\ttry:\n\t\t\txml = urlopen(xml_url)\n\t\texcept:\n\t\t\tlogger.debug( 'error cargando pagina %s' % xml_url)\n\t\t\tcontinue\n\t\tpelis_actual = parse_peliculas(xml)\n\t\t#Agregar las peliculas q no estan todavia\n\t\tfor peli in pelis_actual:\n\t\t\tkey = peli.get('mex_vc', '')\n\t\t\tif key not in peliculas: peliculas[key] = peli\n \n\tfor k, v in peliculas.items():\n\t\turl = base_url_pelicula % k\n\t\thtml = urlopen(url)\n\t\tpelis_obj.append(scrape_pelicula(html, v))\n \n\tfor peli in pelis_obj:\n\t\tcreatePelicula(peli)","def updateComplejos():\n\t\n\turl_base_cd = \"http://m.cinemex.com/estrenos.php?cveciudad=%s\"\n\t\n\tbase_url_complejo = \"http://www.cinemex.com/cinemex/complejos.php?cvecine=%s\"\n\t\n\tciudades = parse_ciudades( urlopen(url_movil) )\n\t\n\tfor ciudad_id in ciudades.keys():\n\t\turl_cd = url_base_cd % ciudad_id\n\t\tcomplejos = parse_complejos( urlopen(url_cd) )\n\t\tfor comp in complejos:\n\t\t\turl = base_url_complejo % comp['id_org']\n\t\t\tcomp_html = urlopen(url)\n\t\t\tcomplejo, platino = scrape_compInfo(comp_html,comp)\n\t\t\t#logger.debug( complejo['id_org'])\n\t\t\tcomplejo['id_ciudad']= ciudad_id\n\t\t\tcreateComplejo(complejo)\n\t\t\tif platino: createComplejoPlatino(complejo)\t\n\t\n\t#Por alguna razon hay algunos cinemex sin nombre y causan problema\n\t#Borra vacios\n\tComplejo.objects.filter(nombre='Cinemex ').delete()","def update_data():\n pass","def actualizar_poderes(self, event):\r\n pass","def atualiza_estoque():\n\n template = 'produtos/form-atualiza-estoque.html'\n form = EnviarProdutosNovosForm()\n config = ConfigMagento.by_id(1)\n\n task = current_app.atualiza_estoque_task\n clear_task = request.args.get('clear_task', None)\n if clear_task and clear_task == 'yes':\n if task and task.state == 'SUCCESS':\n current_app.atualiza_estoque_task = task = None\n return redirect(url_for('produtos.atualiza_estoque'))\n\n produtos = None\n if not task:\n produtos = buscar_estoque_produtos(dthr_sincr=config.dtsincr_estoque)\n\n if form.validate_on_submit() and produtos:\n task = atualiza_estoque_task.apply_async()\n current_app.atualiza_estoque_task = task\n\n success(f'Tarefa iniciada com sucesso')\n return redirect(url_for('produtos.atualiza_estoque'))\n\n result = {\n 'title': 'Produtos',\n 'subtitle': 'Atualizar Estoque',\n 'form': form,\n 'produtos': produtos,\n 'task': task\n }\n return render_template(template, **result)","def cambiar_libro_bcompartida(request, id_biblioteca_compartida, id_libro_compartido=None):\n template = \"libros/cambiar_libro_bcompartida.html\"\n\n bcompartida = BibliotecaCompartida.objects.get(id=id_biblioteca_compartida)\n\n libro_disponible = None\n if id_libro_compartido:\n libro_disponible = LibrosBibliotecaCompartida.objects.get(id=id_libro_compartido)\n\n if request.method == \"POST\":\n form = FormCambiarLibroBCompartida(request.POST)\n if form.is_valid():\n id_libro_cambiado = form.cleaned_data.get(\"id_libro_cambiado\")\n titulo_nuevo_libro = form.cleaned_data.get(\"titulo_recibido\")\n autor_nuevo_libro = form.cleaned_data.get(\"autor_recibido\")\n # = form.cleaned_data.get(\"usuario_cambiar\", None)\n\n libro_cambiado = LibrosBibliotecaCompartida.objects.get(id=id_libro_cambiado)\n libro_cambiado.disponible = False\n libro_cambiado.eliminado = True\n libro_cambiado.save()\n\n nuevo_libro = Libro.objects.create(titulo=titulo_nuevo_libro, autor=autor_nuevo_libro)\n LibrosBibliotecaCompartida.objects.create(libro=nuevo_libro, biblioteca_compartida=bcompartida)\n\n # Crear notificacion\n # if username_cambiar:\n # if Perfil.objects.filter(usuario__username=username_cambiar).exists():\n # usuario_cambiar = Perfil.objects.get(usuario__username=username_cambiar)\n # Notificacion.objects.bcompartida_cambio(biblioteca_compartida, libro_cambiado.libro, nuevo_libro,\n # usuario_cambiar)\n\n Notificacion.objects.bcompartida_cambio(bcompartida, libro_cambiado.libro, nuevo_libro)\n\n return redirect('libros:editar_libros_bcompartida', slug_biblioteca_compartida=bcompartida.slug)\n\n else:\n if id_libro_compartido:\n form = FormCambiarLibroBCompartida(initial={\n 'titulo_inicial': libro_disponible.libro.titulo,\n 'id_libro_cambiado': libro_disponible.id\n })\n else:\n form = FormCambiarLibroBCompartida()\n\n libros_bcompartida = LibrosBibliotecaCompartida.objects.filter(biblioteca_compartida=bcompartida,\n disponible=True).select_related(\"libro\")\n titulos_autocomplete = {}\n for libro_bc in libros_bcompartida:\n titulos_autocomplete[libro_bc.libro.titulo] = libro_bc.id\n\n usuarios_autocomplete_obj = Perfil.objects.filter(ciudad=bcompartida.ciudad).select_related(\"usuario\")\n usernames_autocomplete = [perfil.usuario.username for perfil in usuarios_autocomplete_obj]\n\n context = {'form': form, 'libro_disponible': libro_disponible,\n 'titulos_autocomplete': json.dumps(titulos_autocomplete),\n 'biblioteca_compartida': bcompartida,\n 'usernames_autocomplete': json.dumps(usernames_autocomplete)}\n\n return render(request, template, context)","def listar_atributos(request,idAtributoTI,id_item,ver = None):\n print('el id del item es :',id_item)\n atributos = Atributo_Item.objects.filter(id_item=id_item) #obtiene los valores del atributo del item\n TI=TipoItem.objects.get(id_ti=idAtributoTI) #obtiene el tipo de item\n atributo= Atributo.objects.filter(ti=TI) #lista los atributos del tipo de item\n itemActual=Item.objects.get(id_item=id_item) #el item a ser modificado\n if(request.method=='POST'):\n if request.method == 'POST':\n ''' obtengo el item viejo (itemActual) y actualizo sus valores de boolean a false.'''\n itemActual.actual=False\n itemActual.save()\n '''Ahora en estas variables voy a obtener los valores editados de mi ITEM '''\n nombre_item_editado = request.POST.get('item_nombre')\n descripcion_item_editado = request.POST.get('item_descripcion')\n estado_item_editado = request.POST.get('item_estado')\n costo_item_editado = request.POST.get('item_costo')\n print('nombre es:',nombre_item_editado)\n ''' crear un nuevo item, con un nuevo id con los valores recibidos del form'''\n item_editado = Item(nombre=nombre_item_editado,descripcion=descripcion_item_editado,costo=costo_item_editado,\n actual=True,estado=itemActual.estado,fase_id=TI.fase_id,ti_id=TI.id_ti)\n item_editado.save()\n\n ''' Crear un nuevo registro en la tabla versiones con los valores correspondientes del id_padre y version'''\n version_item_actual= Versiones.objects.get(id_item=itemActual.id_item) #obtengo el registro del item actual\n varsion_item_editado = Versiones(id_item=item_editado.id_item, id_padre= version_item_actual.id_padre,\n id_Version=version_item_actual.id_Version+1)\n varsion_item_editado.save()\n\n diccionario_data = verificar_datos_form_atributo_item(request,TI.id_ti,atributo,request.POST,request.FILES)\n print('el diccionario data es: ',diccionario_data)\n for data in diccionario_data:\n if(data['AtributoTI'].tipo_dato=='File'): #si es de tipo File ,entonces armo la ruta para subir el archivo\n if(data['valor_atributo_item'] != 'Sin archivos adjuntos' and data['valor_atributo_item'] != 'Archivo Elminado' ): # si se recibe algo es porque el edito el archivo, por lo tanto creamos una nueva ruta en la nube\n ruta = str(TI.fase.id_Proyecto.id_proyecto) + \"/\" + str(item_editado.id_item)\n doc = data['valor_atributo_item']\n PATH = f'/{ruta}/{doc[0]}'\n ''' proceso de subir archivo a dropbox'''\n SubirArchivo(doc[0], PATH)\n print('-----',PATH)\n ##se sube archivo a dropbox en segundo plano\n t2 = Thread(\n target=SubirArchivo,\n args=(doc[0], PATH),\n )\n t2.start()\n atributo_item_editado = Atributo_Item(idAtributoTI=data['AtributoTI'], id_item=item_editado,\n valor=PATH)\n atributo_item_editado.save()\n elif(data['valor_atributo_item'] == 'Archivo Elminado'):\n atributo_item_editado = Atributo_Item(idAtributoTI=data['AtributoTI'],\n id_item=item_editado,\n valor='Sin archivos adjuntos')\n atributo_item_editado.save()\n else:\n atributo_item_editado = Atributo_Item(idAtributoTI=data['AtributoTI'], id_item=item_editado,\n valor=Atributo_Item.objects.get(id_item=itemActual,idAtributoTI=data['AtributoTI']).valor)\n print('el editado sin modificar',atributo_item_editado.valor)\n atributo_item_editado.save()\n else:\n print('atributo_id:',data['AtributoTI'].id_atributo,' valor:',data['valor_atributo_item'])\n atributo_item_editado=Atributo_Item(idAtributoTI=data['AtributoTI'], id_item=item_editado,\n valor=data['valor_atributo_item'])\n atributo_item_editado.save()\n '''aca se actulizan las relaciones del item'''\n itemActual_inicio=Relacion.objects.filter(inicio_item=itemActual.id_item) #obtengo todos los items en donde el es el origen\n for relacion in itemActual_inicio:\n nueva_relacion=Relacion(inicio_item=item_editado.id_item,fin_item=relacion.fin_item)\n nueva_relacion.save()\n\n itemActual_fin = Relacion.objects.filter(fin_item=itemActual.id_item) # obtengo todos los items en donde el es el fin\n for relacion in itemActual_fin:\n nueva_relacion = Relacion(inicio_item=relacion.inicio_item, fin_item=item_editado.id_item)\n nueva_relacion.save()\n\n return redirect('gestion:detallesFase',TI.fase_id)\n\n\n context = {\n \"atributos\":atributos,\n \"atributo\":atributo,\n 'proyectos': itemActual.fase.id_Proyecto,\n 'item':itemActual,\n 'true':True,\n 'false':False,\n 'ver':ver\n }\n return render(request, 'items/listar_atributos.html', context)","def atualiza_base():\n template = 'produtos/form-atualiza-base.html'\n form = EnviarProdutosNovosForm()\n\n if form.validate_on_submit():\n produtos = productList()\n atualizar_base(produtos)\n\n success('Base atualizada com sucesso')\n return redirect(url_for('produtos.atualiza_base'))\n\n result = {\n 'title': 'Atualizar Estoque Produtos',\n 'form': form,\n 'produtos': None\n }\n return render_template(template, **result)","def editar_contacto(agenda_path):\n\tcontacto_mod = input('Contacto a modificar: ')\n\t#que contacto quiere modificar el usuario\n\tagenda = descargar_agenda(agenda_path)\n\tif contacto_mod not in agenda:\n\t\tprint ('El contacto no existe, agreguelo desde el menu')\n\t\tcontinue\n\t\t#por si el contacto no esta agendado\n\tfor i in range(len(agenda)):\n\t if agenda[i][0] == nombre:\n\t \tindex = i\n\t \tbreak\n\t if index != None:\n\t \tprint('Omite aquellos campos que no quieras editar para conservar los datos')\n\t \tnombre = input('Nombre:')\n\t \ttelefono = input('Telefono: ')\n\t \tdireccion = input('Dirección') \n\t \tcontacto[index] = [\n nombre if len(nombre) > 0 else agenda[index][0],\n telefono if len(telefono) > 0 else agenda[index][1],\n direccion if len(direccion) > 0 else agenda[index][2] ]\n\t \tcargar_agenda(agenda_path, agenda)\n\t \tprint('Editado con exito!')\n\t\t\t #modificar los datos del contacto que desea el usuario","def actualizar_valores(self,valores): \n self.__valores.update(valores)\n self.set_valores(self.__valores)","def guardar(self, *args):\n self.objeto = self.modelo()\n for e in self.campos:\n print e.name, ': ', e.widget.valor()\n setattr(self.objeto, e.name, e.widget.valor())\n self.objeto.save()\n self.emit('save')\n print 'save', self","def procesa_parametros_factura():\n form = FormParametrosFactura()\n print(form.num_fac_ini.data, form.factura_actual.data)\n\n if request.method == 'POST' and form.validate_on_submit():\n cursor_insert = connectiondb.cursor()\n cursor_update = connectiondb.cursor()\n cursor_select = connectiondb.cursor()\n # verifica si existen datos en la tabla\n sql_select = \"\"\"SELECT num_fac_ini FROM ParametrosFactura\"\"\"\n cursor_select.execute(sql_select)\n resultado = cursor_select.fetchone()\n cursor_select.close()\n\n if resultado is None:\n sql_insert = \"\"\"INSERT INTO ParametrosFactura (num_fac_ini, \\\n num_fac_fin, num_resolucion, fecha_resolucion, \\\n fecha_venc_resolucion, factura_actual) VALUES \\\n ('{}', '{}', '{}', '{}', '{}', '{}');\n \"\"\".format(form.num_fac_ini.data, form.num_fac_fin.data,\n form.num_resolucion.data, form.fecha_resolucion.data,\n form.fecha_venc_resolucion.data, form.factura_actual.data)\n cursor_insert.execute(sql_insert)\n connectiondb.commit()\n cursor_insert.close()\n # connectiondb.close()\n return \"Los datos se guardaron correctamente\"\n else:\n sql_update = \"\"\"UPDATE ParametrosFactura \\\n SET num_fac_ini = '{}', \\\n num_fac_fin = '{}', \\\n num_resolucion = '{}', \\\n fecha_resolucion = '{}',\n fecha_venc_resolucion = '{}', \\\n factura_actual = '{}' \\\n WHERE num_fac_ini = '{}';\n \"\"\".format(form.num_fac_ini.data,\n form.num_fac_fin.data,\n form.num_resolucion.data,\n form.fecha_resolucion.data,\n form.fecha_venc_resolucion.data,\n form.factura_actual.data,\n int(resultado['num_fac_ini']))\n\n cursor_update.execute(sql_update)\n connectiondb.commit()\n cursor_update.close()\n return \"Los datos se guardaron correctamente\"","def apiEditar():\n response.view = 'generic.json'\n\n def GET(*args, **vars):\n # if not request.env.request_method == 'GET': raise HTTP(403)\n # proveedores = db().select(db.proveedor.ALL).as_list()\n return dict()\n\n def POST(*args, **vars):\n # raise HTTP(403)\n pieza = vars[\"pieza\"]\n proveedores_list = vars[\"proveedores_list\"]\n respuesta = \"ok\"\n idProveedor = 0\n\n fechaIn = pieza[\"fechaIn\"].split('/')\n fechaString = fechaIn[2] + '-' + fechaIn[1] + '-' + fechaIn[0]\n\n pieza_data = dict(codigo=pieza[\"codigo\"], nombre=pieza[\"nombre\"],\n descripcion=pieza[\"descripcion\"], cantidad=pieza[\"cantidad\"],\n unidad=pieza[\"unidad\"], precio_entrada=pieza[\"precioIn\"],\n precio_salida=pieza[\"precioOut\"], fecha_entrada=fechaString)\n\n # Verificar si aumento la cantidad. Para asi dar nueva entrada\n cantidad_antigua = db.pieza(pieza[\"id\"]).cantidad\n cantidad_nueva = int(pieza[\"cantidad\"]) - cantidad_antigua if cantidad_antigua < int(\n pieza[\"cantidad\"]) else int(pieza[\"cantidad\"])\n\n registrar_entrada = cantidad_antigua != int(pieza[\"cantidad\"]) and cantidad_nueva != 0\n\n if registrar_entrada:\n id_pieza_entrada = db.pieza_entrada.insert(codigo=pieza[\"codigo\"], nombre=pieza[\"nombre\"],\n descripcion=pieza[\"descripcion\"], cantidad=cantidad_nueva,\n unidad=pieza[\"unidad\"], precio_entrada=pieza[\"precioIn\"],\n precio_salida=pieza[\"precioOut\"], fecha_entrada=datetime.datetime.now().date())\n\n for prov in proveedores_list:\n db.proveedor_entrada.insert(\n id_pieza_entrada=id_pieza_entrada, nombre=prov[\"nombre\"], descripcion=prov[\"descripcion\"],\n direccion=prov[\"direccion\"], telefono=prov[\"telefono\"])\n\n # Actualizo la pieza\n try:\n db(db.pieza.id == pieza[\"id\"]).update(**pieza_data)\n db(db.pieza_proveedor.id_pieza == pieza[\"id\"]).delete()\n\n for prov in proveedores_list:\n db.pieza_proveedor.insert(\n id_pieza=pieza[\"id\"], id_proveedor=prov[\"id\"])\n\n except Exception:\n respuesta = \"error\"\n\n return dict(respuesta=respuesta)\n # return dict(proveedor=proveedor)\n\n return locals()","def editar_fila(self):\n button = self.sender()\n if button:\n row = self.tablaSincronizaciones.indexAt(button.pos()).row()\n self.contenido = self.tablaSincronizaciones.item(row, 0).text()\n self.ventana = QtWidgets.QMainWindow()\n self.ui = Ui_Ventana_Editar()\n self.ui.setupUi(self.ventana)\n self.ui.editar.clicked.connect(self.editar_sincro)\n self.ventana.show()","def editar_empresa(id):\n cadastrando_empresa = False\n\n empresa = Empresa.query.get_or_404(id)\n form = EditarEmpresaForm(obj=empresa)\n\n if form.validate_on_submit():\n empresa.nome = form.nome.data\n empresa.simbolo = form.simbolo.data\n empresa.regiao = form.regiao.data\n empresa.tipo = form.tipo.data\n empresa.abertura = form.abertura.data\n empresa.fechamento = form.fechamento.data\n empresa.zona = form.zona.data\n empresa.moeda = form.moeda.data\n db.session.commit()\n flash('Empresa editada com sucesso!')\n\n return redirect(url_for('home.listar_empresas'))\n\n form.nome.data = empresa.nome\n form.simbolo.data = empresa.abertura \n form.regiao.data = empresa.regiao\n form.tipo.data = empresa.tipo\n form.abertura = empresa.abertura\n form.fechamento = empresa.fechamento\n form.zona.data = empresa.zona\n form.moeda.data = empresa.moeda\n\n\n return render_template('home/empresa.html', action=\"Edit\",\n cadastrando_empresa=cadastrando_empresa, form=form,\n empresa=empresa, title=\"Editar empresa\")","def update(self, id, **kw):\n orden = kw['orden']\n del kw['orden']\n del kw['tareaID']\n if kw['los_archivos_entrada'].filename:\n kw['archivos_entrada'] = kw['los_archivos_entrada'].file.read()\n del kw['los_archivos_entrada']\n if kw['los_archivos_a_comparar'].filename:\n kw['archivos_a_comparar'] = kw['los_archivos_a_comparar'].file.read()\n del kw['los_archivos_a_comparar']\n # TODO : Hacer ventanita mas amigable para cargar esto.\n try:\n kw['archivos_a_guardar'] = tuple(kw['archivos_guardar'].split(','))\n except AttributeError:\n pass\n del kw['archivos_guardar']\n r = validate_set(id, kw)\n flash(_(u'El %s fue actualizado.') % name)\n raise redirect('../list/%d' % r.tarea.id)","def save(self):\n # TODO (Pierre): code","def save_new_carver():\r\n\r\n brand = omVar_new_carver_brand.get() # Erhalten der Eingabewerte.\r\n price = float(enVar_carv_price.get())\r\n length = int(enVar_carv_length.get())\r\n model = en_new_carver_model.get()\r\n typ = omVar_new_carver_typ.get()\r\n\r\n omVar_new_carver_brand.set(carver_brandlist[8]) # \"Bereinigen\" der Eingabefelder.\r\n enVar_carv_price.set(\"\")\r\n enVar_carv_length.set(\"\")\r\n enVar_carv_model.set(\"\")\r\n omVar_new_carver_typ.set(carver_typ[4])\r\n\r\n new_carver = Carver(brand, price, length, model, typ) # Einspeisen der Eingabewerte in Funktion zum anlegen\r\n # eines neuen Carvers nud speichern in Datei \"ski.json\"\r\n new_carver.new_carver_json()\r\n\r\n messagebox.showinfo(\"Neuer Ski\", f'Es wurde folgender Carver dem Bestand hinzugefügt:\\n\\n'\r\n f'{Carver.describe_carver(new_carver)}')","def editar_info_bcompartida(request, slug_biblioteca_compartida):\n\n template = \"libros/editar_info_bcompartida.html\"\n bcompartida = BibliotecaCompartida.objects.get(slug=slug_biblioteca_compartida)\n perfil_usuario = obtener_perfil(request.user)\n\n if AdminsBibliotecaCompartida.objects.filter(biblioteca_compartida=bcompartida, perfil=perfil_usuario).exists():\n pass\n else:\n raise PermissionDenied\n\n if request.method == \"POST\":\n form = EditarBibliotecaCompartida(request.POST, instance=bcompartida)\n\n if form.is_valid():\n form.save()\n\n return HttpResponseRedirect(reverse('libros:biblioteca_compartida',\n kwargs={'slug_biblioteca_compartida': bcompartida.slug}))\n else:\n form = EditarBibliotecaCompartida(initial={\n 'punto_google_maps': bcompartida.punto_google_maps,\n 'nombre': bcompartida.nombre,\n 'direccion': bcompartida.direccion,\n 'imagen': bcompartida.imagen,\n 'direccion_web': bcompartida.direccion_web,\n 'horario_apertura': bcompartida.horario_apertura,\n })\n\n # Si no es un usuario que hiso login con Facebook, entonces hay que mostrar link a editar_info_personal\n if UserSocialAuth.objects.filter(user=request.user).exists():\n es_usuario_social = True\n else:\n es_usuario_social = False\n\n context = {'biblioteca_compartida': bcompartida, 'form': form, 'es_usuario_social': es_usuario_social}\n\n return render(request, template, context)","def update(self, request, *args, **kwargs):\n obj = self.get_object()\n serializer = CambioSerializer(obj, data=request.DATA, partial=True)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","def manage_afterPUT(self, data, marshall_data, file, context, mimetype,\n filename, REQUEST, RESPONSE):\n file.seek(0)\n self.setImage(file)","def aplicar_operacion_fasores(self,fasor1,fasor2,input_tipo_funcion1,input_tipo_funcion2):\n fasor1 = self.controlador.convertir_fasor(fasor1,input_tipo_funcion1)\n fasor2 = self.controlador.convertir_fasor(fasor2,input_tipo_funcion2)\n nuevo_fasor1 = self.controlador.ejecutar_operacion_fasores(fasor1,fasor2)\n print('\\nResultado\\n')\n print( f\"({nuevo_fasor1.amplitud } , { nuevo_fasor1.frecuencia } , { nuevo_fasor1.anguloInicial} )\",'(Amplitud,frecuencia,angulo) --> Forma Coseno' )\n nuevo_fasor1.pasarASeno() # pasamos a seno el angulo inicial y luego imprimimos el nuevo formato\n print( f\"( {nuevo_fasor1.amplitud }, { nuevo_fasor1.frecuencia } , { nuevo_fasor1.anguloInicial} )\",'(Amplitud,frecuencia,angulo) --> Forma seno\\n' )\n print('\\n')","def ticket_edit(request, ticket_id):\n ticket = get_object_or_404(Ticket, code=ticket_id)\n categoria = ticket.input_module.ticket_category\n title = _(\"Modifica ticket\")\n sub_title = ticket\n allegati = ticket.get_allegati_dict()\n path_allegati = get_path(ticket.get_folder())\n form = ticket.compiled_form(files=None, remove_filefields=allegati)\n form_allegati = ticket.compiled_form(files=None,\n remove_filefields=False,\n remove_datafields=True)\n template = \"user/ticket_edit.html\"\n d = {'allegati': allegati,\n 'categoria': categoria,\n 'form': form,\n 'form_allegati': form_allegati,\n 'path_allegati': path_allegati,\n 'sub_title': sub_title,\n 'ticket': ticket,\n 'title': title,}\n if request.method == 'POST':\n fields_to_pop = [settings.TICKET_CONDITIONS_FIELD_ID]\n json_post = get_POST_as_json(request=request,\n fields_to_pop=fields_to_pop)\n json_response=json.loads(json_post)\n # Costruisco il form con il json dei dati inviati e tutti gli allegati\n # json_response[settings.ATTACHMENTS_DICT_PREFIX]=allegati\n # rimuovo solo gli allegati che sono stati già inseriti\n modulo = ticket.get_form_module()\n form = modulo.get_form(data=json_response,\n files=request.FILES,\n remove_filefields=allegati)\n\n d['form'] = form\n\n if form.is_valid():\n if request.FILES:\n json_response[settings.ATTACHMENTS_DICT_PREFIX] = allegati\n path_allegati = get_path(ticket.get_folder())\n for key, value in request.FILES.items():\n nome_allegato = form.cleaned_data[key]._name\n # form.validate_attachment(request.FILES.get(key))\n save_file(form.cleaned_data[key],\n path_allegati,\n nome_allegato)\n json_response[settings.ATTACHMENTS_DICT_PREFIX][\"{}\".format(key)] = \"{}\".format(nome_allegato)\n elif allegati:\n # Se non ho aggiornato i miei allegati lasciandoli invariati rispetto\n # all'inserimento precedente\n json_response[settings.ATTACHMENTS_DICT_PREFIX] = allegati\n # salva il modulo\n ticket.save_data(form.cleaned_data[settings.TICKET_SUBJECT_ID],\n form.cleaned_data[settings.TICKET_DESCRIPTION_ID],\n json_response)\n # data di modifica\n ticket.update_log(user=request.user,\n note=_(\"Ticket modificato\"))\n\n # log action\n logger.info('[{}] user {} edited ticket {}'.format(timezone.now(),\n request.user,\n ticket))\n\n # Allega il messaggio al redirect\n messages.add_message(request, messages.SUCCESS,\n _(\"Modifica effettuata con successo\"))\n return redirect('uni_ticket:ticket_edit', ticket_id=ticket_id)\n else:\n for k,v in get_labeled_errors(form).items():\n messages.add_message(request, messages.ERROR,\n \"<b>{}</b>: {}\".format(k, strip_tags(v)))\n\n return render(request, template, d)","def save():\n pass","def after_successful_edit(self):\n pass","def on_btnAltaOtroServicio_clicked(self,widget):\n try:\n\n codigoReservaServicio=variables.lblCodigoReservaServicio.get_text()\n if (variables.lblCodigoReservaServicio.get_text() != \"\"):\n if(variables.entTipoServicio.get_text()!=\"\"):\n if (variables.entPrecioServicio.get_text() != \"\"):\n existeOtroServicio = False\n for registro in variables.listFactura:\n if registro[0] == variables.entTipoServicio.get_text():\n existeOtroServicio = True\n if existeOtroServicio == False:\n precio = str(variables.entPrecioServicio.get_text())\n concepto = str(variables.entTipoServicio.get_text())\n datos = (codigoReservaServicio, concepto, precio)\n funciones_servicios.insertarServicio(datos)\n\n else:\n variables.mensajeError = \"No puedes insertar otro servicio extra con el mismo nombre\"\n variables.vError.show()\n variables.entTipoServicio.set_text(\"\")\n variables.entPrecioServicio.set_text(\"\")\n\n else:\n variables.mensajeError = \"Debes insertar un precio servicio\"\n variables.vError.show()\n\n else:\n variables.mensajeError = \"Debes insertar un tipo de servicio\"\n variables.vError.show()\n funciones_servicios.listadoServicio(variables.listServicios,codigoReservaServicio)\n funciones_factura.listadoServicios(variables.listFactura,variables.codr,variables.lblHabitacionServicio.get_text())\n funciones_factura.calcularPreciosServicios()\n\n else:\n variables.mensajeError = \"Debes seleccionar un codigo de reserva\"\n variables.vError.show()\n\n except Exception as e:\n print(\"Error alta servicio\")\n print(e)","def __actualizar_jugador(self, ip, datos):\n a, x, y = datos[1:4]\n if self.server.JUGADORES[ip]['activar']:\n a, x, y = (0,0,0)\n self.server.JUGADORES[ip]['estado'] = True\n self.server.JUGADORES[ip]['activar'] = False\n if not self.server.JUGADORES[ip]['estado']:\n a, x, y = (\"-\",\"-\",\"-\")\n self.server.JUGADORES[ip]['pos'] = \"%s,%s,%s\" % (a, x, y)\n self.__actualizar_balas(ip, datos)","def enviar_imagem():\n template = 'produtos/form-enviar-imagem.html'\n\n if current_app.config['UPLOADS_DEFAULT_URL'] is None:\n warning('A URL Base do Sistema não está configurada. ' +\n 'Configure-a para que as imagens possam ser salvas.')\n return redirect(url_for('integrador.produtos_enviar_imagem'))\n\n if request.method == 'POST':\n import os\n import glob\n\n base_path = os.getcwd() + '/uploads/photos/'\n imagem = request.files['file']\n nome = imagem.filename\n sku = int(nome.split('.')[0])\n filepath = base_path + nome\n\n if nome == '':\n return 'Nenhuma Imagem Foi Selecionada', 400\n\n if nome.rfind('.') == -1:\n return 'O arquivo informado não possui extensão definida', 400\n\n ext = nome.split('.')[1].lower()\n if not imageSet.extension_allowed(ext):\n return 'A extensão informada não é válida', 400\n\n if not validar_nome_imagem(nome):\n return 'O nome da imagem precisa ser um codigo válido', 400\n\n # verifca se ja existe alguma imagem na pasta deste item\n # pegando o sku do item e iterando na pasta de destino, verificando\n # se existe alguma imagem e a removendo\n path = os.path.dirname(filepath)\n if os.path.exists(path):\n for im in glob.glob(os.path.join(path, f'{sku}*.*')):\n os.remove(os.path.join(path, im))\n\n image = imageSet.save(imagem)\n image = base_path + image\n genImage(image, image)\n\n # busca o cadastro do produto e se existir altera para atualizar imagem\n produto = MagProduto.by(sku=sku)\n if produto:\n produto.atualiza_imagem = True\n produto.possui_imagem = True\n produto.update()\n\n Log.info(f'[ENVIAR IMAGEM] Enviado Imagem do produto {nome}.')\n\n result = {\n 'title': 'Produtos',\n 'subtitle': 'Enviar Imagens'\n }\n return render_template(template, **result)","def on_btnAltaServicioBasico_clicked(self,widget):\n try:\n conexion.cur.execute('select * from precios')\n precios = conexion.cur.fetchall()\n if(variables.lblCodigoReservaServicio.get_text()!=\"\"):\n codigoReservaServicio = variables.lblCodigoReservaServicio.get_text()\n if(variables.rbDesayuno.get_active()):\n existeDesayuno=False\n for registro in variables.listFactura:\n print(registro[0])\n if registro[0] == \"Desayuno\":\n existeDesayuno=True\n if existeDesayuno==False:\n precio=precios[0][0]\n concepto=\"Desayuno\"\n datos=(codigoReservaServicio,concepto,precio)\n funciones_servicios.insertarServicio(datos)\n else:\n variables.mensajeError = \"No puedes insertar otro desayuno\"\n variables.vError.show()\n\n elif(variables.rbComida.get_active()):\n existeComida = False\n for registro in variables.listFactura:\n if registro[0] == \"Comida\":\n existeComida = True\n if existeComida == False:\n precio = precios[0][1]\n concepto = \"Comida\"\n datos = (codigoReservaServicio, concepto, precio)\n funciones_servicios.insertarServicio(datos)\n else:\n variables.mensajeError = \"No puedes insertar otra comida\"\n variables.vError.show()\n\n\n if(variables.chkParking.get_active()):\n existeParking = False\n for registro in variables.listFactura:\n if registro[0] == \"Parking\":\n existeParking = True\n if existeParking == False:\n precio = precios[0][2]\n concepto = \"Parking\"\n datos = (codigoReservaServicio, concepto, precio)\n funciones_servicios.insertarServicio(datos)\n else:\n variables.mensajeError = \"No puedes insertar otro servicio de parking\"\n variables.vError.show()\n\n\n variables.rbNinguno.set_active(True)\n variables.chkParking.set_active(False)\n funciones_servicios.listadoServicio(variables.listServicios,codigoReservaServicio)\n funciones_factura.listadoServicios(variables.listFactura,variables.codr,variables.lblHabitacionServicio.get_text())\n funciones_factura.calcularPreciosServicios()\n\n else:\n variables.mensajeError=\"Debes seleccionar un codigo de reserva\"\n variables.vError.show()\n\n\n except Exception as e:\n print(\"Error alta servicio\")\n print(e)","def comenzar_nuevo_juego():\n escena_uno.cargarEscena1(screen, display_width, display_height)#Se pone a correr la escena\n #escena_uno.cargarEscena2(screen, display_width, display_height)","def form_valid(self, form):\n with create_revision():\n # form_data = form.save(commit=False)\n bom_name = form.cleaned_data['bom_name']\n parts = get_bom_parts(bom_name)\n total_price = cal_total_price(bom_name)\n qty = form.cleaned_data['quantity']\n product_name = form.cleaned_data['product_name']\n warehouse_name = form.cleaned_data['warehouse_name']\n asset_state = form.cleaned_data['asset_state']\n mfg_date = form.cleaned_data['mfg_date']\n remarks = form.cleaned_data['remarks']\n # multipule_assets = []\n # Outwards for asset creation\n\n # Outward all parts in an asset\n # multiply it with assets `qty` to limit number\n # queries for creation of Outwards\n for part in parts:\n part_obj = Parts.objects.get(part_number=part['PartNumber'])\n authorised_by = \"asset_auto_creation\"\n verified_by = \"asset_auto_creation\"\n mode_of_transport = \"asset_auto_creation\"\n remarks = \"This outward was created due to asset creation.\"\n parts_assets_qty = float(part['Qty']) * qty\n Outward.objects.create(\n part_type=Type.objects.get(type_name=\"ASSETAUTOOUTWARD\"),\n part_number=part_obj,\n part_name=part_obj.part_name,\n quantity=parts_assets_qty,\n from_warehouse_name=warehouse_name,\n to_warehouse_name=warehouse_name,\n authorised_by=authorised_by,\n verified_by=verified_by,\n mode_of_transport=mode_of_transport,\n unit_of_measure=part_obj.unit_of_measure,\n remarks=remarks,\n )\n\n for i in range(qty):\n single_asset = Asset(\n product_name=product_name,\n bom_name=bom_name,\n parts=parts,\n total_price=total_price,\n warehouse_name=warehouse_name,\n asset_state=asset_state,\n remarks=remarks,\n mfg_date=mfg_date,\n )\n single_asset.save()\n revisions.set_user(self.request.user)\n revisions.set_comment(\"Created from web form.\")\n return_value = super().form_valid(single_asset)\n return return_value","def __actualizar_balas(self, ip, datos):\n a, x, y = datos[4:7]\n self.server.JUGADORES[ip]['bala'] = \"%s,%s,%s\" % (a, x, y)\n self.__actualizar_explosiones(ip, datos)","def _update_from_rest_data(self) -> None:","def actualizarLBI(produ,datInv,cant): # Esta sección fue hecha por Valeria \n datInv[produ][2] -= cant\n return datInv","def cobroEfectivo(self):\n if self.total_a_pagar == 0:\n QtGui.QMessageBox.information(self,\"Aviso\",\"El saldo restante a pagar es cero\")\n else:\n self.rbtnEfectivo.setChecked(True)\n monto_a_pagar, ok = QtGui.QInputDialog.getDouble(self,\"Cobro Efectivo\",\"Ingrese monto a pagar\",0,0,2000,2)\n\n if ok:\n if monto_a_pagar >= self.total_a_pagar:\n QtGui.QMessageBox.information(self,\"Cobro Efectivo\",\"Su vuelto es:%.2f\" % (monto_a_pagar - self.total_a_pagar))\n temp = [\"Efectivo\",monto_a_pagar]\n self.detalles_cobro[self.tablePagos.rowCount()] = temp\n self.total_a_pagar = 0\n elif monto_a_pagar == 0:\n QtGui.QMessageBox.information(self,\"Aviso\",\"El monto ingresado no puede ser cero\")\n else:\n temp = [\"Efectivo\",monto_a_pagar]\n self.detalles_cobro[self.tablePagos.rowCount()] = temp\n self.total_a_pagar -= monto_a_pagar\n\n self.actualizar_total()\n self.actualizar_tabla()","def edit():","def save_data_to_db(rq, ji, request):\n if rq['change_privacy_level']:\n privacy_id = request.POST.get('privacy-id', False)\n ji['instance'].privacy = Privacy(pk=privacy_id)\n ji['instance'].save()\n elif rq['delete_img_flag']:\n ji['instance'].save()\n elif rq['delete_target_instance_id']:\n ji['instance'].delete()\n elif ji['form'].is_valid():\n if rq['add_new_profile_data']:\n ji['instance'].save()\n print(\"success adding new data.\")\n else:\n ji['form'].save()\n print(\"success editing data.\")\n if rq['portfolio_images'] and ji['image_form'].is_valid():\n for idx, image in enumerate(rq['portfolio_images']):\n image_instance = Image(\n work_id=ji['instance'].id,\n image=image,\n )\n image_instance.save()\n if idx == 0:\n ji['instance_image'] = image_instance.image\n print(\"success save images.\")","def enviar_novos():\n\n template = 'produtos/form-enviar-produtos.html'\n form = EnviarProdutosNovosForm()\n imagens = read_images().keys()\n\n task = current_app.enviar_novos_task\n clear_task = request.args.get('clear_task', None)\n if clear_task and clear_task == 'yes':\n if task and task.state == 'SUCCESS':\n current_app.enviar_novos_task = task = None\n return redirect(url_for('produtos.enviar_novos'))\n\n produtos = None\n if not task:\n produtos = buscar_produtos_novos()\n\n if not produtos and not task:\n warning('Não existem produtos para serem enviados para o site')\n\n categorias = buscar_categorias_produtos()\n if not categorias:\n warning('Não existem categorias do site cadastradas')\n\n if form.validate_on_submit() and produtos and categorias:\n produtos_task = {}\n\n for p in produtos:\n secao = int(request.form.get(f'secao-{p.idsubproduto}', 0))\n grupo = int(request.form.get(f'grupo-{p.idsubproduto}', 0))\n subgrupo = int(request.form.get(f'subgrupo-{p.idsubproduto}', 0))\n\n if secao and grupo and subgrupo:\n categorias = [secao, grupo, subgrupo]\n produtos_task[p.idsubproduto] = categorias\n\n task = enviar_novos_task.apply_async(args=(produtos_task,))\n current_app.enviar_novos_task = task\n\n success(f'Tarefa iniciada com sucesso')\n return redirect(url_for('produtos.enviar_novos'))\n\n result = {\n 'title': 'Produtos',\n 'subtitle': 'Enviar Novos',\n 'form': form,\n 'imagens': imagens,\n 'produtos': produtos,\n 'categorias': categorias,\n 'task': task\n }\n return render_template(template, **result)","def submit(self, goback=False):\n is_changed = False\n for fields in self.screendata:\n lbl, win = fields[:2]\n caption = lbl.text()\n if caption == 'Uitvoerende:':\n test = win.currentText()\n ix = win.currentIndex() - 1\n if test != self.parent().albumdata['artist']:\n self.parent().albumdata['artist'] = self.parent().artists[ix]\n self.parent().artist = self.parent().albumdata['artist']\n is_changed = True\n elif caption in ('Albumtitel:', 'Locatie/datum:'):\n test = win.text()\n if test != self.parent().albumdata['titel']:\n self.parent().albumdata['titel'] = test\n is_changed = True\n elif caption == 'Label/jaar:':\n win_l, win_y = fields[1:]\n is_changed = self._replace_details(caption, ', '.join((win_l.text(), win_y.text())))\n elif caption in ('Credits:', 'Bezetting:', 'Tevens met:'):\n is_changed = self._replace_details(caption, win.toPlainText())\n else:\n is_changed = self._replace_details(caption, win.text())\n\n if is_changed:\n albumid = 0 if self.new_album else self.parent().album.id\n album, ok = dmla.update_album_details(albumid, self.parent().albumdata)\n # eigenlijk wil ik hier de nieuwe waarden teruggeven om ze op het scherm te zetten\n # zodat je kunt zien wat er daadwerkelijk in de database zit / is bijgewerkt\n if ok:\n if not goback:\n if self.new_album:\n self.add_another()\n else:\n qtw.QMessageBox.information(self, 'Albums', 'Details updated')\n else:\n qtw.QMessageBox.information(self, 'Albums', 'Something went wrong, please try again')\n return\n self.parent().album = album\n if self.new_album:\n self.parent().albums.append(album)\n self.new_album = False\n # eigenlijk zou je hierna de data opnieuw moeten ophalen en het scherm opnieuw\n # opbouwen - wat nu alleen gebeurt als je naar het detailscherm gaat\n else:\n qtw.QMessageBox.information(self, 'Albums', 'Nothing changed')\n\n if self.first_time:\n self.first_time = False\n self.refresh_screen()","def new_parterre_saving():\n f = None\n f = ParterreForm()\n if f.validate_on_submit():\n o = Parterre(name = f.get_name())\n db.session.add(o)\n form = request.form\n longitudes = form.getlist(\"longitudes\")\n latitudes = form.getlist(\"latitudes\")\n num = 0\n for longitude,latitude in zip(longitudes, latitudes):\n c = Coordonnees(x = longitude,\n y = latitude,\n parterre = o.get_id(),\n num = num)\n num = num+1\n try:\n o.add_coordonnee(c)\n except Exception as e:\n db.session.rollback()\n p = Actions(\n contenu = \"Ajout d'un nouveau parterre \"+f.get_name(),\n liste = 1\n )\n db.session.add(p)\n db.session.commit()\n return redirect(url_for('parterre_info', id = o.get_id()))\n return render_template(\n \"create-parterre.html\",\n form = f,\n title = \"Ajouter un nouveau Parterre\",\n param = \"create\")","def actualizar_tabla(self):\n\n self.padre.limpiarTabla(self.tablePagos)\n for row, cobro in enumerate(self.detalles_cobro.values()):\n self.tablePagos.insertRow(row)\n self.tablePagos.setItem(row,0,QtGui.QTableWidgetItem(cobro[0]))\n self.tablePagos.setItem(row,1,QtGui.QTableWidgetItem(\"$\"+str(cobro[1])))","def prestar_libro_bcompartida(request, id_libro_compartido):\n\n template = \"libros/prestar_libro_bcompartida.html\"\n\n libro_disponible_obj = LibrosBibliotecaCompartida.objects.select_related('biblioteca_compartida').get(\n id=id_libro_compartido)\n\n if request.method == \"POST\":\n form = FormPrestarLibroBCompartida(request.POST)\n\n if form.is_valid():\n username = form.cleaned_data.get('usuario', '')\n\n # crear LibrosPrestadosBibliotecaCompartida object\n perfil_usuario_prestamo = Perfil.objects.get(usuario__username=username)\n libro_prestado = LibrosPrestadosBibliotecaCompartida(libro=libro_disponible_obj.libro,\n perfil_prestamo=perfil_usuario_prestamo,\n biblioteca_compartida=libro_disponible_obj.biblioteca_compartida)\n libro_prestado.save()\n\n # poner no disponible a LibrosBibliotecaCompartida object\n libro_disponible_obj.disponible = False\n libro_disponible_obj.prestado = True\n libro_disponible_obj.save()\n\n # Crear notificacion\n Notificacion.objects.bcompartida_presto(libro_disponible_obj.biblioteca_compartida, perfil_usuario_prestamo,\n libro_disponible_obj.libro)\n\n return HttpResponseRedirect(reverse('libros:editar_libros_bcompartida',\n kwargs={\n 'slug_biblioteca_compartida': libro_disponible_obj.biblioteca_compartida.slug}))\n\n else:\n form = FormPrestarLibroBCompartida()\n\n ciudad = libro_disponible_obj.biblioteca_compartida.ciudad\n perfiles_obj = Perfil.objects.filter(ciudad=ciudad).select_related(\"usuario\")\n usernames_autocomplete = [perfil.usuario.username for perfil in perfiles_obj]\n\n context = {'form': form, 'libro_compartido': libro_disponible_obj,\n 'usernames_autocomplete': json.dumps(usernames_autocomplete)}\n\n return render(request, template, context)","def guardar_datos(nombre, datos, \n directory=\"datos_guardados/\",\n method=\"numerado\",\n ): \n # Primera parte del nombre\n filename_1 = \"datos_\"\n nombre += \"_\"\n \n if method == \"fecha\":\n # Tercera parte del nombre (fecha)\n today = datetime.datetime.today()\n tt = today.timetuple()\n\n # Formato de fecha es yyyymmdd_hhmmss\n filename_3 = \"{:4d}{:02d}{:02d}_{:02d}{:02d}{:02d}\"\n filename_3 = filename_3.format(tt.tm_year, tt.tm_mon, tt.tm_mday,\n tt.tm_hour, tt.tm_min, tt.tm_sec)\n\n # Nombre completo\n filename = directory + filename_1 + nombre + filename_3\n\n elif method == \"numerado\":\n p = pathlib.Path(\"./\" + directory)\n archivos = []\n\n for f in p.iterdir():\n archivos.append(str(f))\n \n numero = 0\n while True:\n # Nombre temporal\n filename = directory + filename_1 + nombre + str(numero)\n \n if filename in archivos: numero += 1\n else: break\n\n # Escritura de datos (módulo json)\n with open(filename, \"w\") as file_object:\n json.dump(datos, file_object)\n \n # Mensaje de escritura\n print(\"---------------------------------------\")\n print(\"Datos escritos en:\")\n print(\"\\t\" + filename)\n print(\"---------------------------------------\")","def upload(self):\n\n # building interface\n utils.printer(\"generating interface.cpp\", 'task')\n try: # catch this exception for downward compatibility\n utils.printer(\"generating interface from: %s\" % self.vars_path, 'msg')\n utils.printer(\"using as template: %s\" % self.task_config['interface_template_fname'], 'msg')\n interface_template_fname = self.task_config['interface_template_fname']\n interface_generator.run(self.vars_path, interface_template_fname)\n except KeyError:\n utils.printer(\"generating interface based on %s\" % self.vars_path, 'msg')\n interface_generator.run(self.vars_path)\n\n # uploading code onto arduino\n\n # replace whatever com port is in the platformio.ini with the one from task config\n self.pio_config_path = self.task_folder / \"Arduino\" / \"platformio.ini\"\n pio_config = configparser.ConfigParser()\n pio_config.read(self.pio_config_path)\n\n # get upload port\n upload_port = self.box_config['FSM']['com_port']\n\n for section in pio_config.sections():\n if section.split(\":\")[0] == \"env\":\n pio_config.set(section, \"upload_port\", upload_port)\n\n # write it\n with open(self.pio_config_path, 'w') as fH:\n pio_config.write(fH)\n \n # get current UI arduino variables, backup defaults,\n # write the UI derived and upload those, revert after upload\n # this workaround is necessary to use the get previous variables\n # functionality ... \n\n # backing up original values\n shutil.copy(self.vars_path, self.vars_path.with_suffix('.default'))\n\n # setting the valve calibration factor\n utils.printer(\"setting valve calibration factors\", 'task')\n if 'valves' in self.box_config.sections():\n valves = dict(self.box_config['valves']).keys()\n for valve in valves:\n try:\n utils.printer('setting calibration factor of valve: %s = %s' % (valve, self.box_config['valves'][valve]), 'msg')\n self.VariableController.VariableEditWidget.set_entry(valve, self.box_config['valves'][valve])\n except:\n utils.printer(\"can't set valve calibration factors of valve %s\" % valve, 'error')\n else:\n utils.printer(\"no valves found in box config\", 'msg')\n\n # overwriting vars\n self.VariableController.write_variables(self.vars_path)\n\n # upload\n utils.printer(\"uploading code on arduino\", 'task')\n prev_dir = Path.cwd()\n\n os.chdir(self.task_folder / 'Arduino')\n fH = open(self.run_folder / 'platformio_build_log.txt', 'w')\n platformio_cmd = self.sys_config['system']['platformio_cmd']\n cmd = ' '.join([platformio_cmd, 'run', '--target', 'upload'])\n proc = subprocess.Popen(cmd, shell=True, stdout=fH) # ,stderr=fH)\n proc.communicate()\n fH.close()\n\n os.chdir(prev_dir)\n\n # restoring original variables\n shutil.copy(self.vars_path.with_suffix('.default'), self.vars_path)\n os.remove(self.vars_path.with_suffix('.default'))\n\n utils.printer(\"upload successful\", 'msg')","def limpiarVentana(self):\n\n self.productosAgregados=0\n self.lotesVentas={}\n self.facturaCobrada=False\n self.obraSocialSeleccionada=None\n self.formapago = None\n self.factura = None\n self.data = {}\n self.detallesTabla = {}\n self.lineObra.clear()\n self.lineObra.setEnabled(True)\n self.lineCuit.clear()\n self.lineCuit.setEnabled(True)\n self.tableObra.setVisible(False)\n self.rbtnObra.setChecked(False)\n self.limpiarTabla(self.tableProductos)\n self.limpiarTabla(self.tableFactura)\n self.cargarProductosSinObra()","def save (self):\n pass","def _actualizar_precios(self, request, presup_id, *args, **kwargs):\n obj_presup = Presupuesto.objects.get(pk=presup_id)\n lineas_actualizadas = []\n if not obj_presup._vigente():\n turnoList = obj_presup.get_turnos_activos()\n for turno in turnoList:\n kwargs['pk'] = presup_id\n for linea in turno.ofertatec_linea_set.all():\n obj_ofertatec = OfertaTec.objects.get(pk=linea.ofertatec.id)\n if linea.precio != obj_ofertatec.precio:\n linea.precio = obj_ofertatec.precio\n cantidad = linea.cantidad or 1\n horas = linea.cant_horas or 1\n linea.precio_total = linea.precio * cantidad * horas\n lineas_actualizadas.append(linea)\n if len(lineas_actualizadas) > 0 and obj_presup.fecha_realizado:\n for turno in turnoList:\n lab.views.createRevision(request, pk=turno.pk)\n createRevision(request, pk=obj_presup.pk)\n for linea in lineas_actualizadas:\n linea.save()\n return True\n # Si el presupuesto esta vigente no le hago modificaciones\n else:\n return False","def atualizar_questao():\n try:\n if current_user.is_administrator():\n cod_id = request.args.get('id')\n questao = Questao.query.filter_by(cod_questao = cod_id).one()\n categorias = Categoria.query.all()\n if request.method == 'POST':\n questao.questao = request.form['questao']\n questao.alternativa1 = request.form['alternativa1']\n questao.alternativa2 = request.form['alternativa2']\n questao.alternativa3 = request.form['alternativa3']\n questao.alternativa4 = request.form['alternativa4']\n questao.alternativa5 = request.form['alternativa5']\n questao.alternativa_correta = request.form['resposta']\n questao.cod_categoria = request.form['categoria']\n db.session.commit()\n return redirect(url_for('admin.listar_questoes'))\n return render_template('admin/atualizar_questao.html', questao=questao, categorias=categorias)\n return redirect(url_for('main.index'))\n except Exception as e:\n abort(500, e)","def update_db(request):\n\n reports = []\n pubmedfiles = []\n labels = []\n concepts = []\n usecase = request.session['usecase']\n type1 = request.POST.get('type', None)\n\n\n for filename, file in request.FILES.items():\n if filename.startswith('reports'):\n reports.append(file)\n if filename.startswith('pubmed'):\n pubmedfiles.append(file)\n elif filename.startswith('concepts'):\n concepts.append(file)\n elif filename.startswith('labels'):\n labels.append(file)\n\n jsonDisp = request.POST.get('json_disp', None)\n jsonAnn = request.POST.get('json_ann', None)\n jsonDispUp = request.POST.get('json_disp_update', '')\n jsonAnnUp = request.POST.get('json_ann_update', '')\n jsonAll = request.POST.get('json_all_update', '')\n load_concepts = request.POST.get('exa_concepts',None)\n load_labels = request.POST.get('exa_labels',None)\n batch = request.POST.get('batch',None)\n #print(batch)\n msg = update_db_util(reports,pubmedfiles,labels,concepts,jsonDisp,jsonAnn,jsonDispUp,jsonAnnUp,jsonAll,load_concepts,load_labels,batch)\n if 'message' in list(msg.keys()):\n keys = get_fields_from_json()\n request.session['fields'] = keys['fields']\n request.session['fields_to_ann'] = keys['fields_to_ann']\n if type1 == 'reports':\n get_fields_extractable('update')\n\n if request.session['mode'] == 'Robot':\n\n # fields = UseCase.objects.get(name=usecase)\n # fields_to_extr = fields.extract_fields\n # request.session['fields_to_ann'] = fields_to_extr\n\n workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in\n with open(os.path.join(workpath, './automatic_annotation/auto_fields/auto_fields.json')) as out:\n data = json.load(out)\n request.session['fields_to_ann'] = data['extract_fields'][usecase]\n\n return JsonResponse(msg)","def new_plante_saving():\n f = None\n f = PlanteForm()\n if f.validate_on_submit():\n o = TypePlante(\n nomPlant = f.get_name(),\n comportement = f.get_comportement(),\n taux_humidite = f.get_taux_humidite(),\n quantite = f.get_quantite(),\n parterre_id = f.get_parterre().get_id())\n f.get_parterre().add_plante(o)\n db.session.add(o)\n p = Actions(\n contenu = \"Ajout d'une plante \"+f.get_name() + \" au parterre \"+ f.get_parterre().get_name(),\n liste = 1\n )\n db.session.add(p)\n db.session.commit()\n return redirect(url_for('parterre_info', id = o.get_parterre()))\n return render_template(\n \"create-plante.html\",\n form = f,\n parterre = f.get_parterre().get_id(),\n title = \"Nouvelle plante\",\n param = \"create\")","def buscarFactura(self):\n\n if not self.lineNumero.isEnabled() and self.facturaSeleccionada != None:\n QtGui.QMessageBox.information(self,\"Aviso\",\"Ya se ha seleccionado una factura\")\n elif not self.lineNumero.isEnabled():\n self.lineNumero.setEnabled(True)\n self.lineNumero.clear()\n self.limpiarTabla(self.tableFactura)\n else:\n self.numeroFacturaActual=str(self.lineNumero.text())\n if len(self.numeroFacturaActual)==0:\n QtGui.QMessageBox.information(self,\"Aviso\",QtCore.QString.fromUtf8(\"No se ha ingresado número de factura\"))\n else:\n self.facturaSeleccionada=FacturaModel.existeFactura(int(self.numeroFacturaActual),self.sesion)\n if self.facturaSeleccionada==None:\n QtGui.QMessageBox.warning(self,\"Aviso\",\"La factura seleccionada no existe\")\n elif self.facturaSeleccionada.getNC()!=None:\n QtGui.QMessageBox.information(self,\"Aviso\",QtCore.QString.fromUtf8(\"La factura ya ha posee una Nota de Crédito\"))\n self.facturaSeleccionada = None\n elif self.facturaSeleccionada.getFechaEmision()+timedelta(days=int(self.plazo))<date.today():\n QtGui.QMessageBox.information(self,\"Aviso\",QtCore.QString.fromUtf8(\"El tiempo permitido para la devolución ha expirado\"))\n elif self.facturaSeleccionada.estaLiquidada(self.sesion):\n print self.facturaSeleccionada.estaLiquidada(self.sesion)\n QtGui.QMessageBox.information(self,\"Aviso\",\"La factura se encuentra liquidada a la Obra Social\")\n else:\n self.lineNumero.setEnabled(False)\n self.cargarObjetos(self.tableFactura,self.facturaSeleccionada.getDetalles(self.sesion),\n [\"nro_linea\",\"producto\",\"cantidad\",\"importe\"])","def editarCadastro(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def save(self):\n pass","def save(self):\n pass","def save(self):\n pass","def save(self):\n pass","def save(self):\n pass","def update(self):\n\n # Ordenamos los personajes por fila, para luego dibujarlos correctamente. Para que no se solapen.\n self._personajes.sort(self._comparar_coordenadas_personajes)\n\n for personaje in self._personajes:\n if (personaje.andando): # Si está andando.\n # Si el personaje se encuentra en el centro de la celda a donde debia llegar ...\n if (personaje.obtener_coordenadas() == self._mapa.obtener_coordenadas_por_posicion((personaje.camino[0][0], personaje.camino[0][1]),self._mapa.CENTER)): \n del personaje.camino[:1] # Eliminamos esa celda del camino ha seguir porque ya ha llegado a ella. \n if ((personaje.camino == []) or (personaje.camino == None)): # Si ya no queda camino a seguir...\n personaje.parar() # Paramos al Personaje.\n if not(personaje.accion == None): # Si tiene asignada alguna acción después de haber llegado a su destino ...\n personaje.accion() # Ejecutamos la acción\n personaje.accion = None # Y limpiamos la acción\n if (personaje.nombre != \"Jugador\"): # Si el Personaje no es el Jugador establacemos su dirección final.\n personaje.actualizar_direccion(personaje.direccion_final)\n else: # Calculamos la nueva direccion hacia donde tiene que mover\n # Obtenemos la fila y columna donde se encuenta el personaje.\n origen = self._mapa.obtener_posicion_por_coordenadas(personaje.obtener_coordenadas())\n # Establecemos hacia donde tiene que mirar el Personaje para ir en esa dirección.\n personaje.actualizar_direccion(self._mapa.direcciones.index([personaje.camino[0][0] - origen[0], personaje.camino[0][1] - origen[1]]))\n else: # Si el personaje no esa todavia en el centro de la celda \n if (not self._hay_colision(personaje, (personaje.camino[0][0], personaje.camino[0][1]))): # Si no hay colisión en la celda de destino \n personaje.mover(personaje.obtener_direccion()) # Movemos al personaje en esa dirección.\n else: # Si hay colision\n celda_personaje = self._mapa.obtener_posicion_por_coordenadas(personaje.obtener_coordenadas())\n personaje.actualizar_posicion(self._mapa.obtener_coordenadas_por_posicion((celda_personaje[0], celda_personaje[1]),self._mapa.CENTER))\n # Volvermos a calcular una ruta para llegar al destino.\n self.ir_a(personaje, personaje.destino, personaje.direccion_final)\n \n personaje.update() # Actualizamos el personaje.\n \n for objeto in self._objetos: # Actualizamos los objetos.\n objeto.update()\n \n self._dibujar(\n )","def iniciarEntradas(self):\n tituloEntry = self.crearEntrada(self.master, self.tituloString, 30, 1, 1)\n descripcionEntry = self.crearEntrada(self.master, self.descripcionString, 30, 2, 1)","def save_capteur():\n f = None\n f = CapteurForm()\n a = get_capteur(f.get_id())\n na = a.get_name()\n if f.validate_on_submit():\n a.set_name(f.get_name())\n a.set_num(f.get_phoneNumber())\n a.set_interval(f.get_interval())\n if a.get_parterre() != f.get_parterre().get_id():\n a.set_parterre(f.get_parterre().get_id())\n if a.get_typeMesure() != f.get_typeMesure().get_id():\n a.set_typeMesure(f.get_typeMesure().get_id())\n ac = Actions(\n contenu = \"Modification du capteur \"+na+\" -> \"+a.get_name(),\n liste = 1\n )\n db.session.add(ac)\n db.session.commit()\n return redirect(url_for(\n \"capteur_info\",\n id = f.get_id()))\n f.next.data = \"save_capteur\"\n return render_template(\n \"addCapteur.html\",\n title = a.get_name()+\" - edit\",\n form = f,\n capteur=a,\n param = \"modif\")","def save(self, doc, form=None, creation=False, refresh_index=True, asAuthor=True, onSaveEvent=True, mantainOriginalForm=True):\n method = 'save'\n\n # we process computed fields (refresh the value)\n if form is None:\n form = doc.getForm()\n elif not mantainOriginalForm:\n # questa sostituzione può essere evitata...\n doc.setItem('Form', form.getFormName())\n\n db=self.db # SONO QUI\n if form:\n for f in form.getFormFields(includesubforms=True, doc=doc, applyhidewhen=False):\n mode = f.getFieldMode()\n fieldname = f.id\n if mode in [\"COMPUTED\", \"COMPUTEDONSAVE\"] or (mode==\"CREATION\" and creation):\n result = form.computeFieldValue(fieldname, doc)\n doc.setItem(fieldname, result)\n else:\n # computed for display field are not stored\n pass\n\n # compute the document title\n title_formula = form.getDocumentTitle()\n if title_formula:\n # Use the formula if we have one\n try:\n title = doc.runFormulaScript(\"form_\"+form.id+\"_title\", doc, form.DocumentTitle)\n except PlominoScriptException, e:\n message = 'Title formula failed: %s' % e\n self.set_error(method, message)\n else:\n if title != doc.Title():\n doc.setTitle(title)\n elif creation:\n # If we have no formula and we're creating, use Form's title\n title = form.Title()\n if title != doc.Title():\n # We may be calling save with 'creation=True' on\n # existing documents, in which case we may already have\n # a title.\n doc.setTitle(title)\n\n # update the document id\n if creation and form.getDocumentId():\n new_id = doc.generateNewId()\n if new_id:\n transaction.savepoint(optimistic=True)\n db.documents.manage_renameObject(doc.id, new_id)\n\n # update the Plomino_Authors field with the current user name\n if asAuthor:\n authors = doc.getItem('Plomino_Authors', []) or []\n name = db.getCurrentUser().getUserName()\n\n if name not in authors:\n authors.append(name)\n\n doc.setItem('Plomino_Authors', authors)\n\n # execute the onSaveDocument code of the form\n if form and onSaveEvent:\n try:\n result = doc.runFormulaScript(\"form_\"+form.id+\"_onsave\", doc, form.onSaveDocument)\n# if result and hasattr(doc, 'REQUEST'):\n# doc.REQUEST.set('plominoredirecturl', result)\n except PlominoScriptException, e:\n message = 'Document has been saved but onSave event failed: %s' % e\n self.set_error(method, message)\n# if hasattr(doc, 'REQUEST'):\n# e.reportError('Document has been saved but onSave event failed.')\n# doc_path = doc.REQUEST.physicalPathToURL(doc.doc_path())\n# doc.REQUEST.RESPONSE.redirect(doc_path)\n\n if refresh_index:\n # update index\n db.getIndex().indexDocument(doc)\n # update portal_catalog\n if db.getIndexInPortal():\n db.portal_catalog.catalog_object(doc, \"/\".join(db.getPhysicalPath() + (doc.id,)))","def Save(cls):\n print(\"Saving data...\")\n # Check if the backup directory exists\n backup_dir = FILE_PATH + \"FellesLab_Backup\"\n if not os.path.isdir(backup_dir):\n os.mkdir(backup_dir)\n with open( backup_dir + \"/README\", 'w') as f:\n f.write(BACKUP_README)\n\n #cmd = \"python -m markdown {dir}/README > {dir}/README.html\".format(dir=backup_dir)\n #call([cmd])\n # Check if the Backup directory has a directory for \"today\"\n day = FILE_PATH + \"FellesLab_Backup/\" + cls.dayStamp()\n if not os.path.isdir(day):\n os.mkdir(day)\n\n # Save data for all the sensors\n # TODO: Rewrite, difficult to follow...\n DATA = [ ] # This will become a list of lists, e.g.\n # [ [time, ...], [Temp1, ...], [time, ...], [Temp2, ...] ]\n for ref in cls.__refs__:\n\n ref().File.seek(0) # Rewind file pointer\n\n F = csv.reader(ref().File, delimiter=',', quotechar='|')\n r = [[],[]]\n for row in F:\n for i,num in enumerate(row):\n r[i].append(num)\n if i > 1:\n r[i].append(float(num))\n\n for j in r:\n DATA.append(j)\n ref().File.close()\n\n # Finally, write data file.\n with open( day + \"/\" + cls.timeStamp() + '.csv', 'w') as f:\n csv.writer(f).writerows( it.izip_longest(*DATA, fillvalue='NA') )","def initialize_user_interface(self):\n self.parent.title(\"Formulario Ciudades\")\n self.parent.grid_rowconfigure(0, weight=1)\n self.parent.grid_columnconfigure(0, weight=1)\n self.parent.config(background=\"lavender\")\n\n self.dose_label = tkinter.Label(self.parent, text=\"Nombre:\")\n self.dose_entry = tkinter.Entry(self.parent)\n self.dose_label.grid(row=0, column=0, sticky=tkinter.W)\n self.dose_entry.grid(row=0, column=1)\n\n self.modified_label = tkinter.Label(self.parent, text=\"Codigo Postal:\")\n self.modified_entry = tkinter.Entry(self.parent)\n self.modified_label.grid(row=1, column=0, sticky=tkinter.W)\n self.modified_entry.grid(row=1, column=1)\n\n self.submit_button = tkinter.Button(self.parent, text=\"Update\", command=self.update_data)\n self.submit_button.grid(row=2, column=1, sticky=tkinter.W)\n self.exit_button = tkinter.Button(self.parent, text=\"Exit\", command=self.parent.quit)\n self.exit_button.grid(row=0, column=3)\n\n # Set the treeview\n self.tree = ttk.Treeview(self.parent, columns=('Nombre', 'Codigo Postal'))\n self.tree.heading('#0', text='Numero de Ciudad')\n self.tree.heading('#1', text='Nombre')\n self.tree.heading('#2', text='Codigo Postal')\n self.tree.column('#1', stretch=tkinter.YES)\n self.tree.column('#2', stretch=tkinter.YES)\n self.tree.column('#0', stretch=tkinter.YES)\n self.tree.grid(row=4, columnspan=4, sticky='nsew')\n self.tree.bind(\"<Double-1>\", self.onDoubleClick)\n self.treeview = self.tree\n # agrego datos\n self.insert_data()","def save_click(self):\n acc_name = self.name_entry.get()\n email = self.email_entry.get()\n username = self.user_entry.get()\n password = self.pass_entry.get()\n\n if not acc_name:\n self.error_label.config(text='Introdu numele contului.')\n return\n\n if self.is_new_account and accountdb.account_exists(self.us, acc_name):\n self.error_label.config(text='Un cont cu acest nume există deja.')\n return\n\n # Tell the user what's happening.\n self.error_label.config(text='Se salvează...')\n self.error_label.update()\n\n acc = account.create_account(acc_name, email, username, password, self.us)\n accountdb.change_account(self.us, acc)\n\n self.error_label.config(text='Detaliile contului au fost salvate.')\n\n self.acc = acc\n self.is_new_account = False\n self.load_account_data()","def _recreate(self, req, id, body):\n context = req.environ['meteos.context']\n\n if not self.is_valid_body(body, 'os-recreate'):\n raise exc.HTTPUnprocessableEntity()\n\n b_model = body['os-recreate']\n\n LOG.debug(\"Recreate model with request: %s\", b_model)\n\n try:\n model = self.engine_api.get_model(context, id)\n utils.is_valid_status(model.__class__.__name__,\n model.status,\n constants.STATUS_AVAILABLE)\n experiment = self.engine_api.get_experiment(\n context, model.experiment_id)\n template = self.engine_api.get_template(\n context, experiment.template_id)\n except exception.NotFound:\n raise exc.HTTPNotFound()\n\n display_name = b_model.get('display_name')\n display_description = b_model.get('display_description')\n source_dataset_url = b_model.get('source_dataset_url')\n dataset_format = b_model.get('dataset_format', 'csv')\n model_type = b_model.get('model_type')\n model_params = b_model.get('model_params')\n swift_tenant = b_model.get('swift_tenant')\n swift_username = b_model.get('swift_username')\n swift_password = b_model.get('swift_password')\n\n new_model = self.engine_api.recreate_model(id,\n context,\n display_name,\n display_description,\n source_dataset_url,\n dataset_format,\n model_type,\n model_params,\n template.id,\n template.job_template_id,\n experiment.id,\n experiment.cluster_id,\n swift_tenant,\n swift_username,\n swift_password)\n\n return self._view_builder.detail(req, new_model)","def resubmit(self, _):\n kw = {}\n for k, v in self.form.items():\n if v.edit_text != PARAM_DEFAULT_VALUE:\n kw[k] = v.edit_text\n try:\n self.execution.update_job_params(**kw)\n except QMapError as e:\n self.widget.original_widget = urwid.Text(e)","def uploader_actividad(df,to_model):\n\tengine = create_engine(\"mssql+pyodbc://sa:BaseSQL123@10.160.8.96:1433/vpcanales?driver=SQL+Server+Native+Client+11.0\")\n\n\tfecha = df.loc[0,'Fecha']\n\tprint(fecha.month)\n\tprint(fecha.year)\n\n\tif to_model.__name__==\"Activacion\":\n\n\t\tActivacion.objects.filter(fecha_actividad__month=fecha.month ,\n\t\t\tfecha_actividad__year=fecha.year).delete()\n\n\t\tfor row in df.itertuples():\n\t\t\tconnection = engine.raw_connection()\n\t\t\tcursor=connection.cursor()\n\t\t\t#Se ejecuta el SP por cada registro del dataframe\n\t\t\tstring=\"\"\"exec sp_insert_into_activacion\n\t\t\t@fecha_actividad='{0}',\n\t\t\t@plataforma='{1}',\n\t\t\t@tecnologia='{2}',\n\t\t\t@terminal='{3}',\n\t\t\t@cantidad='{4}',\n\t\t\t@codigo_plan='{5}',\n\t\t\t@mes={6},\n\t\t\t@ano={7},\n\t\t\t@codigo_agente='{8}'\n\t\t\t \"\"\".format(row[2],\n\t\t\trow[5],\n\t\t\trow[6],\n\t\t\trow[7],\n\t\t\trow[-2],\n\t\t\trow[4],\n\t\t\trow[2].month,\n\t\t\trow[2].year,\n\t\t\trow[3])\n\t\t\tcursor.execute(string).commit()\n\n\t\tresults = Activacion.objects.filter(fecha_actividad__month=fecha.month,\\\n\t\t\tfecha_actividad__year=fecha.year)\\\n\t\t\t.aggregate(suma = Sum('cantidad'))\n\n\n\t\tresults.update(Activacion.objects.filter(fecha_actividad__month=fecha.month,\\\n\t\t\tfecha_actividad__year=fecha.year)\\\n\t\t\t.aggregate(count = Count('cantidad')))\n\n\t\tprint(\"*************************\",results)\n\n\t\treturn results\n\n\n\n\telse:\n\n\t\tAlta.objects.filter(fecha_actividad__month=fecha.month ,\n\t\t\tfecha_actividad__year=fecha.year).delete()\n\n\t\tfor row in df.itertuples():\n\t\t\tconnection = engine.raw_connection()\n\t\t\tcursor=connection.cursor()\n\t\t\t#Se ejecuta el SP por cada registro del dataframe\n\t\t\tstring=\"\"\"exec sp_insert_into_alta\n\t @fecha_actividad='{0}',\n\t @plataforma='{1}',\n\t @tecnologia='{2}',\n\t @terminal='{3}',\n\t @cantidad='{4}',\n\t @codigo_plan='{5}',\n\t @mes={6},\n\t @ano={7},\n\t @codigo_agente='{8}' \"\"\".format(row[2],\n\t row[5],\n\t row[6],\n\t row[7],\n\t row[-2],\n\t row[4],\n\t row[2].month,\n\t row[2].year,\n\t row[3])\n\t\t\tcursor.execute(string).commit()\n\n\n\t\tresults = Alta.objects.filter(fecha_actividad__month=fecha.month,\\\n\t\t\tfecha_actividad__year=fecha.year)\\\n\t\t\t.aggregate(suma = Sum('cantidad'))\n\n\n\t\tresults.update(Alta.objects.filter(fecha_actividad__month=fecha.month,\\\n\t\t\tfecha_actividad__year=fecha.year)\\\n\t\t\t.aggregate(count = Count('cantidad')))\n\n\t\tprint(\"*************************\",results)\n\n\n\n\t\treturn results","def editarpermiso():\n if not current_user.is_authenticated():\n flash('Debe loguearse primeramente!!!!', 'loggin')\n return render_template('index.html')\n\n permission = UserRol('ADMINISTRADOR')\n if permission.can():\n p = db_session.query(Permiso).filter_by(codigo=request.args.get('codigo')).first()\n form = PermisoFormulario(request.form,p)\n permiso = db_session.query(Permiso).filter_by(codigo=form.codigo.data).first()\n if request.method == 'POST':\n if form.validate():\n form.populate_obj(permiso)\n db_session.merge(permiso)\n db_session.commit()\n flash('El permiso ha sido modificado con exito','info')\n return redirect('/permiso/administrarpermiso')\n else:\n flash_errors(form)\n else:\n flash_errors(form)\n return render_template('permiso/editarpermiso.html', form=form)\n else:\n flash('Sin permisos para modificar permisos', 'permiso')\n return render_template('index.html')","def editar_prod(self):\n\t codigo=self.ui.codigo_prod.text()\n\t\tnombre=self.ui.nombre_prod.text()\n\t\tdescripcion=self.ui.descripcion_prod.text()\n\t\tmarca=self.ui.marca_prod.text()\n\t\tcolor=self.ui.color_prod.text()\n\t\tresultado=controller.editar_producto(codigo,nombre,descripcion,marca,color)\n\t\tif resultado:\n\t\t self.reject()","def save_data(self):\n pass","def agregar_bolsa(self, letra, cantidad):","def configurar():\n try:\n with open(f'archivos{ruta()}valores_puntajes.json',\n encoding='UTF-8') as f:\n dic = json.load(f)\n except FileNotFoundError:\n dic = crear_valores_puntajes()\n with open(f'archivos{ruta()}valores_puntajes.json', 'w',\n encoding='UTF-8') as f:\n json.dump(dic, f, indent=4)\n\n letras = dic['Personalizada']['bolsa']\n lista_letras= []\n for elemento in letras:\n lista_letras.append(elemento[0])\n valor_letra = letras[0][1] \n valor_punto = dic['Personalizada']['puntos_letra']['a']\n layout=[\n [sg.T(\"Perfil personalizado\", size=(17,1), justification = \"center\",\n font=(\"Georgia\", 17))],\n [sg.T(\"Letra\",font=(\"Georgia\", 12)),\n sg.Combo(lista_letras, size=(8,1), default_value= 'a', enable_events=True),\n sg.T(\"Cantidad:\", font=(\"Georgia\", 12),),\n sg.Slider(range=(1, 12), orientation='h', size=(13, 20), default_value=valor_letra),\n sg.T(\"Valor:\", font=(\"Georgia\", 12),),\n sg.Slider(range=(1, 20), orientation='h', size=(20, 20), default_value=valor_punto)],\n [sg.T(\"Dificultad de la IA: \"),\n sg.DropDown(('Facil','Medio','Dificil'),\n default_value=(dic['Personalizada']['dificultad_IA']),size=(10,1)),\n sg.T(\"Dificultad del tablero: \"),\n sg.DropDown(('Facil','Medio','Dificil'),\n default_value=(dic['Personalizada']['dificultad_Tablero']),size=(10,1))],\n [sg.Button(\"Guardar\", size=(12, 1), key=\"-guardar-\")]\n ]\n\n window_configurar = sg.Window(\"ScrabbleAR - Configurar\").Layout(layout)\n\n while True:\n event, values = window_configurar.Read()\n \"\"\"\n values[0] = retorna la letra elegida\n values[1] = retorna la cantidad de letras\n values[2] = retorna el puntaje seleccionado\n values[3] = retorna la dificultad de la IA\n values[4] = retorna la dificultad del tablero\n \"\"\"\n try:\n for item in letras:\n if item[0] == values[0]:\n valor_letra = int(item[1])\n window_configurar.find_element(1).Update(valor_letra)\n valor_punto = dic['Personalizada']['puntos_letra'][values[0]]\n window_configurar.find_element(2).Update(valor_punto)\n except:\n break\n if event == None:\n break\n elif event == '-guardar-':\n for item in letras:\n if item[0] == values[0]:\n item[1] = int(values[1])\n break\n dic['Personalizada']['bolsa'] = letras\n dic['Personalizada']['puntos_letra'][values[0]] = int(values[2])\n dic['Personalizada']['dificultad_IA'] = values[3]\n dic['Personalizada']['dificultad_Tablero'] = values[4]\n try:\n with open(f'archivos{ruta()}valores_puntajes.json', 'w', encoding='UTF-8') as f:\n json.dump(dic, f, indent=4)\n sg.popup('Se guardaron los cambios', auto_close= True, auto_close_duration= 1)\n except FileNotFoundError:\n sg.popup('No se encuentra el archivo \"valores_puntajes.json\"')\n window_configurar.Close()","def save_model(self, request, obj, form, change):\n\n if form.is_valid():\n cd = form.cleaned_data\n obj.filename = cd['filename']\n obj.society_code = cd['society_code']\n obj.society_name = cd['society_name']\n obj.date = cd['date']\n # TODO move process() to model, and handle messages here\n obj.report = self.process(\n request, obj.society_code, cd['acknowledgement_file'])\n obj.cwr = cd['acknowledgement_file']\n super().save_model(request, obj, form, change)","def edit(self, id, **kw):\n r = validate_get(id)\n r.archivos_guardar = \",\".join(r.archivos_a_guardar)\n return dict(name=name, namepl=namepl, record=r, form=form)","def on_btnBajaServicio_clicked(self, widget):\n try:\n codigoReservaServicio = variables.lblCodigoReservaServicio.get_text()\n if(variables.codigoServicio!= \"\"):\n funciones_servicios.eliminarServicio(variables.codigoServicio)\n funciones_servicios.listadoServicio(variables.listServicios, codigoReservaServicio)\n funciones_factura.listadoServicios(variables.listFactura,variables.codr,variables.lblHabitacionServicio.get_text())\n funciones_factura.calcularPreciosServicios()\n else:\n variables.mensajeError = \"Debes seleccionar un servicio\"\n variables.vError.show()\n\n\n except:\n print (\"error baja servicio\")","def form_valid(self, form):\n self.instance = form.save()[0].prueba\n self.instance.evaluar()\n self.instance.save()\n return super(PresentarView, self).form_valid(form)","def form_valid(self, form):\n with create_revision():\n form_data = form.save(commit=False)\n bom_name = form.cleaned_data['bom_name']\n warehouse_name = form.cleaned_data['warehouse_name']\n initial_parts = form.initial.get('parts')\n updated_parts = form.cleaned_data.get('parts')\n # Using sets to find newly added parts by using set diff\n\n # Forming sets only with PartNumbers instead of PartsDict\n # to make comparisons easy\n initial_partNumbers = {\n part['PartNumber'] for part in initial_parts\n }\n updated_partNumbers = {\n part['PartNumber'] for part in updated_parts\n }\n newly_added_partNumbers = updated_partNumbers - initial_partNumbers\n removed_partNumbers = initial_partNumbers - updated_partNumbers\n\n # Reforming part dicts to access part attributes easily.\n newly_added_parts = [\n part for part in updated_parts\n if part['PartNumber'] in newly_added_partNumbers\n ]\n form_data.total_price = cal_total_price(bom_name)\n # Create outwards for parts used in asset creation.\n for part in newly_added_parts:\n part_obj = Parts.objects.get(part_number=part['PartNumber'])\n authorised_by = \"asset_auto_creation\"\n verified_by = \"asset_auto_creation\"\n mode_of_transport = \"asset_auto_creation\"\n remarks = \"This outward was created due to asset creation.\"\n\n Outward.objects.create(\n part_type=part_obj.part_type,\n part_number=part_obj,\n part_name=part_obj.part_name,\n quantity=part['Qty'],\n from_warehouse_name=warehouse_name,\n to_warehouse_name=warehouse_name,\n authorised_by=authorised_by,\n verified_by=verified_by,\n mode_of_transport=mode_of_transport,\n unit_of_measure=part_obj.unit_of_measure,\n remarks=remarks,\n )\n\n form_data.save()\n revisions.set_user(self.request.user)\n revisions.set_comment(\"Updated from web form.\")\n return_value = super().form_valid(form_data)\n\n return return_value","def form_valid(self, form, factura_form, remito_form, ot_linea_form):\n self.object = form.save()\n factura_form.instance = self.object\n factura_form.save()\n remito_form.instance = self.object\n remito_form.save()\n ot_linea_form.instance = self.object\n ot_linea_form.save()\n return HttpResponseRedirect(self.get_success_url())","def uploaddata(self):\n\t\tself.filename = filedialog.askopenfilename(filetypes=[('CSV', '*.csv')],\n\t\t\t\t\t\t\t\t\t\t\t initialdir='./Tariffs')\n\n\t\tif self.filename: # don't try to import anything if filename is empty\n\t\t\tself.data = pd.read_csv(self.filename)\n\t\t\tself.validate_tariff() # TODO: build out contingency when validation fails.\n\t\t\tself.plot_tariff()\n\t\t\tself.uploadsuccess = True\n\t\t\tself.controller.writetolog('Uploaded tariff file ' + self.filename)\n\t\telse:\n\t\t\tself.uploadsuccess = False\n\t\t\tself.controller.writetolog('Failed to upload tariff file')\n\n\t\tself.dynamicupload()","def cria_receita(request):\n if request.user.is_authenticated:\n if request.method == 'POST':\n nome_receita = request.POST['nome_receita']\n ingredientes = request.POST['ingredientes']\n modo_preparo = request.POST['modo_preparo']\n tempo_preparo = request.POST['tempo_preparo']\n rendimento = request.POST['rendimento']\n categoria = request.POST['categoria']\n foto_receita = request.FILES['foto_receita']#por se tratar de um dado do tipo file\n user = get_object_or_404(User, pk=request.user.id)\n\n if campo_vazio(nome_receita):\n messages.error(request, 'Nome da receita é obrigatório')\n return redirect('cria_receita')\n\n if campo_vazio(ingredientes):\n messages.error(request, 'ingredientes são obrigatórios')\n return redirect('cria_receita')\n\n if campo_vazio(modo_preparo):\n messages.error(request, 'Modo de preparo é obrigatório')\n return redirect('cria_receita')\n\n if campo_vazio(tempo_preparo):\n messages.error(request, 'tempo de preparo é obrigatório')\n return redirect('cria_receita')\n\n if campo_vazio(rendimento):\n messages.error(request, 'rendimento é obrigatórios')\n return redirect('cria_receita')\n\n if campo_vazio(categoria):\n messages.error(request, 'categoria é obrigatório')\n return redirect('cria_receita')\n\n receita = Receita.objects.create(\n pessoa=user,\n nome_receita=nome_receita,\n ingredientes=ingredientes,\n modo_preparo=modo_preparo,\n tempo_preparo=tempo_preparo,\n rendimento=rendimento,\n categoria=categoria,\n publicado=False,\n foto_receita=foto_receita\n )\n receita.save()\n return redirect('dashboard')\n else:\n return render(request, 'cria_receita.html')\n else:\n return redirect('index')","def recarregar_imagem(self):\n self.salvar_imagem(caminho_imagem=self.caminho_temp)\n self.carregar_imagem(caminho_imagem=self.caminho_temp)","def update_geonet_data():\n rs = TremorData()\n rs.update()","def saveData(self):\n pass","def _rebuildOcean(self, tk, templateFile = '', fields = '', id = '', shotNum = '', inprogressBar = ''):\r\n\t\tdebug(app = self.app, method = '_rebuildOcean', message = 'Deleting current Ocean now....', verbose = False)\r\n\t\tself._removeOcean()\r\n\r\n\t\t## Get all the publishes from shotgun now for the blocking or animation step as required..\r\n\t\tif self.app.get_setting('shotStep') == 'Blocking':\r\n\t\t\tgetAnimVersionFolders = tk.paths_from_template(templateFile, {'Step' : 'Blck', 'id' : id, 'Shot' : shotNum})\r\n\t\telif self.app.get_setting('shotStep') == 'Animation':\r\n\t\t\tgetAnimVersionFolders = tk.paths_from_template(templateFile, {'Step' : 'Anm', 'id' : id, 'Shot' : shotNum})\r\n\r\n\t\tdebug(app = self.app, method = '_rebuildOcean', message = 'getAnimVersionFolders...%s' % getAnimVersionFolders, verbose = False)\r\n\r\n\t\t## Fix path for maya to read in properly\r\n\t\tif getAnimVersionFolders:\r\n\t\t\t## now find the highest version folder number\r\n\t\t\thighestVersionFolder = r'%s' % max(getAnimVersionFolders)\r\n\t\t\tdebug(app = self.app, method = '_rebuildOcean', message = 'highestVersionFolder...%s' % highestVersionFolder, verbose = False)\r\n\r\n\t\t\thighestVersionFolder = highestVersionFolder.replace('\\\\', '/')\r\n\t\t\tdebug(app = self.app, method = '_rebuildOcean', message = 'highestVersionFolder replaced \\\\ with /...\\n%s' % highestVersionFolder, verbose = False)\r\n\r\n\t\t\tversionNumber = highestVersionFolder.split('/')[-1]\r\n\t\t\tdebug(app = self.app, method = '_rebuildOcean', message = 'versionNumber: %s' % versionNumber, verbose = False)\r\n\r\n\t\t\tgetCacheFiles = os.listdir(highestVersionFolder)\r\n\t\t\tdebug(app = self.app, method = '_rebuildOcean', message = 'getCacheFiles...\\n%s' % getCacheFiles, verbose = False)\r\n\t\t\t##################################################################################################################\r\n\t\t\t## APPLY OCEAN PRESET TO A NEW OCEAN\r\n\t\t\tif 'publish/fx' in highestVersionFolder: ## The ocean preset.mel is saved out to the fx folder in the animation/publish/fx folders\r\n\t\t\t\tdebug(app = self.app, method = '_rebuildOcean', message = 'Loading ocean files now...', verbose = False)\r\n\r\n\t\t\t\t## Build the group if it doesn't already exist\r\n\t\t\t\tself._buildGroup('FX_CACHES_hrc', versionNumber)\r\n\r\n\t\t\t\t## No maya ocean in scene? Build a new one...\r\n\t\t\t\tif not cmds.objExists('oceanShader1'):\r\n\t\t\t\t\tmel.eval(\"CreateOcean;\")\r\n\r\n\t\t\t\t\t## Clean up the names\r\n\t\t\t\t\t## NOTE we are not renaming the actual OCEAN SHADER here because in Marks scripts we are reconnecting like so\r\n\t\t\t\t\t## cmds.disconnectAttr('oceanShader1.outColor', 'oceanShader1SG.surfaceShader')\r\n\t\t\t\t\theightField = cmds.listRelatives(cmds.ls(selection = True)[0], parent = True, fullPath = True)[0]\r\n\t\t\t\t\tcmds.rename(heightField, 'oceanPreviewPlane_prv')\r\n\t\t\t\t\tcmds.rename('oceanPlane1', 'ocean_srf')\r\n\t\t\t\t\tcmds.rename('oceanPreviewPlane1', 'oceanPreviewPlane_heightF')\r\n\t\t\t\t\tcmds.parent('ocean_srf', 'FX_CACHES_hrc')\r\n\t\t\t\t\tcmds.parent('oceanPreviewPlane_prv', 'FX_CACHES_hrc')\r\n\t\t\t\t\tcmds.rename('oceanShader1', CONST.OCEANDISPSHADER)\r\n\t\t\t\t\tcmds.rename('oceanShader1SG', '%sSG' % CONST.OCEANDISPSHADER)\r\n\r\n\t\t\t\t\t## Lock translateY and rotation so that monkeys don't mess up with the ocean level\r\n\t\t\t\t\ttoLock = ['translateY', 'rotateX', 'rotateY', 'rotateZ']\r\n\t\t\t\t\tfor attr in toLock:\r\n\t\t\t\t\t\tcmds.setAttr('ocean_srf.%s' % attr, lock = True)\r\n\r\n\t\t\t\t\t## Hard set the render tesselation\r\n\t\t\t\t\tcmds.setAttr(\"ocean_srfShape.numberU\", 200)\r\n\t\t\t\t\tcmds.setAttr(\"ocean_srfShape.numberV\", 200)\r\n\r\n\t\t\t\t\tallOceans = cmds.ls(type= 'oceanShader')\r\n\t\t\t\t\tdebug(app = self.app, method = '_rebuildOcean', message = 'allOceans: %s' % allOceans, verbose = False)\r\n\r\n\t\t\t\t\t## now put the ocean in the right position\r\n\t\t\t\t\tif cmds.objExists('fluids_hrc'):\r\n\t\t\t\t\t\tself._setOceanLocation()\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tif cmds.objExists('ocean_srf') and cmds.objExists('oceanPreviewPlane_prv'):\r\n\t\t\t\t\t\t\tif not cmds.isConnected('oceanPreviewPlane_prv.translateX', 'ocean_srf.translateX'):\r\n\t\t\t\t\t\t\t\tcmds.connectAttr('oceanPreviewPlane_prv.translateX', 'ocean_srf.translateX', f = True)\r\n\t\t\t\t\t\t\tif not cmds.isConnected('oceanPreviewPlane_prv.translateZ', 'ocean_srf.translateZ'):\r\n\t\t\t\t\t\t\t\tcmds.connectAttr('oceanPreviewPlane_prv.translateZ', 'ocean_srf.translateZ', f = True)\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tcmds.warning('MISSING ocean_srf or oceanPreviewPlane_prv node from scene...')\r\n\r\n\t\t\t\tfor each in getCacheFiles:\r\n\t\t\t\t\t## Now attach the preset to the maya ocean build a new one if it doesn't already exist\r\n\t\t\t\t\tif each.endswith('.mel'): ## the ocean shader preset saved out\r\n\t\t\t\t\t\tpathToPreset = '%s/%s' % (highestVersionFolder, each)\r\n# presetVersion = each.split('/')\r\n\r\n\t\t\t\t\t\t## Now apply the exported preset to each ocean in the scene. There really should only be ONE\r\n\t\t\t\t\t\tfor eachOcean in allOceans:\r\n\t\t\t\t\t\t\tdebug(app = self.app, method = '_rebuildOcean', message = 'Add version number for ocean preset on Ocean_Hrc', verbose = False)\r\n\t\t\t\t\t\t\tif cmds.objExists('FX_CACHES_hrc'):\r\n\t\t\t\t\t\t\t\tif not cmds.objExists('FX_CACHES_hrc.presetVersion'):\r\n\t\t\t\t\t\t\t\t\tcmds.addAttr('FX_CACHES_hrc', ln='presetVersion', dt='string')\r\n\t\t\t\t\t\t\t\t\tcmds.setAttr('FX_CACHES_hrc.presetVersion' , versionNumber, type='string')\r\n\t\t\t\t\t\t\tdebug(app = self.app, method = '_rebuildOcean', message = 'Applying ocean preset: %s to %s' % (pathToPreset, eachOcean), verbose = False)\r\n\t\t\t\t\t\t\tmel.eval( 'applyPresetToNode \"%s\" \"\" \"\" \"%s\" 1;' %(eachOcean, pathToPreset) )\r\n\t\t\t\t\t\t\tdebug(app = self.app, method = '_rebuildOcean', message = 'Ocean and presets assigned successfully...', verbose = False)\r\n\r\n\t\t\t\t\t## Import ATOM\r\n\t\t\t\t\tif each.endswith('.atom'):\r\n\t\t\t\t\t\tpathToPreset = '%s/%s' % (highestVersionFolder, each)\r\n\t\t\t\t\t\tdebug(app = self.app, method = '_rebuildOcean', message = 'Transferring ocean shader\\'s animation from %s' % pathToPreset, verbose = False)\r\n\r\n\t\t\t\t\t\tmel.eval('performImportAnim 1;')\r\n\t\t\t\t\t\tcmds.select(allOceans, replace = True)\r\n\r\n\t\t\t\t\t\tcmds.file(pathToPreset,\r\n\t\t\t\t\t\ttype = 'atomImport',\r\n\t\t\t\t\t\ti = True,\r\n\t\t\t\t\t\tra = True,\r\n\t\t\t\t\t\tnamespace = 'oceanDisp_shader',\r\n\t\t\t\t\t\toptions = '; ; targetTime = 3; option = insert; match = hierarchy; selected = selectedOnly; search = ; replace = ; prefix = ; suffix = ; mapFile = ;',\r\n\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\tif cmds.window('OptionBoxWindow', exists = True):\r\n\t\t\t\t\t\t\tcmds.deleteUI('OptionBoxWindow', window = True)\r\n\r\n\t\t\t\t\t\tdebug(app = self.app, method = '_rebuildOcean', message = 'Ocean shader\\'s animation transferred successfully.', verbose = False)\r\n\r\n\t\t\t\tdebug(app = self.app, method = '_rebuildOcean', message = 'Ocean imported successfully...', verbose = False)","def save(self):\n\n pass","def test_e_editar_atributo(self): \n print '+++ Edicion de atributo +++'\n request = self._editar_atributo(nombre, 'descrip editada','String')\n print '*-- datos de prueba ::: ' + nombre+', '+ 'descrip editada' + ', ' + str(id_tipo_atributo) +' --*'\n self.assertNotIn('Debe loguearse primeramente!!!!', request.data, 'No se ha logueado correctamente')\n self.assertNotIn('No posee los permisos suficientes para realizar la operacion', request.data, 'No tiene permisos para editar atributos')\n self.assertNotIn('Error', request.data, 'Tiene errores el form')\n self.assertIn('El atributo ha sido editado con exito', request.data, 'Error al editar la atributo')\n print '*-- request result: ' + request._status + ' --*'\n self.assertIn(nombre, request.data, 'El atributo creado no se encuentra en la tabla')\n print '*-- '+nombre+' creado correctamente, aparece en la tabla de atributos--*'\n print '*---test 5 atributo---*'","def _process(self):\n self.output[\"data\"] = get_form_data(self.kwargs[\"collect\"].ona_scan_form_pk)","def modificacion(self, socio):\n\n aux = self.buscar(socio.id)\n print('El socio a modificar en capa de datos:', aux.id, aux.nombre)\n\n if aux == None:\n return False\n else:\n #persona = session.query(Socio).filter(Socio.dni == aux.id)\n aux.nombre = socio.nombre\n aux.apellido = socio.apellido\n aux.dni = socio.dni\n\n session.commit()\n\n return aux"],"string":"[\n \"def actualizar(self):\\n if self.obraSocialSeleccionada!=None:\\n self.cargar_productos(self.obraSocialSeleccionada)\\n else:\\n self.cargarProductosSinObra()\",\n \"def actualizar_puntaje(self):\\r\\n pass\",\n \"def actualizar_tabla(self):\\n self.ajustar_tabla()\\n sincronizaciones = Archivo_crontab.listar_sincronizaciones()\\n self.tablaSincronizaciones.setRowCount(len(sincronizaciones))\\n row = 0\\n for i in sincronizaciones:\\n (btn_borrar, btn_editar) = self.crear_botones_fila()\\n ruta_archivo = QTableWidgetItem(i[0])\\n servidor = QTableWidgetItem(i[2])\\n tiempo = QTableWidgetItem(' ' + i[3] + ' ' + i[4] + ' ' + i[5] + ' ' + i[6])\\n self.tablaSincronizaciones.setItem(row, 0, ruta_archivo)\\n self.tablaSincronizaciones.setItem(row, 1, servidor)\\n self.tablaSincronizaciones.setItem(row, 2, tiempo)\\n self.tablaSincronizaciones.setCellWidget(row, 3, btn_borrar)\\n self.tablaSincronizaciones.setCellWidget(row, 4, btn_editar)\\n row += 1\\n\\n self.tablaSincronizaciones.setEditTriggers(QtWidgets.QTableWidget.NoEditTriggers)\\n self.tablaSincronizaciones.resizeRowsToContents()\",\n \"def actualizar_velocidad(self):\\r\\n pass\",\n \"def ejecutarOperacion(self):\\n\\n if self.option == 1:\\n \\\"\\\"\\\"\\n El usuario desea agregar un punto al mapa\\n \\\"\\\"\\\"\\n # Capturo los x, y\\n k = self.elementoSeleccionado.split(\\\"-\\\")\\n print(\\\"info\\\",k)\\n x = int(k[0])\\n y = int(k[1])\\n # Ya capture los valores los agrego al arbol\\n # Ojo: En modo creacion no entran\\n self.arbol.ADD((x, y), \\\"X\\\")\\n\\n\\n if self.option == 2:\\n \\\"\\\"\\\"\\n El usuario desea eliminar un punto del mapa\\n \\\"\\\"\\\"\\n k= self.elementoSeleccionado.split(\\\"-\\\")\\n x = int(k[0])\\n y = int(k[1])\\n \\n self.arbol.eliminarNodo((x, y))\\n\\n if self.option == 3:\\n # Si no se ha seleccionado el punto A capturelo\\n if self.PUNTOA == None and self.PUNTOB == None:\\n k = self.elementoSeleccionado.split(\\\"-\\\")\\n x = int(k[0])\\n y = int(k[1])\\n self.PUNTOA = (x, y)\\n else:\\n # YA se selecciono el punto A si es diferente de B se captura\\n k = self.elementoSeleccionado.split(\\\"-\\\")\\n x = int(k[0])\\n y = int(k[1])\\n # Creo el punto para poder comparar que A sea diferente de B\\n temp = (x, y)\\n if temp != self.PUNTOA:\\n self.PUNTOB = temp\\n # Lo modifico en el arbol\\n self.arbol.modificarData(self.PUNTOA, self.PUNTOB)\",\n \"def save_parterre():\\n f = None\\n f= ParterreForm()\\n a = get_parterre(f.get_id())\\n na = a.get_name()\\n if f.validate_on_submit():\\n a.set_name(f.get_name())\\n form = request.form\\n longitudes = form.getlist(\\\"longitudes\\\")\\n if longitudes != []:\\n latitudes = form.getlist(\\\"latitudes\\\")\\n num = 0\\n a.remove_coordonnees()\\n for longitude,latitude in zip(longitudes, latitudes):\\n c = Coordonnees(x = longitude,\\n y = latitude,\\n parterre = a.get_id(),\\n num = num)\\n num = num+1\\n a.add_coordonnee(c)\\n p = Actions(\\n contenu = \\\"Modification du parterre \\\"+na+\\\" -> \\\"+a.get_name(),\\n liste = 1\\n )\\n db.session.add(p)\\n db.session.commit()\\n return redirect(url_for(\\\"parterre_info\\\", id = a.get_id()))\\n f.next.data = \\\"save_parterre\\\"\\n return render_template(\\\"create-parterre.html\\\",\\n title= a.get_name()+\\\" - edit\\\",\\n form = f,\\n parterre = a,\\n param = \\\"modif\\\")\",\n \"def save_plante():\\n f = None\\n f = PlanteForm()\\n f.parterre.data = get_parterre(get_plante(f.get_id()).get_parterre())\\n a = get_plante(f.get_id())\\n if f.validate_on_submit():\\n a.set_name(f.get_name())\\n a.set_comportement(f.get_comportement())\\n a.set_taux_humidite(f.get_taux_humidite())\\n a.set_quantite(f.get_quantite())\\n p = Actions(\\n contenu = \\\"Modification de la plante \\\"+a.get_name() + \\\" du parterre \\\"+ get_parterre(a.get_parterre()).get_name(),\\n liste = 1\\n )\\n db.session.add(p)\\n db.session.commit()\\n return redirect(url_for(\\n \\\"plante_info\\\", id = f.get_id()))\\n f.next.data = \\\"save_plante\\\"\\n return render_template(\\n \\\"create-plante.html\\\",\\n title = a.get_name()+\\\" - edit\\\",\\n form = f,\\n plante = a,\\n param = \\\"modif\\\")\",\n \"def VIEJOupdateComplejos():\\n\\txml_ciudades = \\\"http://api2.cinemex.com/rsvr.php?Action=GetFiltrados&IdDev=1\\\"\\n\\txml_complejos = \\\"http://api2.cinemex.com/rsvr.php?Action=GetFiltrados&IdDev=1&ciudad=%s\\\"\\n\\t\\n\\t\\n\\tciudades = parse_ciudadesxml( urlopen(xml_ciudades) )#Diccionario de ciudades de Mexico\\n\\t\\n\\t#xml = urlopen(xml_complejos)\\n\\t#ids = parse_complejosxml(xml)\\n\\t\\n\\tbase_url = \\\"http://www.cinemex.com/cinemex/complejos.php?cvecine=%s\\\"\\n\\t\\n\\tfor ciudad_id in ciudades:\\n\\t\\txml_url = xml_complejos % ciudad_id\\n\\t\\tcomplejos_xml = urlopen(xml_url)\\n\\t\\tcomplejos = parse_complejosxml(complejos_xml)\\n\\t\\tfor comp in complejos:\\n\\t\\t\\turl = base_url % comp['id_org']\\n\\t\\t\\tcomp_html = urlopen(url)\\n\\t\\t\\tcomplejo, platino = scrape_compInfo(comp_html,comp)\\n\\t\\t\\t#logger.debug( complejo['id_org'])\\n\\t\\t\\tcomplejo['id_ciudad']= ciudad_id\\n\\t\\t\\t#createComplejo(complejo)\\n\\t\\t\\tlogger.debug( complejo)\\n\\t\\t\\tlogger.debug( platino)\\n\\t\\t\\t#if platino: createComplejoPlatino(complejo)\",\n \"def save():\",\n \"def actualizar(self, producto, cantidad):\\n producto_id = str(producto)\\n\\n if producto_id in self.carro:\\n self.carro[producto_id]['cantidad'] = cantidad\\n \\n self.guardar()\",\n \"def post(self):\\r\\n piso=self.request.get('piso')\\r\\n numext=self.request.get('numext')\\r\\n numint=self.request.get('numint')\\r\\n piso=self.validonumero(piso)\\r\\n numext=self.validonumero(numext)\\r\\n numint=self.validonumero(numint)\\r\\n \\r\\n empresa=empresas()\\r\\n empresa.nombre=self.request.get('desc')\\r\\n empresa.calle=self.request.get('calle')\\r\\n empresa.numeroExterior=int(numext)\\r\\n empresa.numeroInterior=int(numint)\\r\\n empresa.colonia=self.request.get('colonia')\\r\\n empresa.piso=int(piso)\\r\\n empresa.andador=self.request.get('andador')\\r\\n empresa.codigo_postal=int(self.request.get('cp'))\\r\\n empresa.sitioweb=self.request.get('web')\\r\\n empresa.correo=self.request.get('mail')\\r\\n empresa.nombreContacto=\\\"\\\"\\r\\n empresa.paternoContacto=\\\"\\\"\\r\\n empresa.maternoContacto=\\\"\\\"\\r\\n #### \\r\\n ciudad=self.request.get('ciudad')\\r\\n query=\\\"where ciudad='%s'\\\"%ciudad\\r\\n cd=ciudades.gql(query)\\r\\n city=cd.fetch(1)\\r\\n for lstcd in city:\\r\\n empresa.id_Ciudad=lstcd.key().id()\\r\\n empresa.put()\\r\\n jsondic={}\\r\\n jsondata=[]\\r\\n jsondata+=[self.addKey(jsondic,\\\"Dato\\\", empresa.key().id())]\\r\\n self.response.out.write(simplejson.dumps(jsondata))\\r\\n return False\",\n \"def ejecutarproceso(self):\\n self.generarConsultasLibres()\\n self.aplicarConsultasLibres()\\n self.generarCombinaciones()\\n self.generarConsultasConexion()\\n self.archivoSalida()\",\n \"def cargarObra(self):\\n rowActual=self.tableObra.currentItem().row()\\n self.lineObra.setText(str(self.tableObra.item(rowActual,0).text()))\\n self.lineCuit.setText(str(self.tableObra.item(rowActual,1).text()))\\n self.tableObra.hide()\\n self.lineObra.setEnabled(False)\\n self.lineCuit.setEnabled(False)\\n self.obraSocialSeleccionada = str(self.lineObra.text())\\n self.cargar_productos(self.obraSocialSeleccionada)\\n self.gbProducto.setVisible(True)\",\n \"def updatePeliculas():\\n\\txml_ciudades = \\\"http://api2.cinemex.com/rsvr.php?Action=GetFiltrados&IdDev=1\\\"\\n\\txml_peliculas = \\\"http://api2.cinemex.com/rsvr.php?Action=GetFiltrados&IdDev=1&ciudad=%s&byciudad=1\\\" #id_ciudad\\n \\n\\tciudades = parse_ciudades( urlopen(url_movil) ) \\n\\t\\n\\t\\n\\tbase_url_pelicula = \\\"http://www.cinemex.com/cartelera/pelicula.php?vcode=%s\\\" #mex_vc\\n\\tpeliculas = {}\\n \\n\\tpelis_obj = [] #Contiene toda la info de las peliculas, titulo, sinopsis, etc\\n \\n\\t#Crea un diccionario con el vc y el objeto de la pelicula\\n\\t#De esta forma no hay peliculas repetidas\\n\\tfor ciudad_id in ciudades:\\n\\t\\txml_url = xml_peliculas % ciudad_id\\n\\t\\ttry:\\n\\t\\t\\txml = urlopen(xml_url)\\n\\t\\texcept:\\n\\t\\t\\tlogger.debug( 'error cargando pagina %s' % xml_url)\\n\\t\\t\\tcontinue\\n\\t\\tpelis_actual = parse_peliculas(xml)\\n\\t\\t#Agregar las peliculas q no estan todavia\\n\\t\\tfor peli in pelis_actual:\\n\\t\\t\\tkey = peli.get('mex_vc', '')\\n\\t\\t\\tif key not in peliculas: peliculas[key] = peli\\n \\n\\tfor k, v in peliculas.items():\\n\\t\\turl = base_url_pelicula % k\\n\\t\\thtml = urlopen(url)\\n\\t\\tpelis_obj.append(scrape_pelicula(html, v))\\n \\n\\tfor peli in pelis_obj:\\n\\t\\tcreatePelicula(peli)\",\n \"def updateComplejos():\\n\\t\\n\\turl_base_cd = \\\"http://m.cinemex.com/estrenos.php?cveciudad=%s\\\"\\n\\t\\n\\tbase_url_complejo = \\\"http://www.cinemex.com/cinemex/complejos.php?cvecine=%s\\\"\\n\\t\\n\\tciudades = parse_ciudades( urlopen(url_movil) )\\n\\t\\n\\tfor ciudad_id in ciudades.keys():\\n\\t\\turl_cd = url_base_cd % ciudad_id\\n\\t\\tcomplejos = parse_complejos( urlopen(url_cd) )\\n\\t\\tfor comp in complejos:\\n\\t\\t\\turl = base_url_complejo % comp['id_org']\\n\\t\\t\\tcomp_html = urlopen(url)\\n\\t\\t\\tcomplejo, platino = scrape_compInfo(comp_html,comp)\\n\\t\\t\\t#logger.debug( complejo['id_org'])\\n\\t\\t\\tcomplejo['id_ciudad']= ciudad_id\\n\\t\\t\\tcreateComplejo(complejo)\\n\\t\\t\\tif platino: createComplejoPlatino(complejo)\\t\\n\\t\\n\\t#Por alguna razon hay algunos cinemex sin nombre y causan problema\\n\\t#Borra vacios\\n\\tComplejo.objects.filter(nombre='Cinemex ').delete()\",\n \"def update_data():\\n pass\",\n \"def actualizar_poderes(self, event):\\r\\n pass\",\n \"def atualiza_estoque():\\n\\n template = 'produtos/form-atualiza-estoque.html'\\n form = EnviarProdutosNovosForm()\\n config = ConfigMagento.by_id(1)\\n\\n task = current_app.atualiza_estoque_task\\n clear_task = request.args.get('clear_task', None)\\n if clear_task and clear_task == 'yes':\\n if task and task.state == 'SUCCESS':\\n current_app.atualiza_estoque_task = task = None\\n return redirect(url_for('produtos.atualiza_estoque'))\\n\\n produtos = None\\n if not task:\\n produtos = buscar_estoque_produtos(dthr_sincr=config.dtsincr_estoque)\\n\\n if form.validate_on_submit() and produtos:\\n task = atualiza_estoque_task.apply_async()\\n current_app.atualiza_estoque_task = task\\n\\n success(f'Tarefa iniciada com sucesso')\\n return redirect(url_for('produtos.atualiza_estoque'))\\n\\n result = {\\n 'title': 'Produtos',\\n 'subtitle': 'Atualizar Estoque',\\n 'form': form,\\n 'produtos': produtos,\\n 'task': task\\n }\\n return render_template(template, **result)\",\n \"def cambiar_libro_bcompartida(request, id_biblioteca_compartida, id_libro_compartido=None):\\n template = \\\"libros/cambiar_libro_bcompartida.html\\\"\\n\\n bcompartida = BibliotecaCompartida.objects.get(id=id_biblioteca_compartida)\\n\\n libro_disponible = None\\n if id_libro_compartido:\\n libro_disponible = LibrosBibliotecaCompartida.objects.get(id=id_libro_compartido)\\n\\n if request.method == \\\"POST\\\":\\n form = FormCambiarLibroBCompartida(request.POST)\\n if form.is_valid():\\n id_libro_cambiado = form.cleaned_data.get(\\\"id_libro_cambiado\\\")\\n titulo_nuevo_libro = form.cleaned_data.get(\\\"titulo_recibido\\\")\\n autor_nuevo_libro = form.cleaned_data.get(\\\"autor_recibido\\\")\\n # = form.cleaned_data.get(\\\"usuario_cambiar\\\", None)\\n\\n libro_cambiado = LibrosBibliotecaCompartida.objects.get(id=id_libro_cambiado)\\n libro_cambiado.disponible = False\\n libro_cambiado.eliminado = True\\n libro_cambiado.save()\\n\\n nuevo_libro = Libro.objects.create(titulo=titulo_nuevo_libro, autor=autor_nuevo_libro)\\n LibrosBibliotecaCompartida.objects.create(libro=nuevo_libro, biblioteca_compartida=bcompartida)\\n\\n # Crear notificacion\\n # if username_cambiar:\\n # if Perfil.objects.filter(usuario__username=username_cambiar).exists():\\n # usuario_cambiar = Perfil.objects.get(usuario__username=username_cambiar)\\n # Notificacion.objects.bcompartida_cambio(biblioteca_compartida, libro_cambiado.libro, nuevo_libro,\\n # usuario_cambiar)\\n\\n Notificacion.objects.bcompartida_cambio(bcompartida, libro_cambiado.libro, nuevo_libro)\\n\\n return redirect('libros:editar_libros_bcompartida', slug_biblioteca_compartida=bcompartida.slug)\\n\\n else:\\n if id_libro_compartido:\\n form = FormCambiarLibroBCompartida(initial={\\n 'titulo_inicial': libro_disponible.libro.titulo,\\n 'id_libro_cambiado': libro_disponible.id\\n })\\n else:\\n form = FormCambiarLibroBCompartida()\\n\\n libros_bcompartida = LibrosBibliotecaCompartida.objects.filter(biblioteca_compartida=bcompartida,\\n disponible=True).select_related(\\\"libro\\\")\\n titulos_autocomplete = {}\\n for libro_bc in libros_bcompartida:\\n titulos_autocomplete[libro_bc.libro.titulo] = libro_bc.id\\n\\n usuarios_autocomplete_obj = Perfil.objects.filter(ciudad=bcompartida.ciudad).select_related(\\\"usuario\\\")\\n usernames_autocomplete = [perfil.usuario.username for perfil in usuarios_autocomplete_obj]\\n\\n context = {'form': form, 'libro_disponible': libro_disponible,\\n 'titulos_autocomplete': json.dumps(titulos_autocomplete),\\n 'biblioteca_compartida': bcompartida,\\n 'usernames_autocomplete': json.dumps(usernames_autocomplete)}\\n\\n return render(request, template, context)\",\n \"def listar_atributos(request,idAtributoTI,id_item,ver = None):\\n print('el id del item es :',id_item)\\n atributos = Atributo_Item.objects.filter(id_item=id_item) #obtiene los valores del atributo del item\\n TI=TipoItem.objects.get(id_ti=idAtributoTI) #obtiene el tipo de item\\n atributo= Atributo.objects.filter(ti=TI) #lista los atributos del tipo de item\\n itemActual=Item.objects.get(id_item=id_item) #el item a ser modificado\\n if(request.method=='POST'):\\n if request.method == 'POST':\\n ''' obtengo el item viejo (itemActual) y actualizo sus valores de boolean a false.'''\\n itemActual.actual=False\\n itemActual.save()\\n '''Ahora en estas variables voy a obtener los valores editados de mi ITEM '''\\n nombre_item_editado = request.POST.get('item_nombre')\\n descripcion_item_editado = request.POST.get('item_descripcion')\\n estado_item_editado = request.POST.get('item_estado')\\n costo_item_editado = request.POST.get('item_costo')\\n print('nombre es:',nombre_item_editado)\\n ''' crear un nuevo item, con un nuevo id con los valores recibidos del form'''\\n item_editado = Item(nombre=nombre_item_editado,descripcion=descripcion_item_editado,costo=costo_item_editado,\\n actual=True,estado=itemActual.estado,fase_id=TI.fase_id,ti_id=TI.id_ti)\\n item_editado.save()\\n\\n ''' Crear un nuevo registro en la tabla versiones con los valores correspondientes del id_padre y version'''\\n version_item_actual= Versiones.objects.get(id_item=itemActual.id_item) #obtengo el registro del item actual\\n varsion_item_editado = Versiones(id_item=item_editado.id_item, id_padre= version_item_actual.id_padre,\\n id_Version=version_item_actual.id_Version+1)\\n varsion_item_editado.save()\\n\\n diccionario_data = verificar_datos_form_atributo_item(request,TI.id_ti,atributo,request.POST,request.FILES)\\n print('el diccionario data es: ',diccionario_data)\\n for data in diccionario_data:\\n if(data['AtributoTI'].tipo_dato=='File'): #si es de tipo File ,entonces armo la ruta para subir el archivo\\n if(data['valor_atributo_item'] != 'Sin archivos adjuntos' and data['valor_atributo_item'] != 'Archivo Elminado' ): # si se recibe algo es porque el edito el archivo, por lo tanto creamos una nueva ruta en la nube\\n ruta = str(TI.fase.id_Proyecto.id_proyecto) + \\\"/\\\" + str(item_editado.id_item)\\n doc = data['valor_atributo_item']\\n PATH = f'/{ruta}/{doc[0]}'\\n ''' proceso de subir archivo a dropbox'''\\n SubirArchivo(doc[0], PATH)\\n print('-----',PATH)\\n ##se sube archivo a dropbox en segundo plano\\n t2 = Thread(\\n target=SubirArchivo,\\n args=(doc[0], PATH),\\n )\\n t2.start()\\n atributo_item_editado = Atributo_Item(idAtributoTI=data['AtributoTI'], id_item=item_editado,\\n valor=PATH)\\n atributo_item_editado.save()\\n elif(data['valor_atributo_item'] == 'Archivo Elminado'):\\n atributo_item_editado = Atributo_Item(idAtributoTI=data['AtributoTI'],\\n id_item=item_editado,\\n valor='Sin archivos adjuntos')\\n atributo_item_editado.save()\\n else:\\n atributo_item_editado = Atributo_Item(idAtributoTI=data['AtributoTI'], id_item=item_editado,\\n valor=Atributo_Item.objects.get(id_item=itemActual,idAtributoTI=data['AtributoTI']).valor)\\n print('el editado sin modificar',atributo_item_editado.valor)\\n atributo_item_editado.save()\\n else:\\n print('atributo_id:',data['AtributoTI'].id_atributo,' valor:',data['valor_atributo_item'])\\n atributo_item_editado=Atributo_Item(idAtributoTI=data['AtributoTI'], id_item=item_editado,\\n valor=data['valor_atributo_item'])\\n atributo_item_editado.save()\\n '''aca se actulizan las relaciones del item'''\\n itemActual_inicio=Relacion.objects.filter(inicio_item=itemActual.id_item) #obtengo todos los items en donde el es el origen\\n for relacion in itemActual_inicio:\\n nueva_relacion=Relacion(inicio_item=item_editado.id_item,fin_item=relacion.fin_item)\\n nueva_relacion.save()\\n\\n itemActual_fin = Relacion.objects.filter(fin_item=itemActual.id_item) # obtengo todos los items en donde el es el fin\\n for relacion in itemActual_fin:\\n nueva_relacion = Relacion(inicio_item=relacion.inicio_item, fin_item=item_editado.id_item)\\n nueva_relacion.save()\\n\\n return redirect('gestion:detallesFase',TI.fase_id)\\n\\n\\n context = {\\n \\\"atributos\\\":atributos,\\n \\\"atributo\\\":atributo,\\n 'proyectos': itemActual.fase.id_Proyecto,\\n 'item':itemActual,\\n 'true':True,\\n 'false':False,\\n 'ver':ver\\n }\\n return render(request, 'items/listar_atributos.html', context)\",\n \"def atualiza_base():\\n template = 'produtos/form-atualiza-base.html'\\n form = EnviarProdutosNovosForm()\\n\\n if form.validate_on_submit():\\n produtos = productList()\\n atualizar_base(produtos)\\n\\n success('Base atualizada com sucesso')\\n return redirect(url_for('produtos.atualiza_base'))\\n\\n result = {\\n 'title': 'Atualizar Estoque Produtos',\\n 'form': form,\\n 'produtos': None\\n }\\n return render_template(template, **result)\",\n \"def editar_contacto(agenda_path):\\n\\tcontacto_mod = input('Contacto a modificar: ')\\n\\t#que contacto quiere modificar el usuario\\n\\tagenda = descargar_agenda(agenda_path)\\n\\tif contacto_mod not in agenda:\\n\\t\\tprint ('El contacto no existe, agreguelo desde el menu')\\n\\t\\tcontinue\\n\\t\\t#por si el contacto no esta agendado\\n\\tfor i in range(len(agenda)):\\n\\t if agenda[i][0] == nombre:\\n\\t \\tindex = i\\n\\t \\tbreak\\n\\t if index != None:\\n\\t \\tprint('Omite aquellos campos que no quieras editar para conservar los datos')\\n\\t \\tnombre = input('Nombre:')\\n\\t \\ttelefono = input('Telefono: ')\\n\\t \\tdireccion = input('Dirección') \\n\\t \\tcontacto[index] = [\\n nombre if len(nombre) > 0 else agenda[index][0],\\n telefono if len(telefono) > 0 else agenda[index][1],\\n direccion if len(direccion) > 0 else agenda[index][2] ]\\n\\t \\tcargar_agenda(agenda_path, agenda)\\n\\t \\tprint('Editado con exito!')\\n\\t\\t\\t #modificar los datos del contacto que desea el usuario\",\n \"def actualizar_valores(self,valores): \\n self.__valores.update(valores)\\n self.set_valores(self.__valores)\",\n \"def guardar(self, *args):\\n self.objeto = self.modelo()\\n for e in self.campos:\\n print e.name, ': ', e.widget.valor()\\n setattr(self.objeto, e.name, e.widget.valor())\\n self.objeto.save()\\n self.emit('save')\\n print 'save', self\",\n \"def procesa_parametros_factura():\\n form = FormParametrosFactura()\\n print(form.num_fac_ini.data, form.factura_actual.data)\\n\\n if request.method == 'POST' and form.validate_on_submit():\\n cursor_insert = connectiondb.cursor()\\n cursor_update = connectiondb.cursor()\\n cursor_select = connectiondb.cursor()\\n # verifica si existen datos en la tabla\\n sql_select = \\\"\\\"\\\"SELECT num_fac_ini FROM ParametrosFactura\\\"\\\"\\\"\\n cursor_select.execute(sql_select)\\n resultado = cursor_select.fetchone()\\n cursor_select.close()\\n\\n if resultado is None:\\n sql_insert = \\\"\\\"\\\"INSERT INTO ParametrosFactura (num_fac_ini, \\\\\\n num_fac_fin, num_resolucion, fecha_resolucion, \\\\\\n fecha_venc_resolucion, factura_actual) VALUES \\\\\\n ('{}', '{}', '{}', '{}', '{}', '{}');\\n \\\"\\\"\\\".format(form.num_fac_ini.data, form.num_fac_fin.data,\\n form.num_resolucion.data, form.fecha_resolucion.data,\\n form.fecha_venc_resolucion.data, form.factura_actual.data)\\n cursor_insert.execute(sql_insert)\\n connectiondb.commit()\\n cursor_insert.close()\\n # connectiondb.close()\\n return \\\"Los datos se guardaron correctamente\\\"\\n else:\\n sql_update = \\\"\\\"\\\"UPDATE ParametrosFactura \\\\\\n SET num_fac_ini = '{}', \\\\\\n num_fac_fin = '{}', \\\\\\n num_resolucion = '{}', \\\\\\n fecha_resolucion = '{}',\\n fecha_venc_resolucion = '{}', \\\\\\n factura_actual = '{}' \\\\\\n WHERE num_fac_ini = '{}';\\n \\\"\\\"\\\".format(form.num_fac_ini.data,\\n form.num_fac_fin.data,\\n form.num_resolucion.data,\\n form.fecha_resolucion.data,\\n form.fecha_venc_resolucion.data,\\n form.factura_actual.data,\\n int(resultado['num_fac_ini']))\\n\\n cursor_update.execute(sql_update)\\n connectiondb.commit()\\n cursor_update.close()\\n return \\\"Los datos se guardaron correctamente\\\"\",\n \"def apiEditar():\\n response.view = 'generic.json'\\n\\n def GET(*args, **vars):\\n # if not request.env.request_method == 'GET': raise HTTP(403)\\n # proveedores = db().select(db.proveedor.ALL).as_list()\\n return dict()\\n\\n def POST(*args, **vars):\\n # raise HTTP(403)\\n pieza = vars[\\\"pieza\\\"]\\n proveedores_list = vars[\\\"proveedores_list\\\"]\\n respuesta = \\\"ok\\\"\\n idProveedor = 0\\n\\n fechaIn = pieza[\\\"fechaIn\\\"].split('/')\\n fechaString = fechaIn[2] + '-' + fechaIn[1] + '-' + fechaIn[0]\\n\\n pieza_data = dict(codigo=pieza[\\\"codigo\\\"], nombre=pieza[\\\"nombre\\\"],\\n descripcion=pieza[\\\"descripcion\\\"], cantidad=pieza[\\\"cantidad\\\"],\\n unidad=pieza[\\\"unidad\\\"], precio_entrada=pieza[\\\"precioIn\\\"],\\n precio_salida=pieza[\\\"precioOut\\\"], fecha_entrada=fechaString)\\n\\n # Verificar si aumento la cantidad. Para asi dar nueva entrada\\n cantidad_antigua = db.pieza(pieza[\\\"id\\\"]).cantidad\\n cantidad_nueva = int(pieza[\\\"cantidad\\\"]) - cantidad_antigua if cantidad_antigua < int(\\n pieza[\\\"cantidad\\\"]) else int(pieza[\\\"cantidad\\\"])\\n\\n registrar_entrada = cantidad_antigua != int(pieza[\\\"cantidad\\\"]) and cantidad_nueva != 0\\n\\n if registrar_entrada:\\n id_pieza_entrada = db.pieza_entrada.insert(codigo=pieza[\\\"codigo\\\"], nombre=pieza[\\\"nombre\\\"],\\n descripcion=pieza[\\\"descripcion\\\"], cantidad=cantidad_nueva,\\n unidad=pieza[\\\"unidad\\\"], precio_entrada=pieza[\\\"precioIn\\\"],\\n precio_salida=pieza[\\\"precioOut\\\"], fecha_entrada=datetime.datetime.now().date())\\n\\n for prov in proveedores_list:\\n db.proveedor_entrada.insert(\\n id_pieza_entrada=id_pieza_entrada, nombre=prov[\\\"nombre\\\"], descripcion=prov[\\\"descripcion\\\"],\\n direccion=prov[\\\"direccion\\\"], telefono=prov[\\\"telefono\\\"])\\n\\n # Actualizo la pieza\\n try:\\n db(db.pieza.id == pieza[\\\"id\\\"]).update(**pieza_data)\\n db(db.pieza_proveedor.id_pieza == pieza[\\\"id\\\"]).delete()\\n\\n for prov in proveedores_list:\\n db.pieza_proveedor.insert(\\n id_pieza=pieza[\\\"id\\\"], id_proveedor=prov[\\\"id\\\"])\\n\\n except Exception:\\n respuesta = \\\"error\\\"\\n\\n return dict(respuesta=respuesta)\\n # return dict(proveedor=proveedor)\\n\\n return locals()\",\n \"def editar_fila(self):\\n button = self.sender()\\n if button:\\n row = self.tablaSincronizaciones.indexAt(button.pos()).row()\\n self.contenido = self.tablaSincronizaciones.item(row, 0).text()\\n self.ventana = QtWidgets.QMainWindow()\\n self.ui = Ui_Ventana_Editar()\\n self.ui.setupUi(self.ventana)\\n self.ui.editar.clicked.connect(self.editar_sincro)\\n self.ventana.show()\",\n \"def editar_empresa(id):\\n cadastrando_empresa = False\\n\\n empresa = Empresa.query.get_or_404(id)\\n form = EditarEmpresaForm(obj=empresa)\\n\\n if form.validate_on_submit():\\n empresa.nome = form.nome.data\\n empresa.simbolo = form.simbolo.data\\n empresa.regiao = form.regiao.data\\n empresa.tipo = form.tipo.data\\n empresa.abertura = form.abertura.data\\n empresa.fechamento = form.fechamento.data\\n empresa.zona = form.zona.data\\n empresa.moeda = form.moeda.data\\n db.session.commit()\\n flash('Empresa editada com sucesso!')\\n\\n return redirect(url_for('home.listar_empresas'))\\n\\n form.nome.data = empresa.nome\\n form.simbolo.data = empresa.abertura \\n form.regiao.data = empresa.regiao\\n form.tipo.data = empresa.tipo\\n form.abertura = empresa.abertura\\n form.fechamento = empresa.fechamento\\n form.zona.data = empresa.zona\\n form.moeda.data = empresa.moeda\\n\\n\\n return render_template('home/empresa.html', action=\\\"Edit\\\",\\n cadastrando_empresa=cadastrando_empresa, form=form,\\n empresa=empresa, title=\\\"Editar empresa\\\")\",\n \"def update(self, id, **kw):\\n orden = kw['orden']\\n del kw['orden']\\n del kw['tareaID']\\n if kw['los_archivos_entrada'].filename:\\n kw['archivos_entrada'] = kw['los_archivos_entrada'].file.read()\\n del kw['los_archivos_entrada']\\n if kw['los_archivos_a_comparar'].filename:\\n kw['archivos_a_comparar'] = kw['los_archivos_a_comparar'].file.read()\\n del kw['los_archivos_a_comparar']\\n # TODO : Hacer ventanita mas amigable para cargar esto.\\n try:\\n kw['archivos_a_guardar'] = tuple(kw['archivos_guardar'].split(','))\\n except AttributeError:\\n pass\\n del kw['archivos_guardar']\\n r = validate_set(id, kw)\\n flash(_(u'El %s fue actualizado.') % name)\\n raise redirect('../list/%d' % r.tarea.id)\",\n \"def save(self):\\n # TODO (Pierre): code\",\n \"def save_new_carver():\\r\\n\\r\\n brand = omVar_new_carver_brand.get() # Erhalten der Eingabewerte.\\r\\n price = float(enVar_carv_price.get())\\r\\n length = int(enVar_carv_length.get())\\r\\n model = en_new_carver_model.get()\\r\\n typ = omVar_new_carver_typ.get()\\r\\n\\r\\n omVar_new_carver_brand.set(carver_brandlist[8]) # \\\"Bereinigen\\\" der Eingabefelder.\\r\\n enVar_carv_price.set(\\\"\\\")\\r\\n enVar_carv_length.set(\\\"\\\")\\r\\n enVar_carv_model.set(\\\"\\\")\\r\\n omVar_new_carver_typ.set(carver_typ[4])\\r\\n\\r\\n new_carver = Carver(brand, price, length, model, typ) # Einspeisen der Eingabewerte in Funktion zum anlegen\\r\\n # eines neuen Carvers nud speichern in Datei \\\"ski.json\\\"\\r\\n new_carver.new_carver_json()\\r\\n\\r\\n messagebox.showinfo(\\\"Neuer Ski\\\", f'Es wurde folgender Carver dem Bestand hinzugefügt:\\\\n\\\\n'\\r\\n f'{Carver.describe_carver(new_carver)}')\",\n \"def editar_info_bcompartida(request, slug_biblioteca_compartida):\\n\\n template = \\\"libros/editar_info_bcompartida.html\\\"\\n bcompartida = BibliotecaCompartida.objects.get(slug=slug_biblioteca_compartida)\\n perfil_usuario = obtener_perfil(request.user)\\n\\n if AdminsBibliotecaCompartida.objects.filter(biblioteca_compartida=bcompartida, perfil=perfil_usuario).exists():\\n pass\\n else:\\n raise PermissionDenied\\n\\n if request.method == \\\"POST\\\":\\n form = EditarBibliotecaCompartida(request.POST, instance=bcompartida)\\n\\n if form.is_valid():\\n form.save()\\n\\n return HttpResponseRedirect(reverse('libros:biblioteca_compartida',\\n kwargs={'slug_biblioteca_compartida': bcompartida.slug}))\\n else:\\n form = EditarBibliotecaCompartida(initial={\\n 'punto_google_maps': bcompartida.punto_google_maps,\\n 'nombre': bcompartida.nombre,\\n 'direccion': bcompartida.direccion,\\n 'imagen': bcompartida.imagen,\\n 'direccion_web': bcompartida.direccion_web,\\n 'horario_apertura': bcompartida.horario_apertura,\\n })\\n\\n # Si no es un usuario que hiso login con Facebook, entonces hay que mostrar link a editar_info_personal\\n if UserSocialAuth.objects.filter(user=request.user).exists():\\n es_usuario_social = True\\n else:\\n es_usuario_social = False\\n\\n context = {'biblioteca_compartida': bcompartida, 'form': form, 'es_usuario_social': es_usuario_social}\\n\\n return render(request, template, context)\",\n \"def update(self, request, *args, **kwargs):\\n obj = self.get_object()\\n serializer = CambioSerializer(obj, data=request.DATA, partial=True)\\n if serializer.is_valid():\\n serializer.save()\\n return Response(serializer.data)\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\",\n \"def manage_afterPUT(self, data, marshall_data, file, context, mimetype,\\n filename, REQUEST, RESPONSE):\\n file.seek(0)\\n self.setImage(file)\",\n \"def aplicar_operacion_fasores(self,fasor1,fasor2,input_tipo_funcion1,input_tipo_funcion2):\\n fasor1 = self.controlador.convertir_fasor(fasor1,input_tipo_funcion1)\\n fasor2 = self.controlador.convertir_fasor(fasor2,input_tipo_funcion2)\\n nuevo_fasor1 = self.controlador.ejecutar_operacion_fasores(fasor1,fasor2)\\n print('\\\\nResultado\\\\n')\\n print( f\\\"({nuevo_fasor1.amplitud } , { nuevo_fasor1.frecuencia } , { nuevo_fasor1.anguloInicial} )\\\",'(Amplitud,frecuencia,angulo) --> Forma Coseno' )\\n nuevo_fasor1.pasarASeno() # pasamos a seno el angulo inicial y luego imprimimos el nuevo formato\\n print( f\\\"( {nuevo_fasor1.amplitud }, { nuevo_fasor1.frecuencia } , { nuevo_fasor1.anguloInicial} )\\\",'(Amplitud,frecuencia,angulo) --> Forma seno\\\\n' )\\n print('\\\\n')\",\n \"def ticket_edit(request, ticket_id):\\n ticket = get_object_or_404(Ticket, code=ticket_id)\\n categoria = ticket.input_module.ticket_category\\n title = _(\\\"Modifica ticket\\\")\\n sub_title = ticket\\n allegati = ticket.get_allegati_dict()\\n path_allegati = get_path(ticket.get_folder())\\n form = ticket.compiled_form(files=None, remove_filefields=allegati)\\n form_allegati = ticket.compiled_form(files=None,\\n remove_filefields=False,\\n remove_datafields=True)\\n template = \\\"user/ticket_edit.html\\\"\\n d = {'allegati': allegati,\\n 'categoria': categoria,\\n 'form': form,\\n 'form_allegati': form_allegati,\\n 'path_allegati': path_allegati,\\n 'sub_title': sub_title,\\n 'ticket': ticket,\\n 'title': title,}\\n if request.method == 'POST':\\n fields_to_pop = [settings.TICKET_CONDITIONS_FIELD_ID]\\n json_post = get_POST_as_json(request=request,\\n fields_to_pop=fields_to_pop)\\n json_response=json.loads(json_post)\\n # Costruisco il form con il json dei dati inviati e tutti gli allegati\\n # json_response[settings.ATTACHMENTS_DICT_PREFIX]=allegati\\n # rimuovo solo gli allegati che sono stati già inseriti\\n modulo = ticket.get_form_module()\\n form = modulo.get_form(data=json_response,\\n files=request.FILES,\\n remove_filefields=allegati)\\n\\n d['form'] = form\\n\\n if form.is_valid():\\n if request.FILES:\\n json_response[settings.ATTACHMENTS_DICT_PREFIX] = allegati\\n path_allegati = get_path(ticket.get_folder())\\n for key, value in request.FILES.items():\\n nome_allegato = form.cleaned_data[key]._name\\n # form.validate_attachment(request.FILES.get(key))\\n save_file(form.cleaned_data[key],\\n path_allegati,\\n nome_allegato)\\n json_response[settings.ATTACHMENTS_DICT_PREFIX][\\\"{}\\\".format(key)] = \\\"{}\\\".format(nome_allegato)\\n elif allegati:\\n # Se non ho aggiornato i miei allegati lasciandoli invariati rispetto\\n # all'inserimento precedente\\n json_response[settings.ATTACHMENTS_DICT_PREFIX] = allegati\\n # salva il modulo\\n ticket.save_data(form.cleaned_data[settings.TICKET_SUBJECT_ID],\\n form.cleaned_data[settings.TICKET_DESCRIPTION_ID],\\n json_response)\\n # data di modifica\\n ticket.update_log(user=request.user,\\n note=_(\\\"Ticket modificato\\\"))\\n\\n # log action\\n logger.info('[{}] user {} edited ticket {}'.format(timezone.now(),\\n request.user,\\n ticket))\\n\\n # Allega il messaggio al redirect\\n messages.add_message(request, messages.SUCCESS,\\n _(\\\"Modifica effettuata con successo\\\"))\\n return redirect('uni_ticket:ticket_edit', ticket_id=ticket_id)\\n else:\\n for k,v in get_labeled_errors(form).items():\\n messages.add_message(request, messages.ERROR,\\n \\\"<b>{}</b>: {}\\\".format(k, strip_tags(v)))\\n\\n return render(request, template, d)\",\n \"def save():\\n pass\",\n \"def after_successful_edit(self):\\n pass\",\n \"def on_btnAltaOtroServicio_clicked(self,widget):\\n try:\\n\\n codigoReservaServicio=variables.lblCodigoReservaServicio.get_text()\\n if (variables.lblCodigoReservaServicio.get_text() != \\\"\\\"):\\n if(variables.entTipoServicio.get_text()!=\\\"\\\"):\\n if (variables.entPrecioServicio.get_text() != \\\"\\\"):\\n existeOtroServicio = False\\n for registro in variables.listFactura:\\n if registro[0] == variables.entTipoServicio.get_text():\\n existeOtroServicio = True\\n if existeOtroServicio == False:\\n precio = str(variables.entPrecioServicio.get_text())\\n concepto = str(variables.entTipoServicio.get_text())\\n datos = (codigoReservaServicio, concepto, precio)\\n funciones_servicios.insertarServicio(datos)\\n\\n else:\\n variables.mensajeError = \\\"No puedes insertar otro servicio extra con el mismo nombre\\\"\\n variables.vError.show()\\n variables.entTipoServicio.set_text(\\\"\\\")\\n variables.entPrecioServicio.set_text(\\\"\\\")\\n\\n else:\\n variables.mensajeError = \\\"Debes insertar un precio servicio\\\"\\n variables.vError.show()\\n\\n else:\\n variables.mensajeError = \\\"Debes insertar un tipo de servicio\\\"\\n variables.vError.show()\\n funciones_servicios.listadoServicio(variables.listServicios,codigoReservaServicio)\\n funciones_factura.listadoServicios(variables.listFactura,variables.codr,variables.lblHabitacionServicio.get_text())\\n funciones_factura.calcularPreciosServicios()\\n\\n else:\\n variables.mensajeError = \\\"Debes seleccionar un codigo de reserva\\\"\\n variables.vError.show()\\n\\n except Exception as e:\\n print(\\\"Error alta servicio\\\")\\n print(e)\",\n \"def __actualizar_jugador(self, ip, datos):\\n a, x, y = datos[1:4]\\n if self.server.JUGADORES[ip]['activar']:\\n a, x, y = (0,0,0)\\n self.server.JUGADORES[ip]['estado'] = True\\n self.server.JUGADORES[ip]['activar'] = False\\n if not self.server.JUGADORES[ip]['estado']:\\n a, x, y = (\\\"-\\\",\\\"-\\\",\\\"-\\\")\\n self.server.JUGADORES[ip]['pos'] = \\\"%s,%s,%s\\\" % (a, x, y)\\n self.__actualizar_balas(ip, datos)\",\n \"def enviar_imagem():\\n template = 'produtos/form-enviar-imagem.html'\\n\\n if current_app.config['UPLOADS_DEFAULT_URL'] is None:\\n warning('A URL Base do Sistema não está configurada. ' +\\n 'Configure-a para que as imagens possam ser salvas.')\\n return redirect(url_for('integrador.produtos_enviar_imagem'))\\n\\n if request.method == 'POST':\\n import os\\n import glob\\n\\n base_path = os.getcwd() + '/uploads/photos/'\\n imagem = request.files['file']\\n nome = imagem.filename\\n sku = int(nome.split('.')[0])\\n filepath = base_path + nome\\n\\n if nome == '':\\n return 'Nenhuma Imagem Foi Selecionada', 400\\n\\n if nome.rfind('.') == -1:\\n return 'O arquivo informado não possui extensão definida', 400\\n\\n ext = nome.split('.')[1].lower()\\n if not imageSet.extension_allowed(ext):\\n return 'A extensão informada não é válida', 400\\n\\n if not validar_nome_imagem(nome):\\n return 'O nome da imagem precisa ser um codigo válido', 400\\n\\n # verifca se ja existe alguma imagem na pasta deste item\\n # pegando o sku do item e iterando na pasta de destino, verificando\\n # se existe alguma imagem e a removendo\\n path = os.path.dirname(filepath)\\n if os.path.exists(path):\\n for im in glob.glob(os.path.join(path, f'{sku}*.*')):\\n os.remove(os.path.join(path, im))\\n\\n image = imageSet.save(imagem)\\n image = base_path + image\\n genImage(image, image)\\n\\n # busca o cadastro do produto e se existir altera para atualizar imagem\\n produto = MagProduto.by(sku=sku)\\n if produto:\\n produto.atualiza_imagem = True\\n produto.possui_imagem = True\\n produto.update()\\n\\n Log.info(f'[ENVIAR IMAGEM] Enviado Imagem do produto {nome}.')\\n\\n result = {\\n 'title': 'Produtos',\\n 'subtitle': 'Enviar Imagens'\\n }\\n return render_template(template, **result)\",\n \"def on_btnAltaServicioBasico_clicked(self,widget):\\n try:\\n conexion.cur.execute('select * from precios')\\n precios = conexion.cur.fetchall()\\n if(variables.lblCodigoReservaServicio.get_text()!=\\\"\\\"):\\n codigoReservaServicio = variables.lblCodigoReservaServicio.get_text()\\n if(variables.rbDesayuno.get_active()):\\n existeDesayuno=False\\n for registro in variables.listFactura:\\n print(registro[0])\\n if registro[0] == \\\"Desayuno\\\":\\n existeDesayuno=True\\n if existeDesayuno==False:\\n precio=precios[0][0]\\n concepto=\\\"Desayuno\\\"\\n datos=(codigoReservaServicio,concepto,precio)\\n funciones_servicios.insertarServicio(datos)\\n else:\\n variables.mensajeError = \\\"No puedes insertar otro desayuno\\\"\\n variables.vError.show()\\n\\n elif(variables.rbComida.get_active()):\\n existeComida = False\\n for registro in variables.listFactura:\\n if registro[0] == \\\"Comida\\\":\\n existeComida = True\\n if existeComida == False:\\n precio = precios[0][1]\\n concepto = \\\"Comida\\\"\\n datos = (codigoReservaServicio, concepto, precio)\\n funciones_servicios.insertarServicio(datos)\\n else:\\n variables.mensajeError = \\\"No puedes insertar otra comida\\\"\\n variables.vError.show()\\n\\n\\n if(variables.chkParking.get_active()):\\n existeParking = False\\n for registro in variables.listFactura:\\n if registro[0] == \\\"Parking\\\":\\n existeParking = True\\n if existeParking == False:\\n precio = precios[0][2]\\n concepto = \\\"Parking\\\"\\n datos = (codigoReservaServicio, concepto, precio)\\n funciones_servicios.insertarServicio(datos)\\n else:\\n variables.mensajeError = \\\"No puedes insertar otro servicio de parking\\\"\\n variables.vError.show()\\n\\n\\n variables.rbNinguno.set_active(True)\\n variables.chkParking.set_active(False)\\n funciones_servicios.listadoServicio(variables.listServicios,codigoReservaServicio)\\n funciones_factura.listadoServicios(variables.listFactura,variables.codr,variables.lblHabitacionServicio.get_text())\\n funciones_factura.calcularPreciosServicios()\\n\\n else:\\n variables.mensajeError=\\\"Debes seleccionar un codigo de reserva\\\"\\n variables.vError.show()\\n\\n\\n except Exception as e:\\n print(\\\"Error alta servicio\\\")\\n print(e)\",\n \"def comenzar_nuevo_juego():\\n escena_uno.cargarEscena1(screen, display_width, display_height)#Se pone a correr la escena\\n #escena_uno.cargarEscena2(screen, display_width, display_height)\",\n \"def form_valid(self, form):\\n with create_revision():\\n # form_data = form.save(commit=False)\\n bom_name = form.cleaned_data['bom_name']\\n parts = get_bom_parts(bom_name)\\n total_price = cal_total_price(bom_name)\\n qty = form.cleaned_data['quantity']\\n product_name = form.cleaned_data['product_name']\\n warehouse_name = form.cleaned_data['warehouse_name']\\n asset_state = form.cleaned_data['asset_state']\\n mfg_date = form.cleaned_data['mfg_date']\\n remarks = form.cleaned_data['remarks']\\n # multipule_assets = []\\n # Outwards for asset creation\\n\\n # Outward all parts in an asset\\n # multiply it with assets `qty` to limit number\\n # queries for creation of Outwards\\n for part in parts:\\n part_obj = Parts.objects.get(part_number=part['PartNumber'])\\n authorised_by = \\\"asset_auto_creation\\\"\\n verified_by = \\\"asset_auto_creation\\\"\\n mode_of_transport = \\\"asset_auto_creation\\\"\\n remarks = \\\"This outward was created due to asset creation.\\\"\\n parts_assets_qty = float(part['Qty']) * qty\\n Outward.objects.create(\\n part_type=Type.objects.get(type_name=\\\"ASSETAUTOOUTWARD\\\"),\\n part_number=part_obj,\\n part_name=part_obj.part_name,\\n quantity=parts_assets_qty,\\n from_warehouse_name=warehouse_name,\\n to_warehouse_name=warehouse_name,\\n authorised_by=authorised_by,\\n verified_by=verified_by,\\n mode_of_transport=mode_of_transport,\\n unit_of_measure=part_obj.unit_of_measure,\\n remarks=remarks,\\n )\\n\\n for i in range(qty):\\n single_asset = Asset(\\n product_name=product_name,\\n bom_name=bom_name,\\n parts=parts,\\n total_price=total_price,\\n warehouse_name=warehouse_name,\\n asset_state=asset_state,\\n remarks=remarks,\\n mfg_date=mfg_date,\\n )\\n single_asset.save()\\n revisions.set_user(self.request.user)\\n revisions.set_comment(\\\"Created from web form.\\\")\\n return_value = super().form_valid(single_asset)\\n return return_value\",\n \"def __actualizar_balas(self, ip, datos):\\n a, x, y = datos[4:7]\\n self.server.JUGADORES[ip]['bala'] = \\\"%s,%s,%s\\\" % (a, x, y)\\n self.__actualizar_explosiones(ip, datos)\",\n \"def _update_from_rest_data(self) -> None:\",\n \"def actualizarLBI(produ,datInv,cant): # Esta sección fue hecha por Valeria \\n datInv[produ][2] -= cant\\n return datInv\",\n \"def cobroEfectivo(self):\\n if self.total_a_pagar == 0:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"El saldo restante a pagar es cero\\\")\\n else:\\n self.rbtnEfectivo.setChecked(True)\\n monto_a_pagar, ok = QtGui.QInputDialog.getDouble(self,\\\"Cobro Efectivo\\\",\\\"Ingrese monto a pagar\\\",0,0,2000,2)\\n\\n if ok:\\n if monto_a_pagar >= self.total_a_pagar:\\n QtGui.QMessageBox.information(self,\\\"Cobro Efectivo\\\",\\\"Su vuelto es:%.2f\\\" % (monto_a_pagar - self.total_a_pagar))\\n temp = [\\\"Efectivo\\\",monto_a_pagar]\\n self.detalles_cobro[self.tablePagos.rowCount()] = temp\\n self.total_a_pagar = 0\\n elif monto_a_pagar == 0:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"El monto ingresado no puede ser cero\\\")\\n else:\\n temp = [\\\"Efectivo\\\",monto_a_pagar]\\n self.detalles_cobro[self.tablePagos.rowCount()] = temp\\n self.total_a_pagar -= monto_a_pagar\\n\\n self.actualizar_total()\\n self.actualizar_tabla()\",\n \"def edit():\",\n \"def save_data_to_db(rq, ji, request):\\n if rq['change_privacy_level']:\\n privacy_id = request.POST.get('privacy-id', False)\\n ji['instance'].privacy = Privacy(pk=privacy_id)\\n ji['instance'].save()\\n elif rq['delete_img_flag']:\\n ji['instance'].save()\\n elif rq['delete_target_instance_id']:\\n ji['instance'].delete()\\n elif ji['form'].is_valid():\\n if rq['add_new_profile_data']:\\n ji['instance'].save()\\n print(\\\"success adding new data.\\\")\\n else:\\n ji['form'].save()\\n print(\\\"success editing data.\\\")\\n if rq['portfolio_images'] and ji['image_form'].is_valid():\\n for idx, image in enumerate(rq['portfolio_images']):\\n image_instance = Image(\\n work_id=ji['instance'].id,\\n image=image,\\n )\\n image_instance.save()\\n if idx == 0:\\n ji['instance_image'] = image_instance.image\\n print(\\\"success save images.\\\")\",\n \"def enviar_novos():\\n\\n template = 'produtos/form-enviar-produtos.html'\\n form = EnviarProdutosNovosForm()\\n imagens = read_images().keys()\\n\\n task = current_app.enviar_novos_task\\n clear_task = request.args.get('clear_task', None)\\n if clear_task and clear_task == 'yes':\\n if task and task.state == 'SUCCESS':\\n current_app.enviar_novos_task = task = None\\n return redirect(url_for('produtos.enviar_novos'))\\n\\n produtos = None\\n if not task:\\n produtos = buscar_produtos_novos()\\n\\n if not produtos and not task:\\n warning('Não existem produtos para serem enviados para o site')\\n\\n categorias = buscar_categorias_produtos()\\n if not categorias:\\n warning('Não existem categorias do site cadastradas')\\n\\n if form.validate_on_submit() and produtos and categorias:\\n produtos_task = {}\\n\\n for p in produtos:\\n secao = int(request.form.get(f'secao-{p.idsubproduto}', 0))\\n grupo = int(request.form.get(f'grupo-{p.idsubproduto}', 0))\\n subgrupo = int(request.form.get(f'subgrupo-{p.idsubproduto}', 0))\\n\\n if secao and grupo and subgrupo:\\n categorias = [secao, grupo, subgrupo]\\n produtos_task[p.idsubproduto] = categorias\\n\\n task = enviar_novos_task.apply_async(args=(produtos_task,))\\n current_app.enviar_novos_task = task\\n\\n success(f'Tarefa iniciada com sucesso')\\n return redirect(url_for('produtos.enviar_novos'))\\n\\n result = {\\n 'title': 'Produtos',\\n 'subtitle': 'Enviar Novos',\\n 'form': form,\\n 'imagens': imagens,\\n 'produtos': produtos,\\n 'categorias': categorias,\\n 'task': task\\n }\\n return render_template(template, **result)\",\n \"def submit(self, goback=False):\\n is_changed = False\\n for fields in self.screendata:\\n lbl, win = fields[:2]\\n caption = lbl.text()\\n if caption == 'Uitvoerende:':\\n test = win.currentText()\\n ix = win.currentIndex() - 1\\n if test != self.parent().albumdata['artist']:\\n self.parent().albumdata['artist'] = self.parent().artists[ix]\\n self.parent().artist = self.parent().albumdata['artist']\\n is_changed = True\\n elif caption in ('Albumtitel:', 'Locatie/datum:'):\\n test = win.text()\\n if test != self.parent().albumdata['titel']:\\n self.parent().albumdata['titel'] = test\\n is_changed = True\\n elif caption == 'Label/jaar:':\\n win_l, win_y = fields[1:]\\n is_changed = self._replace_details(caption, ', '.join((win_l.text(), win_y.text())))\\n elif caption in ('Credits:', 'Bezetting:', 'Tevens met:'):\\n is_changed = self._replace_details(caption, win.toPlainText())\\n else:\\n is_changed = self._replace_details(caption, win.text())\\n\\n if is_changed:\\n albumid = 0 if self.new_album else self.parent().album.id\\n album, ok = dmla.update_album_details(albumid, self.parent().albumdata)\\n # eigenlijk wil ik hier de nieuwe waarden teruggeven om ze op het scherm te zetten\\n # zodat je kunt zien wat er daadwerkelijk in de database zit / is bijgewerkt\\n if ok:\\n if not goback:\\n if self.new_album:\\n self.add_another()\\n else:\\n qtw.QMessageBox.information(self, 'Albums', 'Details updated')\\n else:\\n qtw.QMessageBox.information(self, 'Albums', 'Something went wrong, please try again')\\n return\\n self.parent().album = album\\n if self.new_album:\\n self.parent().albums.append(album)\\n self.new_album = False\\n # eigenlijk zou je hierna de data opnieuw moeten ophalen en het scherm opnieuw\\n # opbouwen - wat nu alleen gebeurt als je naar het detailscherm gaat\\n else:\\n qtw.QMessageBox.information(self, 'Albums', 'Nothing changed')\\n\\n if self.first_time:\\n self.first_time = False\\n self.refresh_screen()\",\n \"def new_parterre_saving():\\n f = None\\n f = ParterreForm()\\n if f.validate_on_submit():\\n o = Parterre(name = f.get_name())\\n db.session.add(o)\\n form = request.form\\n longitudes = form.getlist(\\\"longitudes\\\")\\n latitudes = form.getlist(\\\"latitudes\\\")\\n num = 0\\n for longitude,latitude in zip(longitudes, latitudes):\\n c = Coordonnees(x = longitude,\\n y = latitude,\\n parterre = o.get_id(),\\n num = num)\\n num = num+1\\n try:\\n o.add_coordonnee(c)\\n except Exception as e:\\n db.session.rollback()\\n p = Actions(\\n contenu = \\\"Ajout d'un nouveau parterre \\\"+f.get_name(),\\n liste = 1\\n )\\n db.session.add(p)\\n db.session.commit()\\n return redirect(url_for('parterre_info', id = o.get_id()))\\n return render_template(\\n \\\"create-parterre.html\\\",\\n form = f,\\n title = \\\"Ajouter un nouveau Parterre\\\",\\n param = \\\"create\\\")\",\n \"def actualizar_tabla(self):\\n\\n self.padre.limpiarTabla(self.tablePagos)\\n for row, cobro in enumerate(self.detalles_cobro.values()):\\n self.tablePagos.insertRow(row)\\n self.tablePagos.setItem(row,0,QtGui.QTableWidgetItem(cobro[0]))\\n self.tablePagos.setItem(row,1,QtGui.QTableWidgetItem(\\\"$\\\"+str(cobro[1])))\",\n \"def prestar_libro_bcompartida(request, id_libro_compartido):\\n\\n template = \\\"libros/prestar_libro_bcompartida.html\\\"\\n\\n libro_disponible_obj = LibrosBibliotecaCompartida.objects.select_related('biblioteca_compartida').get(\\n id=id_libro_compartido)\\n\\n if request.method == \\\"POST\\\":\\n form = FormPrestarLibroBCompartida(request.POST)\\n\\n if form.is_valid():\\n username = form.cleaned_data.get('usuario', '')\\n\\n # crear LibrosPrestadosBibliotecaCompartida object\\n perfil_usuario_prestamo = Perfil.objects.get(usuario__username=username)\\n libro_prestado = LibrosPrestadosBibliotecaCompartida(libro=libro_disponible_obj.libro,\\n perfil_prestamo=perfil_usuario_prestamo,\\n biblioteca_compartida=libro_disponible_obj.biblioteca_compartida)\\n libro_prestado.save()\\n\\n # poner no disponible a LibrosBibliotecaCompartida object\\n libro_disponible_obj.disponible = False\\n libro_disponible_obj.prestado = True\\n libro_disponible_obj.save()\\n\\n # Crear notificacion\\n Notificacion.objects.bcompartida_presto(libro_disponible_obj.biblioteca_compartida, perfil_usuario_prestamo,\\n libro_disponible_obj.libro)\\n\\n return HttpResponseRedirect(reverse('libros:editar_libros_bcompartida',\\n kwargs={\\n 'slug_biblioteca_compartida': libro_disponible_obj.biblioteca_compartida.slug}))\\n\\n else:\\n form = FormPrestarLibroBCompartida()\\n\\n ciudad = libro_disponible_obj.biblioteca_compartida.ciudad\\n perfiles_obj = Perfil.objects.filter(ciudad=ciudad).select_related(\\\"usuario\\\")\\n usernames_autocomplete = [perfil.usuario.username for perfil in perfiles_obj]\\n\\n context = {'form': form, 'libro_compartido': libro_disponible_obj,\\n 'usernames_autocomplete': json.dumps(usernames_autocomplete)}\\n\\n return render(request, template, context)\",\n \"def guardar_datos(nombre, datos, \\n directory=\\\"datos_guardados/\\\",\\n method=\\\"numerado\\\",\\n ): \\n # Primera parte del nombre\\n filename_1 = \\\"datos_\\\"\\n nombre += \\\"_\\\"\\n \\n if method == \\\"fecha\\\":\\n # Tercera parte del nombre (fecha)\\n today = datetime.datetime.today()\\n tt = today.timetuple()\\n\\n # Formato de fecha es yyyymmdd_hhmmss\\n filename_3 = \\\"{:4d}{:02d}{:02d}_{:02d}{:02d}{:02d}\\\"\\n filename_3 = filename_3.format(tt.tm_year, tt.tm_mon, tt.tm_mday,\\n tt.tm_hour, tt.tm_min, tt.tm_sec)\\n\\n # Nombre completo\\n filename = directory + filename_1 + nombre + filename_3\\n\\n elif method == \\\"numerado\\\":\\n p = pathlib.Path(\\\"./\\\" + directory)\\n archivos = []\\n\\n for f in p.iterdir():\\n archivos.append(str(f))\\n \\n numero = 0\\n while True:\\n # Nombre temporal\\n filename = directory + filename_1 + nombre + str(numero)\\n \\n if filename in archivos: numero += 1\\n else: break\\n\\n # Escritura de datos (módulo json)\\n with open(filename, \\\"w\\\") as file_object:\\n json.dump(datos, file_object)\\n \\n # Mensaje de escritura\\n print(\\\"---------------------------------------\\\")\\n print(\\\"Datos escritos en:\\\")\\n print(\\\"\\\\t\\\" + filename)\\n print(\\\"---------------------------------------\\\")\",\n \"def upload(self):\\n\\n # building interface\\n utils.printer(\\\"generating interface.cpp\\\", 'task')\\n try: # catch this exception for downward compatibility\\n utils.printer(\\\"generating interface from: %s\\\" % self.vars_path, 'msg')\\n utils.printer(\\\"using as template: %s\\\" % self.task_config['interface_template_fname'], 'msg')\\n interface_template_fname = self.task_config['interface_template_fname']\\n interface_generator.run(self.vars_path, interface_template_fname)\\n except KeyError:\\n utils.printer(\\\"generating interface based on %s\\\" % self.vars_path, 'msg')\\n interface_generator.run(self.vars_path)\\n\\n # uploading code onto arduino\\n\\n # replace whatever com port is in the platformio.ini with the one from task config\\n self.pio_config_path = self.task_folder / \\\"Arduino\\\" / \\\"platformio.ini\\\"\\n pio_config = configparser.ConfigParser()\\n pio_config.read(self.pio_config_path)\\n\\n # get upload port\\n upload_port = self.box_config['FSM']['com_port']\\n\\n for section in pio_config.sections():\\n if section.split(\\\":\\\")[0] == \\\"env\\\":\\n pio_config.set(section, \\\"upload_port\\\", upload_port)\\n\\n # write it\\n with open(self.pio_config_path, 'w') as fH:\\n pio_config.write(fH)\\n \\n # get current UI arduino variables, backup defaults,\\n # write the UI derived and upload those, revert after upload\\n # this workaround is necessary to use the get previous variables\\n # functionality ... \\n\\n # backing up original values\\n shutil.copy(self.vars_path, self.vars_path.with_suffix('.default'))\\n\\n # setting the valve calibration factor\\n utils.printer(\\\"setting valve calibration factors\\\", 'task')\\n if 'valves' in self.box_config.sections():\\n valves = dict(self.box_config['valves']).keys()\\n for valve in valves:\\n try:\\n utils.printer('setting calibration factor of valve: %s = %s' % (valve, self.box_config['valves'][valve]), 'msg')\\n self.VariableController.VariableEditWidget.set_entry(valve, self.box_config['valves'][valve])\\n except:\\n utils.printer(\\\"can't set valve calibration factors of valve %s\\\" % valve, 'error')\\n else:\\n utils.printer(\\\"no valves found in box config\\\", 'msg')\\n\\n # overwriting vars\\n self.VariableController.write_variables(self.vars_path)\\n\\n # upload\\n utils.printer(\\\"uploading code on arduino\\\", 'task')\\n prev_dir = Path.cwd()\\n\\n os.chdir(self.task_folder / 'Arduino')\\n fH = open(self.run_folder / 'platformio_build_log.txt', 'w')\\n platformio_cmd = self.sys_config['system']['platformio_cmd']\\n cmd = ' '.join([platformio_cmd, 'run', '--target', 'upload'])\\n proc = subprocess.Popen(cmd, shell=True, stdout=fH) # ,stderr=fH)\\n proc.communicate()\\n fH.close()\\n\\n os.chdir(prev_dir)\\n\\n # restoring original variables\\n shutil.copy(self.vars_path.with_suffix('.default'), self.vars_path)\\n os.remove(self.vars_path.with_suffix('.default'))\\n\\n utils.printer(\\\"upload successful\\\", 'msg')\",\n \"def limpiarVentana(self):\\n\\n self.productosAgregados=0\\n self.lotesVentas={}\\n self.facturaCobrada=False\\n self.obraSocialSeleccionada=None\\n self.formapago = None\\n self.factura = None\\n self.data = {}\\n self.detallesTabla = {}\\n self.lineObra.clear()\\n self.lineObra.setEnabled(True)\\n self.lineCuit.clear()\\n self.lineCuit.setEnabled(True)\\n self.tableObra.setVisible(False)\\n self.rbtnObra.setChecked(False)\\n self.limpiarTabla(self.tableProductos)\\n self.limpiarTabla(self.tableFactura)\\n self.cargarProductosSinObra()\",\n \"def save (self):\\n pass\",\n \"def _actualizar_precios(self, request, presup_id, *args, **kwargs):\\n obj_presup = Presupuesto.objects.get(pk=presup_id)\\n lineas_actualizadas = []\\n if not obj_presup._vigente():\\n turnoList = obj_presup.get_turnos_activos()\\n for turno in turnoList:\\n kwargs['pk'] = presup_id\\n for linea in turno.ofertatec_linea_set.all():\\n obj_ofertatec = OfertaTec.objects.get(pk=linea.ofertatec.id)\\n if linea.precio != obj_ofertatec.precio:\\n linea.precio = obj_ofertatec.precio\\n cantidad = linea.cantidad or 1\\n horas = linea.cant_horas or 1\\n linea.precio_total = linea.precio * cantidad * horas\\n lineas_actualizadas.append(linea)\\n if len(lineas_actualizadas) > 0 and obj_presup.fecha_realizado:\\n for turno in turnoList:\\n lab.views.createRevision(request, pk=turno.pk)\\n createRevision(request, pk=obj_presup.pk)\\n for linea in lineas_actualizadas:\\n linea.save()\\n return True\\n # Si el presupuesto esta vigente no le hago modificaciones\\n else:\\n return False\",\n \"def atualizar_questao():\\n try:\\n if current_user.is_administrator():\\n cod_id = request.args.get('id')\\n questao = Questao.query.filter_by(cod_questao = cod_id).one()\\n categorias = Categoria.query.all()\\n if request.method == 'POST':\\n questao.questao = request.form['questao']\\n questao.alternativa1 = request.form['alternativa1']\\n questao.alternativa2 = request.form['alternativa2']\\n questao.alternativa3 = request.form['alternativa3']\\n questao.alternativa4 = request.form['alternativa4']\\n questao.alternativa5 = request.form['alternativa5']\\n questao.alternativa_correta = request.form['resposta']\\n questao.cod_categoria = request.form['categoria']\\n db.session.commit()\\n return redirect(url_for('admin.listar_questoes'))\\n return render_template('admin/atualizar_questao.html', questao=questao, categorias=categorias)\\n return redirect(url_for('main.index'))\\n except Exception as e:\\n abort(500, e)\",\n \"def update_db(request):\\n\\n reports = []\\n pubmedfiles = []\\n labels = []\\n concepts = []\\n usecase = request.session['usecase']\\n type1 = request.POST.get('type', None)\\n\\n\\n for filename, file in request.FILES.items():\\n if filename.startswith('reports'):\\n reports.append(file)\\n if filename.startswith('pubmed'):\\n pubmedfiles.append(file)\\n elif filename.startswith('concepts'):\\n concepts.append(file)\\n elif filename.startswith('labels'):\\n labels.append(file)\\n\\n jsonDisp = request.POST.get('json_disp', None)\\n jsonAnn = request.POST.get('json_ann', None)\\n jsonDispUp = request.POST.get('json_disp_update', '')\\n jsonAnnUp = request.POST.get('json_ann_update', '')\\n jsonAll = request.POST.get('json_all_update', '')\\n load_concepts = request.POST.get('exa_concepts',None)\\n load_labels = request.POST.get('exa_labels',None)\\n batch = request.POST.get('batch',None)\\n #print(batch)\\n msg = update_db_util(reports,pubmedfiles,labels,concepts,jsonDisp,jsonAnn,jsonDispUp,jsonAnnUp,jsonAll,load_concepts,load_labels,batch)\\n if 'message' in list(msg.keys()):\\n keys = get_fields_from_json()\\n request.session['fields'] = keys['fields']\\n request.session['fields_to_ann'] = keys['fields_to_ann']\\n if type1 == 'reports':\\n get_fields_extractable('update')\\n\\n if request.session['mode'] == 'Robot':\\n\\n # fields = UseCase.objects.get(name=usecase)\\n # fields_to_extr = fields.extract_fields\\n # request.session['fields_to_ann'] = fields_to_extr\\n\\n workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in\\n with open(os.path.join(workpath, './automatic_annotation/auto_fields/auto_fields.json')) as out:\\n data = json.load(out)\\n request.session['fields_to_ann'] = data['extract_fields'][usecase]\\n\\n return JsonResponse(msg)\",\n \"def new_plante_saving():\\n f = None\\n f = PlanteForm()\\n if f.validate_on_submit():\\n o = TypePlante(\\n nomPlant = f.get_name(),\\n comportement = f.get_comportement(),\\n taux_humidite = f.get_taux_humidite(),\\n quantite = f.get_quantite(),\\n parterre_id = f.get_parterre().get_id())\\n f.get_parterre().add_plante(o)\\n db.session.add(o)\\n p = Actions(\\n contenu = \\\"Ajout d'une plante \\\"+f.get_name() + \\\" au parterre \\\"+ f.get_parterre().get_name(),\\n liste = 1\\n )\\n db.session.add(p)\\n db.session.commit()\\n return redirect(url_for('parterre_info', id = o.get_parterre()))\\n return render_template(\\n \\\"create-plante.html\\\",\\n form = f,\\n parterre = f.get_parterre().get_id(),\\n title = \\\"Nouvelle plante\\\",\\n param = \\\"create\\\")\",\n \"def buscarFactura(self):\\n\\n if not self.lineNumero.isEnabled() and self.facturaSeleccionada != None:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"Ya se ha seleccionado una factura\\\")\\n elif not self.lineNumero.isEnabled():\\n self.lineNumero.setEnabled(True)\\n self.lineNumero.clear()\\n self.limpiarTabla(self.tableFactura)\\n else:\\n self.numeroFacturaActual=str(self.lineNumero.text())\\n if len(self.numeroFacturaActual)==0:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",QtCore.QString.fromUtf8(\\\"No se ha ingresado número de factura\\\"))\\n else:\\n self.facturaSeleccionada=FacturaModel.existeFactura(int(self.numeroFacturaActual),self.sesion)\\n if self.facturaSeleccionada==None:\\n QtGui.QMessageBox.warning(self,\\\"Aviso\\\",\\\"La factura seleccionada no existe\\\")\\n elif self.facturaSeleccionada.getNC()!=None:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",QtCore.QString.fromUtf8(\\\"La factura ya ha posee una Nota de Crédito\\\"))\\n self.facturaSeleccionada = None\\n elif self.facturaSeleccionada.getFechaEmision()+timedelta(days=int(self.plazo))<date.today():\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",QtCore.QString.fromUtf8(\\\"El tiempo permitido para la devolución ha expirado\\\"))\\n elif self.facturaSeleccionada.estaLiquidada(self.sesion):\\n print self.facturaSeleccionada.estaLiquidada(self.sesion)\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"La factura se encuentra liquidada a la Obra Social\\\")\\n else:\\n self.lineNumero.setEnabled(False)\\n self.cargarObjetos(self.tableFactura,self.facturaSeleccionada.getDetalles(self.sesion),\\n [\\\"nro_linea\\\",\\\"producto\\\",\\\"cantidad\\\",\\\"importe\\\"])\",\n \"def editarCadastro(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def save(self):\\n pass\",\n \"def update(self):\\n\\n # Ordenamos los personajes por fila, para luego dibujarlos correctamente. Para que no se solapen.\\n self._personajes.sort(self._comparar_coordenadas_personajes)\\n\\n for personaje in self._personajes:\\n if (personaje.andando): # Si está andando.\\n # Si el personaje se encuentra en el centro de la celda a donde debia llegar ...\\n if (personaje.obtener_coordenadas() == self._mapa.obtener_coordenadas_por_posicion((personaje.camino[0][0], personaje.camino[0][1]),self._mapa.CENTER)): \\n del personaje.camino[:1] # Eliminamos esa celda del camino ha seguir porque ya ha llegado a ella. \\n if ((personaje.camino == []) or (personaje.camino == None)): # Si ya no queda camino a seguir...\\n personaje.parar() # Paramos al Personaje.\\n if not(personaje.accion == None): # Si tiene asignada alguna acción después de haber llegado a su destino ...\\n personaje.accion() # Ejecutamos la acción\\n personaje.accion = None # Y limpiamos la acción\\n if (personaje.nombre != \\\"Jugador\\\"): # Si el Personaje no es el Jugador establacemos su dirección final.\\n personaje.actualizar_direccion(personaje.direccion_final)\\n else: # Calculamos la nueva direccion hacia donde tiene que mover\\n # Obtenemos la fila y columna donde se encuenta el personaje.\\n origen = self._mapa.obtener_posicion_por_coordenadas(personaje.obtener_coordenadas())\\n # Establecemos hacia donde tiene que mirar el Personaje para ir en esa dirección.\\n personaje.actualizar_direccion(self._mapa.direcciones.index([personaje.camino[0][0] - origen[0], personaje.camino[0][1] - origen[1]]))\\n else: # Si el personaje no esa todavia en el centro de la celda \\n if (not self._hay_colision(personaje, (personaje.camino[0][0], personaje.camino[0][1]))): # Si no hay colisión en la celda de destino \\n personaje.mover(personaje.obtener_direccion()) # Movemos al personaje en esa dirección.\\n else: # Si hay colision\\n celda_personaje = self._mapa.obtener_posicion_por_coordenadas(personaje.obtener_coordenadas())\\n personaje.actualizar_posicion(self._mapa.obtener_coordenadas_por_posicion((celda_personaje[0], celda_personaje[1]),self._mapa.CENTER))\\n # Volvermos a calcular una ruta para llegar al destino.\\n self.ir_a(personaje, personaje.destino, personaje.direccion_final)\\n \\n personaje.update() # Actualizamos el personaje.\\n \\n for objeto in self._objetos: # Actualizamos los objetos.\\n objeto.update()\\n \\n self._dibujar(\\n )\",\n \"def iniciarEntradas(self):\\n tituloEntry = self.crearEntrada(self.master, self.tituloString, 30, 1, 1)\\n descripcionEntry = self.crearEntrada(self.master, self.descripcionString, 30, 2, 1)\",\n \"def save_capteur():\\n f = None\\n f = CapteurForm()\\n a = get_capteur(f.get_id())\\n na = a.get_name()\\n if f.validate_on_submit():\\n a.set_name(f.get_name())\\n a.set_num(f.get_phoneNumber())\\n a.set_interval(f.get_interval())\\n if a.get_parterre() != f.get_parterre().get_id():\\n a.set_parterre(f.get_parterre().get_id())\\n if a.get_typeMesure() != f.get_typeMesure().get_id():\\n a.set_typeMesure(f.get_typeMesure().get_id())\\n ac = Actions(\\n contenu = \\\"Modification du capteur \\\"+na+\\\" -> \\\"+a.get_name(),\\n liste = 1\\n )\\n db.session.add(ac)\\n db.session.commit()\\n return redirect(url_for(\\n \\\"capteur_info\\\",\\n id = f.get_id()))\\n f.next.data = \\\"save_capteur\\\"\\n return render_template(\\n \\\"addCapteur.html\\\",\\n title = a.get_name()+\\\" - edit\\\",\\n form = f,\\n capteur=a,\\n param = \\\"modif\\\")\",\n \"def save(self, doc, form=None, creation=False, refresh_index=True, asAuthor=True, onSaveEvent=True, mantainOriginalForm=True):\\n method = 'save'\\n\\n # we process computed fields (refresh the value)\\n if form is None:\\n form = doc.getForm()\\n elif not mantainOriginalForm:\\n # questa sostituzione può essere evitata...\\n doc.setItem('Form', form.getFormName())\\n\\n db=self.db # SONO QUI\\n if form:\\n for f in form.getFormFields(includesubforms=True, doc=doc, applyhidewhen=False):\\n mode = f.getFieldMode()\\n fieldname = f.id\\n if mode in [\\\"COMPUTED\\\", \\\"COMPUTEDONSAVE\\\"] or (mode==\\\"CREATION\\\" and creation):\\n result = form.computeFieldValue(fieldname, doc)\\n doc.setItem(fieldname, result)\\n else:\\n # computed for display field are not stored\\n pass\\n\\n # compute the document title\\n title_formula = form.getDocumentTitle()\\n if title_formula:\\n # Use the formula if we have one\\n try:\\n title = doc.runFormulaScript(\\\"form_\\\"+form.id+\\\"_title\\\", doc, form.DocumentTitle)\\n except PlominoScriptException, e:\\n message = 'Title formula failed: %s' % e\\n self.set_error(method, message)\\n else:\\n if title != doc.Title():\\n doc.setTitle(title)\\n elif creation:\\n # If we have no formula and we're creating, use Form's title\\n title = form.Title()\\n if title != doc.Title():\\n # We may be calling save with 'creation=True' on\\n # existing documents, in which case we may already have\\n # a title.\\n doc.setTitle(title)\\n\\n # update the document id\\n if creation and form.getDocumentId():\\n new_id = doc.generateNewId()\\n if new_id:\\n transaction.savepoint(optimistic=True)\\n db.documents.manage_renameObject(doc.id, new_id)\\n\\n # update the Plomino_Authors field with the current user name\\n if asAuthor:\\n authors = doc.getItem('Plomino_Authors', []) or []\\n name = db.getCurrentUser().getUserName()\\n\\n if name not in authors:\\n authors.append(name)\\n\\n doc.setItem('Plomino_Authors', authors)\\n\\n # execute the onSaveDocument code of the form\\n if form and onSaveEvent:\\n try:\\n result = doc.runFormulaScript(\\\"form_\\\"+form.id+\\\"_onsave\\\", doc, form.onSaveDocument)\\n# if result and hasattr(doc, 'REQUEST'):\\n# doc.REQUEST.set('plominoredirecturl', result)\\n except PlominoScriptException, e:\\n message = 'Document has been saved but onSave event failed: %s' % e\\n self.set_error(method, message)\\n# if hasattr(doc, 'REQUEST'):\\n# e.reportError('Document has been saved but onSave event failed.')\\n# doc_path = doc.REQUEST.physicalPathToURL(doc.doc_path())\\n# doc.REQUEST.RESPONSE.redirect(doc_path)\\n\\n if refresh_index:\\n # update index\\n db.getIndex().indexDocument(doc)\\n # update portal_catalog\\n if db.getIndexInPortal():\\n db.portal_catalog.catalog_object(doc, \\\"/\\\".join(db.getPhysicalPath() + (doc.id,)))\",\n \"def Save(cls):\\n print(\\\"Saving data...\\\")\\n # Check if the backup directory exists\\n backup_dir = FILE_PATH + \\\"FellesLab_Backup\\\"\\n if not os.path.isdir(backup_dir):\\n os.mkdir(backup_dir)\\n with open( backup_dir + \\\"/README\\\", 'w') as f:\\n f.write(BACKUP_README)\\n\\n #cmd = \\\"python -m markdown {dir}/README > {dir}/README.html\\\".format(dir=backup_dir)\\n #call([cmd])\\n # Check if the Backup directory has a directory for \\\"today\\\"\\n day = FILE_PATH + \\\"FellesLab_Backup/\\\" + cls.dayStamp()\\n if not os.path.isdir(day):\\n os.mkdir(day)\\n\\n # Save data for all the sensors\\n # TODO: Rewrite, difficult to follow...\\n DATA = [ ] # This will become a list of lists, e.g.\\n # [ [time, ...], [Temp1, ...], [time, ...], [Temp2, ...] ]\\n for ref in cls.__refs__:\\n\\n ref().File.seek(0) # Rewind file pointer\\n\\n F = csv.reader(ref().File, delimiter=',', quotechar='|')\\n r = [[],[]]\\n for row in F:\\n for i,num in enumerate(row):\\n r[i].append(num)\\n if i > 1:\\n r[i].append(float(num))\\n\\n for j in r:\\n DATA.append(j)\\n ref().File.close()\\n\\n # Finally, write data file.\\n with open( day + \\\"/\\\" + cls.timeStamp() + '.csv', 'w') as f:\\n csv.writer(f).writerows( it.izip_longest(*DATA, fillvalue='NA') )\",\n \"def initialize_user_interface(self):\\n self.parent.title(\\\"Formulario Ciudades\\\")\\n self.parent.grid_rowconfigure(0, weight=1)\\n self.parent.grid_columnconfigure(0, weight=1)\\n self.parent.config(background=\\\"lavender\\\")\\n\\n self.dose_label = tkinter.Label(self.parent, text=\\\"Nombre:\\\")\\n self.dose_entry = tkinter.Entry(self.parent)\\n self.dose_label.grid(row=0, column=0, sticky=tkinter.W)\\n self.dose_entry.grid(row=0, column=1)\\n\\n self.modified_label = tkinter.Label(self.parent, text=\\\"Codigo Postal:\\\")\\n self.modified_entry = tkinter.Entry(self.parent)\\n self.modified_label.grid(row=1, column=0, sticky=tkinter.W)\\n self.modified_entry.grid(row=1, column=1)\\n\\n self.submit_button = tkinter.Button(self.parent, text=\\\"Update\\\", command=self.update_data)\\n self.submit_button.grid(row=2, column=1, sticky=tkinter.W)\\n self.exit_button = tkinter.Button(self.parent, text=\\\"Exit\\\", command=self.parent.quit)\\n self.exit_button.grid(row=0, column=3)\\n\\n # Set the treeview\\n self.tree = ttk.Treeview(self.parent, columns=('Nombre', 'Codigo Postal'))\\n self.tree.heading('#0', text='Numero de Ciudad')\\n self.tree.heading('#1', text='Nombre')\\n self.tree.heading('#2', text='Codigo Postal')\\n self.tree.column('#1', stretch=tkinter.YES)\\n self.tree.column('#2', stretch=tkinter.YES)\\n self.tree.column('#0', stretch=tkinter.YES)\\n self.tree.grid(row=4, columnspan=4, sticky='nsew')\\n self.tree.bind(\\\"<Double-1>\\\", self.onDoubleClick)\\n self.treeview = self.tree\\n # agrego datos\\n self.insert_data()\",\n \"def save_click(self):\\n acc_name = self.name_entry.get()\\n email = self.email_entry.get()\\n username = self.user_entry.get()\\n password = self.pass_entry.get()\\n\\n if not acc_name:\\n self.error_label.config(text='Introdu numele contului.')\\n return\\n\\n if self.is_new_account and accountdb.account_exists(self.us, acc_name):\\n self.error_label.config(text='Un cont cu acest nume există deja.')\\n return\\n\\n # Tell the user what's happening.\\n self.error_label.config(text='Se salvează...')\\n self.error_label.update()\\n\\n acc = account.create_account(acc_name, email, username, password, self.us)\\n accountdb.change_account(self.us, acc)\\n\\n self.error_label.config(text='Detaliile contului au fost salvate.')\\n\\n self.acc = acc\\n self.is_new_account = False\\n self.load_account_data()\",\n \"def _recreate(self, req, id, body):\\n context = req.environ['meteos.context']\\n\\n if not self.is_valid_body(body, 'os-recreate'):\\n raise exc.HTTPUnprocessableEntity()\\n\\n b_model = body['os-recreate']\\n\\n LOG.debug(\\\"Recreate model with request: %s\\\", b_model)\\n\\n try:\\n model = self.engine_api.get_model(context, id)\\n utils.is_valid_status(model.__class__.__name__,\\n model.status,\\n constants.STATUS_AVAILABLE)\\n experiment = self.engine_api.get_experiment(\\n context, model.experiment_id)\\n template = self.engine_api.get_template(\\n context, experiment.template_id)\\n except exception.NotFound:\\n raise exc.HTTPNotFound()\\n\\n display_name = b_model.get('display_name')\\n display_description = b_model.get('display_description')\\n source_dataset_url = b_model.get('source_dataset_url')\\n dataset_format = b_model.get('dataset_format', 'csv')\\n model_type = b_model.get('model_type')\\n model_params = b_model.get('model_params')\\n swift_tenant = b_model.get('swift_tenant')\\n swift_username = b_model.get('swift_username')\\n swift_password = b_model.get('swift_password')\\n\\n new_model = self.engine_api.recreate_model(id,\\n context,\\n display_name,\\n display_description,\\n source_dataset_url,\\n dataset_format,\\n model_type,\\n model_params,\\n template.id,\\n template.job_template_id,\\n experiment.id,\\n experiment.cluster_id,\\n swift_tenant,\\n swift_username,\\n swift_password)\\n\\n return self._view_builder.detail(req, new_model)\",\n \"def resubmit(self, _):\\n kw = {}\\n for k, v in self.form.items():\\n if v.edit_text != PARAM_DEFAULT_VALUE:\\n kw[k] = v.edit_text\\n try:\\n self.execution.update_job_params(**kw)\\n except QMapError as e:\\n self.widget.original_widget = urwid.Text(e)\",\n \"def uploader_actividad(df,to_model):\\n\\tengine = create_engine(\\\"mssql+pyodbc://sa:BaseSQL123@10.160.8.96:1433/vpcanales?driver=SQL+Server+Native+Client+11.0\\\")\\n\\n\\tfecha = df.loc[0,'Fecha']\\n\\tprint(fecha.month)\\n\\tprint(fecha.year)\\n\\n\\tif to_model.__name__==\\\"Activacion\\\":\\n\\n\\t\\tActivacion.objects.filter(fecha_actividad__month=fecha.month ,\\n\\t\\t\\tfecha_actividad__year=fecha.year).delete()\\n\\n\\t\\tfor row in df.itertuples():\\n\\t\\t\\tconnection = engine.raw_connection()\\n\\t\\t\\tcursor=connection.cursor()\\n\\t\\t\\t#Se ejecuta el SP por cada registro del dataframe\\n\\t\\t\\tstring=\\\"\\\"\\\"exec sp_insert_into_activacion\\n\\t\\t\\t@fecha_actividad='{0}',\\n\\t\\t\\t@plataforma='{1}',\\n\\t\\t\\t@tecnologia='{2}',\\n\\t\\t\\t@terminal='{3}',\\n\\t\\t\\t@cantidad='{4}',\\n\\t\\t\\t@codigo_plan='{5}',\\n\\t\\t\\t@mes={6},\\n\\t\\t\\t@ano={7},\\n\\t\\t\\t@codigo_agente='{8}'\\n\\t\\t\\t \\\"\\\"\\\".format(row[2],\\n\\t\\t\\trow[5],\\n\\t\\t\\trow[6],\\n\\t\\t\\trow[7],\\n\\t\\t\\trow[-2],\\n\\t\\t\\trow[4],\\n\\t\\t\\trow[2].month,\\n\\t\\t\\trow[2].year,\\n\\t\\t\\trow[3])\\n\\t\\t\\tcursor.execute(string).commit()\\n\\n\\t\\tresults = Activacion.objects.filter(fecha_actividad__month=fecha.month,\\\\\\n\\t\\t\\tfecha_actividad__year=fecha.year)\\\\\\n\\t\\t\\t.aggregate(suma = Sum('cantidad'))\\n\\n\\n\\t\\tresults.update(Activacion.objects.filter(fecha_actividad__month=fecha.month,\\\\\\n\\t\\t\\tfecha_actividad__year=fecha.year)\\\\\\n\\t\\t\\t.aggregate(count = Count('cantidad')))\\n\\n\\t\\tprint(\\\"*************************\\\",results)\\n\\n\\t\\treturn results\\n\\n\\n\\n\\telse:\\n\\n\\t\\tAlta.objects.filter(fecha_actividad__month=fecha.month ,\\n\\t\\t\\tfecha_actividad__year=fecha.year).delete()\\n\\n\\t\\tfor row in df.itertuples():\\n\\t\\t\\tconnection = engine.raw_connection()\\n\\t\\t\\tcursor=connection.cursor()\\n\\t\\t\\t#Se ejecuta el SP por cada registro del dataframe\\n\\t\\t\\tstring=\\\"\\\"\\\"exec sp_insert_into_alta\\n\\t @fecha_actividad='{0}',\\n\\t @plataforma='{1}',\\n\\t @tecnologia='{2}',\\n\\t @terminal='{3}',\\n\\t @cantidad='{4}',\\n\\t @codigo_plan='{5}',\\n\\t @mes={6},\\n\\t @ano={7},\\n\\t @codigo_agente='{8}' \\\"\\\"\\\".format(row[2],\\n\\t row[5],\\n\\t row[6],\\n\\t row[7],\\n\\t row[-2],\\n\\t row[4],\\n\\t row[2].month,\\n\\t row[2].year,\\n\\t row[3])\\n\\t\\t\\tcursor.execute(string).commit()\\n\\n\\n\\t\\tresults = Alta.objects.filter(fecha_actividad__month=fecha.month,\\\\\\n\\t\\t\\tfecha_actividad__year=fecha.year)\\\\\\n\\t\\t\\t.aggregate(suma = Sum('cantidad'))\\n\\n\\n\\t\\tresults.update(Alta.objects.filter(fecha_actividad__month=fecha.month,\\\\\\n\\t\\t\\tfecha_actividad__year=fecha.year)\\\\\\n\\t\\t\\t.aggregate(count = Count('cantidad')))\\n\\n\\t\\tprint(\\\"*************************\\\",results)\\n\\n\\n\\n\\t\\treturn results\",\n \"def editarpermiso():\\n if not current_user.is_authenticated():\\n flash('Debe loguearse primeramente!!!!', 'loggin')\\n return render_template('index.html')\\n\\n permission = UserRol('ADMINISTRADOR')\\n if permission.can():\\n p = db_session.query(Permiso).filter_by(codigo=request.args.get('codigo')).first()\\n form = PermisoFormulario(request.form,p)\\n permiso = db_session.query(Permiso).filter_by(codigo=form.codigo.data).first()\\n if request.method == 'POST':\\n if form.validate():\\n form.populate_obj(permiso)\\n db_session.merge(permiso)\\n db_session.commit()\\n flash('El permiso ha sido modificado con exito','info')\\n return redirect('/permiso/administrarpermiso')\\n else:\\n flash_errors(form)\\n else:\\n flash_errors(form)\\n return render_template('permiso/editarpermiso.html', form=form)\\n else:\\n flash('Sin permisos para modificar permisos', 'permiso')\\n return render_template('index.html')\",\n \"def editar_prod(self):\\n\\t codigo=self.ui.codigo_prod.text()\\n\\t\\tnombre=self.ui.nombre_prod.text()\\n\\t\\tdescripcion=self.ui.descripcion_prod.text()\\n\\t\\tmarca=self.ui.marca_prod.text()\\n\\t\\tcolor=self.ui.color_prod.text()\\n\\t\\tresultado=controller.editar_producto(codigo,nombre,descripcion,marca,color)\\n\\t\\tif resultado:\\n\\t\\t self.reject()\",\n \"def save_data(self):\\n pass\",\n \"def agregar_bolsa(self, letra, cantidad):\",\n \"def configurar():\\n try:\\n with open(f'archivos{ruta()}valores_puntajes.json',\\n encoding='UTF-8') as f:\\n dic = json.load(f)\\n except FileNotFoundError:\\n dic = crear_valores_puntajes()\\n with open(f'archivos{ruta()}valores_puntajes.json', 'w',\\n encoding='UTF-8') as f:\\n json.dump(dic, f, indent=4)\\n\\n letras = dic['Personalizada']['bolsa']\\n lista_letras= []\\n for elemento in letras:\\n lista_letras.append(elemento[0])\\n valor_letra = letras[0][1] \\n valor_punto = dic['Personalizada']['puntos_letra']['a']\\n layout=[\\n [sg.T(\\\"Perfil personalizado\\\", size=(17,1), justification = \\\"center\\\",\\n font=(\\\"Georgia\\\", 17))],\\n [sg.T(\\\"Letra\\\",font=(\\\"Georgia\\\", 12)),\\n sg.Combo(lista_letras, size=(8,1), default_value= 'a', enable_events=True),\\n sg.T(\\\"Cantidad:\\\", font=(\\\"Georgia\\\", 12),),\\n sg.Slider(range=(1, 12), orientation='h', size=(13, 20), default_value=valor_letra),\\n sg.T(\\\"Valor:\\\", font=(\\\"Georgia\\\", 12),),\\n sg.Slider(range=(1, 20), orientation='h', size=(20, 20), default_value=valor_punto)],\\n [sg.T(\\\"Dificultad de la IA: \\\"),\\n sg.DropDown(('Facil','Medio','Dificil'),\\n default_value=(dic['Personalizada']['dificultad_IA']),size=(10,1)),\\n sg.T(\\\"Dificultad del tablero: \\\"),\\n sg.DropDown(('Facil','Medio','Dificil'),\\n default_value=(dic['Personalizada']['dificultad_Tablero']),size=(10,1))],\\n [sg.Button(\\\"Guardar\\\", size=(12, 1), key=\\\"-guardar-\\\")]\\n ]\\n\\n window_configurar = sg.Window(\\\"ScrabbleAR - Configurar\\\").Layout(layout)\\n\\n while True:\\n event, values = window_configurar.Read()\\n \\\"\\\"\\\"\\n values[0] = retorna la letra elegida\\n values[1] = retorna la cantidad de letras\\n values[2] = retorna el puntaje seleccionado\\n values[3] = retorna la dificultad de la IA\\n values[4] = retorna la dificultad del tablero\\n \\\"\\\"\\\"\\n try:\\n for item in letras:\\n if item[0] == values[0]:\\n valor_letra = int(item[1])\\n window_configurar.find_element(1).Update(valor_letra)\\n valor_punto = dic['Personalizada']['puntos_letra'][values[0]]\\n window_configurar.find_element(2).Update(valor_punto)\\n except:\\n break\\n if event == None:\\n break\\n elif event == '-guardar-':\\n for item in letras:\\n if item[0] == values[0]:\\n item[1] = int(values[1])\\n break\\n dic['Personalizada']['bolsa'] = letras\\n dic['Personalizada']['puntos_letra'][values[0]] = int(values[2])\\n dic['Personalizada']['dificultad_IA'] = values[3]\\n dic['Personalizada']['dificultad_Tablero'] = values[4]\\n try:\\n with open(f'archivos{ruta()}valores_puntajes.json', 'w', encoding='UTF-8') as f:\\n json.dump(dic, f, indent=4)\\n sg.popup('Se guardaron los cambios', auto_close= True, auto_close_duration= 1)\\n except FileNotFoundError:\\n sg.popup('No se encuentra el archivo \\\"valores_puntajes.json\\\"')\\n window_configurar.Close()\",\n \"def save_model(self, request, obj, form, change):\\n\\n if form.is_valid():\\n cd = form.cleaned_data\\n obj.filename = cd['filename']\\n obj.society_code = cd['society_code']\\n obj.society_name = cd['society_name']\\n obj.date = cd['date']\\n # TODO move process() to model, and handle messages here\\n obj.report = self.process(\\n request, obj.society_code, cd['acknowledgement_file'])\\n obj.cwr = cd['acknowledgement_file']\\n super().save_model(request, obj, form, change)\",\n \"def edit(self, id, **kw):\\n r = validate_get(id)\\n r.archivos_guardar = \\\",\\\".join(r.archivos_a_guardar)\\n return dict(name=name, namepl=namepl, record=r, form=form)\",\n \"def on_btnBajaServicio_clicked(self, widget):\\n try:\\n codigoReservaServicio = variables.lblCodigoReservaServicio.get_text()\\n if(variables.codigoServicio!= \\\"\\\"):\\n funciones_servicios.eliminarServicio(variables.codigoServicio)\\n funciones_servicios.listadoServicio(variables.listServicios, codigoReservaServicio)\\n funciones_factura.listadoServicios(variables.listFactura,variables.codr,variables.lblHabitacionServicio.get_text())\\n funciones_factura.calcularPreciosServicios()\\n else:\\n variables.mensajeError = \\\"Debes seleccionar un servicio\\\"\\n variables.vError.show()\\n\\n\\n except:\\n print (\\\"error baja servicio\\\")\",\n \"def form_valid(self, form):\\n self.instance = form.save()[0].prueba\\n self.instance.evaluar()\\n self.instance.save()\\n return super(PresentarView, self).form_valid(form)\",\n \"def form_valid(self, form):\\n with create_revision():\\n form_data = form.save(commit=False)\\n bom_name = form.cleaned_data['bom_name']\\n warehouse_name = form.cleaned_data['warehouse_name']\\n initial_parts = form.initial.get('parts')\\n updated_parts = form.cleaned_data.get('parts')\\n # Using sets to find newly added parts by using set diff\\n\\n # Forming sets only with PartNumbers instead of PartsDict\\n # to make comparisons easy\\n initial_partNumbers = {\\n part['PartNumber'] for part in initial_parts\\n }\\n updated_partNumbers = {\\n part['PartNumber'] for part in updated_parts\\n }\\n newly_added_partNumbers = updated_partNumbers - initial_partNumbers\\n removed_partNumbers = initial_partNumbers - updated_partNumbers\\n\\n # Reforming part dicts to access part attributes easily.\\n newly_added_parts = [\\n part for part in updated_parts\\n if part['PartNumber'] in newly_added_partNumbers\\n ]\\n form_data.total_price = cal_total_price(bom_name)\\n # Create outwards for parts used in asset creation.\\n for part in newly_added_parts:\\n part_obj = Parts.objects.get(part_number=part['PartNumber'])\\n authorised_by = \\\"asset_auto_creation\\\"\\n verified_by = \\\"asset_auto_creation\\\"\\n mode_of_transport = \\\"asset_auto_creation\\\"\\n remarks = \\\"This outward was created due to asset creation.\\\"\\n\\n Outward.objects.create(\\n part_type=part_obj.part_type,\\n part_number=part_obj,\\n part_name=part_obj.part_name,\\n quantity=part['Qty'],\\n from_warehouse_name=warehouse_name,\\n to_warehouse_name=warehouse_name,\\n authorised_by=authorised_by,\\n verified_by=verified_by,\\n mode_of_transport=mode_of_transport,\\n unit_of_measure=part_obj.unit_of_measure,\\n remarks=remarks,\\n )\\n\\n form_data.save()\\n revisions.set_user(self.request.user)\\n revisions.set_comment(\\\"Updated from web form.\\\")\\n return_value = super().form_valid(form_data)\\n\\n return return_value\",\n \"def form_valid(self, form, factura_form, remito_form, ot_linea_form):\\n self.object = form.save()\\n factura_form.instance = self.object\\n factura_form.save()\\n remito_form.instance = self.object\\n remito_form.save()\\n ot_linea_form.instance = self.object\\n ot_linea_form.save()\\n return HttpResponseRedirect(self.get_success_url())\",\n \"def uploaddata(self):\\n\\t\\tself.filename = filedialog.askopenfilename(filetypes=[('CSV', '*.csv')],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t initialdir='./Tariffs')\\n\\n\\t\\tif self.filename: # don't try to import anything if filename is empty\\n\\t\\t\\tself.data = pd.read_csv(self.filename)\\n\\t\\t\\tself.validate_tariff() # TODO: build out contingency when validation fails.\\n\\t\\t\\tself.plot_tariff()\\n\\t\\t\\tself.uploadsuccess = True\\n\\t\\t\\tself.controller.writetolog('Uploaded tariff file ' + self.filename)\\n\\t\\telse:\\n\\t\\t\\tself.uploadsuccess = False\\n\\t\\t\\tself.controller.writetolog('Failed to upload tariff file')\\n\\n\\t\\tself.dynamicupload()\",\n \"def cria_receita(request):\\n if request.user.is_authenticated:\\n if request.method == 'POST':\\n nome_receita = request.POST['nome_receita']\\n ingredientes = request.POST['ingredientes']\\n modo_preparo = request.POST['modo_preparo']\\n tempo_preparo = request.POST['tempo_preparo']\\n rendimento = request.POST['rendimento']\\n categoria = request.POST['categoria']\\n foto_receita = request.FILES['foto_receita']#por se tratar de um dado do tipo file\\n user = get_object_or_404(User, pk=request.user.id)\\n\\n if campo_vazio(nome_receita):\\n messages.error(request, 'Nome da receita é obrigatório')\\n return redirect('cria_receita')\\n\\n if campo_vazio(ingredientes):\\n messages.error(request, 'ingredientes são obrigatórios')\\n return redirect('cria_receita')\\n\\n if campo_vazio(modo_preparo):\\n messages.error(request, 'Modo de preparo é obrigatório')\\n return redirect('cria_receita')\\n\\n if campo_vazio(tempo_preparo):\\n messages.error(request, 'tempo de preparo é obrigatório')\\n return redirect('cria_receita')\\n\\n if campo_vazio(rendimento):\\n messages.error(request, 'rendimento é obrigatórios')\\n return redirect('cria_receita')\\n\\n if campo_vazio(categoria):\\n messages.error(request, 'categoria é obrigatório')\\n return redirect('cria_receita')\\n\\n receita = Receita.objects.create(\\n pessoa=user,\\n nome_receita=nome_receita,\\n ingredientes=ingredientes,\\n modo_preparo=modo_preparo,\\n tempo_preparo=tempo_preparo,\\n rendimento=rendimento,\\n categoria=categoria,\\n publicado=False,\\n foto_receita=foto_receita\\n )\\n receita.save()\\n return redirect('dashboard')\\n else:\\n return render(request, 'cria_receita.html')\\n else:\\n return redirect('index')\",\n \"def recarregar_imagem(self):\\n self.salvar_imagem(caminho_imagem=self.caminho_temp)\\n self.carregar_imagem(caminho_imagem=self.caminho_temp)\",\n \"def update_geonet_data():\\n rs = TremorData()\\n rs.update()\",\n \"def saveData(self):\\n pass\",\n \"def _rebuildOcean(self, tk, templateFile = '', fields = '', id = '', shotNum = '', inprogressBar = ''):\\r\\n\\t\\tdebug(app = self.app, method = '_rebuildOcean', message = 'Deleting current Ocean now....', verbose = False)\\r\\n\\t\\tself._removeOcean()\\r\\n\\r\\n\\t\\t## Get all the publishes from shotgun now for the blocking or animation step as required..\\r\\n\\t\\tif self.app.get_setting('shotStep') == 'Blocking':\\r\\n\\t\\t\\tgetAnimVersionFolders = tk.paths_from_template(templateFile, {'Step' : 'Blck', 'id' : id, 'Shot' : shotNum})\\r\\n\\t\\telif self.app.get_setting('shotStep') == 'Animation':\\r\\n\\t\\t\\tgetAnimVersionFolders = tk.paths_from_template(templateFile, {'Step' : 'Anm', 'id' : id, 'Shot' : shotNum})\\r\\n\\r\\n\\t\\tdebug(app = self.app, method = '_rebuildOcean', message = 'getAnimVersionFolders...%s' % getAnimVersionFolders, verbose = False)\\r\\n\\r\\n\\t\\t## Fix path for maya to read in properly\\r\\n\\t\\tif getAnimVersionFolders:\\r\\n\\t\\t\\t## now find the highest version folder number\\r\\n\\t\\t\\thighestVersionFolder = r'%s' % max(getAnimVersionFolders)\\r\\n\\t\\t\\tdebug(app = self.app, method = '_rebuildOcean', message = 'highestVersionFolder...%s' % highestVersionFolder, verbose = False)\\r\\n\\r\\n\\t\\t\\thighestVersionFolder = highestVersionFolder.replace('\\\\\\\\', '/')\\r\\n\\t\\t\\tdebug(app = self.app, method = '_rebuildOcean', message = 'highestVersionFolder replaced \\\\\\\\ with /...\\\\n%s' % highestVersionFolder, verbose = False)\\r\\n\\r\\n\\t\\t\\tversionNumber = highestVersionFolder.split('/')[-1]\\r\\n\\t\\t\\tdebug(app = self.app, method = '_rebuildOcean', message = 'versionNumber: %s' % versionNumber, verbose = False)\\r\\n\\r\\n\\t\\t\\tgetCacheFiles = os.listdir(highestVersionFolder)\\r\\n\\t\\t\\tdebug(app = self.app, method = '_rebuildOcean', message = 'getCacheFiles...\\\\n%s' % getCacheFiles, verbose = False)\\r\\n\\t\\t\\t##################################################################################################################\\r\\n\\t\\t\\t## APPLY OCEAN PRESET TO A NEW OCEAN\\r\\n\\t\\t\\tif 'publish/fx' in highestVersionFolder: ## The ocean preset.mel is saved out to the fx folder in the animation/publish/fx folders\\r\\n\\t\\t\\t\\tdebug(app = self.app, method = '_rebuildOcean', message = 'Loading ocean files now...', verbose = False)\\r\\n\\r\\n\\t\\t\\t\\t## Build the group if it doesn't already exist\\r\\n\\t\\t\\t\\tself._buildGroup('FX_CACHES_hrc', versionNumber)\\r\\n\\r\\n\\t\\t\\t\\t## No maya ocean in scene? Build a new one...\\r\\n\\t\\t\\t\\tif not cmds.objExists('oceanShader1'):\\r\\n\\t\\t\\t\\t\\tmel.eval(\\\"CreateOcean;\\\")\\r\\n\\r\\n\\t\\t\\t\\t\\t## Clean up the names\\r\\n\\t\\t\\t\\t\\t## NOTE we are not renaming the actual OCEAN SHADER here because in Marks scripts we are reconnecting like so\\r\\n\\t\\t\\t\\t\\t## cmds.disconnectAttr('oceanShader1.outColor', 'oceanShader1SG.surfaceShader')\\r\\n\\t\\t\\t\\t\\theightField = cmds.listRelatives(cmds.ls(selection = True)[0], parent = True, fullPath = True)[0]\\r\\n\\t\\t\\t\\t\\tcmds.rename(heightField, 'oceanPreviewPlane_prv')\\r\\n\\t\\t\\t\\t\\tcmds.rename('oceanPlane1', 'ocean_srf')\\r\\n\\t\\t\\t\\t\\tcmds.rename('oceanPreviewPlane1', 'oceanPreviewPlane_heightF')\\r\\n\\t\\t\\t\\t\\tcmds.parent('ocean_srf', 'FX_CACHES_hrc')\\r\\n\\t\\t\\t\\t\\tcmds.parent('oceanPreviewPlane_prv', 'FX_CACHES_hrc')\\r\\n\\t\\t\\t\\t\\tcmds.rename('oceanShader1', CONST.OCEANDISPSHADER)\\r\\n\\t\\t\\t\\t\\tcmds.rename('oceanShader1SG', '%sSG' % CONST.OCEANDISPSHADER)\\r\\n\\r\\n\\t\\t\\t\\t\\t## Lock translateY and rotation so that monkeys don't mess up with the ocean level\\r\\n\\t\\t\\t\\t\\ttoLock = ['translateY', 'rotateX', 'rotateY', 'rotateZ']\\r\\n\\t\\t\\t\\t\\tfor attr in toLock:\\r\\n\\t\\t\\t\\t\\t\\tcmds.setAttr('ocean_srf.%s' % attr, lock = True)\\r\\n\\r\\n\\t\\t\\t\\t\\t## Hard set the render tesselation\\r\\n\\t\\t\\t\\t\\tcmds.setAttr(\\\"ocean_srfShape.numberU\\\", 200)\\r\\n\\t\\t\\t\\t\\tcmds.setAttr(\\\"ocean_srfShape.numberV\\\", 200)\\r\\n\\r\\n\\t\\t\\t\\t\\tallOceans = cmds.ls(type= 'oceanShader')\\r\\n\\t\\t\\t\\t\\tdebug(app = self.app, method = '_rebuildOcean', message = 'allOceans: %s' % allOceans, verbose = False)\\r\\n\\r\\n\\t\\t\\t\\t\\t## now put the ocean in the right position\\r\\n\\t\\t\\t\\t\\tif cmds.objExists('fluids_hrc'):\\r\\n\\t\\t\\t\\t\\t\\tself._setOceanLocation()\\r\\n\\t\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t\\tif cmds.objExists('ocean_srf') and cmds.objExists('oceanPreviewPlane_prv'):\\r\\n\\t\\t\\t\\t\\t\\t\\tif not cmds.isConnected('oceanPreviewPlane_prv.translateX', 'ocean_srf.translateX'):\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tcmds.connectAttr('oceanPreviewPlane_prv.translateX', 'ocean_srf.translateX', f = True)\\r\\n\\t\\t\\t\\t\\t\\t\\tif not cmds.isConnected('oceanPreviewPlane_prv.translateZ', 'ocean_srf.translateZ'):\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tcmds.connectAttr('oceanPreviewPlane_prv.translateZ', 'ocean_srf.translateZ', f = True)\\r\\n\\t\\t\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\t\\t\\tcmds.warning('MISSING ocean_srf or oceanPreviewPlane_prv node from scene...')\\r\\n\\r\\n\\t\\t\\t\\tfor each in getCacheFiles:\\r\\n\\t\\t\\t\\t\\t## Now attach the preset to the maya ocean build a new one if it doesn't already exist\\r\\n\\t\\t\\t\\t\\tif each.endswith('.mel'): ## the ocean shader preset saved out\\r\\n\\t\\t\\t\\t\\t\\tpathToPreset = '%s/%s' % (highestVersionFolder, each)\\r\\n# presetVersion = each.split('/')\\r\\n\\r\\n\\t\\t\\t\\t\\t\\t## Now apply the exported preset to each ocean in the scene. There really should only be ONE\\r\\n\\t\\t\\t\\t\\t\\tfor eachOcean in allOceans:\\r\\n\\t\\t\\t\\t\\t\\t\\tdebug(app = self.app, method = '_rebuildOcean', message = 'Add version number for ocean preset on Ocean_Hrc', verbose = False)\\r\\n\\t\\t\\t\\t\\t\\t\\tif cmds.objExists('FX_CACHES_hrc'):\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tif not cmds.objExists('FX_CACHES_hrc.presetVersion'):\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tcmds.addAttr('FX_CACHES_hrc', ln='presetVersion', dt='string')\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tcmds.setAttr('FX_CACHES_hrc.presetVersion' , versionNumber, type='string')\\r\\n\\t\\t\\t\\t\\t\\t\\tdebug(app = self.app, method = '_rebuildOcean', message = 'Applying ocean preset: %s to %s' % (pathToPreset, eachOcean), verbose = False)\\r\\n\\t\\t\\t\\t\\t\\t\\tmel.eval( 'applyPresetToNode \\\"%s\\\" \\\"\\\" \\\"\\\" \\\"%s\\\" 1;' %(eachOcean, pathToPreset) )\\r\\n\\t\\t\\t\\t\\t\\t\\tdebug(app = self.app, method = '_rebuildOcean', message = 'Ocean and presets assigned successfully...', verbose = False)\\r\\n\\r\\n\\t\\t\\t\\t\\t## Import ATOM\\r\\n\\t\\t\\t\\t\\tif each.endswith('.atom'):\\r\\n\\t\\t\\t\\t\\t\\tpathToPreset = '%s/%s' % (highestVersionFolder, each)\\r\\n\\t\\t\\t\\t\\t\\tdebug(app = self.app, method = '_rebuildOcean', message = 'Transferring ocean shader\\\\'s animation from %s' % pathToPreset, verbose = False)\\r\\n\\r\\n\\t\\t\\t\\t\\t\\tmel.eval('performImportAnim 1;')\\r\\n\\t\\t\\t\\t\\t\\tcmds.select(allOceans, replace = True)\\r\\n\\r\\n\\t\\t\\t\\t\\t\\tcmds.file(pathToPreset,\\r\\n\\t\\t\\t\\t\\t\\ttype = 'atomImport',\\r\\n\\t\\t\\t\\t\\t\\ti = True,\\r\\n\\t\\t\\t\\t\\t\\tra = True,\\r\\n\\t\\t\\t\\t\\t\\tnamespace = 'oceanDisp_shader',\\r\\n\\t\\t\\t\\t\\t\\toptions = '; ; targetTime = 3; option = insert; match = hierarchy; selected = selectedOnly; search = ; replace = ; prefix = ; suffix = ; mapFile = ;',\\r\\n\\t\\t\\t\\t\\t\\t)\\r\\n\\r\\n\\t\\t\\t\\t\\t\\tif cmds.window('OptionBoxWindow', exists = True):\\r\\n\\t\\t\\t\\t\\t\\t\\tcmds.deleteUI('OptionBoxWindow', window = True)\\r\\n\\r\\n\\t\\t\\t\\t\\t\\tdebug(app = self.app, method = '_rebuildOcean', message = 'Ocean shader\\\\'s animation transferred successfully.', verbose = False)\\r\\n\\r\\n\\t\\t\\t\\tdebug(app = self.app, method = '_rebuildOcean', message = 'Ocean imported successfully...', verbose = False)\",\n \"def save(self):\\n\\n pass\",\n \"def test_e_editar_atributo(self): \\n print '+++ Edicion de atributo +++'\\n request = self._editar_atributo(nombre, 'descrip editada','String')\\n print '*-- datos de prueba ::: ' + nombre+', '+ 'descrip editada' + ', ' + str(id_tipo_atributo) +' --*'\\n self.assertNotIn('Debe loguearse primeramente!!!!', request.data, 'No se ha logueado correctamente')\\n self.assertNotIn('No posee los permisos suficientes para realizar la operacion', request.data, 'No tiene permisos para editar atributos')\\n self.assertNotIn('Error', request.data, 'Tiene errores el form')\\n self.assertIn('El atributo ha sido editado con exito', request.data, 'Error al editar la atributo')\\n print '*-- request result: ' + request._status + ' --*'\\n self.assertIn(nombre, request.data, 'El atributo creado no se encuentra en la tabla')\\n print '*-- '+nombre+' creado correctamente, aparece en la tabla de atributos--*'\\n print '*---test 5 atributo---*'\",\n \"def _process(self):\\n self.output[\\\"data\\\"] = get_form_data(self.kwargs[\\\"collect\\\"].ona_scan_form_pk)\",\n \"def modificacion(self, socio):\\n\\n aux = self.buscar(socio.id)\\n print('El socio a modificar en capa de datos:', aux.id, aux.nombre)\\n\\n if aux == None:\\n return False\\n else:\\n #persona = session.query(Socio).filter(Socio.dni == aux.id)\\n aux.nombre = socio.nombre\\n aux.apellido = socio.apellido\\n aux.dni = socio.dni\\n\\n session.commit()\\n\\n return aux\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63073975","0.62418467","0.59441423","0.5581971","0.5581573","0.55443275","0.550863","0.5449518","0.5426941","0.5420971","0.53737736","0.5362197","0.5340136","0.5321031","0.5316212","0.5314762","0.5310911","0.52641654","0.5248064","0.5235256","0.52185756","0.520786","0.51849943","0.5180972","0.5176461","0.51633745","0.5130371","0.51218575","0.5121614","0.51104176","0.5109217","0.5098305","0.5088389","0.5085578","0.5083518","0.5070142","0.5058826","0.5056736","0.5054686","0.50493693","0.5045992","0.50371385","0.5030971","0.5023413","0.5022722","0.502223","0.49938607","0.49935025","0.49906018","0.49880153","0.49857917","0.4985791","0.49829873","0.49649328","0.4963716","0.49611717","0.4960378","0.49553782","0.49490452","0.49475458","0.49473268","0.49344411","0.49290982","0.492505","0.491386","0.49106976","0.49106976","0.49106976","0.49106976","0.49106976","0.4905012","0.48967192","0.48942995","0.48915765","0.48897374","0.48824176","0.4882315","0.48812273","0.4881127","0.487969","0.48753667","0.4872709","0.48659822","0.48618823","0.48592713","0.4855463","0.48500285","0.48490396","0.48478815","0.4843182","0.48421255","0.48307475","0.48299626","0.4823069","0.482121","0.48155427","0.47980177","0.47960487","0.4795877","0.4791763","0.47881576"],"string":"[\n \"0.63073975\",\n \"0.62418467\",\n \"0.59441423\",\n \"0.5581971\",\n \"0.5581573\",\n \"0.55443275\",\n \"0.550863\",\n \"0.5449518\",\n \"0.5426941\",\n \"0.5420971\",\n \"0.53737736\",\n \"0.5362197\",\n \"0.5340136\",\n \"0.5321031\",\n \"0.5316212\",\n \"0.5314762\",\n \"0.5310911\",\n \"0.52641654\",\n \"0.5248064\",\n \"0.5235256\",\n \"0.52185756\",\n \"0.520786\",\n \"0.51849943\",\n \"0.5180972\",\n \"0.5176461\",\n \"0.51633745\",\n \"0.5130371\",\n \"0.51218575\",\n \"0.5121614\",\n \"0.51104176\",\n \"0.5109217\",\n \"0.5098305\",\n \"0.5088389\",\n \"0.5085578\",\n \"0.5083518\",\n \"0.5070142\",\n \"0.5058826\",\n \"0.5056736\",\n \"0.5054686\",\n \"0.50493693\",\n \"0.5045992\",\n \"0.50371385\",\n \"0.5030971\",\n \"0.5023413\",\n \"0.5022722\",\n \"0.502223\",\n \"0.49938607\",\n \"0.49935025\",\n \"0.49906018\",\n \"0.49880153\",\n \"0.49857917\",\n \"0.4985791\",\n \"0.49829873\",\n \"0.49649328\",\n \"0.4963716\",\n \"0.49611717\",\n \"0.4960378\",\n \"0.49553782\",\n \"0.49490452\",\n \"0.49475458\",\n \"0.49473268\",\n \"0.49344411\",\n \"0.49290982\",\n \"0.492505\",\n \"0.491386\",\n \"0.49106976\",\n \"0.49106976\",\n \"0.49106976\",\n \"0.49106976\",\n \"0.49106976\",\n \"0.4905012\",\n \"0.48967192\",\n \"0.48942995\",\n \"0.48915765\",\n \"0.48897374\",\n \"0.48824176\",\n \"0.4882315\",\n \"0.48812273\",\n \"0.4881127\",\n \"0.487969\",\n \"0.48753667\",\n \"0.4872709\",\n \"0.48659822\",\n \"0.48618823\",\n \"0.48592713\",\n \"0.4855463\",\n \"0.48500285\",\n \"0.48490396\",\n \"0.48478815\",\n \"0.4843182\",\n \"0.48421255\",\n \"0.48307475\",\n \"0.48299626\",\n \"0.4823069\",\n \"0.482121\",\n \"0.48155427\",\n \"0.47980177\",\n \"0.47960487\",\n \"0.4795877\",\n \"0.4791763\",\n \"0.47881576\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76670,"cells":{"query":{"kind":"string","value":"Permite elegir un dato de la base para cambiar o borrar"},"document":{"kind":"string","value":"def selectTree(self, event):\n item = self.verDatos.selection()\n self.idInteger.set(self.verDatos.item(item)['values'][0]) \n self.tituloString.set(self.verDatos.item(item)['values'][1]) \n self.descripcionString.set(self.verDatos.item(item)['values'][2])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def SaveData(self, conn):\n t = time.process_time()\n log = Logger()\n cursor = conn.cursor()\n sqlQuery = \"insert into old_data \" \\\n \"select * from new_data\"\n cursor.execute(sqlQuery)\n conn.commit()\n conn.close()\n elapsed_t = time.process_time() - t\n log.log('Datos de cambios copiados a tabla', elapsed_t)","def restore_data(self):\n self.R = self._Ro\n del self._Ro","def datamunge(cls, data):\n pass","def lee_base_covid(fecha):\n zipname = \"data/\"+fecha+\"COVID19MEXICO.zip\"\n filename = fecha+\"COVID19MEXICO.csv\"\n\n try:\n start_time = time()\n zipfile = ZipFile(zipname)\n df = pd.read_csv(zipfile.open(filename), parse_dates=True)\n end_time = time()\n total_time = end_time - start_time\n except FileNotFoundError:\n print(\"La base de datos no se encuentra en la carpeta.\")\n\n print(f\"La base de datos tardó en cargarse {total_time} segundos\")\n return df","def migrar(self):\r\n start = time.time()\r\n if not os.path.exists('output'):\r\n os.makedirs('output')\r\n # seleciona as tabelas\r\n res = self.cur_origem.execute(\r\n \"select rdb$relation_name from rdb$relations where rdb$view_blr is null and (rdb$system_flag is null or rdb$system_flag = 0) %s;\" % self.filtro)\r\n\r\n # para cada tabela\r\n for row, in res.fetchall():\r\n row = row.strip()\r\n\r\n # conta os registros\r\n countsql = self.cur_origem.execute(\r\n \"select count(*) as total from %s \" % row)\r\n count, = countsql.fetchall()[0]\r\n start_time = time.time()\r\n start_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(\"MIGRANDO: %s\\n NRO REGISTROS: %s registros\\n INICIO: %s\" % (\r\n row, count, start_datetime))\r\n\r\n # gera o create table e trunca a tabela ( se ja existir )\r\n create, tipos = self.ddl_table(row)\r\n self.cur_destino.execute(create)\r\n self.cur_destino.execute(\"TRUNCATE TABLE %s\" % row)\r\n\r\n # busca os dados\r\n self.cur_origem.execute(\"select * from %s \" % (row))\r\n\r\n # grava os dados no TXT\r\n with open(\"output/%s.txt\" % row, \"wb\") as f:\r\n writer = csv.writer(f, delimiter='|')\r\n writer.writerows(self.cur_origem.fetchall())\r\n\r\n # le o arquivo gravado e copia para o banco destino\r\n with open(\"output/%s.txt\" % row, \"r\") as f:\r\n try:\r\n self.cur_destino.copy_expert(\r\n \"\"\"COPY %s FROM STDIN WITH QUOTE '\"' DELIMITER '|' NULL '' CSV \"\"\" % row, f)\r\n except Exception as e:\r\n self.erros.append([\"%s\" % row, e])\r\n end_time = time.time()\r\n end_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(\" FIM: %s\\n TEMPO: %ss\\n TABELA COM ERRO %s\" %\r\n (end_datetime, round(end_time-start_time, 0), e))\r\n else:\r\n end_time = time.time()\r\n end_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(\" FIM: %s\\n TEMPO: %ss\\n OK\" %\r\n (end_datetime, round(end_time-start_time, 0)))\r\n end = time.time()\r\n print(\"TEMPO GASTO: %s s\" % (end-start))","def D_Base_to_Exel(self):\n# for item in sorted(self.dbase.keys()): # for every key/cell add to a dataFRAME\n# self.dataFRAME[item]=self.dbase[item]\n \n self.dataFRAME = self.Dbase_to_DF()\n writer = ExcelWriter(self.path+'/ALLwells'+ self.filetype) # assign a path for the file\n self.dataFRAME.to_excel(writer, 'Sheet1') # create a file in the same path the original files came from\n writer.save()","def cargar_otras(self):\n\n stream_cargar = open ('yo_otros.txt', 'rt',encoding=\"utf-8\")\n datos=stream_cargar.readlines()\n \n # print(datos)\n # print (len(kasino.maquinas))\n\n lista_maquinas=[]\n lista_deco =[]\n day=\"\"\n money=\"\"\n\n contador=0\n dia_o_dinero=\"dia\"\n\n for i in datos[0]:\n # print(contador,i)\n if contador <8:\n lista_maquinas.append(i)\n contador+=1\n\n elif contador <17:\n lista_deco.append(i)\n contador+=1\n\n\n elif contador >= 17 and dia_o_dinero ==\"dia\":\n if i ==\"D\":\n pass\n elif i ==\"M\":\n dia_o_dinero=\"dinero\"\n else:\n day+=i\n elif contador >= 17 and dia_o_dinero == \"dinero\":\n money+=i\n \n \n\n # print(\"lm\",lista_maquinas)\n # print (\"ld\",lista_deco)\n # print(day,money)\n\n contador=0\n for i in kasino.maquinas:\n kasino.maquinas[i]=int(lista_maquinas[contador])\n contador+=1\n\n contador=0\n for i in kasino.decoracion:\n kasino.decoracion[i]=int(lista_deco[contador])\n contador+=1\n\n kasino.dia=int( day)\n kasino.dinero=int(money)","def uvozi_filme(cur):\n cur.execute(\"DELETE FROM narocila;\")\n with open('podatki/narocila.csv') as datoteka:\n podatki = csv.reader(datoteka)\n stolpci = next(podatki)\n poizvedba = \"\"\"\n INSERT INTO narocila VALUES ({})\n \"\"\".format(', '.join([\"?\"] * len(stolpci))) #---------zakaj tukaj vprašaji???-------------\n for vrstica in podatki:\n cur.execute(poizvedba, vrstica)","def loaddata(self):\n # Connect to the db\n self.conn, self.c = self.connect_db(self.dbname)\n # create the bdefile table to \n self.c.execute(oeeutil.sql_create_bdefile_table)\n # Delete any previous records\n self.c.execute('DELETE FROM bdefile')\n # hold the content for analysis\n for item in self.content:\n self.c.execute('INSERT INTO bdefile VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?)', item)\n self.c.executescript(oeeutil.sql_create_bdefile_view)\n self.conn.commit()","def formulate(self):\n # initialize new empty file\n draft_win = np.empty([])\n f = open('draft_win.txt','wb') \n pickle.dump(draft_win,f)\n f.close()\n # read lineup and outcome from target_mmr\n target_mmr = pd.read_csv('target_mmr.csv')\n draft_win_df = target_mmr.loc[:, ['radiant_team', 'dire_team', 'radiant_win']]\n # convert into numpy array\n # Radiant\n draft_win_df_r = pd.DataFrame(draft_win_df.radiant_team.str.split(',',4).tolist(), columns = ['r1','r2','r3','r4','r5'])\n # Dire\n draft_win_df_d = pd.DataFrame(draft_win_df.dire_team.str.split(',',4).tolist(), columns = ['d1','d2','d3','d4','d5'])\n # outcome\n draft_win_df_w = draft_win_df.radiant_win\n # join\n draft_win_df = draft_win_df_r.join(draft_win_df_d).join(draft_win_df_w)\n # save into numpy array\n draft_win_np = draft_win_df.values\n # str to int\n draft_win_np = draft_win_np.astype(np.int)\n # sort lineup (hero id)\n draft_win_np[:, :5].sort()\n draft_win_np[:, 5:10].sort()\n print(draft_win_np.shape)\n # augment data by swap the lineup\n augmented = draft_win_np.copy()\n augmented[:, :5] = draft_win_np[:, 5:10]\n augmented[:, 5:10] = draft_win_np[:, :5]\n augmented[:, -1] = 1 - draft_win_np[:, -1]\n draft_win_np_augmented = np.concatenate((draft_win_np, augmented), axis=0)\n # pickle\n f = open('draft_win.txt','wb') \n pickle.dump(draft_win_np_augmented,f)\n f.close()\n # read pickle numpy array\n f = open('draft_win.txt','rb') \n draft_win = pickle.load(f) \n f.close()\n print('draft_win shape:\\n', draft_win.shape)\n print('draft_win:\\n', draft_win[0:2])","def archivos_de_texto():\n palabra = \"\" \n palabras_candidatas = [] #lista donde se guardara las palabras candidatas de cada linea\n palabra_cantidad = {} #diccionario con la palabra candidata de clave y las veces que esta repetida en cada texto de valor\n with open(\"Cuentos.txt\",\"r\") as Cuentos: \n for linea_Cuentos in Cuentos: #cada ciclo del for es una linea del texto\n for caracter in linea_Cuentos: #cada ciclo del for es una caracter de la linea \n if caracter.isalpha():\n caracter = quitar_tilde_y_may(caracter) #se transformas caracteres mayusculas y tildes\n palabra += caracter #cada caracter ira formando la palabra\n if not caracter.isalpha():\n if len(palabra) >= 5: #se analiza que la palabra tenga 5 o mas caracteres\n palabras_candidatas.append(palabra) \n palabra = \"\" #se vacia la palabra ya analizada\n for palabra_en_lista in palabras_candidatas: #se introduce las palabras candidatas a un diccionario\n if palabra_en_lista not in palabra_cantidad:\n palabra_cantidad[palabra_en_lista] = [1,0,0]\n else:\n palabra_cantidad[palabra_en_lista] = [int(palabra_cantidad[palabra_en_lista][0]) + 1 , 0, 0]\n palabras_candidatas = []\n with open(\"La araña negra - tomo 1.txt\",\"r\") as La_arana_negra:#se repite el mismo proceso con los otros dos textos\n for linea_Cuentos in La_arana_negra:\n for caracter in linea_Cuentos:\n if caracter.isalpha():\n caracter = quitar_tilde_y_may(caracter)\n palabra += caracter\n if not caracter.isalpha():\n if len(palabra) >= 5:\n palabras_candidatas.append(palabra)\n palabra = \"\"\n for palabra_en_lista in palabras_candidatas:\n if palabra_en_lista not in palabra_cantidad:\n palabra_cantidad[palabra_en_lista] = [0,1,0]\n else:\n palabra_cantidad[palabra_en_lista] = [palabra_cantidad[palabra_en_lista][0] , int(palabra_cantidad[palabra_en_lista][1]) + 1, 0]\n palabras_candidatas = [] \n with open(\"Las 1000 Noches y 1 Noche.txt\",\"r\") as muchas_noches: \n for linea_Cuentos in muchas_noches:\n for caracter in linea_Cuentos:\n if caracter.isalpha():\n caracter = quitar_tilde_y_may(caracter)\n palabra += caracter\n if not caracter.isalpha():\n if len(palabra) >= 5:\n palabras_candidatas.append(palabra)\n palabra = \"\"\n for palabra_en_lista in palabras_candidatas:\n if palabra_en_lista not in palabra_cantidad:\n palabra_cantidad[palabra_en_lista] = [0,0,1]\n else:\n palabra_cantidad[palabra_en_lista] = [palabra_cantidad[palabra_en_lista][0] ,palabra_cantidad[palabra_en_lista][1], int(palabra_cantidad[palabra_en_lista][2]) + 1]\n palabras_candidatas = [] \n palabra_cantidad = dict(sorted(palabra_cantidad.items())) #se ordena el diccionario alfabeticamente\n with open(\"palabras.csv\",\"w\") as palabras_csv: # se agrga el diccionario a un arcivo .csv\n for palabra in palabra_cantidad:\n palabras_csv.write(palabra)\n palabras_csv.write(\",\")\n palabras_csv.write(str(palabra_cantidad[palabra][0]))\n palabras_csv.write(\",\")\n palabras_csv.write(str(palabra_cantidad[palabra][1]))\n palabras_csv.write(\",\")\n palabras_csv.write(str(palabra_cantidad[palabra][2]))\n palabras_csv.write(\"\\n\")\n return palabra_cantidad","def actualizarLBI(produ,datInv,cant): # Esta sección fue hecha por Valeria \n datInv[produ][2] -= cant\n return datInv","def caricaDataSet(self):\n # salvataggio del percorso della cartella contenente i dataset\n currentDir = os.path.dirname(os.path.abspath(__file__))\n pathDataset = os.path.join(currentDir, 'dataset')\n # permetto il solo caricamento di file csv\n filter = \"csv(*.csv)\"\n try:\n # apro la schermata di caricamento\n nomeFile = QFileDialog.getOpenFileName(None,None,pathDataset,filter)\n percorso = nomeFile[0]\n datafilename = percorso\n d = ','\n f = open(datafilename, 'r')\n reader = csv.reader(f, delimiter=d)\n ncol = len(next(reader))\n if(ncol != 5):\n raise ValueError\n\n except:\n messaggio = 'err: apertura file'\n self.ui.comunicazione.setText(messaggio)\n\n # controllo che sia stato caricato il dataset\n try:\n self.__dataSet = percorso\n\n if(len(self.__dataSet) == 0):\n messaggio = 'err: dataSet non caricato'\n raise NameError\n except:\n # mostro il messaggio di errore\n self.ui.comunicazione.setText(messaggio)","def descompactar(caminho, nome, formato):\n\n # Abre e le o arquivo\n arquivo = Arquivo(caminho, nome, formato)\n cabecalho_compactado = arquivo.ler()\n\n #----------------------------------------\n\n # Descompactacao do cabecalho por meio do gzip\n cabecalho = g.decompress(cabecalho_compactado)\n\n #----------------------------------------\n\n # Cabecalho\n numero_magico = int.from_bytes(cabecalho[0:1], byteorder=\"big\") \n if numero_magico != 42:\n exit()\n\n largura = int.from_bytes(cabecalho[1:3], byteorder=\"big\")\n altura = int.from_bytes(cabecalho[3:5], byteorder=\"big\")\n quantidade_cores = int.from_bytes(cabecalho[5:6], byteorder=\"big\")\n lista = list(cabecalho[6:262])\n conteudo = list(cabecalho[262:len(cabecalho)])\n\n #----------------------------------------\n \n # Amplia para 8 bits\n bit = Bitwise(6)\n imagem_rotulada = bit.ampliar(conteudo, largura*altura)\n\n #----------------------------------------\n\n # Cria a tabela\n matriz = [[lista[y], lista[y+1], lista[y+2], lista[y+3]] for y in range(0, len(lista), 4)]\n tabela = Tabela(matriz)\n\n #----------------------------------------\n\n # Cria um objeto da classe Imagem\n final = Imagem(caminho, nome+\"_som\", \"TIFF\")\n # Salva a imagem\n final.salvar(largura, altura, imagem_rotulada, tabela)","def make_data_raw (mdp,do_makedata,filename):\n #\n fin = open(filename,'r')\n for line in fin:\n lsp = line.split(' ')\n if len(lsp) > 1: # skip empty lines\n if lsp[0] == \"for\": # indicates when to get correlator\n lsp.pop(0)\n update_params(mdp,lsp)\n ## -- do_makedata tells it to go ahead with generating a new data output file\n ## -- otherwise, just saves parameters to metadata\n if do_makedata:\n try:\n # open correlator file\n mdp.corr_file = open(mdp.input_path + '/' + mdp.input_fname,'r')\n except IOError:\n print \"Could not open file \",mdp.input_fname\n continue\n print mdp.input_fname,',',mdp.tag\n if not mdp.flag_out_open:\n try:\n if mdp.flag_overwrite:\n ## -- open save file for read+write\n try:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0) # go to beginning\n mdp.save_file.truncate() # delete whatever was there before\n except IOError:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\n mdp.save_file.close()\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.flag_out_open = True\n # write first header\n #corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",\\\n # int(mdp.corr_num.strip(\"[]\").split(',')[0]))\n #uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\n #uf.write_section(mdp.save_file,mdp.key)\n for num,key in zip(mdp.corr_num,mdp.key):\n corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\n uf.write_section(mdp.save_file,key)\n mdp.flag_overwrite= False\n else:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0,2) # seek the end of file\n mdp.flag_out_open = True\n # write another header\n #corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",\\\n # int(mdp.corr_num.strip(\"[]\").split(',')[0]))\n #uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\n #uf.write_section(mdp.save_file,mdp.key)\n for num,key in zip(mdp.corr_num,mdp.key):\n corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\n uf.write_section(mdp.save_file,key)\n #except (IOError):\n # pass\n except (AttributeError):\n print \"Attempted to open invalid output file\"\n ##endif ! flag_out_open\n save_data(mdp)\n mdp.corr_file.close()\n ##endif do_makedata\n ##else \"for\" not found in control file\n else:\n update_params(mdp,lsp)\n ##endif lsp[0]==for\n ##endif len(lsp) > 1\n try:\n mdp.save_file.close()\n mdp.flag_out_open = False\n except (IOError,AttributeError):\n pass\n fin.close()\n return","def adelanta_camion(self, entorno, operacion, medio_de_origen_o_destino, camion, tipo):\r\n # TODO mejorar implementacion\r\n\r\n if tipo == \"Operacion\":\r\n\r\n operacion.recurso.cola.remove(camion)\r\n operacion.recurso.cola = \\\r\n operacion.recurso.cola[0:operacion.recurso.cola.index(self)] \\\r\n + [camion] + operacion.recurso.cola[operacion.recurso.cola.index(self):]\r\n\r\n print str(camion) + \" adelantado bajo criterio de \" + str(self) + \" \" + str(entorno.now)\r\n print \"\\tEn sistema: \" + str(operacion.recurso.cola) + \" Hora: \" + str(entorno.now)\r\n\r\n elif tipo == \"Almacen\":\r\n\r\n medio_de_origen_o_destino.cola.remove(camion)\r\n medio_de_origen_o_destino.cola = \\\r\n medio_de_origen_o_destino.cola[0:medio_de_origen_o_destino.cola.index(self)] \\\r\n + [camion] + medio_de_origen_o_destino.cola[medio_de_origen_o_destino.cola.index(self):]\r\n\r\n print str(camion) + \" adelantado bajo criterio de \" + str(self) + \" \" + str(entorno.now)\r\n print \"\\t\" + medio_de_origen_o_destino.nombre + \":\" \\\r\n + str(medio_de_origen_o_destino.cola) + \" Hora: \" + str(entorno.now)","def make_data_raw_fast(mdp,do_makedata,filename):\n #\n fin = open(filename,'r')\n for line in fin:\n lsp = line.split(' ')\n if len(lsp) > 1: # skip empty lines\n if lsp[0] == \"for\": # indicates when to get correlator\n lsp.pop(0)\n update_params(mdp,lsp)\n ## -- do_makedata tells it to go ahead with generating a new data output file\n ## -- otherwise, just saves parameters to metadata\n if do_makedata:\n try:\n # open correlator file\n mdp.corr_file = open(mdp.input_path + '/' + mdp.input_fname,'r')\n except IOError:\n print \"Could not open file \",mdp.input_fname\n continue\n print mdp.input_fname\n if not mdp.flag_out_open:\n try:\n if mdp.flag_overwrite:\n ## -- open save file for read+write\n try:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0) # go to beginning\n mdp.save_file.truncate() # delete whatever was there before\n except IOError:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\n mdp.save_file.close()\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.flag_out_open = True\n #for num,key in zip(mdp.corr_num,mdp.key):\n # corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n mdp.flag_overwrite= False\n else:\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\n mdp.save_file.seek(0,2) # seek the end of file\n mdp.flag_out_open = True\n #for num,key in zip(mdp.corr_num,mdp.key):\n # corr_key=uf.get_str_key(mdp.corr_file,\"correlator_key\",num)\n #except (IOError):\n # pass\n except (AttributeError):\n print \"Attempted to open invalid output file\"\n ##endif ! flag_out_open\n save_data_fast(mdp)\n mdp.corr_file.close()\n ##endif do_makedata\n ##else \"for\" not found in control file\n else:\n update_params(mdp,lsp)\n ##endif lsp[0]==for\n ##endif len(lsp) > 1\n try:\n mdp.save_file.close()\n mdp.flag_out_open = False\n except (IOError,AttributeError):\n pass\n fin.close()\n return","def write_base_data(self, daty):\r\n self.liczba_pelnych_linii = len(self.output_lista_cegiel) / 5\r\n if len(self.output_lista_cegiel)-self.liczba_pelnych_linii * 5 != 0:\r\n self.niepelna_liniia = True\r\n else:\r\n self.niepelna_liniia = False\r\n\r\n self.write_drugs_name()\r\n self.write_cegla_name()\r\n self.write_date(daty)","def ubicar_fragata():\n tamano = Fragata.tamano #se importa el tamano del barco desde su clase\n cantidad = Fragata.cantidad #se importa la cantidad de barcos desde su clase\n orientacion = orientaciones[(randint(0, 1))] #elige aleatoriamente el index de la tupla orientaciones = (\"Vertical\", \"Horizontal\")\n seguir_coordenadas = True\n while seguir_coordenadas:\n mal_ubicado = \"no\"\n if orientacion == \"Vertical\":\n #se eligen random las filas y las columnas\n coor_fila = randint(1, numero_filas)\n coor_columna = randint (1, numero_columnas)\n while (coor_fila + tamano) > 10: #como su orientacion es vertical la fila incial del barco mas su tamano (2) no puede ser mayor que 10 porque se saldria del mapa\n coor_fila = randint(1,numero_filas)\n ubicacion = (coor_fila, coor_columna)\n lista_temporal.append(ubicacion) #lista donde se ubicaran temporalmente las ubicaciones de los barcos\n while len(lista_temporal) < tamano: #sacar las posiciones restantes \n coor_fila += 1\n ubicacion = (coor_fila, coor_columna)\n lista_temporal.append(ubicacion)\n for x in lista_ubicacion_barco:\n for y in lista_temporal:\n if x == y:\n mal_ubicado = \"si\" #si alguna coordenada de este barco coincide con la de otro la variable mal ubicado sera \"si\" \n elif (y[0] == x[0] or (y[0]+1) == x[0] or (y[0]-1) == x[0]) and ((y[1]) == x[1] or (y[1]+1) == x[1] or (y[1]- 1) == x[1]): #validacion para que no se ubique el barco al lado o diagonalmente contiguo a otro\n mal_ubicado = \"si\" #si esta validacion sucede la variable mal ubicado sera \"si\" \n if orientacion == \"Horizontal\":\n #se eligen random las filas y las columnas\n coor_fila = randint(1, numero_filas)\n coor_columna = randint(1, numero_columnas)\n while (coor_columna + tamano) > 10: #como su orientacion es horizontal la columna incial del barco mas su tamano (2) no puede ser mayor que 10 porque se saldria del mapa\n coor_columna = randint(1, numero_columnas)\n ubicacion = (coor_fila, coor_columna)\n lista_temporal.append(ubicacion) #lista donde se ubicaran temporalmente las ubicaciones de los barcos\n while len(lista_temporal) < tamano: #sacar las posiciones restantes \n coor_columna += 1\n ubicacion = (coor_fila, coor_columna)\n lista_temporal.append(ubicacion)\n for x in lista_ubicacion_barco:\n for y in lista_temporal:\n if x == y:\n mal_ubicado = \"si\" #si alguna coordenada de este barco coincide con la de otro la variable mal ubicado sera \"si\" \n elif (y[0] == x[0] or (y[0]+1) == x[0] or (y[0]-1) == x[0]) and ((y[1]) == x[1] or (y[1]+1) == x[1] or (y[1]- 1) == x[1]): #validacion para que no se ubique el barco al lado o diagonalmente contiguo a otro\n mal_ubicado = \"si\" #si esta validacion sucede la variable mal ubicado sera \"si\" \n if mal_ubicado == \"si\": #si la variable mal ubicado es \"si\" se repetira el proceso otra vez\n seguir_coordenadas = True\n lista_temporal.clear()\n elif mal_ubicado == \"no\": #si la variable es \"no\" se continuara el proceso\n for x in lista_temporal:\n lista_ubicacion_barco.append(x) #se agregan las posiciones a la lista general\n coordenadas_fragata.append(x) \n lista_temporal.clear() #se limpia la lista temporal para usarla en el otro barco\n seguir_coordenadas = False","def busca_por_data(self, data_inicial, data_final):\r\n # data_inicial/data_final formato 'DDMMYYYY'\r\n\tserie = \"\" \r\n\tchave_acesso = \"\" \r\n self.__busca_notas(\"DATA\", data_inicial, data_final, serie, chave_acesso)","def read(self):\n try:\n datos = self.base.readData()\n for i in range(len(datos)):\n self.verDatos.insert('', i+1, text = i+1, values = (datos[i][0], datos[i][1], datos[i][2]))\n except:\n showerror(\"Error\", exc_info()[1])","def load_data(self):","def reverse(self):\n self.data_encap.reverse()","def _copy_data_from(self, original):\n raise NotImplementedError()","def crear_vista_base(self, master, nombre_base=False, nombre_tabla=False, campo_referencia=False, dato_busqueda=False):\n if not nombre_base:\n print(\"No se ha especificado la base de datos a acceder.\\n\")\n return False\n else:\n if not nombre_tabla:\n print(\"La base de datos especificada no contiene tablas.\\n\")\n self.tree[\"columns\"] = (1,2,3)\n for row in self.tree.get_children():\n self.tree.delete(row)\n return False\n\n contador_campos = 0\n nombres = []\n for campo in base.retornar_esquema(nombre_base, nombre_tabla):\n nombre = campo[1] # escruta el nombre de la columna\n contador_campos += 1\n nombres.append(nombre)\n\n self.tree[\"columns\"] = tuple([x for x in range(1,contador_campos+1)] if contador_campos>=7 else [x for x in range(1,8)])\n\n if len(nombres) <= 6:\n for i in range(1,len(self.tree[\"columns\"])+1):\n if i > 6:\n self.tree.column(f\"#{i}\",\n width=886-(140*6),\n stretch=tk.NO)\n self.tree.heading(f\"#{i}\", \n text=f\"\")\n break\n elif i <= 6 and i > len(nombres):\n self.tree.column(f\"#{i}\",\n width=140,\n stretch=tk.NO)\n self.tree.heading(f\"#{i}\", \n text=f\"\")\n else:\n self.tree.column(f\"#{i}\",\n width=140,\n stretch=tk.NO,\n anchor='center')\n self.tree.heading(f\"#{i}\", \n text=f\"{nombres[i-1].title() if nombres[i-1] not in ('id','ID') else nombres[i-1]}\",\n anchor=\"center\")\n else:\n for i in range(1,len(self.tree[\"columns\"])+1):\n self.tree.column(f\"#{i}\",\n width=140,\n stretch=tk.NO,\n anchor='center')\n self.tree.heading(f\"#{i}\", \n text=f\"{nombres[i-1].title() if nombres[i-1] not in ('id','ID') else nombres[i-1]}\",\n anchor=\"center\")\n\n if campo_referencia:\n if dato_busqueda:\n if isinstance(dato_busqueda, (str,int,float,bool)):\n for row in self.tree.get_children():\n self.tree.delete(row)\n filas = base.traer_datos(nombre_base, nombre_tabla, campo_referencia, dato_busqueda)\n for fila in filas:\n self.tree.insert(\"\", tk.END, values=fila)\n elif isinstance(dato_busqueda, (tuple,list,dict)):\n for row in self.tree.get_children():\n self.tree.delete(row)\n for valor in dato_busqueda:\n filas = base.traer_datos(nombre_base, nombre_tabla, campo_referencia, valor)\n for fila in filas:\n self.tree.insert(\"\", tk.END, values=fila)\n else:\n for row in self.tree.get_children():\n self.tree.delete(row)\n filas = base.retornar_datos(nombre_base, nombre_tabla)\n for fila in filas:\n self.tree.insert(\"\", tk.END, values=fila)\n else:\n for row in self.tree.get_children():\n self.tree.delete(row)\n filas = base.retornar_datos_segun_tiempo(nombre_base, nombre_tabla)\n for fila in filas:\n self.tree.insert(\"\", tk.END, values=fila)","def guardar_otras (self,maquinas,decoracion,dia,dinero):#dicc,dicc,int,int\n\n stream_guardar = open(\"yo_otros.txt\",\"wt\",encoding=\"utf-8\")\n for i in maquinas:\n stream_guardar.write(str(maquinas[i]))\n\n for i in decoracion:\n stream_guardar.write(str(decoracion[i]))\n\n stream_guardar.write(\"D\"+str(dia))\n stream_guardar.write(\"M\"+str(dinero))","def getDbase(self):\n for item in self.sqlData: # for every colummn name in the data\n self.sqdbase[item]=np.array(self.sqlData[item]) # add to the dictionary the clomunm name and the corresponding data\n \n self.sqlData['index'] = list(range(len(self.sqlData['time']))) # since we sometimes have even a column for index(pandas put it automatically) and sometimes not which will not be used for Stats we dropp it out\n self.sqdbase.pop('index') # we make sure that all dataFRames we are working with has inde column and the drop it\n return self.sqdbase","def migrar(self):\r\n start = time.time()\r\n if not os.path.exists('output'):\r\n os.makedirs('output')\r\n # seleciona as tabelas\r\n res = self.cur_origem.execute(\r\n \"SELECT table_name FROM dba_tables WHERE owner = '%s' %s order by table_name\" % (self.schema_origem, self.filtro))\r\n\r\n # para cada tabela\r\n for row, in res.fetchall():\r\n row = row.strip()\r\n # conta os registros\r\n countsql = self.cur_origem.execute(\r\n \"select count(*) as total from %s \" % row)\r\n count, = countsql.fetchall()[0]\r\n start_time = time.time()\r\n start_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(\"MIGRANDO: %s\\n NRO REGISTROS: %s registros\\n INICIO: %s\" % (\r\n row, count, start_datetime))\r\n\r\n # gera o create table e trunca a tabela ( se ja existir )\r\n create, tipos = self.ddl_table(row)\r\n self.cur_destino.execute(create)\r\n self.cur_destino.execute(\"TRUNCATE TABLE %s\" % row)\r\n\r\n # gera as colunas\r\n cols = \"\"\r\n # tratamento exclusivo SONNER\r\n for id, [col, tipo] in tipos.iteritems():\r\n if col == \"SENHA\" or (col == \"DADOS\" and row == \"CADARQUIVODIGITAL\"):\r\n cols += \"NULL AS %s,\" % col\r\n else:\r\n cols += \"%s,\" % col\r\n # padrao seria:\r\n # for id, [col, tipo] in tipos.iteritems():\r\n # cols += \"%s,\" % col\r\n # print \"select %s from %s \" % (cols[:-1], row)\r\n\r\n # busca os dados\r\n self.cur_origem.execute(\"select %s from %s \" % (cols[:-1], row))\r\n\r\n # grava os dados no TXT\r\n with open(\"output/%s.txt\" % row, \"wb\") as f:\r\n w = csv.writer(\r\n f, delimiter='|', quotechar='\"')\r\n try:\r\n\r\n # execao SONNER (dados TEXT tipo cblob necessita de conversao anterior por conta de encoding)\r\n if row == \"CADPESSOACONTSOC\":\r\n for cada in self.cur_origem.fetchall():\r\n cada = list(cada)\r\n if cada[5] is not None:\r\n cada[5] = cada[5].read().encode(\"latin-1\")\r\n w.writerow(cada)\r\n\r\n else:\r\n w.writerows(self.cur_origem.fetchall())\r\n except Exception as e:\r\n self.erros.append([\"%s\" % row, e])\r\n end_time = time.time()\r\n end_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(\"\\tFIM: %s\\tTEMPO: %ss\\tTABELA COM ERRO %s\" %\r\n (end_datetime, round(end_time-start_time, 0), e))\r\n\r\n # le o arquivo gravado e copia para o banco destino\r\n with open(\"output/%s.txt\" % row, \"r\") as f:\r\n try:\r\n self.cur_destino.copy_expert(\r\n \"\"\"COPY %s FROM STDIN WITH QUOTE '\"' DELIMITER '|' NULL '' CSV \"\"\" % row, f)\r\n except Exception as e:\r\n self.erros.append([\"%s\" % row, e])\r\n end_time = time.time()\r\n end_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(\" FIM: %s\\n TEMPO: %ss\\n TABELA COM ERRO %s\" %\r\n (end_datetime, round(end_time-start_time, 0), e))\r\n else:\r\n end_time = time.time()\r\n end_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(\" FIM: %s\\n TEMPO: %ss\\n OK\" %\r\n (end_datetime, round(end_time-start_time, 0)))\r\n end = time.time()\r\n print(\"TEMPO GASTO: %s s\" % (end-start))","def __preproc_covid(self):\n\n # processar datas\n \n self.covidbr['data'] = pd.to_datetime(self.covidbr['data'], format='%d/%m/%Y')\n self.covidbr['data'] = self.covidbr['data'].astype(pd.DatetimeTZDtype(tz='America/Sao_Paulo'))\n\n # 2020-06-02: O Ministério da Saúde cagou os dados de população na planilha divulgada diariamente.\n # devemos consertá-lo\n # regex: ^((?:\\d{1,3}\\.)*\\d*)\n\n # preencher populacao de areas dos estados que não estão em nenhum município\n\n pop_estados = self.covidbr[(self.covidbr.estado.notnull()) & (self.covidbr.codmun.isnull())].\\\n groupby('coduf').populacaoTCU2019.first()\n df = self.covidbr.merge(right=pop_estados, on='coduf', how='left')\n populacaoTCU2019 = df.populacaoTCU2019_x.where(df.populacaoTCU2019_x.notnull(), other=df.populacaoTCU2019_y)\n\n # filtrar dados espúrios de população\n\n populacaoTCU2019 = populacaoTCU2019.str.extract(r'^((?:\\d{1,3}\\.)*\\d*)', expand=False).\\\n str.replace('.','').astype(float)\n\n # converter tipos\n\n df['populacaoTCU2019'] = populacaoTCU2019\n df.drop(columns=[ 'populacaoTCU2019_' + mergedir for mergedir in ['x', 'y'] ], inplace=True)\n self.covidbr = df\n\n self.covidbr = self.covidbr.astype(\n { converter: 'Int64' for converter in ['coduf', 'codmun', 'codRegiaoSaude', 'populacaoTCU2019',\n 'Recuperadosnovos', 'emAcompanhamentoNovos'] }\n )","def guardar_datos(self, archivo):\r\n with open(archivo, \"wb\") as csv_file:\r\n writer = csv.writer(csv_file, delimiter=';')\r\n writer.writerow(\r\n [\"Camion\", \"Carga\", \"Tipo\", \"Peso Final\", \"Operacion\", \"Recurso\", \"Dia\",\r\n \"Arribo\", \"Espera M. O/D\", \"Espera R\", \"Espera P.\", \"Espera T.\", \"Inicio\", \"Fin\",\r\n \"Medio de Almacenamiento\", \"Nivel\"])\r\n for linea in self.datos:\r\n writer.writerow(linea)","def recarregar_imagem(self):\n self.salvar_imagem(caminho_imagem=self.caminho_temp)\n self.carregar_imagem(caminho_imagem=self.caminho_temp)","def normalize_dataset(self):","def getBaseMonetaria(self):\n #Obtener la url de descarga del cvs\n urlPackage=\"https://datos.gob.ar/api/3/action/package_show?id=sspm-factores-explicacion-base-monetaria\"\n s=requests.get(urlPackage).content\n objJson = json.loads(s)\n resultado = objJson['result']['resources']\n selector = 0\n ultimoResultado = resultado[selector]\n urlDescarga = ultimoResultado['url']\n descripcion = ultimoResultado['description']\n print(\"Descargando: {}\".format(descripcion))\n print(\"Archivo: {}\".format(urlDescarga))\n \n #Descargar la url con cvs y generar pandas dataframe\n contenidoCVS = requests.get(urlDescarga).content\n flujoCVS = io.StringIO(contenidoCVS.decode('utf-8'))\n df_temp = pd.read_csv(flujoCVS)\n \n #transform string to datetime\n df_temp['indice_tiempo'] = pd.to_datetime(df_temp['indice_tiempo'], format='%Y-%m-%d', errors='ignore')\n df_temp['indice_tiempo'] = df_temp['indice_tiempo'].dt.date\n #set index\n df_temp.set_index('indice_tiempo', inplace=True)\n \n return df_temp","def analisa_backup(self):\n #obtem as pastas que precisam ser criadas na pasta de destino \n self.obter_pastas_inexistentes_no_destino()\n \n #enquanto existir subpasta na pasta atual, faça backup recursivo\n while(self.sub_pastas_nao_finalizadas):\n \n #pega ultima pasta da lista\n sub_pasta = self.sub_pastas_nao_finalizadas[-1]\n \n #cria um objeto backup, a partir da criação de um objeto pasta \n #(com base na subpasta de origem) e de uma sub_pasta de mesmo nome\n #na pasta de destino não é necessário que esta subpasta, na pasta \n #de destino exista. Após a criação do objeto Backup, chama-se o \n #método analisa_backup, que por meio de recursão varre todas as \n #subpastas verificando a necessidade de backup\n Backup(Pasta(sub_pasta),Pasta(os.path.join(self.pasta_destino.obter_caminho(),os.path.basename(sub_pasta)))).analisa_backup()\n \n #subpasta finalizada, retira a mesma da lista\n self.sub_pastas_nao_finalizadas.pop()\n\n #Pega somente o nome do arquivo a partir de uma lista com os caminhos \n #absolutos de cada arquivo\n arquivos_origem = [os.path.basename(arq) \\\n for arq in self.pasta_origem.obter_lista_arquivos()]\n \n arquivos_destino = [os.path.basename(arq) \\\n for arq in self.pasta_destino.obter_lista_arquivos()]\n\n for arquivo_origem in arquivos_origem:\n \n #Verifica se o arquivo de origem esta na pasta de destino, \n #caso não esteja, deve ser feito o backup\"\n if arquivo_origem not in arquivos_destino:\n \n #insere na lista operacao, um tupla (arquivo,pasta_origem,\n #pasta_destino) de modo a que no final o backup seja realizado\"\n self.__class__.operacoes_copiar.append(\\\n (arquivo_origem,\\\n self.pasta_origem.obter_caminho(),\\\n self.pasta_destino.obter_caminho())\\\n )","def desapilar(pila):\n dato = pila.datos[pila.tope]\n pila.tope -= 1\n return dato","def inicializar_tablero(self):\n\n cont_pixels_x = 0 # Para contar los pixeles en x, y tenerlos como referencia en la casilla correspondiente\n cont_pixels_y = 0 # Para contar los pixeles en y, y tenerlos como referencia en la casilla correspondiente\n pixels_cuadro = 100\n\n for f in range(len(self._matriz)): # Inicializando la matriz del tablero que va a contener los objetos casilla\n cont_pixels_y += pixels_cuadro\n for c in range(len(self._matriz[f])):\n cont_pixels_x += pixels_cuadro\n\n # Por el momento solo se estara utilizando una ficha para pruebas.\n if f == 0 and c == 1: #(superior_izq, superior_der, inferior_izq, inferior_der )\n self._matriz[f][c] = Casilla (Ficha('f_marron.png'), (f, c), (cont_pixels_x - pixels_cuadro, cont_pixels_x, cont_pixels_y - pixels_cuadro, cont_pixels_y))\n\n else:\n self._matriz[f][c] = Casilla(None, (f, c), (cont_pixels_x - pixels_cuadro, cont_pixels_x, cont_pixels_y - pixels_cuadro, cont_pixels_y))\n\n cont_pixels_x = 0 # Bajar la columna\n\n\n return self._matriz","def format_data(self, raw_data):\n opz = raw_data.copy()\n opz['datetime'] = pd.to_datetime(opz['Datum-tijd'], format='%Y-%m-%dT%H:%M:%SZ')\n opz.drop(['Datum-tijd'],axis=1, inplace=True)\n opz['dag']=opz['datetime'].dt.day\n opz['tijd'] = opz['datetime'].dt.time\n #voeg open/dicht data toe en bepaal momenten waarop dit wisselt\n opz['Opzetstuk Noord (°)'] = opz['Opzetstuk Noord (°)'].str.replace(',', '.').astype(float)\n opz['Opzetstuk Zuid (°)'] = opz['Opzetstuk Zuid (°)'].str.replace(',', '.').astype(float)\n opz['Opzetstuk Noord (°)'].fillna(opz['Opzetstuk Zuid (°)'], inplace=True)\n opz['Opzetstuk Zuid (°)'].fillna(opz['Opzetstuk Noord (°)'], inplace=True)\n return opz","def transform(self, data):","def revise():","def rewind():","def restore_base_data():\n\n db_dirname = os.path.dirname(os.path.realpath(__file__))\n shutil.copyfile(src=os.path.join(db_dirname, 'consolemini.base.json'),\n dst=os.path.join(db_dirname, 'consolemini.test.json'))","def saveData(self):\n pass","def migrar(self):\r\n start = time.time()\r\n if not os.path.exists('output'):\r\n os.makedirs('output')\r\n # seleciona as tabelas\r\n res = self.cur_origem.execute(\r\n \"select table_name from INFORMATION_SCHEMA.tables where TABLE_SCHEMA = '%s' %s order by table_name;\" % (self.schema_origem, self.filtro))\r\n\r\n # para cada tabela\r\n for row, in res.fetchall():\r\n row = row.strip()\r\n # conta os registros\r\n countsql = self.cur_origem.execute(\r\n \"select count(*) as total from %s.%s \" % (self.schema_origem,row))\r\n count, = countsql.fetchall()[0]\r\n start_time = time.time()\r\n start_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(\"MIGRANDO: %s\\n NRO REGISTROS: %s registros\\n INICIO: %s\" % (\r\n row, count, start_datetime))\r\n\r\n # gera o create table e trunca a tabela ( se ja existir )\r\n create, tipos = self.ddl_table(row)\r\n self.cur_destino.execute(create)\r\n self.cur_destino.execute(\"TRUNCATE TABLE %s\" % row)\r\n\r\n # gera as colunas\r\n cols = \"\"\r\n for id, [col, tipo] in tipos.items():\r\n cols += \"%s,\" % col\r\n # print( \"select %s from %s.%s \" % (cols[:-1], self.schema_origem,row))\r\n\r\n # busca os dados\r\n \r\n\r\n # grava os dados no TXT\r\n with open(\"output/%s.txt\" % row, \"w\", newline='', encoding=\"latin-1\", errors='ignore') as f:\r\n w = csv.writer(\r\n f, delimiter='|', quotechar='\"')\r\n try:\r\n self.cur_origem.execute(\"select %s from %s.%s \" % (cols[:-1], self.schema_origem,row))\r\n w.writerows(self.cur_origem.fetchall())\r\n except Exception as e:\r\n self.erros.append([\"%s\" % row, e])\r\n end_time = time.time()\r\n end_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(\"\\tFIM: %s\\tTEMPO: %ss\\tTABELA COM ERRO %s\" %\r\n (end_datetime, round(end_time-start_time, 0), e))\r\n\r\n # le o arquivo gravado e copia para o banco destino\r\n with open(\"output/%s.txt\" % row, \"r\", encoding=\"latin-1\", errors='ignore') as f:\r\n try:\r\n self.cur_destino.copy_expert(\r\n \"\"\"COPY %s FROM STDIN WITH QUOTE '\"' DELIMITER '|' NULL '' CSV \"\"\" % row, f)\r\n except Exception as e:\r\n self.erros.append([\"%s\" % row, e])\r\n end_time = time.time()\r\n end_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(\" FIM: %s\\n TEMPO: %ss\\n TABELA COM ERRO %s\" %\r\n (end_datetime, round(end_time-start_time, 0), e))\r\n else:\r\n end_time = time.time()\r\n end_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(\" FIM: %s\\n TEMPO: %ss\\n OK\" %\r\n (end_datetime, round(end_time-start_time, 0)))\r\n end = time.time()\r\n print(\"TEMPO GASTO: %s s\" % (end-start))","def update_original_data(self):\n pass","def Convertir(self,inicio:list,fin:list,direccion:str,color:int) -> None:\n\t\t\n\t\t#coordenada de inicio\n\t\tx1=inicio[0]\n\t\ty1=inicio[1]\n\n\t\t#coordenada de destino\n\t\tx2=fin[0]\n\t\ty2=fin[1]\n\n\t\tif direccion==\"derecha\":\n\t\t\twhile y1!=y2:\n\t\t\t\tself.tablero[x1][y1]=color\n\t\t\t\ty1=y1+1\n\t\telif direccion==\"izquierda\":\n\t\t\twhile y1!=y2:\n\t\t\t\tself.tablero[x1][y1]=color\n\t\t\t\ty1=y1-1\n\t\telif direccion==\"arriba\":\n\t\t\twhile x1!=x2:\n\t\t\t\tself.tablero[x1][y1]=color\n\t\t\t\tx1=x1-1\n\t\telif direccion==\"abajo\":\n\t\t\twhile x1!=x2:\n\t\t\t\tself.tablero[x1][y1]=color\n\t\t\t\tx1=x1+1\n\t\telif direccion==\"arriba-derecha\":\n\t\t\twhile x1!=x2:\n\t\t\t\tself.tablero[x1][y1]=color\n\t\t\t\tx1=x1-1\n\t\t\t\ty1=y1+1\n\t\telif direccion==\"arriba-izquierda\":\n\t\t\twhile x1!=x2:\n\t\t\t\tself.tablero[x1][y1]=color\n\t\t\t\tx1=x1-1\n\t\t\t\ty1=y1-1\n\t\telif direccion==\"abajo-derecha\":\n\t\t\twhile x1!=x2:\n\t\t\t\tself.tablero[x1][y1]=color\n\t\t\t\tx1=x1+1\n\t\t\t\ty1=y1+1\n\t\telif direccion==\"abajo-izquierda\":\n\t\t\twhile x1!=x2:\n\t\t\t\tself.tablero[x1][y1]=color\n\t\t\t\tx1=x1+1\n\t\t\t\ty1=y1-1","def _make_resized_data(self, odata):\n\n\n nmax = odata['file_id'].shape[1]\n new_nmax = odata['ncutout'].max()\n if new_nmax < 2:\n new_nmax = 2\n temp_obj_data = odata\n\n nobj = temp_obj_data.size\n\n new_data = meds.util.get_meds_output_struct(\n nobj,\n new_nmax,\n extra_fields=self._get_fields(new_nmax),\n )\n new_data = self._add_cat_fields(new_data, copy=False)\n\n for name in new_data.dtype.names:\n if name in temp_obj_data.dtype.names:\n\n shape = new_data[name].shape\n lshape = len(shape)\n\n if lshape > 1 and shape[1] == new_nmax:\n new_data[name][:,:] = temp_obj_data[name][:,0:new_nmax]\n else:\n new_data[name][:] = temp_obj_data[name][:]\n\n del temp_obj_data\n\n return new_data","def rebase(self):\n self.cm.rebase()","def load_database(self, main_class):\n main_class.database.delete_all(\"render\")\n main_class.database.delete_all(\"object\")\n #main_class.database.delete_all(\"output\")\n render_csv = os.path.join(self.filepath, \"Render_data.csv\")\n object_csv = os.path.join(self.filepath, \"Obj_data.csv\")\n main_class.database.import_excel(render_csv, \"render\")\n main_class.database.import_excel(object_csv, \"object\")\n\n render_dic=main_class.database.get_database_dict(\"render\")\n\n main_class.render_database = main_class.database.get_data_from_database(\"render\")\n main_class.object_database = main_class.database.get_data_from_database(\"object\")\n\n main_class.background_picture_list = main_class.database.get_background_pictures_names()\n main_class.packaging_picture_list = main_class.database.get_bubble_wrap_pictures_names()\n\n main_class.camera_settings.append([0, 0, 0, 0, 100])\n for obj in main_class.render_database:\n \"\"\"\n extracting Camerasetting from Database and set all important angles and distances\n \"\"\"\n if obj[render_dic[\"object_type\"]] == \"camera\":\n for i in range(0, int(obj[render_dic[\"polar_angle_segments\"]])):\n for j in range(0, int(obj[render_dic[\"azimuth_angle_segments\"]])):\n pol_min = obj[render_dic[\"polar_angle_min\"]]\n pol_max = obj[render_dic[\"polar_anglel_max\"]]\n pol_segments= obj[render_dic[\"polar_angle_segments\"]]\n pol_random=obj[render_dic[\"polar_angle_random_rad\"]]\n try:\n pol_min = float( pol_min.replace(',','.'))\n except:\n pass\n try:\n pol_max = float( pol_max.replace(',','.'))\n except:\n pass\n try:\n pol_segments = float( pol_segments.replace(',','.'))\n except:\n pass\n try:\n pol_random = float( pol_random.replace(',','.'))\n except:\n pass\n polar_angle = (pol_min + ((pol_max - pol_min)/(pol_segments))*i)\n\n azi_min = obj[render_dic[\"azimuth_angle_min\"]]\n azi_max = obj[render_dic[\"azimuth_angle_max\"]]\n azi_segments= obj[render_dic[\"azimuth_angle_segments\"]]\n azi_random= obj[render_dic[\"azimuth_angle_random_rad\"]]\n\n try:\n azi_min = float( azi_min.replace(',','.'))\n except:\n pass\n try:\n azi_max = float( azi_max.replace(',','.'))\n except:\n pass\n try:\n azi_segments = float( azi_segments.replace(',','.'))\n except:\n pass\n try:\n azi_random = float( azi_random.replace(',','.'))\n except:\n pass\n azimuth_angle = (azi_min + ((azi_max - azi_min)/(azi_segments))*j)\n\n position=[polar_angle, pol_random, azimuth_angle, azi_random, obj[render_dic[\"radius\"]] ]\n print(\"camera position added: \",position)\n main_class.camera_settings.append(position)\n \n if obj[render_dic[\"object_type\"]]==\"light\":\n\n if obj[render_dic[\"name\"]]==\"SUN\":\n radius= obj[render_dic[\"radius\"]]\n try:\n radius = float( radius.replace(',','.'))\n except:\n pass\n light_obj=[ obj[render_dic[\"name\"]] , [0,0, radius ] ]\n main_class.light_settings.append(light_obj)\n print(\"sun added to list\")\n\n if obj[render_dic[\"name\"]]==\"SPOT\":\n for i in range(0, int(obj[render_dic[\"polar_angle_segments\"]])):\n for j in range(0, int(obj[render_dic[\"azimuth_angle_segments\"]])):\n pol_min = obj[render_dic[\"polar_angle_min\"]]\n pol_max = obj[render_dic[\"polar_anglel_max\"]]\n pol_segments= obj[render_dic[\"polar_angle_segments\"]]\n pol_random=obj[render_dic[\"polar_angle_random_rad\"]]\n try:\n pol_min = float( pol_min.replace(',','.'))\n except:\n pass\n try:\n pol_max = float( pol_max.replace(',','.'))\n except:\n pass\n try:\n pol_segments = float( pol_segments.replace(',','.'))\n except:\n pass\n try:\n pol_random = float( pol_random.replace(',','.'))\n except:\n pass\n polar_angle = (pol_min + ((pol_max - pol_min)/(pol_segments))*i)\n\n azi_min = obj[render_dic[\"azimuth_angle_min\"]]\n azi_max = obj[render_dic[\"azimuth_angle_max\"]]\n azi_segments= obj[render_dic[\"azimuth_angle_segments\"]]\n azi_random= obj[render_dic[\"azimuth_angle_random_rad\"]]\n try:\n azi_min = float( azi_min.replace(',','.'))\n except:\n pass\n try:\n azi_max = float( azi_max.replace(',','.'))\n except:\n pass\n try:\n azi_segments = float( azi_segments.replace(',','.'))\n except:\n pass\n try:\n azi_random = float( azi_random.replace(',','.'))\n except:\n pass\n azimuth_angle = (azi_min + ((azi_max - azi_min)/(azi_segments))*j)\n position=[polar_angle, pol_random, azimuth_angle, azi_random, obj[render_dic[\"radius\"]] ]\n light_obj=[ obj[render_dic[\"name\"]] , position, obj[render_dic[\"tracking_obj\"]],1000 ]\n print(\"added light_obj: \", light_obj)\n main_class.light_settings.append(light_obj)\n main_class.max_loop_count=len(main_class.camera_settings)*len(main_class.light_settings)\n print(\"loop count is:\", main_class.max_loop_count)\n return","def load_data(self) -> None:","def save_mem_load(self):\n if len(self.get_data_shape())==4 and self._img:\n data = np.zeros(self.get_data_shape())\n self._data = np.rot90(data)\n self._loaded_time_list = [0]\n self._data[..., 0] = np.rot90(self._img.dataobj[..., 0])\n else:\n self._loaded_time_list = [0]\n data = self._img.get_data(caching='unchanged')\n self._data = np.rot90(data)","def reset_data():\n shutil.copy2(\n 'data/one_producer_many_consumers.ORIG.json',\n 'data/one_producer_many_consumers.json'\n )","def getArchivoVotacion():","def merge_roi_data(cnn_dir, db_dir, subj_id):\n roi_list = dataio.vim2_roi_info(db_dir, subj_id)\n train_ts = None\n val_ts = None\n vxl_idx = None\n for roi in roi_list:\n idx, tx, vx = dataio.load_vim2_fmri(db_dir, subj_id, roi)\n if not isinstance(train_ts, np.ndarray):\n train_ts = tx\n val_ts = vx\n vxl_idx = idx\n else:\n train_ts = np.vstack((train_ts, tx))\n val_ts = np.vstack((val_ts, vx))\n vxl_idx = np.concatenate((vxl_idx, idx))\n outfile = os.path.join(cnn_dir, 'roi_orig_fmri')\n np.savez(outfile, train_ts=train_ts, val_ts=val_ts, vxl_idx=vxl_idx)","def reloadData(self):\n self.dto.readFromData()\n print(\"Record reloaded.\")","def getCambios(self, fechaInicio, fechaFin, usuarioColaborador=\"\"):\n\tif usuarioColaborador == \"\" and fechaInicio == \"\" and fechaFin == \"\":\n\t return self.conexion.ejecutarSQL(\"\"\"select id, fecha, hora, codigo_Producto_entra, codigo_Producto_sale, id_Venta, excedente, usuario_Colaborador\n from cambios\"\"\")\n elif usuarioColaborador == \"\":\n return self.conexion.ejecutarSQL(\"\"\"select id, fecha, hora, codigo_Producto_entra, codigo_Producto_sale, id_Venta, excedente, usuario_Colaborador\n from cambios\n where fecha between '%s' and '%s'\"\"\" %(fechaInicio,fechaFin))\n else:\n return self.conexion.ejecutarSQL(\"\"\"select id, fecha, hora, codigo_Producto_entra, codigo_Producto_sale, id_Venta, excedente, usuario_Colaborador\n from cambios\n where fecha between '%s' and '%s'\n and usuario_Colaborador = '%s'\"\"\" %(fechaInicio,fechaFin,usuarioColaborador))","def _convert_rosetta_db_to_basic_db(self):\n pass","def load_data(self):\n with open(self.config.drv_log, 'r') as f:\n add_data = [] # addtional data require angle smoothing\n ori_data = [] # given data\n ddir = os.path.dirname(self.config.drv_log)\n lines = list(csv.reader(f))\n np.random.shuffle(lines)\n for line in lines:\n if line[0] == 'center':\n continue\n ste_ang = float(line[3])\n if ste_ang == 0. and random.random() < 0.8:\n continue\n if '12_01' in line[0]:\n ori_data.append([os.path.join(ddir, line[0].strip()), ste_ang])\n ori_data.append([os.path.join(ddir, line[1].strip()), ste_ang])\n ori_data.append([os.path.join(ddir, line[2].strip()), ste_ang])\n else:\n add_data.append([os.path.join(ddir, line[0].strip()), ste_ang])\n add_data.append([os.path.join(ddir, line[1].strip()), ste_ang])\n add_data.append([os.path.join(ddir, line[2].strip()), ste_ang])\n \n def angle_smoothing(data):\n \"\"\"helper function for smoothing steering angles.\n reference: \n https://carnd-forums.udacity.com/questions/24807517/tips-for-behavior-cloning\n \"\"\"\n angles = np.asarray([itm[1] for itm in data])\n fwd = pd.ewma(angles, span=20)\n bwd = pd.ewma(angles[::-1], span=20)\n smooth = np.vstack((fwd, bwd[::-1]))\n smooth = np.mean(smooth, axis=0)\n angles = np.ndarray.tolist(smooth)\n smooth_data = []\n for i in range(len(data)):\n smooth_data.append([data[i][0], angles[i]])\n return smooth_data\n #all_data = ori_data + angle_smoothing(add_data) #the smoothing method didn't work well\n all_data = ori_data + add_data\n n1 = int(len(all_data) * 0.7)\n n2 = int(len(all_data) * 0.85)\n self.train = self.data_generator(all_data[:n1])\n self.valid = self.data_generator(all_data[n1:n2])\n self.test = self.data_generator(all_data[n2:])\n self.train_len = n1 // self.config.batch_size * self.config.batch_size\n self.valid_len = (n2 - n1) // self.config.batch_size * self.config.batch_size\n self.test_len = (len(all_data) - n2) // self.config.batch_size * self.config.batch_size","def reset_data(self):\n self.data = []","def __restoreBackup(self):\n pass #FIXME!!!","def _original_data(self, data: np.ndarray):\n if self._raw_data is None:\n self._raw_data = data","def borrar_CSV(self):\n archivo = input(\"Indique el nombre del archivo a eliminar: \")\n os.remove(f\"{archivo}.txt\")\n print(\n f\"\"\"\n ==========================================\n == SE HAN ELIMINADO LOS DATOS ==\n ==========================================\n \"\"\"\n )","def importar_visualizar_tabela(self):\r\n\r\n self.tabela_clientes = pd.read_csv('telecom_users.csv') # armazenando arquivo csv em uma variavel\r\n self.tabela_clientes = self.tabela_clientes.drop([\"Unnamed: 0\"], axis=1) # apagando a coluna Unnamed: 0, axist=1 -> para excluir a coluna, axist=0 -> excluir a linha (exist = eixo)\r\n print(self.tabela_clientes)\r\n # print(self.tabela_clientes.columns) # para mostrar todas as colunas da tabela \r\n self.tabela_clientes['NovaColuna'] = 1 # criar uma nova coluna se não existir, se caso ja exista, irá substituir todos os valores na coluna para 1\r","def abrir(self):\n self.x0 = [val for val in self.x0]\n self.x = [val for val in self.x]\n self.tipos = [val for val in self.tipos]\n self.mask_fr = [val for val in self.mask_fr]\n self.mask_in = [val for val in self.mask_in]\n self.open = True","def delete(self, *args, **kwargs):\n campo = Campostagimg.objects.filter(tag=self.tag, imagen=self.imagen)\n for c in campo:\n c.medidas = \"\"\n c.save()\n c.precision = 0\n c.save()\n c.v_esperado = \"\"\n c.save() \n\n super(Campos_defecto, self).delete(*args, **kwargs)","def binary_reconstruct(self, data_path, width, height, dtype=\"uint16\",\n output_dir=\"\", focal_spot_image_name=\"focal_spot\", \n sinogram_image_name=\"sinogram\"):\n\n focal_spot, sinogram = reconstruct_from_cr_data(data_path, width, height, dtype=dtype)\n\n focal_spot = img_as_ubyte(equalize_adapthist(focal_spot))\n focal_spot_path = os.path.join(output_dir, \"%s.png\" % focal_spot_image_name)\n sinogram = img_as_ubyte(equalize_adapthist(sinogram))\n sinogram_path = os.path.join(output_dir, \"%s.png\" % sinogram_image_name)\n\n io.imsave(focal_spot_path, focal_spot)\n io.imsave(sinogram_path, sinogram)","def cargarProductosSinObra(self):\n\n self.limpiarTabla(self.tableProductos)\n\n ##Cnsulta para obtener todos los productos del sistema, con su correspondiente\n ##codigo de barra, monodroga, descuento, importe\n query=self.sesion.query(ProductoModel.codigo_barra,ProductoModel.id_medicamento,ProductoModel.id_presentacion,MonodrogaModel.nombre,ProductoModel.importe).\\\n join(MedicamentoModel).filter(ProductoModel.id_medicamento==MedicamentoModel.nombre_comercial).\\\n join(MonodrogaModel).filter(MedicamentoModel.id_monodroga==MonodrogaModel.nombre).\\\n filter(ProductoModel.baja==False).order_by(ProductoModel.codigo_barra)\n\n ##Se cargan los datos obtenidos en la tabla de Producto\n for n, obj in enumerate(query):\n self.tableProductos.insertRow(n)\n self.tableProductos.setItem(n, 0, QtGui.QTableWidgetItem(str(obj[0])))\n self.tableProductos.setItem(n, 1, QtGui.QTableWidgetItem(str(obj[1])))\n self.tableProductos.setItem(n, 2, QtGui.QTableWidgetItem(str(obj[2])))\n self.tableProductos.setItem(n, 3, QtGui.QTableWidgetItem(str(obj[3])))\n self.tableProductos.setItem(n, 4, QtGui.QTableWidgetItem(str(0)))\n self.tableProductos.setItem(n, 5, QtGui.QTableWidgetItem(str(obj[4])))\n\n ##Se carga la cantidad de cada producto en la tabla\n for row,producto in enumerate(ProductoModel.buscarTodos(ProductoModel.codigo_barra,self.sesion)):\n self.tableProductos.setItem(row,6,QtGui.QTableWidgetItem(str(producto.getCantidad(self.sesion))))","def cleanData(data):\r\n \r\n indexes = data.index[pd.isnull(data[\"release date\"])]\r\n # data = data.drop(data.index[[2172, 3781, 7245, 12475, 14756, 15292, 49295, 93523, 99723]],axis=0,inplace=False)\r\n data = data.drop(indexes,axis=0,inplace=False)\r\n data = data.drop([\"video release date\", 'IMDb URL'],axis=1)\r\n \r\n \r\n data.gender = data.gender.astype(\"category\")\r\n data.occupation = data.occupation.astype(\"category\")\r\n \r\n categorical_variables = [\"gender\", \"occupation\"]\r\n for variable in categorical_variables:\r\n dummies = pd.get_dummies(data[variable], prefix=variable)\r\n data = pd.concat([data, dummies], axis=1)\r\n data.drop([variable], axis=1, inplace=True)\r\n data['movieID'] = data['movieID'].astype('int')\r\n data['userID'] = data['userID'].astype('int')\r\n \r\n\t#data = dateTime(data)\r\n \r\n return data","def FrameBase_to_Sqlite(self):\n sql3 = Sql3(self.dataFRAME) # we import the created from us class Sql3 to add the data \n sql3.sql_write() # very simple and easy","def load_dataset():\n\n df_ = pd.read_excel(\"D:\\VERİBİLİMİOKULU\\VERİSETLERİ\\post_bilgileri.xlsx\")\n df = df_.copy()\n return df","def Devolver_estado(self,estado:list) -> None:\n\t\tfor i in range(6):\n\t\t\tfor j in range(6):\n\t\t\t\t#Usamos deepcopy para que el minimax o el alfabeta realice cambios en copias y no en el tablero\n\t\t\t\tself.tablero[i][j]=copy.deepcopy(estado[i][j])","def realizar_movimiento(atacante, defensor, gestor_archivos):\r\n \r\n movimiento = atacante.movimiento_a_realizar\r\n # Movimiento de ataque normal\r\n if movimiento.categoria == 'Physical':\r\n d_base = 15 * movimiento.poder * (atacante.atk / defensor.defe) / 50\r\n \r\n stab = True if atacante.tipo == movimiento.tipo else False\r\n rand = random.randrange(8, 11) / 10\r\n mod_defensor = 1\r\n \r\n print(f'DEBUG: atacante tipo = {atacante.tipo}')\r\n print(f'DEBUG: defensor tipo = {defensor.tipo}')\r\n \r\n for tipo_atk in atacante.tipo.split(','):\r\n for tipo_def in defensor.tipo.split(','):\r\n mod_defensor *= float(gestor_archivos.diccionario_tabla_tipos[tipo_atk][tipo_def])\r\n \r\n d_total = int(d_base * rand * mod_defensor * (1.5 if stab else 1))\r\n \r\n defensor.hp -= d_total\r\n \r\n print(f'DEBUG: movimiento_poder = {movimiento.poder}')\r\n print(f'DEBUG: atk = {atacante.atk}')\r\n print(f'DEBUG: defe = {defensor.defe}')\r\n print(f'DEBUG: d_base = {d_base}')\r\n print(f'DEBUG: rand = {rand}')\r\n print(f'DEBUG: stab = {stab}')\r\n print(f'DEBUG: d_total = {d_total}')\r\n \r\n output_manager.imprimir_mensaje(f'{atacante.nombre} ha atacado a {defensor.nombre}, quitandole {d_total} de HP. {defensor.nombre} ha quedado con {defensor.hp} de HP.')\r\n \r\n # Movimiento de ataque especial\r\n elif movimiento.categoria == 'Special':\r\n d_base = 15 * movimiento.poder * (atacante.spa / defensor.spd) / 50\r\n \r\n stab = True if atacante.tipo == movimiento.tipo else False\r\n rand = random.randrange(8, 11) / 10\r\n mod_defensor = 1\r\n \r\n print(f'DEBUG: atacante tipo = {atacante.tipo}')\r\n print(f'DEBUG: defensor tipo = {defensor.tipo}')\r\n \r\n for tipo_atk in atacante.tipo.split(','):\r\n for tipo_def in defensor.tipo.split(','):\r\n mod_defensor *= float(gestor_archivos.diccionario_tabla_tipos[tipo_atk][tipo_def])\r\n \r\n d_total = int(d_base * rand * mod_defensor * (1.5 if stab else 1))\r\n \r\n defensor.hp -= d_total\r\n \r\n print(f'DEBUG: movimiento_poder = {movimiento.poder}')\r\n print(f'DEBUG: spa = {atacante.spa}')\r\n print(f'DEBUG: spd = {defensor.spd}')\r\n print(f'DEBUG: d_base = {d_base}')\r\n print(f'DEBUG: rand = {rand}')\r\n print(f'DEBUG: stab = {stab}')\r\n print(f'DEBUG: d_total = {d_total}')\r\n \r\n output_manager.imprimir_mensaje(f'{atacante.nombre} ha atacado a {defensor.nombre}, quitandole {d_total} de HP. {defensor.nombre} ha quedado con {defensor.hp} de HP.')\r\n \r\n # Movimiento de sanacion \r\n elif movimiento.categoria == 'Status' and movimiento.objetivo == 'self' and movimiento.stats == '':\r\n atacante.hp += atacante.hp_max // 2 \r\n \r\n # Movimeinto de empoderamiento a si mismo\r\n elif movimiento.categoria == 'Status' and movimiento.objetivo == 'self' and movimiento.stats != '':\r\n for stat in movimiento.stats.split(','):\r\n output_manager.imprimir_mensaje(f'Duplicando el {stat} de {atacante.nombre}')\r\n \r\n if stat == 'atk': \r\n atacante.atk *= 2\r\n elif stat == 'def': \r\n atacante.defe *= 2\r\n elif stat == 'spe': \r\n atacante.spe *= 2\r\n \r\n # Movimeinto de empoderamiento al oponente\r\n elif movimiento.categoria == 'Status' and movimiento.objetivo == 'normal' and movimiento.stats != '':\r\n for stat in movimiento.stats.split(','):\r\n output_manager.imprimir_mensaje(f'Reduciendo a la mitad el {stat} de {defensor.nombre}')\r\n \r\n if stat == 'atk':\r\n defensor.atk *= 0.5\r\n elif stat == 'def':\r\n defensor.defe *= 0.5\r\n elif stat == 'spe':\r\n defensor.spe *= 0.5","def Tratamentos_dos_dados(self):\r\n self.tabela_clientes[\"TotalGasto\"] = pd.to_numeric(self.tabela_clientes[\"TotalGasto\"], errors=\"coerce\") # transformar coluna que deveria ser número e está como texto em número, errors=\"coerce\" -> se der erro em algo deixa vazio\r\n \r\n self.tabela_clientes = self.tabela_clientes.dropna(how='all', axis=1) # remover as colunas que estam 100% vazia, how='all' -> todas\r\n \r\n self.tabela_clientes = self.tabela_clientes.dropna() # remover a linha que tem algum valor vazio\r\n\r\n print(self.tabela_clientes.info()) # informações sobre a tabela \r","def save_data (mdp):\n for num,key in zip(mdp.corr_num,mdp.key):\n lnum = find_corr(mdp,int(num)) # get the line number of the correlator, if possible\n if lnum > -1:\n cdat = extract_data(mdp,lnum) # found the correlator, save to array\n try: # write it to file\n ## -- organizing is too slow, just write to end of file\n mdp.save_file.seek(0,2) # seek the end of file\n mdp.save_file.write( key + ' ' + \\\n ' '.join('{:e}'.format(cdat[x]) for x in range(0,mdp.corr_len))+'\\n')\n #lsec = uf.find_data_section(mdp.save_file,key)\n #mdp.save_file.write( key + ' ' + ' '.\\\n #uf.ins_line(mdp.save_file, key + ' ' + ' '.\\\n # join('{:e}'.format(cdat[x]) for x in range(0,mdp.corr_len))\\\n # , lsec[1]+1\\\n # )\n #write_fname(mdp,lsec[0])\n except IndexError:\n print \"-- In file\",mdp.corr_file.name\n print \"Could not extract data from file\"\n else:\n print \"-- In file\",mdp.corr_file.name\n print \"Failed to find correlator #\",num","def _reload(self):\n if os.path.exists(self.filename):\n self.data = pd.read_csv(self.filename)\n else:\n self.data = pd.DataFrame(columns=self.unique_keys)\n\n # Set these default values\n # if 'weight_rescale' not in self.data.columns:\n # self.data['weight_rescale'] = 'none'\n # if 'norm' not in self.data.columns:\n # self.data['norm'] = 'softmax'\n # if 'update' not in self.data.columns:\n # self.data['update'] = 'all'\n # if 'replay' not in self.data.columns:\n # self.data['replay'] = False\n if 'debug' not in self.data.columns:\n self.data['debug'] = False\n\n # if 'tie' not in self.data.columns:\n # self.data['tie'] = False\n\n if 'update_length' not in self.data.columns:\n self.data['update_length'] = 0\n # for key in self.unique_keys:\n # self.data[key] = np.nan\n # Remaining set to None\n # for k in self.check_keys:\n # if k not in self.data.columns:\n # self.data[k] = None","def update_database():\n\n # We obtain the data from the official database\n df = getData.extractData()\n\n # We save the dataframe for later use in the API\n auxiliary.saveToCsv(df, 'app/resources')","def datos_usuario():\n usuario_partida.clear()\n puntaje = 0\n disparos_efectuados = 100\n username = input(\"Ingrese su username: \")#el usuario ingresa su username\n usuarios = []\n nombre_lista = []\n username_repetido = True\n while username_repetido: #se verifica si el username ya jugo antes\n with open(\"BaseDatos.txt\", \"r\") as bd:\n datos = bd.readlines()\n for x in datos:\n usuario = x[:-1].split(',') #si el username esta en la base de datos no necesita ingresar sus datos\n usuarios.append(usuario[0])\n #se procede a extraer los datos del username guardado y copiarlos \n if username in usuarios:\n print(\"Veo que ya has jugado antes, gracias por volver :)\\n\")\n sleep(2)\n username = usuario[0]\n nombre = usuario[1].split(\" \")\n nombre = nombre[1].title()\n edad = usuario[2].split(\" \")\n edad = edad[1]\n genero = usuario[3].split(\" \")\n genero = genero[1]\n puntaje = usuario[4].split(\" \")\n puntaje = puntaje[1]\n disparos_efectuados = usuario[5].split(\" \")\n disparos_efectuados = disparos_efectuados[1]\n usuario_partida.append(username)\n with open(\"Basedatos.txt\", \"r\") as db: \n lineas = db.readlines()\n with open(\"Basedatos.txt\", \"w\") as db: #se reescribe la lista en la base de datos sin el usuario actual \n for linea in lineas:\n if username not in linea:\n db.write(linea) \n with open(\"Basedatos.txt\", \"a\") as bd: #se agrega a la base de datos el usuario actual que ya ha jugado antes, se mantienen todos sus datos pasados\n bd.write(\"{}, {}, {}, {}, {}, {}\\n\".format(username, nombre, edad, genero, puntaje, disparos_efectuados))\n return Usuario(username, nombre, edad, genero, puntaje, disparos_efectuados)\n username_repetido = False\n else:\n validacion_username = username.islower() #el username no se enconrro en la base de datos\n while validacion_username == False or len(username) > 30 or \" \" in username: #Validacion para username\n print(\"{}Su usuario solo puede contener minusculas y numeros sin ningun espacio{}\\n\".format(Fore.LIGHTRED_EX, Fore.RESET))\n username = input(\"Ingerese su username nuevamente: \")\n validacion_username = username.islower()\n usuario_partida.append(username)\n nombre = input(\"Ingrese su nombre completo: \") #el usuario ingresa su nombre\n validacion_nombre = bool(re.fullmatch('[A-Za-z]{2,25}( [A-Za-z]{2,25})+?', nombre)) #se valida que el nombre este correcto\n while validacion_nombre == False:\n print(\"{}Este nombre no es valido{}\".format(Fore.LIGHTRED_EX, Fore.RESET))\n nombre = input(\"Ingrese su nombre completo: \")\n validacion_nombre = bool(re.fullmatch('[A-Za-z]{2,25}( [A-Za-z]{2,25})+?', nombre))\n nombre = nombre.title() \n while True: #Validacion para edad\n try:\n edad = int(input(\"Ingresa su edad: \")) #el usuario ingresa su edad\n if edad < 5 or edad > 100:\n raise ValueError\n break\n except ValueError:\n print(\"{}Tu edad no es adecuada para jugar{}\".format(Fore.LIGHTRED_EX, Fore.RESET))\n while True: #Validacion para genero\n try:\n genero = int(input(\"Ingrese su genero: \\n1) Femenino \\n2) Masculino \\n3) Ninguno\\n\")) #el usuario elige su genero\n if genero < 1 or genero > 3:\n raise ValueError\n if genero == 1:\n genero = \"Femenino\"\n elif genero == 2: \n genero = \"Masculino\"\n elif genero == 3:\n genero = \"Ninguno\"\n break\n except ValueError:\n print(\"{}El valor ingresado no es valido{}\".format(Fore.LIGHTRED_EX, Fore.RESET))\n with open(\"Basedatos.txt\", \"a+\") as bd: \n bd.write(\"{}, {}, {}, {}, {}, {}\\n\".format(username, nombre, edad, genero, puntaje, disparos_efectuados)) #se agrega el usuario a la base de datos\n return Usuario(username, nombre, edad, genero, puntaje, disparos_efectuados)\n username_repetido = False","def _load_data(self):\n\n path_data_x = '/workspace/base-ml/data/dizzyreg/t%s_df.csv' % \\\n self.task_num\n path_data_y = '/workspace/base-ml/data/dizzyreg/label_df_t%s.csv' % self.task_num\n path_meta = '/workspace/base-ml/data/dizzyreg/meta_df_t%s.csv' % self.task_num\n path_numerical_columns = '/workspace/base-ml/data/dizzyreg/num_columns_v2.csv'\n path_nonnumerical_columns = '/workspace/base-ml/data/dizzyreg/non_num_columns_v2.csv'\n\n read_data_x = pd.read_csv(path_data_x)\n read_data_y = pd.read_csv(path_data_y)\n read_data_meta = pd.read_csv(path_meta)\n\n # Drop columns if it only contains 1 unique element\n read_data_x = pd.DataFrame(self.drop_one_elem_columns(read_data_x))\n\n num_col = pd.read_csv(path_numerical_columns)\n num_col = read_data_x.columns.isin(num_col['0'].values).nonzero()[0]\n col_idx = np.arange(read_data_x.shape[-1])\n non_num_col = np.setdiff1d(col_idx, num_col)\n\n # new_data_x = np.array(read_data_x).astype(np.float32)\n new_data_x = np.array(read_data_x)\n new_data_y = np.array(read_data_y).astype(np.float32)\n new_data_meta = np.array(read_data_meta).astype(np.float32)\n\n print(new_data_x.shape, new_data_y.shape, new_data_meta.shape)\n\n\n # Winsorize dataset\n len_feat = new_data_x.shape[-1]\n idx_list = list(num_col)\n for i in range(len_feat):\n if i in idx_list:\n cur_data = new_data_x[:, i]\n cur_data = np.array(cur_data)\n lower_p = np.percentile(cur_data, 5)\n higher_p = np.percentile(cur_data, 95)\n cur_data[cur_data < lower_p] = lower_p\n cur_data[cur_data > higher_p] = higher_p\n new_data_x[:, i] = cur_data\n\n # Make sure target data is one-hot encoded\n if new_data_y.shape[-1] == 1:\n num_class = len(np.unique(new_data_y))\n new_data_y = np.eye(num_class)[new_data_y.astype(int).reshape(-1)]\n new_data_y = new_data_y.astype('float32')\n self.orig_column_names = read_data_x.columns\n self.data_x = new_data_x # N x F\n self.data_y = new_data_y # N x C\n self.numerical_idx = num_col # list of idx\n self.non_num_idx = non_num_col # None\n\n # Calculate adjacency matrix\n self.meta_inf = new_data_meta.astype('float32') # N x 3\n if self.args.graph_type:\n self.adj = self.get_adjacency()","def concatenate_data():","def save_data(self):\n pass","def guardar_datos(nombre, datos, \n directory=\"datos_guardados/\",\n method=\"numerado\",\n ): \n # Primera parte del nombre\n filename_1 = \"datos_\"\n nombre += \"_\"\n \n if method == \"fecha\":\n # Tercera parte del nombre (fecha)\n today = datetime.datetime.today()\n tt = today.timetuple()\n\n # Formato de fecha es yyyymmdd_hhmmss\n filename_3 = \"{:4d}{:02d}{:02d}_{:02d}{:02d}{:02d}\"\n filename_3 = filename_3.format(tt.tm_year, tt.tm_mon, tt.tm_mday,\n tt.tm_hour, tt.tm_min, tt.tm_sec)\n\n # Nombre completo\n filename = directory + filename_1 + nombre + filename_3\n\n elif method == \"numerado\":\n p = pathlib.Path(\"./\" + directory)\n archivos = []\n\n for f in p.iterdir():\n archivos.append(str(f))\n \n numero = 0\n while True:\n # Nombre temporal\n filename = directory + filename_1 + nombre + str(numero)\n \n if filename in archivos: numero += 1\n else: break\n\n # Escritura de datos (módulo json)\n with open(filename, \"w\") as file_object:\n json.dump(datos, file_object)\n \n # Mensaje de escritura\n print(\"---------------------------------------\")\n print(\"Datos escritos en:\")\n print(\"\\t\" + filename)\n print(\"---------------------------------------\")","def uadb_ascii_to_dataframe(file=''): \n \n if debug:\n print(\"Running uadb_ascii_to_dataframe for: \", file) \n \n data = check_read_file(file=file, read=True) # TODO\n \n #source_file = [l for l in file.split('/') if '.txt' in l][0]\n\n nmiss = 0\n search_h = False \n read_data = []\n \n usi,idate, usi, lat, lon, lat, stype, press, gph, temp, rh, wdir, wspd = np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan\n\n #usi,idate, usi, lat, lon, lat, stype, press, gph, temp, rh, wdir, wspd, iday, ident, numlev= 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\n obs_id = 0\n stations_id = [] \n \n for i, line in enumerate(data):\n if line[0] == 'H':\n try:\n # Header\n usi = int(line[2:14]) # unique station identifier\n \n ident = int(line[15:21].replace(' ',''))# WMO\n if ident not in stations_id:\n stations_id.append(ident)\n \n #if len(ident) == 4:\n # ident = '0' + ident \n #idflag = int(line[22:24]) # id flag\n #d_src = int(line[25:28]) # source dataset\n #version = float(line[29:34]) # version\n #dateflag = int(line[35:37]) # date flag\n year = line[38:42] # year\n month = \"%02d\" % int(line[43:45])\n day = \"%02d\" % int(line[46:48])\n hour = line[49:53]\n #locflag = int(line[54:56]) # Location Flag\n lat = float(line[57:67])\n lon = float(line[68:78])\n #ele = float(line[79:85])\n #stype = int(line[86:88])\n numlev = int(line[89:93])\n #pvers = line[94:102]\n\n if '99' in hour:\n hour = hour.replace('99', '00')\n \n if '99' in day:\n search_h = True\n continue\n \n minutes = int(hour) % 100 \n hour = \"%02d\" % (int(hour) // 100)\n if minutes > 60 or minutes < 0:\n minutes = 0\n minutes = \"%02d\" % minutes\n idate = datetime.strptime(year + month + day + hour + minutes, '%Y%m%d%H%M')\n iday = int(year + month + day)\n #pday = int(day)\n search_h = False\n\n except Exception as e:\n #print(\"Error: \", i, line, repr(e), \"Skipping Block:\")\n search_h = True\n #iprev = i\n\n elif search_h:\n nmiss += 1\n continue # Skipping block\n\n else:\n # Data\n #ltyp = int(line[0:4])\n p = float(line[5:13])\n \n if p != -99999.0 and p != 9999.9: \n press = float(line[5:13])*100 # converting to Pa, since P is given in mb (1 mb = 1 hPa) \n else:\n press = np.nan \n \n gph = float(line[14:22]) # gph [m]\n \n if gph == -999.0 or gph == -99999.00 or gph >= 99999.0:\n gph = np.nan\n \n temp = float(line[23:29])\n if temp == -999.0:\n temp = np.nan \n else:\n temp = temp + 273.15\n \n rh = float(line[30:36]) # %\n if rh == -999.0:\n rh = np.nan\n else:\n rh = rh / 100. # convert to absolute ratio TODO\n\n wdir = float(line[37:43]) \n if wdir == -999.0 or wdir == -999 :\n wdir = np.nan\n \n wspd = float(line[44:50]) # [m/s], module of the velocity\n if wspd <0 :\n wspd = np.nan \n \n try:\n \n for value,var in zip([ gph, temp, wspd, wdir, rh], [ 'gph', 'temperature', 'wind_speed', 'wind_direction', 'relative_humidity'] ):\n obs_id = obs_id +1\n if not np.isnan(press): # when pressure is available, z_coord== pressure and z_type==1\n z_type = 1 \n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, press, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\n elif (np.isnan(press) and not np.isnan(gph) ) : # when pressure is not available, z_coord== gph and z_type==2 \n z_type = 2 \n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, gph, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\n else:\n z_type = -2147483648 \n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, press, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\n \n except:\n 0\n \n \n \n #column_names = ['source_file', 'product_code', 'report_id', 'observation_id', 'report_timestamp' , 'iday', 'station_id', 'lat@hdr', 'lon@hdr', 'vertco_reference_1@body', 'obsvalue@body', 'varno@body' , 'units', 'number_of_pressure_levels' ]\n \n df = pd.DataFrame(data= read_data, columns= column_names) \n \n df['observation_id'] = np.chararray.zfill( (df['observation_id'].astype(int)) .astype('S'+str(id_string_length ) ), id_string_length ) #converting to fixed length bite objects \n df['report_id'] = np.chararray.zfill( (df['report_id'].astype(int)).astype ('S'+str(id_string_length ) ), id_string_length )\n \n df = df.replace([-999.9, -9999, -999, -999.0, -99999.0, -99999.9, 99999.0, -99999.00 ], np.nan)\n \n #df['observations_id'] =numpy.char.zfill(numpy.arange(ivar.shape[0]).astype('S10'), 10)\n \n df.sort_values(by = ['record_timestamp', 'vertco_reference_1@body' ] ) \n #df['report_id'] = numpy.int64 (df['report_id'] ) \n #df['observation_id'] = numpy.int64 (df['observation_id'] ) \n df = df.sort_values(by = ['record_timestamp', 'vertco_reference_1@body' ] ) \n \n print('Done reading DF')\n return df , stations_id","def read_precios(path=\".\"):\n\n here = Path(path)\n sucursales = sucursales_prov(here)\n productos = pd.read_csv(here / \"productos.csv\")\n precios = [read_precio(f, sucursales) for f in Path(path).glob(\"precios_*.csv\")]\n\n # aplico merges de todos los precios\n def merge(left, right):\n return (\n pd.merge(left, right, on=[\"producto_id\", \"id_prov\", \"provincia\"], how=\"inner\").dropna()\n # .drop([\"sucursal_id_x\", \"sucursal_id_y\"], axis=1)\n )\n\n precios = functools.reduce(merge, precios)\n # eliminar columnas repetidas\n precios = precios.loc[:, ~precios.columns.duplicated()]\n precios = precios.rename(columns={\"cadena_x\": \"cadena\"})\n\n # elimina columnas innecesarias.\n # basicamente sucursal_* y cadena_y (queda cadena, renombrada antes)\n for c in set(precios.columns):\n if c.startswith((\"sucursal\", \"cadena_\")):\n precios.drop(c, axis=1, inplace=True)\n\n # cruzamos datos con productos\n precios = pd.merge(\n precios,\n productos[[\"id\", \"marca\", \"nombre\", \"categoria1\", \"categoria2\", \"categoria3\"]],\n left_on=\"producto_id\",\n right_on=\"id\",\n ).drop([\"id\", \"id_prov\"], axis=1)\n\n precios.sort_index(axis=1, inplace=True)\n\n # obtenemos el periodo maximo inicio vs fin (asumiendo columnas ordenadas)\n inicio, *_, fin = [c for c in precios.columns if c.startswith(\"precio_\")]\n\n precios[\"variacion\"] = precios[fin] - precios[inicio]\n precios[\"variacion_relativa\"] = precios[\"variacion\"] / precios[inicio] * 100\n return precios","def prepare_data(self):","def _importInDjango(self):\n\n with open(settings.DATA_PATH, 'r', encoding='latin-1') as csv_file:\n reader = csv.DictReader(csv_file, delimiter=';')\n for raw in reader:\n\n # Créer ou mettre à jour la division\n division, created = Division.objects.get_or_create(\n nom=raw['Division']\n )\n if created:\n self.stdout.write(\n 'Divion {} ajoutée'.format(division.nom)\n )\n\n # Créer ou mettre à jour les équipes\n equipeDom, created = Equipe.objects.get_or_create(\n nom=raw['Equipe 1'],\n division=division\n )\n if created:\n self.stdout.write(\n 'Equipe {} ajoutée'.format(equipeDom.nom)\n )\n\n equipeExt, created = Equipe.objects.get_or_create(\n nom=raw['Equipe 2'],\n division=division\n )\n if created:\n self.stdout.write(\n 'Equipe {} ajoutée'.format(equipeExt.nom)\n )\n\n # Créer ou mettre à jour la rencontre\n scoreDom = 0 if raw['Score 1'] == '' else int(raw['Score 1'])\n scoreExt = 0 if raw['Score 2'] == '' else int(raw['Score 2'])\n forfaitDom = True if raw['Forfait 1'] == 'true' else False\n forfaitExt = True if raw['Forfait 2'] == 'true' else False\n date = datetime.datetime.strptime(raw['Date de rencontre'], '%d/%m/%Y')\n heure = datetime.datetime.strptime(raw['Heure'], '%H:%M')\n rencontre, created = Rencontre.objects.update_or_create(\n numero=int(raw['N° de match']),\n equipeDom=equipeDom,\n equipeExt=equipeExt,\n defaults={\n 'date': date,\n 'heure': heure,\n 'scoreDom': scoreDom,\n 'scoreExt': scoreExt,\n 'forfaitDom': forfaitDom,\n 'forfaitExt': forfaitExt,\n }\n )\n if created:\n self.stdout.write(\n 'Rencontre {} / {} ajoutée'.format(\n rencontre.equipeDom,\n rencontre.equipeExt\n )\n )","def dataset()-> pd.DataFrame:\n old= pd.read_csv(DIRECT_OLD)\n new= pd.read_csv(DIRECT_NEW)\n return pd.concat([old, new])","def load_and_fix(self):\n # Read in json\n self.read_json()\n\n if self.size_to_load:\n self.data = self.data[:self.size_to_load]\n\n # Add names from database given _bsn:\n self.extend_dataframe_with_personnames()\n\n # Clean rows in the data_frame where the names column is empty - > thus no response from the database\n self.clean_none_response()\n\n # Fix path from A09.pdf to A09.json\n self.fix_path()\n\n # Get the correct names from the database response\n self.parse_names_from_response()\n\n print(\" --- Final Shape Data ---\")\n print(self.data.shape)\n print(list(self.data))\n\n # Save pickled object in ./data map\n self.save_obj(self.data, self.file_name_to_save)","def load_data():\r\n #读取User数据\r\n users_title = ['UserID', 'Gender', 'Age', 'JobID', 'Zip-code']\r\n users = pd.read_csv('./ml-1m/users.dat', sep='::', header=None, names=users_title, engine = 'python')\r\n users = users.filter(regex='UserID|Gender|Age|JobID')\r\n users_orig = users.values\r\n #改变User数据中性别和年龄\r\n gender_map = {'F':0, 'M':1}\r\n users['Gender'] = users['Gender'].map(gender_map)\r\n\r\n age_map = {val:ii for ii,val in enumerate(set(users['Age']))}\r\n users['Age'] = users['Age'].map(age_map)\r\n\r\n #读取Movie数据集\r\n movies_title = ['MovieID', 'Title', 'Genres']\r\n movies = pd.read_csv('./ml-1m/movies.dat', sep='::', header=None, names=movies_title, engine = 'python')\r\n movies_orig = movies.values\r\n #将Title中的年份去掉\r\n pattern = re.compile(r'^(.*)\\((\\d+)\\)$')\r\n\r\n title_map = {val:pattern.match(val).group(1) for ii,val in enumerate(set(movies['Title']))}\r\n movies['Title'] = movies['Title'].map(title_map)\r\n\r\n #电影类型转数字字典\r\n genres_set = set()\r\n for val in movies['Genres'].str.split('|'):\r\n genres_set.update(val)\r\n\r\n genres_set.add('<PAD>')\r\n genres2int = {val:ii for ii, val in enumerate(genres_set)}\r\n\r\n #将电影类型转成等长数字列表，长度是18\r\n genres_map = {val:[genres2int[row] for row in val.split('|')] for ii,val in enumerate(set(movies['Genres']))}\r\n\r\n for key in genres_map:\r\n for cnt in range(max(genres2int.values()) - len(genres_map[key])):\r\n genres_map[key].insert(len(genres_map[key]) + cnt,genres2int['<PAD>'])\r\n \r\n movies['Genres'] = movies['Genres'].map(genres_map)\r\n\r\n #电影Title转数字字典\r\n title_set = set()\r\n for val in movies['Title'].str.split():\r\n title_set.update(val)\r\n \r\n title_set.add('<PAD>')\r\n title2int = {val:ii for ii, val in enumerate(title_set)}\r\n\r\n #将电影Title转成等长数字列表，长度是15\r\n title_count = 15\r\n title_map = {val:[title2int[row] for row in val.split()] for ii,val in enumerate(set(movies['Title']))}\r\n \r\n for key in title_map:\r\n for cnt in range(title_count - len(title_map[key])):\r\n title_map[key].insert(len(title_map[key]) + cnt,title2int['<PAD>'])\r\n \r\n movies['Title'] = movies['Title'].map(title_map)\r\n\r\n #读取评分数据集\r\n ratings_title = ['UserID','MovieID', 'ratings', 'timestamps']\r\n ratings = pd.read_csv('./ml-1m/ratings.dat', sep='::', header=None, names=ratings_title, engine = 'python')\r\n ratings = ratings.filter(regex='UserID|MovieID|ratings')\r\n\r\n #合并三个表\r\n data = pd.merge(pd.merge(ratings, users), movies)\r\n \r\n #将数据分成X和y两张表\r\n target_fields = ['ratings']\r\n features_pd, targets_pd = data.drop(target_fields, axis=1), data[target_fields]\r\n \r\n features = features_pd.values\r\n targets_values = targets_pd.values\r\n \r\n return title_count, title_set, genres2int, features, targets_values, ratings, users, movies, data, movies_orig, users_orig","def demodulate_data_original(self, data):\n bank = self.bank\n demod = np.zeros_like(data)\n t = np.arange(data.shape[0])\n for n, ich in enumerate(self.readout_selection):\n phi0 = self.phases[ich]\n k = self.tone_bins[bank,ich]\n m = self.fft_bins[bank,ich]\n if m >= self.nfft / 2:\n sign = -1.0\n doconj = True\n else:\n sign = -1.0\n doconj = False\n nfft = self.nfft\n ns = self.tone_nsamp\n foffs = (k * nfft - m * ns) / float(ns)\n demod[:, n] = np.exp(sign * 1j * (2 * np.pi * foffs * t + phi0)) * data[:, n]\n if doconj:\n demod[:, n] = np.conjugate(demod[:, n])\n return demod*self.wavenorm","def save_calib_data(self):\r\n \r\n #get data to save\r\n x0 = self.ui.x0.value()\r\n x1 = self.ui.x1.value()\r\n y0 = self.ui.y0.value()\r\n y1 = self.ui.y1.value()\r\n \r\n directory, fileName = os.path.split(self.ui.imagePath.text())\r\n nofpixels = (max(x0,x1)-min(x0,x1))*(max(y0,y1)-min(y0,y1))\r\n\r\n #create a list with everything to be saved \r\n #(fast way in Python to build a string)\r\n strList = [self.ui.filmNumber.text(),#indentifier\r\n fileName, #file name\r\n \"{:d}\".format(x0),#coordinates\r\n \"{:d}\".format(y0),\r\n \"{:d}\".format(x1),\r\n \"{:d}\".format(y1),\r\n \"{:d}\".format(nofpixels)] \r\n \r\n \r\n #save the channel data\r\n for channel in [0,1,2]:\r\n avg = np.average(self.npImg[y0:y1,x0:x1,channel])\r\n std = np.std(self.npImg[y0:y1,x0:x1,channel])\r\n strList.append(\"{:.3f}\".format(avg))\r\n strList.append(\"{:.3f}\".format(std))\r\n\r\n #concatenate the list, using tab as a seperator\r\n saveStr = '\\t'.join(strList)+\"\\n\"\r\n \r\n self.saveTablePath = self.check_save_table_path(self.ui.saveTablePath.text())\r\n \r\n if self.saveTablePath == \"\":\r\n logging.error(\"no valid file selected, nothing written\")\r\n else:\r\n with open(self.saveTablePath,\"a\") as saveTable:\r\n saveTable.write(saveStr)\r\n logging.info((\"info for \"+self.ui.filmNumber.text()+\" written to file\"))","def Save(cls):\n print(\"Saving data...\")\n # Check if the backup directory exists\n backup_dir = FILE_PATH + \"FellesLab_Backup\"\n if not os.path.isdir(backup_dir):\n os.mkdir(backup_dir)\n with open( backup_dir + \"/README\", 'w') as f:\n f.write(BACKUP_README)\n\n #cmd = \"python -m markdown {dir}/README > {dir}/README.html\".format(dir=backup_dir)\n #call([cmd])\n # Check if the Backup directory has a directory for \"today\"\n day = FILE_PATH + \"FellesLab_Backup/\" + cls.dayStamp()\n if not os.path.isdir(day):\n os.mkdir(day)\n\n # Save data for all the sensors\n # TODO: Rewrite, difficult to follow...\n DATA = [ ] # This will become a list of lists, e.g.\n # [ [time, ...], [Temp1, ...], [time, ...], [Temp2, ...] ]\n for ref in cls.__refs__:\n\n ref().File.seek(0) # Rewind file pointer\n\n F = csv.reader(ref().File, delimiter=',', quotechar='|')\n r = [[],[]]\n for row in F:\n for i,num in enumerate(row):\n r[i].append(num)\n if i > 1:\n r[i].append(float(num))\n\n for j in r:\n DATA.append(j)\n ref().File.close()\n\n # Finally, write data file.\n with open( day + \"/\" + cls.timeStamp() + '.csv', 'w') as f:\n csv.writer(f).writerows( it.izip_longest(*DATA, fillvalue='NA') )","def reset_data(self):\n self.data = None","def restore(self):\n raise NotImplementedError","def Dbase_to_DF(self):\n for item in sorted(self.dbase.keys()):\n self.dataFRAME[item]=self.dbase[item]\n return self.dataFRAME","def uvozi_osebe(cur):\n cur.execute(\"DELETE FROM oseba;\")\n with open('podatki/oseba.csv') as datoteka:\n podatki = csv.reader(datoteka)\n stolpci = next(podatki)\n poizvedba = \"\"\"\n INSERT INTO oseba VALUES ({})\n \"\"\".format(', '.join([\"?\"] * len(stolpci)))\n for vrstica in podatki:\n cur.execute(poizvedba, vrstica)","def actualizar_tabla(self):\n self.ajustar_tabla()\n sincronizaciones = Archivo_crontab.listar_sincronizaciones()\n self.tablaSincronizaciones.setRowCount(len(sincronizaciones))\n row = 0\n for i in sincronizaciones:\n (btn_borrar, btn_editar) = self.crear_botones_fila()\n ruta_archivo = QTableWidgetItem(i[0])\n servidor = QTableWidgetItem(i[2])\n tiempo = QTableWidgetItem(' ' + i[3] + ' ' + i[4] + ' ' + i[5] + ' ' + i[6])\n self.tablaSincronizaciones.setItem(row, 0, ruta_archivo)\n self.tablaSincronizaciones.setItem(row, 1, servidor)\n self.tablaSincronizaciones.setItem(row, 2, tiempo)\n self.tablaSincronizaciones.setCellWidget(row, 3, btn_borrar)\n self.tablaSincronizaciones.setCellWidget(row, 4, btn_editar)\n row += 1\n\n self.tablaSincronizaciones.setEditTriggers(QtWidgets.QTableWidget.NoEditTriggers)\n self.tablaSincronizaciones.resizeRowsToContents()","def apilar(pila, dato):\n pila.cima += 1\n pila.datos[pila.cima] = dato","def BorrarListas(self,evt):\n self.l_bal = []\n self.l_muestras = []\n self.l_vol = []\n self.l_den = []\n self.l_calidad = []\n self.idact = 0","def actualizar_tabla(self):\n\n self.padre.limpiarTabla(self.tablePagos)\n for row, cobro in enumerate(self.detalles_cobro.values()):\n self.tablePagos.insertRow(row)\n self.tablePagos.setItem(row,0,QtGui.QTableWidgetItem(cobro[0]))\n self.tablePagos.setItem(row,1,QtGui.QTableWidgetItem(\"$\"+str(cobro[1])))","def __remover_jugador(self, ip, datos):\n if MAKELOG:\n key = \"Jugador Removido %s\" % time.time()\n APPEND_LOG({key: ip})\n self.server.JUGADORES[ip]['pos'] = \"-,-,-\"\n self.server.JUGADORES[ip]['bala'] = \"-,-,-\"","def borrar_primero(self):\t\t\n\t\tif self.len == 0:\n\t\t\traise ValueError(\"Lista vacia\")\n\t\tdato = self.prim.dato\n\t\tself.prim = self.prim.prox\n\t\tself.len -= 1\n\t\treturn dato","def MergeDataframe(clip_im_dir, matting_dir):\r\n\r\n clip_dict = MergeClipData(clip_im_dir)\r\n mate_dict = MergeMateData(matting_dir)\r\n\r\n clip_df = pd.DataFrame.from_dict(clip_dict)\r\n mate_df = pd.DataFrame.from_dict(mate_dict)\r\n\r\n data_df = pd.merge(clip_df, mate_df, left_index=True, right_index=True)\r\n\r\n # save\r\n data_df.to_csv('H:/Projet/ia/blurry_background/data/protrait.csv', index=False)\r\n\r\n return data_df"],"string":"[\n \"def SaveData(self, conn):\\n t = time.process_time()\\n log = Logger()\\n cursor = conn.cursor()\\n sqlQuery = \\\"insert into old_data \\\" \\\\\\n \\\"select * from new_data\\\"\\n cursor.execute(sqlQuery)\\n conn.commit()\\n conn.close()\\n elapsed_t = time.process_time() - t\\n log.log('Datos de cambios copiados a tabla', elapsed_t)\",\n \"def restore_data(self):\\n self.R = self._Ro\\n del self._Ro\",\n \"def datamunge(cls, data):\\n pass\",\n \"def lee_base_covid(fecha):\\n zipname = \\\"data/\\\"+fecha+\\\"COVID19MEXICO.zip\\\"\\n filename = fecha+\\\"COVID19MEXICO.csv\\\"\\n\\n try:\\n start_time = time()\\n zipfile = ZipFile(zipname)\\n df = pd.read_csv(zipfile.open(filename), parse_dates=True)\\n end_time = time()\\n total_time = end_time - start_time\\n except FileNotFoundError:\\n print(\\\"La base de datos no se encuentra en la carpeta.\\\")\\n\\n print(f\\\"La base de datos tardó en cargarse {total_time} segundos\\\")\\n return df\",\n \"def migrar(self):\\r\\n start = time.time()\\r\\n if not os.path.exists('output'):\\r\\n os.makedirs('output')\\r\\n # seleciona as tabelas\\r\\n res = self.cur_origem.execute(\\r\\n \\\"select rdb$relation_name from rdb$relations where rdb$view_blr is null and (rdb$system_flag is null or rdb$system_flag = 0) %s;\\\" % self.filtro)\\r\\n\\r\\n # para cada tabela\\r\\n for row, in res.fetchall():\\r\\n row = row.strip()\\r\\n\\r\\n # conta os registros\\r\\n countsql = self.cur_origem.execute(\\r\\n \\\"select count(*) as total from %s \\\" % row)\\r\\n count, = countsql.fetchall()[0]\\r\\n start_time = time.time()\\r\\n start_datetime = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n print(\\\"MIGRANDO: %s\\\\n NRO REGISTROS: %s registros\\\\n INICIO: %s\\\" % (\\r\\n row, count, start_datetime))\\r\\n\\r\\n # gera o create table e trunca a tabela ( se ja existir )\\r\\n create, tipos = self.ddl_table(row)\\r\\n self.cur_destino.execute(create)\\r\\n self.cur_destino.execute(\\\"TRUNCATE TABLE %s\\\" % row)\\r\\n\\r\\n # busca os dados\\r\\n self.cur_origem.execute(\\\"select * from %s \\\" % (row))\\r\\n\\r\\n # grava os dados no TXT\\r\\n with open(\\\"output/%s.txt\\\" % row, \\\"wb\\\") as f:\\r\\n writer = csv.writer(f, delimiter='|')\\r\\n writer.writerows(self.cur_origem.fetchall())\\r\\n\\r\\n # le o arquivo gravado e copia para o banco destino\\r\\n with open(\\\"output/%s.txt\\\" % row, \\\"r\\\") as f:\\r\\n try:\\r\\n self.cur_destino.copy_expert(\\r\\n \\\"\\\"\\\"COPY %s FROM STDIN WITH QUOTE '\\\"' DELIMITER '|' NULL '' CSV \\\"\\\"\\\" % row, f)\\r\\n except Exception as e:\\r\\n self.erros.append([\\\"%s\\\" % row, e])\\r\\n end_time = time.time()\\r\\n end_datetime = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n print(\\\" FIM: %s\\\\n TEMPO: %ss\\\\n TABELA COM ERRO %s\\\" %\\r\\n (end_datetime, round(end_time-start_time, 0), e))\\r\\n else:\\r\\n end_time = time.time()\\r\\n end_datetime = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n print(\\\" FIM: %s\\\\n TEMPO: %ss\\\\n OK\\\" %\\r\\n (end_datetime, round(end_time-start_time, 0)))\\r\\n end = time.time()\\r\\n print(\\\"TEMPO GASTO: %s s\\\" % (end-start))\",\n \"def D_Base_to_Exel(self):\\n# for item in sorted(self.dbase.keys()): # for every key/cell add to a dataFRAME\\n# self.dataFRAME[item]=self.dbase[item]\\n \\n self.dataFRAME = self.Dbase_to_DF()\\n writer = ExcelWriter(self.path+'/ALLwells'+ self.filetype) # assign a path for the file\\n self.dataFRAME.to_excel(writer, 'Sheet1') # create a file in the same path the original files came from\\n writer.save()\",\n \"def cargar_otras(self):\\n\\n stream_cargar = open ('yo_otros.txt', 'rt',encoding=\\\"utf-8\\\")\\n datos=stream_cargar.readlines()\\n \\n # print(datos)\\n # print (len(kasino.maquinas))\\n\\n lista_maquinas=[]\\n lista_deco =[]\\n day=\\\"\\\"\\n money=\\\"\\\"\\n\\n contador=0\\n dia_o_dinero=\\\"dia\\\"\\n\\n for i in datos[0]:\\n # print(contador,i)\\n if contador <8:\\n lista_maquinas.append(i)\\n contador+=1\\n\\n elif contador <17:\\n lista_deco.append(i)\\n contador+=1\\n\\n\\n elif contador >= 17 and dia_o_dinero ==\\\"dia\\\":\\n if i ==\\\"D\\\":\\n pass\\n elif i ==\\\"M\\\":\\n dia_o_dinero=\\\"dinero\\\"\\n else:\\n day+=i\\n elif contador >= 17 and dia_o_dinero == \\\"dinero\\\":\\n money+=i\\n \\n \\n\\n # print(\\\"lm\\\",lista_maquinas)\\n # print (\\\"ld\\\",lista_deco)\\n # print(day,money)\\n\\n contador=0\\n for i in kasino.maquinas:\\n kasino.maquinas[i]=int(lista_maquinas[contador])\\n contador+=1\\n\\n contador=0\\n for i in kasino.decoracion:\\n kasino.decoracion[i]=int(lista_deco[contador])\\n contador+=1\\n\\n kasino.dia=int( day)\\n kasino.dinero=int(money)\",\n \"def uvozi_filme(cur):\\n cur.execute(\\\"DELETE FROM narocila;\\\")\\n with open('podatki/narocila.csv') as datoteka:\\n podatki = csv.reader(datoteka)\\n stolpci = next(podatki)\\n poizvedba = \\\"\\\"\\\"\\n INSERT INTO narocila VALUES ({})\\n \\\"\\\"\\\".format(', '.join([\\\"?\\\"] * len(stolpci))) #---------zakaj tukaj vprašaji???-------------\\n for vrstica in podatki:\\n cur.execute(poizvedba, vrstica)\",\n \"def loaddata(self):\\n # Connect to the db\\n self.conn, self.c = self.connect_db(self.dbname)\\n # create the bdefile table to \\n self.c.execute(oeeutil.sql_create_bdefile_table)\\n # Delete any previous records\\n self.c.execute('DELETE FROM bdefile')\\n # hold the content for analysis\\n for item in self.content:\\n self.c.execute('INSERT INTO bdefile VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?)', item)\\n self.c.executescript(oeeutil.sql_create_bdefile_view)\\n self.conn.commit()\",\n \"def formulate(self):\\n # initialize new empty file\\n draft_win = np.empty([])\\n f = open('draft_win.txt','wb') \\n pickle.dump(draft_win,f)\\n f.close()\\n # read lineup and outcome from target_mmr\\n target_mmr = pd.read_csv('target_mmr.csv')\\n draft_win_df = target_mmr.loc[:, ['radiant_team', 'dire_team', 'radiant_win']]\\n # convert into numpy array\\n # Radiant\\n draft_win_df_r = pd.DataFrame(draft_win_df.radiant_team.str.split(',',4).tolist(), columns = ['r1','r2','r3','r4','r5'])\\n # Dire\\n draft_win_df_d = pd.DataFrame(draft_win_df.dire_team.str.split(',',4).tolist(), columns = ['d1','d2','d3','d4','d5'])\\n # outcome\\n draft_win_df_w = draft_win_df.radiant_win\\n # join\\n draft_win_df = draft_win_df_r.join(draft_win_df_d).join(draft_win_df_w)\\n # save into numpy array\\n draft_win_np = draft_win_df.values\\n # str to int\\n draft_win_np = draft_win_np.astype(np.int)\\n # sort lineup (hero id)\\n draft_win_np[:, :5].sort()\\n draft_win_np[:, 5:10].sort()\\n print(draft_win_np.shape)\\n # augment data by swap the lineup\\n augmented = draft_win_np.copy()\\n augmented[:, :5] = draft_win_np[:, 5:10]\\n augmented[:, 5:10] = draft_win_np[:, :5]\\n augmented[:, -1] = 1 - draft_win_np[:, -1]\\n draft_win_np_augmented = np.concatenate((draft_win_np, augmented), axis=0)\\n # pickle\\n f = open('draft_win.txt','wb') \\n pickle.dump(draft_win_np_augmented,f)\\n f.close()\\n # read pickle numpy array\\n f = open('draft_win.txt','rb') \\n draft_win = pickle.load(f) \\n f.close()\\n print('draft_win shape:\\\\n', draft_win.shape)\\n print('draft_win:\\\\n', draft_win[0:2])\",\n \"def archivos_de_texto():\\n palabra = \\\"\\\" \\n palabras_candidatas = [] #lista donde se guardara las palabras candidatas de cada linea\\n palabra_cantidad = {} #diccionario con la palabra candidata de clave y las veces que esta repetida en cada texto de valor\\n with open(\\\"Cuentos.txt\\\",\\\"r\\\") as Cuentos: \\n for linea_Cuentos in Cuentos: #cada ciclo del for es una linea del texto\\n for caracter in linea_Cuentos: #cada ciclo del for es una caracter de la linea \\n if caracter.isalpha():\\n caracter = quitar_tilde_y_may(caracter) #se transformas caracteres mayusculas y tildes\\n palabra += caracter #cada caracter ira formando la palabra\\n if not caracter.isalpha():\\n if len(palabra) >= 5: #se analiza que la palabra tenga 5 o mas caracteres\\n palabras_candidatas.append(palabra) \\n palabra = \\\"\\\" #se vacia la palabra ya analizada\\n for palabra_en_lista in palabras_candidatas: #se introduce las palabras candidatas a un diccionario\\n if palabra_en_lista not in palabra_cantidad:\\n palabra_cantidad[palabra_en_lista] = [1,0,0]\\n else:\\n palabra_cantidad[palabra_en_lista] = [int(palabra_cantidad[palabra_en_lista][0]) + 1 , 0, 0]\\n palabras_candidatas = []\\n with open(\\\"La araña negra - tomo 1.txt\\\",\\\"r\\\") as La_arana_negra:#se repite el mismo proceso con los otros dos textos\\n for linea_Cuentos in La_arana_negra:\\n for caracter in linea_Cuentos:\\n if caracter.isalpha():\\n caracter = quitar_tilde_y_may(caracter)\\n palabra += caracter\\n if not caracter.isalpha():\\n if len(palabra) >= 5:\\n palabras_candidatas.append(palabra)\\n palabra = \\\"\\\"\\n for palabra_en_lista in palabras_candidatas:\\n if palabra_en_lista not in palabra_cantidad:\\n palabra_cantidad[palabra_en_lista] = [0,1,0]\\n else:\\n palabra_cantidad[palabra_en_lista] = [palabra_cantidad[palabra_en_lista][0] , int(palabra_cantidad[palabra_en_lista][1]) + 1, 0]\\n palabras_candidatas = [] \\n with open(\\\"Las 1000 Noches y 1 Noche.txt\\\",\\\"r\\\") as muchas_noches: \\n for linea_Cuentos in muchas_noches:\\n for caracter in linea_Cuentos:\\n if caracter.isalpha():\\n caracter = quitar_tilde_y_may(caracter)\\n palabra += caracter\\n if not caracter.isalpha():\\n if len(palabra) >= 5:\\n palabras_candidatas.append(palabra)\\n palabra = \\\"\\\"\\n for palabra_en_lista in palabras_candidatas:\\n if palabra_en_lista not in palabra_cantidad:\\n palabra_cantidad[palabra_en_lista] = [0,0,1]\\n else:\\n palabra_cantidad[palabra_en_lista] = [palabra_cantidad[palabra_en_lista][0] ,palabra_cantidad[palabra_en_lista][1], int(palabra_cantidad[palabra_en_lista][2]) + 1]\\n palabras_candidatas = [] \\n palabra_cantidad = dict(sorted(palabra_cantidad.items())) #se ordena el diccionario alfabeticamente\\n with open(\\\"palabras.csv\\\",\\\"w\\\") as palabras_csv: # se agrga el diccionario a un arcivo .csv\\n for palabra in palabra_cantidad:\\n palabras_csv.write(palabra)\\n palabras_csv.write(\\\",\\\")\\n palabras_csv.write(str(palabra_cantidad[palabra][0]))\\n palabras_csv.write(\\\",\\\")\\n palabras_csv.write(str(palabra_cantidad[palabra][1]))\\n palabras_csv.write(\\\",\\\")\\n palabras_csv.write(str(palabra_cantidad[palabra][2]))\\n palabras_csv.write(\\\"\\\\n\\\")\\n return palabra_cantidad\",\n \"def actualizarLBI(produ,datInv,cant): # Esta sección fue hecha por Valeria \\n datInv[produ][2] -= cant\\n return datInv\",\n \"def caricaDataSet(self):\\n # salvataggio del percorso della cartella contenente i dataset\\n currentDir = os.path.dirname(os.path.abspath(__file__))\\n pathDataset = os.path.join(currentDir, 'dataset')\\n # permetto il solo caricamento di file csv\\n filter = \\\"csv(*.csv)\\\"\\n try:\\n # apro la schermata di caricamento\\n nomeFile = QFileDialog.getOpenFileName(None,None,pathDataset,filter)\\n percorso = nomeFile[0]\\n datafilename = percorso\\n d = ','\\n f = open(datafilename, 'r')\\n reader = csv.reader(f, delimiter=d)\\n ncol = len(next(reader))\\n if(ncol != 5):\\n raise ValueError\\n\\n except:\\n messaggio = 'err: apertura file'\\n self.ui.comunicazione.setText(messaggio)\\n\\n # controllo che sia stato caricato il dataset\\n try:\\n self.__dataSet = percorso\\n\\n if(len(self.__dataSet) == 0):\\n messaggio = 'err: dataSet non caricato'\\n raise NameError\\n except:\\n # mostro il messaggio di errore\\n self.ui.comunicazione.setText(messaggio)\",\n \"def descompactar(caminho, nome, formato):\\n\\n # Abre e le o arquivo\\n arquivo = Arquivo(caminho, nome, formato)\\n cabecalho_compactado = arquivo.ler()\\n\\n #----------------------------------------\\n\\n # Descompactacao do cabecalho por meio do gzip\\n cabecalho = g.decompress(cabecalho_compactado)\\n\\n #----------------------------------------\\n\\n # Cabecalho\\n numero_magico = int.from_bytes(cabecalho[0:1], byteorder=\\\"big\\\") \\n if numero_magico != 42:\\n exit()\\n\\n largura = int.from_bytes(cabecalho[1:3], byteorder=\\\"big\\\")\\n altura = int.from_bytes(cabecalho[3:5], byteorder=\\\"big\\\")\\n quantidade_cores = int.from_bytes(cabecalho[5:6], byteorder=\\\"big\\\")\\n lista = list(cabecalho[6:262])\\n conteudo = list(cabecalho[262:len(cabecalho)])\\n\\n #----------------------------------------\\n \\n # Amplia para 8 bits\\n bit = Bitwise(6)\\n imagem_rotulada = bit.ampliar(conteudo, largura*altura)\\n\\n #----------------------------------------\\n\\n # Cria a tabela\\n matriz = [[lista[y], lista[y+1], lista[y+2], lista[y+3]] for y in range(0, len(lista), 4)]\\n tabela = Tabela(matriz)\\n\\n #----------------------------------------\\n\\n # Cria um objeto da classe Imagem\\n final = Imagem(caminho, nome+\\\"_som\\\", \\\"TIFF\\\")\\n # Salva a imagem\\n final.salvar(largura, altura, imagem_rotulada, tabela)\",\n \"def make_data_raw (mdp,do_makedata,filename):\\n #\\n fin = open(filename,'r')\\n for line in fin:\\n lsp = line.split(' ')\\n if len(lsp) > 1: # skip empty lines\\n if lsp[0] == \\\"for\\\": # indicates when to get correlator\\n lsp.pop(0)\\n update_params(mdp,lsp)\\n ## -- do_makedata tells it to go ahead with generating a new data output file\\n ## -- otherwise, just saves parameters to metadata\\n if do_makedata:\\n try:\\n # open correlator file\\n mdp.corr_file = open(mdp.input_path + '/' + mdp.input_fname,'r')\\n except IOError:\\n print \\\"Could not open file \\\",mdp.input_fname\\n continue\\n print mdp.input_fname,',',mdp.tag\\n if not mdp.flag_out_open:\\n try:\\n if mdp.flag_overwrite:\\n ## -- open save file for read+write\\n try:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0) # go to beginning\\n mdp.save_file.truncate() # delete whatever was there before\\n except IOError:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\\n mdp.save_file.close()\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.flag_out_open = True\\n # write first header\\n #corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",\\\\\\n # int(mdp.corr_num.strip(\\\"[]\\\").split(',')[0]))\\n #uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\\n #uf.write_section(mdp.save_file,mdp.key)\\n for num,key in zip(mdp.corr_num,mdp.key):\\n corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\\n uf.write_section(mdp.save_file,key)\\n mdp.flag_overwrite= False\\n else:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0,2) # seek the end of file\\n mdp.flag_out_open = True\\n # write another header\\n #corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",\\\\\\n # int(mdp.corr_num.strip(\\\"[]\\\").split(',')[0]))\\n #uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\\n #uf.write_section(mdp.save_file,mdp.key)\\n for num,key in zip(mdp.corr_num,mdp.key):\\n corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n uf.write_header(mdp.save_file,corr_key,mdp.corr_len)\\n uf.write_section(mdp.save_file,key)\\n #except (IOError):\\n # pass\\n except (AttributeError):\\n print \\\"Attempted to open invalid output file\\\"\\n ##endif ! flag_out_open\\n save_data(mdp)\\n mdp.corr_file.close()\\n ##endif do_makedata\\n ##else \\\"for\\\" not found in control file\\n else:\\n update_params(mdp,lsp)\\n ##endif lsp[0]==for\\n ##endif len(lsp) > 1\\n try:\\n mdp.save_file.close()\\n mdp.flag_out_open = False\\n except (IOError,AttributeError):\\n pass\\n fin.close()\\n return\",\n \"def adelanta_camion(self, entorno, operacion, medio_de_origen_o_destino, camion, tipo):\\r\\n # TODO mejorar implementacion\\r\\n\\r\\n if tipo == \\\"Operacion\\\":\\r\\n\\r\\n operacion.recurso.cola.remove(camion)\\r\\n operacion.recurso.cola = \\\\\\r\\n operacion.recurso.cola[0:operacion.recurso.cola.index(self)] \\\\\\r\\n + [camion] + operacion.recurso.cola[operacion.recurso.cola.index(self):]\\r\\n\\r\\n print str(camion) + \\\" adelantado bajo criterio de \\\" + str(self) + \\\" \\\" + str(entorno.now)\\r\\n print \\\"\\\\tEn sistema: \\\" + str(operacion.recurso.cola) + \\\" Hora: \\\" + str(entorno.now)\\r\\n\\r\\n elif tipo == \\\"Almacen\\\":\\r\\n\\r\\n medio_de_origen_o_destino.cola.remove(camion)\\r\\n medio_de_origen_o_destino.cola = \\\\\\r\\n medio_de_origen_o_destino.cola[0:medio_de_origen_o_destino.cola.index(self)] \\\\\\r\\n + [camion] + medio_de_origen_o_destino.cola[medio_de_origen_o_destino.cola.index(self):]\\r\\n\\r\\n print str(camion) + \\\" adelantado bajo criterio de \\\" + str(self) + \\\" \\\" + str(entorno.now)\\r\\n print \\\"\\\\t\\\" + medio_de_origen_o_destino.nombre + \\\":\\\" \\\\\\r\\n + str(medio_de_origen_o_destino.cola) + \\\" Hora: \\\" + str(entorno.now)\",\n \"def make_data_raw_fast(mdp,do_makedata,filename):\\n #\\n fin = open(filename,'r')\\n for line in fin:\\n lsp = line.split(' ')\\n if len(lsp) > 1: # skip empty lines\\n if lsp[0] == \\\"for\\\": # indicates when to get correlator\\n lsp.pop(0)\\n update_params(mdp,lsp)\\n ## -- do_makedata tells it to go ahead with generating a new data output file\\n ## -- otherwise, just saves parameters to metadata\\n if do_makedata:\\n try:\\n # open correlator file\\n mdp.corr_file = open(mdp.input_path + '/' + mdp.input_fname,'r')\\n except IOError:\\n print \\\"Could not open file \\\",mdp.input_fname\\n continue\\n print mdp.input_fname\\n if not mdp.flag_out_open:\\n try:\\n if mdp.flag_overwrite:\\n ## -- open save file for read+write\\n try:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0) # go to beginning\\n mdp.save_file.truncate() # delete whatever was there before\\n except IOError:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'w')\\n mdp.save_file.close()\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.flag_out_open = True\\n #for num,key in zip(mdp.corr_num,mdp.key):\\n # corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n mdp.flag_overwrite= False\\n else:\\n mdp.save_file = open(mdp.output_path + '/' + mdp.output_fname,'r+')\\n mdp.save_file.seek(0,2) # seek the end of file\\n mdp.flag_out_open = True\\n #for num,key in zip(mdp.corr_num,mdp.key):\\n # corr_key=uf.get_str_key(mdp.corr_file,\\\"correlator_key\\\",num)\\n #except (IOError):\\n # pass\\n except (AttributeError):\\n print \\\"Attempted to open invalid output file\\\"\\n ##endif ! flag_out_open\\n save_data_fast(mdp)\\n mdp.corr_file.close()\\n ##endif do_makedata\\n ##else \\\"for\\\" not found in control file\\n else:\\n update_params(mdp,lsp)\\n ##endif lsp[0]==for\\n ##endif len(lsp) > 1\\n try:\\n mdp.save_file.close()\\n mdp.flag_out_open = False\\n except (IOError,AttributeError):\\n pass\\n fin.close()\\n return\",\n \"def write_base_data(self, daty):\\r\\n self.liczba_pelnych_linii = len(self.output_lista_cegiel) / 5\\r\\n if len(self.output_lista_cegiel)-self.liczba_pelnych_linii * 5 != 0:\\r\\n self.niepelna_liniia = True\\r\\n else:\\r\\n self.niepelna_liniia = False\\r\\n\\r\\n self.write_drugs_name()\\r\\n self.write_cegla_name()\\r\\n self.write_date(daty)\",\n \"def ubicar_fragata():\\n tamano = Fragata.tamano #se importa el tamano del barco desde su clase\\n cantidad = Fragata.cantidad #se importa la cantidad de barcos desde su clase\\n orientacion = orientaciones[(randint(0, 1))] #elige aleatoriamente el index de la tupla orientaciones = (\\\"Vertical\\\", \\\"Horizontal\\\")\\n seguir_coordenadas = True\\n while seguir_coordenadas:\\n mal_ubicado = \\\"no\\\"\\n if orientacion == \\\"Vertical\\\":\\n #se eligen random las filas y las columnas\\n coor_fila = randint(1, numero_filas)\\n coor_columna = randint (1, numero_columnas)\\n while (coor_fila + tamano) > 10: #como su orientacion es vertical la fila incial del barco mas su tamano (2) no puede ser mayor que 10 porque se saldria del mapa\\n coor_fila = randint(1,numero_filas)\\n ubicacion = (coor_fila, coor_columna)\\n lista_temporal.append(ubicacion) #lista donde se ubicaran temporalmente las ubicaciones de los barcos\\n while len(lista_temporal) < tamano: #sacar las posiciones restantes \\n coor_fila += 1\\n ubicacion = (coor_fila, coor_columna)\\n lista_temporal.append(ubicacion)\\n for x in lista_ubicacion_barco:\\n for y in lista_temporal:\\n if x == y:\\n mal_ubicado = \\\"si\\\" #si alguna coordenada de este barco coincide con la de otro la variable mal ubicado sera \\\"si\\\" \\n elif (y[0] == x[0] or (y[0]+1) == x[0] or (y[0]-1) == x[0]) and ((y[1]) == x[1] or (y[1]+1) == x[1] or (y[1]- 1) == x[1]): #validacion para que no se ubique el barco al lado o diagonalmente contiguo a otro\\n mal_ubicado = \\\"si\\\" #si esta validacion sucede la variable mal ubicado sera \\\"si\\\" \\n if orientacion == \\\"Horizontal\\\":\\n #se eligen random las filas y las columnas\\n coor_fila = randint(1, numero_filas)\\n coor_columna = randint(1, numero_columnas)\\n while (coor_columna + tamano) > 10: #como su orientacion es horizontal la columna incial del barco mas su tamano (2) no puede ser mayor que 10 porque se saldria del mapa\\n coor_columna = randint(1, numero_columnas)\\n ubicacion = (coor_fila, coor_columna)\\n lista_temporal.append(ubicacion) #lista donde se ubicaran temporalmente las ubicaciones de los barcos\\n while len(lista_temporal) < tamano: #sacar las posiciones restantes \\n coor_columna += 1\\n ubicacion = (coor_fila, coor_columna)\\n lista_temporal.append(ubicacion)\\n for x in lista_ubicacion_barco:\\n for y in lista_temporal:\\n if x == y:\\n mal_ubicado = \\\"si\\\" #si alguna coordenada de este barco coincide con la de otro la variable mal ubicado sera \\\"si\\\" \\n elif (y[0] == x[0] or (y[0]+1) == x[0] or (y[0]-1) == x[0]) and ((y[1]) == x[1] or (y[1]+1) == x[1] or (y[1]- 1) == x[1]): #validacion para que no se ubique el barco al lado o diagonalmente contiguo a otro\\n mal_ubicado = \\\"si\\\" #si esta validacion sucede la variable mal ubicado sera \\\"si\\\" \\n if mal_ubicado == \\\"si\\\": #si la variable mal ubicado es \\\"si\\\" se repetira el proceso otra vez\\n seguir_coordenadas = True\\n lista_temporal.clear()\\n elif mal_ubicado == \\\"no\\\": #si la variable es \\\"no\\\" se continuara el proceso\\n for x in lista_temporal:\\n lista_ubicacion_barco.append(x) #se agregan las posiciones a la lista general\\n coordenadas_fragata.append(x) \\n lista_temporal.clear() #se limpia la lista temporal para usarla en el otro barco\\n seguir_coordenadas = False\",\n \"def busca_por_data(self, data_inicial, data_final):\\r\\n # data_inicial/data_final formato 'DDMMYYYY'\\r\\n\\tserie = \\\"\\\" \\r\\n\\tchave_acesso = \\\"\\\" \\r\\n self.__busca_notas(\\\"DATA\\\", data_inicial, data_final, serie, chave_acesso)\",\n \"def read(self):\\n try:\\n datos = self.base.readData()\\n for i in range(len(datos)):\\n self.verDatos.insert('', i+1, text = i+1, values = (datos[i][0], datos[i][1], datos[i][2]))\\n except:\\n showerror(\\\"Error\\\", exc_info()[1])\",\n \"def load_data(self):\",\n \"def reverse(self):\\n self.data_encap.reverse()\",\n \"def _copy_data_from(self, original):\\n raise NotImplementedError()\",\n \"def crear_vista_base(self, master, nombre_base=False, nombre_tabla=False, campo_referencia=False, dato_busqueda=False):\\n if not nombre_base:\\n print(\\\"No se ha especificado la base de datos a acceder.\\\\n\\\")\\n return False\\n else:\\n if not nombre_tabla:\\n print(\\\"La base de datos especificada no contiene tablas.\\\\n\\\")\\n self.tree[\\\"columns\\\"] = (1,2,3)\\n for row in self.tree.get_children():\\n self.tree.delete(row)\\n return False\\n\\n contador_campos = 0\\n nombres = []\\n for campo in base.retornar_esquema(nombre_base, nombre_tabla):\\n nombre = campo[1] # escruta el nombre de la columna\\n contador_campos += 1\\n nombres.append(nombre)\\n\\n self.tree[\\\"columns\\\"] = tuple([x for x in range(1,contador_campos+1)] if contador_campos>=7 else [x for x in range(1,8)])\\n\\n if len(nombres) <= 6:\\n for i in range(1,len(self.tree[\\\"columns\\\"])+1):\\n if i > 6:\\n self.tree.column(f\\\"#{i}\\\",\\n width=886-(140*6),\\n stretch=tk.NO)\\n self.tree.heading(f\\\"#{i}\\\", \\n text=f\\\"\\\")\\n break\\n elif i <= 6 and i > len(nombres):\\n self.tree.column(f\\\"#{i}\\\",\\n width=140,\\n stretch=tk.NO)\\n self.tree.heading(f\\\"#{i}\\\", \\n text=f\\\"\\\")\\n else:\\n self.tree.column(f\\\"#{i}\\\",\\n width=140,\\n stretch=tk.NO,\\n anchor='center')\\n self.tree.heading(f\\\"#{i}\\\", \\n text=f\\\"{nombres[i-1].title() if nombres[i-1] not in ('id','ID') else nombres[i-1]}\\\",\\n anchor=\\\"center\\\")\\n else:\\n for i in range(1,len(self.tree[\\\"columns\\\"])+1):\\n self.tree.column(f\\\"#{i}\\\",\\n width=140,\\n stretch=tk.NO,\\n anchor='center')\\n self.tree.heading(f\\\"#{i}\\\", \\n text=f\\\"{nombres[i-1].title() if nombres[i-1] not in ('id','ID') else nombres[i-1]}\\\",\\n anchor=\\\"center\\\")\\n\\n if campo_referencia:\\n if dato_busqueda:\\n if isinstance(dato_busqueda, (str,int,float,bool)):\\n for row in self.tree.get_children():\\n self.tree.delete(row)\\n filas = base.traer_datos(nombre_base, nombre_tabla, campo_referencia, dato_busqueda)\\n for fila in filas:\\n self.tree.insert(\\\"\\\", tk.END, values=fila)\\n elif isinstance(dato_busqueda, (tuple,list,dict)):\\n for row in self.tree.get_children():\\n self.tree.delete(row)\\n for valor in dato_busqueda:\\n filas = base.traer_datos(nombre_base, nombre_tabla, campo_referencia, valor)\\n for fila in filas:\\n self.tree.insert(\\\"\\\", tk.END, values=fila)\\n else:\\n for row in self.tree.get_children():\\n self.tree.delete(row)\\n filas = base.retornar_datos(nombre_base, nombre_tabla)\\n for fila in filas:\\n self.tree.insert(\\\"\\\", tk.END, values=fila)\\n else:\\n for row in self.tree.get_children():\\n self.tree.delete(row)\\n filas = base.retornar_datos_segun_tiempo(nombre_base, nombre_tabla)\\n for fila in filas:\\n self.tree.insert(\\\"\\\", tk.END, values=fila)\",\n \"def guardar_otras (self,maquinas,decoracion,dia,dinero):#dicc,dicc,int,int\\n\\n stream_guardar = open(\\\"yo_otros.txt\\\",\\\"wt\\\",encoding=\\\"utf-8\\\")\\n for i in maquinas:\\n stream_guardar.write(str(maquinas[i]))\\n\\n for i in decoracion:\\n stream_guardar.write(str(decoracion[i]))\\n\\n stream_guardar.write(\\\"D\\\"+str(dia))\\n stream_guardar.write(\\\"M\\\"+str(dinero))\",\n \"def getDbase(self):\\n for item in self.sqlData: # for every colummn name in the data\\n self.sqdbase[item]=np.array(self.sqlData[item]) # add to the dictionary the clomunm name and the corresponding data\\n \\n self.sqlData['index'] = list(range(len(self.sqlData['time']))) # since we sometimes have even a column for index(pandas put it automatically) and sometimes not which will not be used for Stats we dropp it out\\n self.sqdbase.pop('index') # we make sure that all dataFRames we are working with has inde column and the drop it\\n return self.sqdbase\",\n \"def migrar(self):\\r\\n start = time.time()\\r\\n if not os.path.exists('output'):\\r\\n os.makedirs('output')\\r\\n # seleciona as tabelas\\r\\n res = self.cur_origem.execute(\\r\\n \\\"SELECT table_name FROM dba_tables WHERE owner = '%s' %s order by table_name\\\" % (self.schema_origem, self.filtro))\\r\\n\\r\\n # para cada tabela\\r\\n for row, in res.fetchall():\\r\\n row = row.strip()\\r\\n # conta os registros\\r\\n countsql = self.cur_origem.execute(\\r\\n \\\"select count(*) as total from %s \\\" % row)\\r\\n count, = countsql.fetchall()[0]\\r\\n start_time = time.time()\\r\\n start_datetime = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n print(\\\"MIGRANDO: %s\\\\n NRO REGISTROS: %s registros\\\\n INICIO: %s\\\" % (\\r\\n row, count, start_datetime))\\r\\n\\r\\n # gera o create table e trunca a tabela ( se ja existir )\\r\\n create, tipos = self.ddl_table(row)\\r\\n self.cur_destino.execute(create)\\r\\n self.cur_destino.execute(\\\"TRUNCATE TABLE %s\\\" % row)\\r\\n\\r\\n # gera as colunas\\r\\n cols = \\\"\\\"\\r\\n # tratamento exclusivo SONNER\\r\\n for id, [col, tipo] in tipos.iteritems():\\r\\n if col == \\\"SENHA\\\" or (col == \\\"DADOS\\\" and row == \\\"CADARQUIVODIGITAL\\\"):\\r\\n cols += \\\"NULL AS %s,\\\" % col\\r\\n else:\\r\\n cols += \\\"%s,\\\" % col\\r\\n # padrao seria:\\r\\n # for id, [col, tipo] in tipos.iteritems():\\r\\n # cols += \\\"%s,\\\" % col\\r\\n # print \\\"select %s from %s \\\" % (cols[:-1], row)\\r\\n\\r\\n # busca os dados\\r\\n self.cur_origem.execute(\\\"select %s from %s \\\" % (cols[:-1], row))\\r\\n\\r\\n # grava os dados no TXT\\r\\n with open(\\\"output/%s.txt\\\" % row, \\\"wb\\\") as f:\\r\\n w = csv.writer(\\r\\n f, delimiter='|', quotechar='\\\"')\\r\\n try:\\r\\n\\r\\n # execao SONNER (dados TEXT tipo cblob necessita de conversao anterior por conta de encoding)\\r\\n if row == \\\"CADPESSOACONTSOC\\\":\\r\\n for cada in self.cur_origem.fetchall():\\r\\n cada = list(cada)\\r\\n if cada[5] is not None:\\r\\n cada[5] = cada[5].read().encode(\\\"latin-1\\\")\\r\\n w.writerow(cada)\\r\\n\\r\\n else:\\r\\n w.writerows(self.cur_origem.fetchall())\\r\\n except Exception as e:\\r\\n self.erros.append([\\\"%s\\\" % row, e])\\r\\n end_time = time.time()\\r\\n end_datetime = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n print(\\\"\\\\tFIM: %s\\\\tTEMPO: %ss\\\\tTABELA COM ERRO %s\\\" %\\r\\n (end_datetime, round(end_time-start_time, 0), e))\\r\\n\\r\\n # le o arquivo gravado e copia para o banco destino\\r\\n with open(\\\"output/%s.txt\\\" % row, \\\"r\\\") as f:\\r\\n try:\\r\\n self.cur_destino.copy_expert(\\r\\n \\\"\\\"\\\"COPY %s FROM STDIN WITH QUOTE '\\\"' DELIMITER '|' NULL '' CSV \\\"\\\"\\\" % row, f)\\r\\n except Exception as e:\\r\\n self.erros.append([\\\"%s\\\" % row, e])\\r\\n end_time = time.time()\\r\\n end_datetime = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n print(\\\" FIM: %s\\\\n TEMPO: %ss\\\\n TABELA COM ERRO %s\\\" %\\r\\n (end_datetime, round(end_time-start_time, 0), e))\\r\\n else:\\r\\n end_time = time.time()\\r\\n end_datetime = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n print(\\\" FIM: %s\\\\n TEMPO: %ss\\\\n OK\\\" %\\r\\n (end_datetime, round(end_time-start_time, 0)))\\r\\n end = time.time()\\r\\n print(\\\"TEMPO GASTO: %s s\\\" % (end-start))\",\n \"def __preproc_covid(self):\\n\\n # processar datas\\n \\n self.covidbr['data'] = pd.to_datetime(self.covidbr['data'], format='%d/%m/%Y')\\n self.covidbr['data'] = self.covidbr['data'].astype(pd.DatetimeTZDtype(tz='America/Sao_Paulo'))\\n\\n # 2020-06-02: O Ministério da Saúde cagou os dados de população na planilha divulgada diariamente.\\n # devemos consertá-lo\\n # regex: ^((?:\\\\d{1,3}\\\\.)*\\\\d*)\\n\\n # preencher populacao de areas dos estados que não estão em nenhum município\\n\\n pop_estados = self.covidbr[(self.covidbr.estado.notnull()) & (self.covidbr.codmun.isnull())].\\\\\\n groupby('coduf').populacaoTCU2019.first()\\n df = self.covidbr.merge(right=pop_estados, on='coduf', how='left')\\n populacaoTCU2019 = df.populacaoTCU2019_x.where(df.populacaoTCU2019_x.notnull(), other=df.populacaoTCU2019_y)\\n\\n # filtrar dados espúrios de população\\n\\n populacaoTCU2019 = populacaoTCU2019.str.extract(r'^((?:\\\\d{1,3}\\\\.)*\\\\d*)', expand=False).\\\\\\n str.replace('.','').astype(float)\\n\\n # converter tipos\\n\\n df['populacaoTCU2019'] = populacaoTCU2019\\n df.drop(columns=[ 'populacaoTCU2019_' + mergedir for mergedir in ['x', 'y'] ], inplace=True)\\n self.covidbr = df\\n\\n self.covidbr = self.covidbr.astype(\\n { converter: 'Int64' for converter in ['coduf', 'codmun', 'codRegiaoSaude', 'populacaoTCU2019',\\n 'Recuperadosnovos', 'emAcompanhamentoNovos'] }\\n )\",\n \"def guardar_datos(self, archivo):\\r\\n with open(archivo, \\\"wb\\\") as csv_file:\\r\\n writer = csv.writer(csv_file, delimiter=';')\\r\\n writer.writerow(\\r\\n [\\\"Camion\\\", \\\"Carga\\\", \\\"Tipo\\\", \\\"Peso Final\\\", \\\"Operacion\\\", \\\"Recurso\\\", \\\"Dia\\\",\\r\\n \\\"Arribo\\\", \\\"Espera M. O/D\\\", \\\"Espera R\\\", \\\"Espera P.\\\", \\\"Espera T.\\\", \\\"Inicio\\\", \\\"Fin\\\",\\r\\n \\\"Medio de Almacenamiento\\\", \\\"Nivel\\\"])\\r\\n for linea in self.datos:\\r\\n writer.writerow(linea)\",\n \"def recarregar_imagem(self):\\n self.salvar_imagem(caminho_imagem=self.caminho_temp)\\n self.carregar_imagem(caminho_imagem=self.caminho_temp)\",\n \"def normalize_dataset(self):\",\n \"def getBaseMonetaria(self):\\n #Obtener la url de descarga del cvs\\n urlPackage=\\\"https://datos.gob.ar/api/3/action/package_show?id=sspm-factores-explicacion-base-monetaria\\\"\\n s=requests.get(urlPackage).content\\n objJson = json.loads(s)\\n resultado = objJson['result']['resources']\\n selector = 0\\n ultimoResultado = resultado[selector]\\n urlDescarga = ultimoResultado['url']\\n descripcion = ultimoResultado['description']\\n print(\\\"Descargando: {}\\\".format(descripcion))\\n print(\\\"Archivo: {}\\\".format(urlDescarga))\\n \\n #Descargar la url con cvs y generar pandas dataframe\\n contenidoCVS = requests.get(urlDescarga).content\\n flujoCVS = io.StringIO(contenidoCVS.decode('utf-8'))\\n df_temp = pd.read_csv(flujoCVS)\\n \\n #transform string to datetime\\n df_temp['indice_tiempo'] = pd.to_datetime(df_temp['indice_tiempo'], format='%Y-%m-%d', errors='ignore')\\n df_temp['indice_tiempo'] = df_temp['indice_tiempo'].dt.date\\n #set index\\n df_temp.set_index('indice_tiempo', inplace=True)\\n \\n return df_temp\",\n \"def analisa_backup(self):\\n #obtem as pastas que precisam ser criadas na pasta de destino \\n self.obter_pastas_inexistentes_no_destino()\\n \\n #enquanto existir subpasta na pasta atual, faça backup recursivo\\n while(self.sub_pastas_nao_finalizadas):\\n \\n #pega ultima pasta da lista\\n sub_pasta = self.sub_pastas_nao_finalizadas[-1]\\n \\n #cria um objeto backup, a partir da criação de um objeto pasta \\n #(com base na subpasta de origem) e de uma sub_pasta de mesmo nome\\n #na pasta de destino não é necessário que esta subpasta, na pasta \\n #de destino exista. Após a criação do objeto Backup, chama-se o \\n #método analisa_backup, que por meio de recursão varre todas as \\n #subpastas verificando a necessidade de backup\\n Backup(Pasta(sub_pasta),Pasta(os.path.join(self.pasta_destino.obter_caminho(),os.path.basename(sub_pasta)))).analisa_backup()\\n \\n #subpasta finalizada, retira a mesma da lista\\n self.sub_pastas_nao_finalizadas.pop()\\n\\n #Pega somente o nome do arquivo a partir de uma lista com os caminhos \\n #absolutos de cada arquivo\\n arquivos_origem = [os.path.basename(arq) \\\\\\n for arq in self.pasta_origem.obter_lista_arquivos()]\\n \\n arquivos_destino = [os.path.basename(arq) \\\\\\n for arq in self.pasta_destino.obter_lista_arquivos()]\\n\\n for arquivo_origem in arquivos_origem:\\n \\n #Verifica se o arquivo de origem esta na pasta de destino, \\n #caso não esteja, deve ser feito o backup\\\"\\n if arquivo_origem not in arquivos_destino:\\n \\n #insere na lista operacao, um tupla (arquivo,pasta_origem,\\n #pasta_destino) de modo a que no final o backup seja realizado\\\"\\n self.__class__.operacoes_copiar.append(\\\\\\n (arquivo_origem,\\\\\\n self.pasta_origem.obter_caminho(),\\\\\\n self.pasta_destino.obter_caminho())\\\\\\n )\",\n \"def desapilar(pila):\\n dato = pila.datos[pila.tope]\\n pila.tope -= 1\\n return dato\",\n \"def inicializar_tablero(self):\\n\\n cont_pixels_x = 0 # Para contar los pixeles en x, y tenerlos como referencia en la casilla correspondiente\\n cont_pixels_y = 0 # Para contar los pixeles en y, y tenerlos como referencia en la casilla correspondiente\\n pixels_cuadro = 100\\n\\n for f in range(len(self._matriz)): # Inicializando la matriz del tablero que va a contener los objetos casilla\\n cont_pixels_y += pixels_cuadro\\n for c in range(len(self._matriz[f])):\\n cont_pixels_x += pixels_cuadro\\n\\n # Por el momento solo se estara utilizando una ficha para pruebas.\\n if f == 0 and c == 1: #(superior_izq, superior_der, inferior_izq, inferior_der )\\n self._matriz[f][c] = Casilla (Ficha('f_marron.png'), (f, c), (cont_pixels_x - pixels_cuadro, cont_pixels_x, cont_pixels_y - pixels_cuadro, cont_pixels_y))\\n\\n else:\\n self._matriz[f][c] = Casilla(None, (f, c), (cont_pixels_x - pixels_cuadro, cont_pixels_x, cont_pixels_y - pixels_cuadro, cont_pixels_y))\\n\\n cont_pixels_x = 0 # Bajar la columna\\n\\n\\n return self._matriz\",\n \"def format_data(self, raw_data):\\n opz = raw_data.copy()\\n opz['datetime'] = pd.to_datetime(opz['Datum-tijd'], format='%Y-%m-%dT%H:%M:%SZ')\\n opz.drop(['Datum-tijd'],axis=1, inplace=True)\\n opz['dag']=opz['datetime'].dt.day\\n opz['tijd'] = opz['datetime'].dt.time\\n #voeg open/dicht data toe en bepaal momenten waarop dit wisselt\\n opz['Opzetstuk Noord (°)'] = opz['Opzetstuk Noord (°)'].str.replace(',', '.').astype(float)\\n opz['Opzetstuk Zuid (°)'] = opz['Opzetstuk Zuid (°)'].str.replace(',', '.').astype(float)\\n opz['Opzetstuk Noord (°)'].fillna(opz['Opzetstuk Zuid (°)'], inplace=True)\\n opz['Opzetstuk Zuid (°)'].fillna(opz['Opzetstuk Noord (°)'], inplace=True)\\n return opz\",\n \"def transform(self, data):\",\n \"def revise():\",\n \"def rewind():\",\n \"def restore_base_data():\\n\\n db_dirname = os.path.dirname(os.path.realpath(__file__))\\n shutil.copyfile(src=os.path.join(db_dirname, 'consolemini.base.json'),\\n dst=os.path.join(db_dirname, 'consolemini.test.json'))\",\n \"def saveData(self):\\n pass\",\n \"def migrar(self):\\r\\n start = time.time()\\r\\n if not os.path.exists('output'):\\r\\n os.makedirs('output')\\r\\n # seleciona as tabelas\\r\\n res = self.cur_origem.execute(\\r\\n \\\"select table_name from INFORMATION_SCHEMA.tables where TABLE_SCHEMA = '%s' %s order by table_name;\\\" % (self.schema_origem, self.filtro))\\r\\n\\r\\n # para cada tabela\\r\\n for row, in res.fetchall():\\r\\n row = row.strip()\\r\\n # conta os registros\\r\\n countsql = self.cur_origem.execute(\\r\\n \\\"select count(*) as total from %s.%s \\\" % (self.schema_origem,row))\\r\\n count, = countsql.fetchall()[0]\\r\\n start_time = time.time()\\r\\n start_datetime = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n print(\\\"MIGRANDO: %s\\\\n NRO REGISTROS: %s registros\\\\n INICIO: %s\\\" % (\\r\\n row, count, start_datetime))\\r\\n\\r\\n # gera o create table e trunca a tabela ( se ja existir )\\r\\n create, tipos = self.ddl_table(row)\\r\\n self.cur_destino.execute(create)\\r\\n self.cur_destino.execute(\\\"TRUNCATE TABLE %s\\\" % row)\\r\\n\\r\\n # gera as colunas\\r\\n cols = \\\"\\\"\\r\\n for id, [col, tipo] in tipos.items():\\r\\n cols += \\\"%s,\\\" % col\\r\\n # print( \\\"select %s from %s.%s \\\" % (cols[:-1], self.schema_origem,row))\\r\\n\\r\\n # busca os dados\\r\\n \\r\\n\\r\\n # grava os dados no TXT\\r\\n with open(\\\"output/%s.txt\\\" % row, \\\"w\\\", newline='', encoding=\\\"latin-1\\\", errors='ignore') as f:\\r\\n w = csv.writer(\\r\\n f, delimiter='|', quotechar='\\\"')\\r\\n try:\\r\\n self.cur_origem.execute(\\\"select %s from %s.%s \\\" % (cols[:-1], self.schema_origem,row))\\r\\n w.writerows(self.cur_origem.fetchall())\\r\\n except Exception as e:\\r\\n self.erros.append([\\\"%s\\\" % row, e])\\r\\n end_time = time.time()\\r\\n end_datetime = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n print(\\\"\\\\tFIM: %s\\\\tTEMPO: %ss\\\\tTABELA COM ERRO %s\\\" %\\r\\n (end_datetime, round(end_time-start_time, 0), e))\\r\\n\\r\\n # le o arquivo gravado e copia para o banco destino\\r\\n with open(\\\"output/%s.txt\\\" % row, \\\"r\\\", encoding=\\\"latin-1\\\", errors='ignore') as f:\\r\\n try:\\r\\n self.cur_destino.copy_expert(\\r\\n \\\"\\\"\\\"COPY %s FROM STDIN WITH QUOTE '\\\"' DELIMITER '|' NULL '' CSV \\\"\\\"\\\" % row, f)\\r\\n except Exception as e:\\r\\n self.erros.append([\\\"%s\\\" % row, e])\\r\\n end_time = time.time()\\r\\n end_datetime = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n print(\\\" FIM: %s\\\\n TEMPO: %ss\\\\n TABELA COM ERRO %s\\\" %\\r\\n (end_datetime, round(end_time-start_time, 0), e))\\r\\n else:\\r\\n end_time = time.time()\\r\\n end_datetime = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n print(\\\" FIM: %s\\\\n TEMPO: %ss\\\\n OK\\\" %\\r\\n (end_datetime, round(end_time-start_time, 0)))\\r\\n end = time.time()\\r\\n print(\\\"TEMPO GASTO: %s s\\\" % (end-start))\",\n \"def update_original_data(self):\\n pass\",\n \"def Convertir(self,inicio:list,fin:list,direccion:str,color:int) -> None:\\n\\t\\t\\n\\t\\t#coordenada de inicio\\n\\t\\tx1=inicio[0]\\n\\t\\ty1=inicio[1]\\n\\n\\t\\t#coordenada de destino\\n\\t\\tx2=fin[0]\\n\\t\\ty2=fin[1]\\n\\n\\t\\tif direccion==\\\"derecha\\\":\\n\\t\\t\\twhile y1!=y2:\\n\\t\\t\\t\\tself.tablero[x1][y1]=color\\n\\t\\t\\t\\ty1=y1+1\\n\\t\\telif direccion==\\\"izquierda\\\":\\n\\t\\t\\twhile y1!=y2:\\n\\t\\t\\t\\tself.tablero[x1][y1]=color\\n\\t\\t\\t\\ty1=y1-1\\n\\t\\telif direccion==\\\"arriba\\\":\\n\\t\\t\\twhile x1!=x2:\\n\\t\\t\\t\\tself.tablero[x1][y1]=color\\n\\t\\t\\t\\tx1=x1-1\\n\\t\\telif direccion==\\\"abajo\\\":\\n\\t\\t\\twhile x1!=x2:\\n\\t\\t\\t\\tself.tablero[x1][y1]=color\\n\\t\\t\\t\\tx1=x1+1\\n\\t\\telif direccion==\\\"arriba-derecha\\\":\\n\\t\\t\\twhile x1!=x2:\\n\\t\\t\\t\\tself.tablero[x1][y1]=color\\n\\t\\t\\t\\tx1=x1-1\\n\\t\\t\\t\\ty1=y1+1\\n\\t\\telif direccion==\\\"arriba-izquierda\\\":\\n\\t\\t\\twhile x1!=x2:\\n\\t\\t\\t\\tself.tablero[x1][y1]=color\\n\\t\\t\\t\\tx1=x1-1\\n\\t\\t\\t\\ty1=y1-1\\n\\t\\telif direccion==\\\"abajo-derecha\\\":\\n\\t\\t\\twhile x1!=x2:\\n\\t\\t\\t\\tself.tablero[x1][y1]=color\\n\\t\\t\\t\\tx1=x1+1\\n\\t\\t\\t\\ty1=y1+1\\n\\t\\telif direccion==\\\"abajo-izquierda\\\":\\n\\t\\t\\twhile x1!=x2:\\n\\t\\t\\t\\tself.tablero[x1][y1]=color\\n\\t\\t\\t\\tx1=x1+1\\n\\t\\t\\t\\ty1=y1-1\",\n \"def _make_resized_data(self, odata):\\n\\n\\n nmax = odata['file_id'].shape[1]\\n new_nmax = odata['ncutout'].max()\\n if new_nmax < 2:\\n new_nmax = 2\\n temp_obj_data = odata\\n\\n nobj = temp_obj_data.size\\n\\n new_data = meds.util.get_meds_output_struct(\\n nobj,\\n new_nmax,\\n extra_fields=self._get_fields(new_nmax),\\n )\\n new_data = self._add_cat_fields(new_data, copy=False)\\n\\n for name in new_data.dtype.names:\\n if name in temp_obj_data.dtype.names:\\n\\n shape = new_data[name].shape\\n lshape = len(shape)\\n\\n if lshape > 1 and shape[1] == new_nmax:\\n new_data[name][:,:] = temp_obj_data[name][:,0:new_nmax]\\n else:\\n new_data[name][:] = temp_obj_data[name][:]\\n\\n del temp_obj_data\\n\\n return new_data\",\n \"def rebase(self):\\n self.cm.rebase()\",\n \"def load_database(self, main_class):\\n main_class.database.delete_all(\\\"render\\\")\\n main_class.database.delete_all(\\\"object\\\")\\n #main_class.database.delete_all(\\\"output\\\")\\n render_csv = os.path.join(self.filepath, \\\"Render_data.csv\\\")\\n object_csv = os.path.join(self.filepath, \\\"Obj_data.csv\\\")\\n main_class.database.import_excel(render_csv, \\\"render\\\")\\n main_class.database.import_excel(object_csv, \\\"object\\\")\\n\\n render_dic=main_class.database.get_database_dict(\\\"render\\\")\\n\\n main_class.render_database = main_class.database.get_data_from_database(\\\"render\\\")\\n main_class.object_database = main_class.database.get_data_from_database(\\\"object\\\")\\n\\n main_class.background_picture_list = main_class.database.get_background_pictures_names()\\n main_class.packaging_picture_list = main_class.database.get_bubble_wrap_pictures_names()\\n\\n main_class.camera_settings.append([0, 0, 0, 0, 100])\\n for obj in main_class.render_database:\\n \\\"\\\"\\\"\\n extracting Camerasetting from Database and set all important angles and distances\\n \\\"\\\"\\\"\\n if obj[render_dic[\\\"object_type\\\"]] == \\\"camera\\\":\\n for i in range(0, int(obj[render_dic[\\\"polar_angle_segments\\\"]])):\\n for j in range(0, int(obj[render_dic[\\\"azimuth_angle_segments\\\"]])):\\n pol_min = obj[render_dic[\\\"polar_angle_min\\\"]]\\n pol_max = obj[render_dic[\\\"polar_anglel_max\\\"]]\\n pol_segments= obj[render_dic[\\\"polar_angle_segments\\\"]]\\n pol_random=obj[render_dic[\\\"polar_angle_random_rad\\\"]]\\n try:\\n pol_min = float( pol_min.replace(',','.'))\\n except:\\n pass\\n try:\\n pol_max = float( pol_max.replace(',','.'))\\n except:\\n pass\\n try:\\n pol_segments = float( pol_segments.replace(',','.'))\\n except:\\n pass\\n try:\\n pol_random = float( pol_random.replace(',','.'))\\n except:\\n pass\\n polar_angle = (pol_min + ((pol_max - pol_min)/(pol_segments))*i)\\n\\n azi_min = obj[render_dic[\\\"azimuth_angle_min\\\"]]\\n azi_max = obj[render_dic[\\\"azimuth_angle_max\\\"]]\\n azi_segments= obj[render_dic[\\\"azimuth_angle_segments\\\"]]\\n azi_random= obj[render_dic[\\\"azimuth_angle_random_rad\\\"]]\\n\\n try:\\n azi_min = float( azi_min.replace(',','.'))\\n except:\\n pass\\n try:\\n azi_max = float( azi_max.replace(',','.'))\\n except:\\n pass\\n try:\\n azi_segments = float( azi_segments.replace(',','.'))\\n except:\\n pass\\n try:\\n azi_random = float( azi_random.replace(',','.'))\\n except:\\n pass\\n azimuth_angle = (azi_min + ((azi_max - azi_min)/(azi_segments))*j)\\n\\n position=[polar_angle, pol_random, azimuth_angle, azi_random, obj[render_dic[\\\"radius\\\"]] ]\\n print(\\\"camera position added: \\\",position)\\n main_class.camera_settings.append(position)\\n \\n if obj[render_dic[\\\"object_type\\\"]]==\\\"light\\\":\\n\\n if obj[render_dic[\\\"name\\\"]]==\\\"SUN\\\":\\n radius= obj[render_dic[\\\"radius\\\"]]\\n try:\\n radius = float( radius.replace(',','.'))\\n except:\\n pass\\n light_obj=[ obj[render_dic[\\\"name\\\"]] , [0,0, radius ] ]\\n main_class.light_settings.append(light_obj)\\n print(\\\"sun added to list\\\")\\n\\n if obj[render_dic[\\\"name\\\"]]==\\\"SPOT\\\":\\n for i in range(0, int(obj[render_dic[\\\"polar_angle_segments\\\"]])):\\n for j in range(0, int(obj[render_dic[\\\"azimuth_angle_segments\\\"]])):\\n pol_min = obj[render_dic[\\\"polar_angle_min\\\"]]\\n pol_max = obj[render_dic[\\\"polar_anglel_max\\\"]]\\n pol_segments= obj[render_dic[\\\"polar_angle_segments\\\"]]\\n pol_random=obj[render_dic[\\\"polar_angle_random_rad\\\"]]\\n try:\\n pol_min = float( pol_min.replace(',','.'))\\n except:\\n pass\\n try:\\n pol_max = float( pol_max.replace(',','.'))\\n except:\\n pass\\n try:\\n pol_segments = float( pol_segments.replace(',','.'))\\n except:\\n pass\\n try:\\n pol_random = float( pol_random.replace(',','.'))\\n except:\\n pass\\n polar_angle = (pol_min + ((pol_max - pol_min)/(pol_segments))*i)\\n\\n azi_min = obj[render_dic[\\\"azimuth_angle_min\\\"]]\\n azi_max = obj[render_dic[\\\"azimuth_angle_max\\\"]]\\n azi_segments= obj[render_dic[\\\"azimuth_angle_segments\\\"]]\\n azi_random= obj[render_dic[\\\"azimuth_angle_random_rad\\\"]]\\n try:\\n azi_min = float( azi_min.replace(',','.'))\\n except:\\n pass\\n try:\\n azi_max = float( azi_max.replace(',','.'))\\n except:\\n pass\\n try:\\n azi_segments = float( azi_segments.replace(',','.'))\\n except:\\n pass\\n try:\\n azi_random = float( azi_random.replace(',','.'))\\n except:\\n pass\\n azimuth_angle = (azi_min + ((azi_max - azi_min)/(azi_segments))*j)\\n position=[polar_angle, pol_random, azimuth_angle, azi_random, obj[render_dic[\\\"radius\\\"]] ]\\n light_obj=[ obj[render_dic[\\\"name\\\"]] , position, obj[render_dic[\\\"tracking_obj\\\"]],1000 ]\\n print(\\\"added light_obj: \\\", light_obj)\\n main_class.light_settings.append(light_obj)\\n main_class.max_loop_count=len(main_class.camera_settings)*len(main_class.light_settings)\\n print(\\\"loop count is:\\\", main_class.max_loop_count)\\n return\",\n \"def load_data(self) -> None:\",\n \"def save_mem_load(self):\\n if len(self.get_data_shape())==4 and self._img:\\n data = np.zeros(self.get_data_shape())\\n self._data = np.rot90(data)\\n self._loaded_time_list = [0]\\n self._data[..., 0] = np.rot90(self._img.dataobj[..., 0])\\n else:\\n self._loaded_time_list = [0]\\n data = self._img.get_data(caching='unchanged')\\n self._data = np.rot90(data)\",\n \"def reset_data():\\n shutil.copy2(\\n 'data/one_producer_many_consumers.ORIG.json',\\n 'data/one_producer_many_consumers.json'\\n )\",\n \"def getArchivoVotacion():\",\n \"def merge_roi_data(cnn_dir, db_dir, subj_id):\\n roi_list = dataio.vim2_roi_info(db_dir, subj_id)\\n train_ts = None\\n val_ts = None\\n vxl_idx = None\\n for roi in roi_list:\\n idx, tx, vx = dataio.load_vim2_fmri(db_dir, subj_id, roi)\\n if not isinstance(train_ts, np.ndarray):\\n train_ts = tx\\n val_ts = vx\\n vxl_idx = idx\\n else:\\n train_ts = np.vstack((train_ts, tx))\\n val_ts = np.vstack((val_ts, vx))\\n vxl_idx = np.concatenate((vxl_idx, idx))\\n outfile = os.path.join(cnn_dir, 'roi_orig_fmri')\\n np.savez(outfile, train_ts=train_ts, val_ts=val_ts, vxl_idx=vxl_idx)\",\n \"def reloadData(self):\\n self.dto.readFromData()\\n print(\\\"Record reloaded.\\\")\",\n \"def getCambios(self, fechaInicio, fechaFin, usuarioColaborador=\\\"\\\"):\\n\\tif usuarioColaborador == \\\"\\\" and fechaInicio == \\\"\\\" and fechaFin == \\\"\\\":\\n\\t return self.conexion.ejecutarSQL(\\\"\\\"\\\"select id, fecha, hora, codigo_Producto_entra, codigo_Producto_sale, id_Venta, excedente, usuario_Colaborador\\n from cambios\\\"\\\"\\\")\\n elif usuarioColaborador == \\\"\\\":\\n return self.conexion.ejecutarSQL(\\\"\\\"\\\"select id, fecha, hora, codigo_Producto_entra, codigo_Producto_sale, id_Venta, excedente, usuario_Colaborador\\n from cambios\\n where fecha between '%s' and '%s'\\\"\\\"\\\" %(fechaInicio,fechaFin))\\n else:\\n return self.conexion.ejecutarSQL(\\\"\\\"\\\"select id, fecha, hora, codigo_Producto_entra, codigo_Producto_sale, id_Venta, excedente, usuario_Colaborador\\n from cambios\\n where fecha between '%s' and '%s'\\n and usuario_Colaborador = '%s'\\\"\\\"\\\" %(fechaInicio,fechaFin,usuarioColaborador))\",\n \"def _convert_rosetta_db_to_basic_db(self):\\n pass\",\n \"def load_data(self):\\n with open(self.config.drv_log, 'r') as f:\\n add_data = [] # addtional data require angle smoothing\\n ori_data = [] # given data\\n ddir = os.path.dirname(self.config.drv_log)\\n lines = list(csv.reader(f))\\n np.random.shuffle(lines)\\n for line in lines:\\n if line[0] == 'center':\\n continue\\n ste_ang = float(line[3])\\n if ste_ang == 0. and random.random() < 0.8:\\n continue\\n if '12_01' in line[0]:\\n ori_data.append([os.path.join(ddir, line[0].strip()), ste_ang])\\n ori_data.append([os.path.join(ddir, line[1].strip()), ste_ang])\\n ori_data.append([os.path.join(ddir, line[2].strip()), ste_ang])\\n else:\\n add_data.append([os.path.join(ddir, line[0].strip()), ste_ang])\\n add_data.append([os.path.join(ddir, line[1].strip()), ste_ang])\\n add_data.append([os.path.join(ddir, line[2].strip()), ste_ang])\\n \\n def angle_smoothing(data):\\n \\\"\\\"\\\"helper function for smoothing steering angles.\\n reference: \\n https://carnd-forums.udacity.com/questions/24807517/tips-for-behavior-cloning\\n \\\"\\\"\\\"\\n angles = np.asarray([itm[1] for itm in data])\\n fwd = pd.ewma(angles, span=20)\\n bwd = pd.ewma(angles[::-1], span=20)\\n smooth = np.vstack((fwd, bwd[::-1]))\\n smooth = np.mean(smooth, axis=0)\\n angles = np.ndarray.tolist(smooth)\\n smooth_data = []\\n for i in range(len(data)):\\n smooth_data.append([data[i][0], angles[i]])\\n return smooth_data\\n #all_data = ori_data + angle_smoothing(add_data) #the smoothing method didn't work well\\n all_data = ori_data + add_data\\n n1 = int(len(all_data) * 0.7)\\n n2 = int(len(all_data) * 0.85)\\n self.train = self.data_generator(all_data[:n1])\\n self.valid = self.data_generator(all_data[n1:n2])\\n self.test = self.data_generator(all_data[n2:])\\n self.train_len = n1 // self.config.batch_size * self.config.batch_size\\n self.valid_len = (n2 - n1) // self.config.batch_size * self.config.batch_size\\n self.test_len = (len(all_data) - n2) // self.config.batch_size * self.config.batch_size\",\n \"def reset_data(self):\\n self.data = []\",\n \"def __restoreBackup(self):\\n pass #FIXME!!!\",\n \"def _original_data(self, data: np.ndarray):\\n if self._raw_data is None:\\n self._raw_data = data\",\n \"def borrar_CSV(self):\\n archivo = input(\\\"Indique el nombre del archivo a eliminar: \\\")\\n os.remove(f\\\"{archivo}.txt\\\")\\n print(\\n f\\\"\\\"\\\"\\n ==========================================\\n == SE HAN ELIMINADO LOS DATOS ==\\n ==========================================\\n \\\"\\\"\\\"\\n )\",\n \"def importar_visualizar_tabela(self):\\r\\n\\r\\n self.tabela_clientes = pd.read_csv('telecom_users.csv') # armazenando arquivo csv em uma variavel\\r\\n self.tabela_clientes = self.tabela_clientes.drop([\\\"Unnamed: 0\\\"], axis=1) # apagando a coluna Unnamed: 0, axist=1 -> para excluir a coluna, axist=0 -> excluir a linha (exist = eixo)\\r\\n print(self.tabela_clientes)\\r\\n # print(self.tabela_clientes.columns) # para mostrar todas as colunas da tabela \\r\\n self.tabela_clientes['NovaColuna'] = 1 # criar uma nova coluna se não existir, se caso ja exista, irá substituir todos os valores na coluna para 1\\r\",\n \"def abrir(self):\\n self.x0 = [val for val in self.x0]\\n self.x = [val for val in self.x]\\n self.tipos = [val for val in self.tipos]\\n self.mask_fr = [val for val in self.mask_fr]\\n self.mask_in = [val for val in self.mask_in]\\n self.open = True\",\n \"def delete(self, *args, **kwargs):\\n campo = Campostagimg.objects.filter(tag=self.tag, imagen=self.imagen)\\n for c in campo:\\n c.medidas = \\\"\\\"\\n c.save()\\n c.precision = 0\\n c.save()\\n c.v_esperado = \\\"\\\"\\n c.save() \\n\\n super(Campos_defecto, self).delete(*args, **kwargs)\",\n \"def binary_reconstruct(self, data_path, width, height, dtype=\\\"uint16\\\",\\n output_dir=\\\"\\\", focal_spot_image_name=\\\"focal_spot\\\", \\n sinogram_image_name=\\\"sinogram\\\"):\\n\\n focal_spot, sinogram = reconstruct_from_cr_data(data_path, width, height, dtype=dtype)\\n\\n focal_spot = img_as_ubyte(equalize_adapthist(focal_spot))\\n focal_spot_path = os.path.join(output_dir, \\\"%s.png\\\" % focal_spot_image_name)\\n sinogram = img_as_ubyte(equalize_adapthist(sinogram))\\n sinogram_path = os.path.join(output_dir, \\\"%s.png\\\" % sinogram_image_name)\\n\\n io.imsave(focal_spot_path, focal_spot)\\n io.imsave(sinogram_path, sinogram)\",\n \"def cargarProductosSinObra(self):\\n\\n self.limpiarTabla(self.tableProductos)\\n\\n ##Cnsulta para obtener todos los productos del sistema, con su correspondiente\\n ##codigo de barra, monodroga, descuento, importe\\n query=self.sesion.query(ProductoModel.codigo_barra,ProductoModel.id_medicamento,ProductoModel.id_presentacion,MonodrogaModel.nombre,ProductoModel.importe).\\\\\\n join(MedicamentoModel).filter(ProductoModel.id_medicamento==MedicamentoModel.nombre_comercial).\\\\\\n join(MonodrogaModel).filter(MedicamentoModel.id_monodroga==MonodrogaModel.nombre).\\\\\\n filter(ProductoModel.baja==False).order_by(ProductoModel.codigo_barra)\\n\\n ##Se cargan los datos obtenidos en la tabla de Producto\\n for n, obj in enumerate(query):\\n self.tableProductos.insertRow(n)\\n self.tableProductos.setItem(n, 0, QtGui.QTableWidgetItem(str(obj[0])))\\n self.tableProductos.setItem(n, 1, QtGui.QTableWidgetItem(str(obj[1])))\\n self.tableProductos.setItem(n, 2, QtGui.QTableWidgetItem(str(obj[2])))\\n self.tableProductos.setItem(n, 3, QtGui.QTableWidgetItem(str(obj[3])))\\n self.tableProductos.setItem(n, 4, QtGui.QTableWidgetItem(str(0)))\\n self.tableProductos.setItem(n, 5, QtGui.QTableWidgetItem(str(obj[4])))\\n\\n ##Se carga la cantidad de cada producto en la tabla\\n for row,producto in enumerate(ProductoModel.buscarTodos(ProductoModel.codigo_barra,self.sesion)):\\n self.tableProductos.setItem(row,6,QtGui.QTableWidgetItem(str(producto.getCantidad(self.sesion))))\",\n \"def cleanData(data):\\r\\n \\r\\n indexes = data.index[pd.isnull(data[\\\"release date\\\"])]\\r\\n # data = data.drop(data.index[[2172, 3781, 7245, 12475, 14756, 15292, 49295, 93523, 99723]],axis=0,inplace=False)\\r\\n data = data.drop(indexes,axis=0,inplace=False)\\r\\n data = data.drop([\\\"video release date\\\", 'IMDb URL'],axis=1)\\r\\n \\r\\n \\r\\n data.gender = data.gender.astype(\\\"category\\\")\\r\\n data.occupation = data.occupation.astype(\\\"category\\\")\\r\\n \\r\\n categorical_variables = [\\\"gender\\\", \\\"occupation\\\"]\\r\\n for variable in categorical_variables:\\r\\n dummies = pd.get_dummies(data[variable], prefix=variable)\\r\\n data = pd.concat([data, dummies], axis=1)\\r\\n data.drop([variable], axis=1, inplace=True)\\r\\n data['movieID'] = data['movieID'].astype('int')\\r\\n data['userID'] = data['userID'].astype('int')\\r\\n \\r\\n\\t#data = dateTime(data)\\r\\n \\r\\n return data\",\n \"def FrameBase_to_Sqlite(self):\\n sql3 = Sql3(self.dataFRAME) # we import the created from us class Sql3 to add the data \\n sql3.sql_write() # very simple and easy\",\n \"def load_dataset():\\n\\n df_ = pd.read_excel(\\\"D:\\\\VERİBİLİMİOKULU\\\\VERİSETLERİ\\\\post_bilgileri.xlsx\\\")\\n df = df_.copy()\\n return df\",\n \"def Devolver_estado(self,estado:list) -> None:\\n\\t\\tfor i in range(6):\\n\\t\\t\\tfor j in range(6):\\n\\t\\t\\t\\t#Usamos deepcopy para que el minimax o el alfabeta realice cambios en copias y no en el tablero\\n\\t\\t\\t\\tself.tablero[i][j]=copy.deepcopy(estado[i][j])\",\n \"def realizar_movimiento(atacante, defensor, gestor_archivos):\\r\\n \\r\\n movimiento = atacante.movimiento_a_realizar\\r\\n # Movimiento de ataque normal\\r\\n if movimiento.categoria == 'Physical':\\r\\n d_base = 15 * movimiento.poder * (atacante.atk / defensor.defe) / 50\\r\\n \\r\\n stab = True if atacante.tipo == movimiento.tipo else False\\r\\n rand = random.randrange(8, 11) / 10\\r\\n mod_defensor = 1\\r\\n \\r\\n print(f'DEBUG: atacante tipo = {atacante.tipo}')\\r\\n print(f'DEBUG: defensor tipo = {defensor.tipo}')\\r\\n \\r\\n for tipo_atk in atacante.tipo.split(','):\\r\\n for tipo_def in defensor.tipo.split(','):\\r\\n mod_defensor *= float(gestor_archivos.diccionario_tabla_tipos[tipo_atk][tipo_def])\\r\\n \\r\\n d_total = int(d_base * rand * mod_defensor * (1.5 if stab else 1))\\r\\n \\r\\n defensor.hp -= d_total\\r\\n \\r\\n print(f'DEBUG: movimiento_poder = {movimiento.poder}')\\r\\n print(f'DEBUG: atk = {atacante.atk}')\\r\\n print(f'DEBUG: defe = {defensor.defe}')\\r\\n print(f'DEBUG: d_base = {d_base}')\\r\\n print(f'DEBUG: rand = {rand}')\\r\\n print(f'DEBUG: stab = {stab}')\\r\\n print(f'DEBUG: d_total = {d_total}')\\r\\n \\r\\n output_manager.imprimir_mensaje(f'{atacante.nombre} ha atacado a {defensor.nombre}, quitandole {d_total} de HP. {defensor.nombre} ha quedado con {defensor.hp} de HP.')\\r\\n \\r\\n # Movimiento de ataque especial\\r\\n elif movimiento.categoria == 'Special':\\r\\n d_base = 15 * movimiento.poder * (atacante.spa / defensor.spd) / 50\\r\\n \\r\\n stab = True if atacante.tipo == movimiento.tipo else False\\r\\n rand = random.randrange(8, 11) / 10\\r\\n mod_defensor = 1\\r\\n \\r\\n print(f'DEBUG: atacante tipo = {atacante.tipo}')\\r\\n print(f'DEBUG: defensor tipo = {defensor.tipo}')\\r\\n \\r\\n for tipo_atk in atacante.tipo.split(','):\\r\\n for tipo_def in defensor.tipo.split(','):\\r\\n mod_defensor *= float(gestor_archivos.diccionario_tabla_tipos[tipo_atk][tipo_def])\\r\\n \\r\\n d_total = int(d_base * rand * mod_defensor * (1.5 if stab else 1))\\r\\n \\r\\n defensor.hp -= d_total\\r\\n \\r\\n print(f'DEBUG: movimiento_poder = {movimiento.poder}')\\r\\n print(f'DEBUG: spa = {atacante.spa}')\\r\\n print(f'DEBUG: spd = {defensor.spd}')\\r\\n print(f'DEBUG: d_base = {d_base}')\\r\\n print(f'DEBUG: rand = {rand}')\\r\\n print(f'DEBUG: stab = {stab}')\\r\\n print(f'DEBUG: d_total = {d_total}')\\r\\n \\r\\n output_manager.imprimir_mensaje(f'{atacante.nombre} ha atacado a {defensor.nombre}, quitandole {d_total} de HP. {defensor.nombre} ha quedado con {defensor.hp} de HP.')\\r\\n \\r\\n # Movimiento de sanacion \\r\\n elif movimiento.categoria == 'Status' and movimiento.objetivo == 'self' and movimiento.stats == '':\\r\\n atacante.hp += atacante.hp_max // 2 \\r\\n \\r\\n # Movimeinto de empoderamiento a si mismo\\r\\n elif movimiento.categoria == 'Status' and movimiento.objetivo == 'self' and movimiento.stats != '':\\r\\n for stat in movimiento.stats.split(','):\\r\\n output_manager.imprimir_mensaje(f'Duplicando el {stat} de {atacante.nombre}')\\r\\n \\r\\n if stat == 'atk': \\r\\n atacante.atk *= 2\\r\\n elif stat == 'def': \\r\\n atacante.defe *= 2\\r\\n elif stat == 'spe': \\r\\n atacante.spe *= 2\\r\\n \\r\\n # Movimeinto de empoderamiento al oponente\\r\\n elif movimiento.categoria == 'Status' and movimiento.objetivo == 'normal' and movimiento.stats != '':\\r\\n for stat in movimiento.stats.split(','):\\r\\n output_manager.imprimir_mensaje(f'Reduciendo a la mitad el {stat} de {defensor.nombre}')\\r\\n \\r\\n if stat == 'atk':\\r\\n defensor.atk *= 0.5\\r\\n elif stat == 'def':\\r\\n defensor.defe *= 0.5\\r\\n elif stat == 'spe':\\r\\n defensor.spe *= 0.5\",\n \"def Tratamentos_dos_dados(self):\\r\\n self.tabela_clientes[\\\"TotalGasto\\\"] = pd.to_numeric(self.tabela_clientes[\\\"TotalGasto\\\"], errors=\\\"coerce\\\") # transformar coluna que deveria ser número e está como texto em número, errors=\\\"coerce\\\" -> se der erro em algo deixa vazio\\r\\n \\r\\n self.tabela_clientes = self.tabela_clientes.dropna(how='all', axis=1) # remover as colunas que estam 100% vazia, how='all' -> todas\\r\\n \\r\\n self.tabela_clientes = self.tabela_clientes.dropna() # remover a linha que tem algum valor vazio\\r\\n\\r\\n print(self.tabela_clientes.info()) # informações sobre a tabela \\r\",\n \"def save_data (mdp):\\n for num,key in zip(mdp.corr_num,mdp.key):\\n lnum = find_corr(mdp,int(num)) # get the line number of the correlator, if possible\\n if lnum > -1:\\n cdat = extract_data(mdp,lnum) # found the correlator, save to array\\n try: # write it to file\\n ## -- organizing is too slow, just write to end of file\\n mdp.save_file.seek(0,2) # seek the end of file\\n mdp.save_file.write( key + ' ' + \\\\\\n ' '.join('{:e}'.format(cdat[x]) for x in range(0,mdp.corr_len))+'\\\\n')\\n #lsec = uf.find_data_section(mdp.save_file,key)\\n #mdp.save_file.write( key + ' ' + ' '.\\\\\\n #uf.ins_line(mdp.save_file, key + ' ' + ' '.\\\\\\n # join('{:e}'.format(cdat[x]) for x in range(0,mdp.corr_len))\\\\\\n # , lsec[1]+1\\\\\\n # )\\n #write_fname(mdp,lsec[0])\\n except IndexError:\\n print \\\"-- In file\\\",mdp.corr_file.name\\n print \\\"Could not extract data from file\\\"\\n else:\\n print \\\"-- In file\\\",mdp.corr_file.name\\n print \\\"Failed to find correlator #\\\",num\",\n \"def _reload(self):\\n if os.path.exists(self.filename):\\n self.data = pd.read_csv(self.filename)\\n else:\\n self.data = pd.DataFrame(columns=self.unique_keys)\\n\\n # Set these default values\\n # if 'weight_rescale' not in self.data.columns:\\n # self.data['weight_rescale'] = 'none'\\n # if 'norm' not in self.data.columns:\\n # self.data['norm'] = 'softmax'\\n # if 'update' not in self.data.columns:\\n # self.data['update'] = 'all'\\n # if 'replay' not in self.data.columns:\\n # self.data['replay'] = False\\n if 'debug' not in self.data.columns:\\n self.data['debug'] = False\\n\\n # if 'tie' not in self.data.columns:\\n # self.data['tie'] = False\\n\\n if 'update_length' not in self.data.columns:\\n self.data['update_length'] = 0\\n # for key in self.unique_keys:\\n # self.data[key] = np.nan\\n # Remaining set to None\\n # for k in self.check_keys:\\n # if k not in self.data.columns:\\n # self.data[k] = None\",\n \"def update_database():\\n\\n # We obtain the data from the official database\\n df = getData.extractData()\\n\\n # We save the dataframe for later use in the API\\n auxiliary.saveToCsv(df, 'app/resources')\",\n \"def datos_usuario():\\n usuario_partida.clear()\\n puntaje = 0\\n disparos_efectuados = 100\\n username = input(\\\"Ingrese su username: \\\")#el usuario ingresa su username\\n usuarios = []\\n nombre_lista = []\\n username_repetido = True\\n while username_repetido: #se verifica si el username ya jugo antes\\n with open(\\\"BaseDatos.txt\\\", \\\"r\\\") as bd:\\n datos = bd.readlines()\\n for x in datos:\\n usuario = x[:-1].split(',') #si el username esta en la base de datos no necesita ingresar sus datos\\n usuarios.append(usuario[0])\\n #se procede a extraer los datos del username guardado y copiarlos \\n if username in usuarios:\\n print(\\\"Veo que ya has jugado antes, gracias por volver :)\\\\n\\\")\\n sleep(2)\\n username = usuario[0]\\n nombre = usuario[1].split(\\\" \\\")\\n nombre = nombre[1].title()\\n edad = usuario[2].split(\\\" \\\")\\n edad = edad[1]\\n genero = usuario[3].split(\\\" \\\")\\n genero = genero[1]\\n puntaje = usuario[4].split(\\\" \\\")\\n puntaje = puntaje[1]\\n disparos_efectuados = usuario[5].split(\\\" \\\")\\n disparos_efectuados = disparos_efectuados[1]\\n usuario_partida.append(username)\\n with open(\\\"Basedatos.txt\\\", \\\"r\\\") as db: \\n lineas = db.readlines()\\n with open(\\\"Basedatos.txt\\\", \\\"w\\\") as db: #se reescribe la lista en la base de datos sin el usuario actual \\n for linea in lineas:\\n if username not in linea:\\n db.write(linea) \\n with open(\\\"Basedatos.txt\\\", \\\"a\\\") as bd: #se agrega a la base de datos el usuario actual que ya ha jugado antes, se mantienen todos sus datos pasados\\n bd.write(\\\"{}, {}, {}, {}, {}, {}\\\\n\\\".format(username, nombre, edad, genero, puntaje, disparos_efectuados))\\n return Usuario(username, nombre, edad, genero, puntaje, disparos_efectuados)\\n username_repetido = False\\n else:\\n validacion_username = username.islower() #el username no se enconrro en la base de datos\\n while validacion_username == False or len(username) > 30 or \\\" \\\" in username: #Validacion para username\\n print(\\\"{}Su usuario solo puede contener minusculas y numeros sin ningun espacio{}\\\\n\\\".format(Fore.LIGHTRED_EX, Fore.RESET))\\n username = input(\\\"Ingerese su username nuevamente: \\\")\\n validacion_username = username.islower()\\n usuario_partida.append(username)\\n nombre = input(\\\"Ingrese su nombre completo: \\\") #el usuario ingresa su nombre\\n validacion_nombre = bool(re.fullmatch('[A-Za-z]{2,25}( [A-Za-z]{2,25})+?', nombre)) #se valida que el nombre este correcto\\n while validacion_nombre == False:\\n print(\\\"{}Este nombre no es valido{}\\\".format(Fore.LIGHTRED_EX, Fore.RESET))\\n nombre = input(\\\"Ingrese su nombre completo: \\\")\\n validacion_nombre = bool(re.fullmatch('[A-Za-z]{2,25}( [A-Za-z]{2,25})+?', nombre))\\n nombre = nombre.title() \\n while True: #Validacion para edad\\n try:\\n edad = int(input(\\\"Ingresa su edad: \\\")) #el usuario ingresa su edad\\n if edad < 5 or edad > 100:\\n raise ValueError\\n break\\n except ValueError:\\n print(\\\"{}Tu edad no es adecuada para jugar{}\\\".format(Fore.LIGHTRED_EX, Fore.RESET))\\n while True: #Validacion para genero\\n try:\\n genero = int(input(\\\"Ingrese su genero: \\\\n1) Femenino \\\\n2) Masculino \\\\n3) Ninguno\\\\n\\\")) #el usuario elige su genero\\n if genero < 1 or genero > 3:\\n raise ValueError\\n if genero == 1:\\n genero = \\\"Femenino\\\"\\n elif genero == 2: \\n genero = \\\"Masculino\\\"\\n elif genero == 3:\\n genero = \\\"Ninguno\\\"\\n break\\n except ValueError:\\n print(\\\"{}El valor ingresado no es valido{}\\\".format(Fore.LIGHTRED_EX, Fore.RESET))\\n with open(\\\"Basedatos.txt\\\", \\\"a+\\\") as bd: \\n bd.write(\\\"{}, {}, {}, {}, {}, {}\\\\n\\\".format(username, nombre, edad, genero, puntaje, disparos_efectuados)) #se agrega el usuario a la base de datos\\n return Usuario(username, nombre, edad, genero, puntaje, disparos_efectuados)\\n username_repetido = False\",\n \"def _load_data(self):\\n\\n path_data_x = '/workspace/base-ml/data/dizzyreg/t%s_df.csv' % \\\\\\n self.task_num\\n path_data_y = '/workspace/base-ml/data/dizzyreg/label_df_t%s.csv' % self.task_num\\n path_meta = '/workspace/base-ml/data/dizzyreg/meta_df_t%s.csv' % self.task_num\\n path_numerical_columns = '/workspace/base-ml/data/dizzyreg/num_columns_v2.csv'\\n path_nonnumerical_columns = '/workspace/base-ml/data/dizzyreg/non_num_columns_v2.csv'\\n\\n read_data_x = pd.read_csv(path_data_x)\\n read_data_y = pd.read_csv(path_data_y)\\n read_data_meta = pd.read_csv(path_meta)\\n\\n # Drop columns if it only contains 1 unique element\\n read_data_x = pd.DataFrame(self.drop_one_elem_columns(read_data_x))\\n\\n num_col = pd.read_csv(path_numerical_columns)\\n num_col = read_data_x.columns.isin(num_col['0'].values).nonzero()[0]\\n col_idx = np.arange(read_data_x.shape[-1])\\n non_num_col = np.setdiff1d(col_idx, num_col)\\n\\n # new_data_x = np.array(read_data_x).astype(np.float32)\\n new_data_x = np.array(read_data_x)\\n new_data_y = np.array(read_data_y).astype(np.float32)\\n new_data_meta = np.array(read_data_meta).astype(np.float32)\\n\\n print(new_data_x.shape, new_data_y.shape, new_data_meta.shape)\\n\\n\\n # Winsorize dataset\\n len_feat = new_data_x.shape[-1]\\n idx_list = list(num_col)\\n for i in range(len_feat):\\n if i in idx_list:\\n cur_data = new_data_x[:, i]\\n cur_data = np.array(cur_data)\\n lower_p = np.percentile(cur_data, 5)\\n higher_p = np.percentile(cur_data, 95)\\n cur_data[cur_data < lower_p] = lower_p\\n cur_data[cur_data > higher_p] = higher_p\\n new_data_x[:, i] = cur_data\\n\\n # Make sure target data is one-hot encoded\\n if new_data_y.shape[-1] == 1:\\n num_class = len(np.unique(new_data_y))\\n new_data_y = np.eye(num_class)[new_data_y.astype(int).reshape(-1)]\\n new_data_y = new_data_y.astype('float32')\\n self.orig_column_names = read_data_x.columns\\n self.data_x = new_data_x # N x F\\n self.data_y = new_data_y # N x C\\n self.numerical_idx = num_col # list of idx\\n self.non_num_idx = non_num_col # None\\n\\n # Calculate adjacency matrix\\n self.meta_inf = new_data_meta.astype('float32') # N x 3\\n if self.args.graph_type:\\n self.adj = self.get_adjacency()\",\n \"def concatenate_data():\",\n \"def save_data(self):\\n pass\",\n \"def guardar_datos(nombre, datos, \\n directory=\\\"datos_guardados/\\\",\\n method=\\\"numerado\\\",\\n ): \\n # Primera parte del nombre\\n filename_1 = \\\"datos_\\\"\\n nombre += \\\"_\\\"\\n \\n if method == \\\"fecha\\\":\\n # Tercera parte del nombre (fecha)\\n today = datetime.datetime.today()\\n tt = today.timetuple()\\n\\n # Formato de fecha es yyyymmdd_hhmmss\\n filename_3 = \\\"{:4d}{:02d}{:02d}_{:02d}{:02d}{:02d}\\\"\\n filename_3 = filename_3.format(tt.tm_year, tt.tm_mon, tt.tm_mday,\\n tt.tm_hour, tt.tm_min, tt.tm_sec)\\n\\n # Nombre completo\\n filename = directory + filename_1 + nombre + filename_3\\n\\n elif method == \\\"numerado\\\":\\n p = pathlib.Path(\\\"./\\\" + directory)\\n archivos = []\\n\\n for f in p.iterdir():\\n archivos.append(str(f))\\n \\n numero = 0\\n while True:\\n # Nombre temporal\\n filename = directory + filename_1 + nombre + str(numero)\\n \\n if filename in archivos: numero += 1\\n else: break\\n\\n # Escritura de datos (módulo json)\\n with open(filename, \\\"w\\\") as file_object:\\n json.dump(datos, file_object)\\n \\n # Mensaje de escritura\\n print(\\\"---------------------------------------\\\")\\n print(\\\"Datos escritos en:\\\")\\n print(\\\"\\\\t\\\" + filename)\\n print(\\\"---------------------------------------\\\")\",\n \"def uadb_ascii_to_dataframe(file=''): \\n \\n if debug:\\n print(\\\"Running uadb_ascii_to_dataframe for: \\\", file) \\n \\n data = check_read_file(file=file, read=True) # TODO\\n \\n #source_file = [l for l in file.split('/') if '.txt' in l][0]\\n\\n nmiss = 0\\n search_h = False \\n read_data = []\\n \\n usi,idate, usi, lat, lon, lat, stype, press, gph, temp, rh, wdir, wspd = np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan\\n\\n #usi,idate, usi, lat, lon, lat, stype, press, gph, temp, rh, wdir, wspd, iday, ident, numlev= 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0\\n obs_id = 0\\n stations_id = [] \\n \\n for i, line in enumerate(data):\\n if line[0] == 'H':\\n try:\\n # Header\\n usi = int(line[2:14]) # unique station identifier\\n \\n ident = int(line[15:21].replace(' ',''))# WMO\\n if ident not in stations_id:\\n stations_id.append(ident)\\n \\n #if len(ident) == 4:\\n # ident = '0' + ident \\n #idflag = int(line[22:24]) # id flag\\n #d_src = int(line[25:28]) # source dataset\\n #version = float(line[29:34]) # version\\n #dateflag = int(line[35:37]) # date flag\\n year = line[38:42] # year\\n month = \\\"%02d\\\" % int(line[43:45])\\n day = \\\"%02d\\\" % int(line[46:48])\\n hour = line[49:53]\\n #locflag = int(line[54:56]) # Location Flag\\n lat = float(line[57:67])\\n lon = float(line[68:78])\\n #ele = float(line[79:85])\\n #stype = int(line[86:88])\\n numlev = int(line[89:93])\\n #pvers = line[94:102]\\n\\n if '99' in hour:\\n hour = hour.replace('99', '00')\\n \\n if '99' in day:\\n search_h = True\\n continue\\n \\n minutes = int(hour) % 100 \\n hour = \\\"%02d\\\" % (int(hour) // 100)\\n if minutes > 60 or minutes < 0:\\n minutes = 0\\n minutes = \\\"%02d\\\" % minutes\\n idate = datetime.strptime(year + month + day + hour + minutes, '%Y%m%d%H%M')\\n iday = int(year + month + day)\\n #pday = int(day)\\n search_h = False\\n\\n except Exception as e:\\n #print(\\\"Error: \\\", i, line, repr(e), \\\"Skipping Block:\\\")\\n search_h = True\\n #iprev = i\\n\\n elif search_h:\\n nmiss += 1\\n continue # Skipping block\\n\\n else:\\n # Data\\n #ltyp = int(line[0:4])\\n p = float(line[5:13])\\n \\n if p != -99999.0 and p != 9999.9: \\n press = float(line[5:13])*100 # converting to Pa, since P is given in mb (1 mb = 1 hPa) \\n else:\\n press = np.nan \\n \\n gph = float(line[14:22]) # gph [m]\\n \\n if gph == -999.0 or gph == -99999.00 or gph >= 99999.0:\\n gph = np.nan\\n \\n temp = float(line[23:29])\\n if temp == -999.0:\\n temp = np.nan \\n else:\\n temp = temp + 273.15\\n \\n rh = float(line[30:36]) # %\\n if rh == -999.0:\\n rh = np.nan\\n else:\\n rh = rh / 100. # convert to absolute ratio TODO\\n\\n wdir = float(line[37:43]) \\n if wdir == -999.0 or wdir == -999 :\\n wdir = np.nan\\n \\n wspd = float(line[44:50]) # [m/s], module of the velocity\\n if wspd <0 :\\n wspd = np.nan \\n \\n try:\\n \\n for value,var in zip([ gph, temp, wspd, wdir, rh], [ 'gph', 'temperature', 'wind_speed', 'wind_direction', 'relative_humidity'] ):\\n obs_id = obs_id +1\\n if not np.isnan(press): # when pressure is available, z_coord== pressure and z_type==1\\n z_type = 1 \\n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, press, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\\n elif (np.isnan(press) and not np.isnan(gph) ) : # when pressure is not available, z_coord== gph and z_type==2 \\n z_type = 2 \\n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, gph, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\\n else:\\n z_type = -2147483648 \\n read_data.append( ( 'NCAR'.rjust(10), int(usi), int(obs_id), idate, iday, ident, lat, lon, press, value, cdmvar_dic[var]['cdm_var'] , int(cdmvar_dic[var]['cdm_unit']), numlev , z_type) )\\n \\n except:\\n 0\\n \\n \\n \\n #column_names = ['source_file', 'product_code', 'report_id', 'observation_id', 'report_timestamp' , 'iday', 'station_id', 'lat@hdr', 'lon@hdr', 'vertco_reference_1@body', 'obsvalue@body', 'varno@body' , 'units', 'number_of_pressure_levels' ]\\n \\n df = pd.DataFrame(data= read_data, columns= column_names) \\n \\n df['observation_id'] = np.chararray.zfill( (df['observation_id'].astype(int)) .astype('S'+str(id_string_length ) ), id_string_length ) #converting to fixed length bite objects \\n df['report_id'] = np.chararray.zfill( (df['report_id'].astype(int)).astype ('S'+str(id_string_length ) ), id_string_length )\\n \\n df = df.replace([-999.9, -9999, -999, -999.0, -99999.0, -99999.9, 99999.0, -99999.00 ], np.nan)\\n \\n #df['observations_id'] =numpy.char.zfill(numpy.arange(ivar.shape[0]).astype('S10'), 10)\\n \\n df.sort_values(by = ['record_timestamp', 'vertco_reference_1@body' ] ) \\n #df['report_id'] = numpy.int64 (df['report_id'] ) \\n #df['observation_id'] = numpy.int64 (df['observation_id'] ) \\n df = df.sort_values(by = ['record_timestamp', 'vertco_reference_1@body' ] ) \\n \\n print('Done reading DF')\\n return df , stations_id\",\n \"def read_precios(path=\\\".\\\"):\\n\\n here = Path(path)\\n sucursales = sucursales_prov(here)\\n productos = pd.read_csv(here / \\\"productos.csv\\\")\\n precios = [read_precio(f, sucursales) for f in Path(path).glob(\\\"precios_*.csv\\\")]\\n\\n # aplico merges de todos los precios\\n def merge(left, right):\\n return (\\n pd.merge(left, right, on=[\\\"producto_id\\\", \\\"id_prov\\\", \\\"provincia\\\"], how=\\\"inner\\\").dropna()\\n # .drop([\\\"sucursal_id_x\\\", \\\"sucursal_id_y\\\"], axis=1)\\n )\\n\\n precios = functools.reduce(merge, precios)\\n # eliminar columnas repetidas\\n precios = precios.loc[:, ~precios.columns.duplicated()]\\n precios = precios.rename(columns={\\\"cadena_x\\\": \\\"cadena\\\"})\\n\\n # elimina columnas innecesarias.\\n # basicamente sucursal_* y cadena_y (queda cadena, renombrada antes)\\n for c in set(precios.columns):\\n if c.startswith((\\\"sucursal\\\", \\\"cadena_\\\")):\\n precios.drop(c, axis=1, inplace=True)\\n\\n # cruzamos datos con productos\\n precios = pd.merge(\\n precios,\\n productos[[\\\"id\\\", \\\"marca\\\", \\\"nombre\\\", \\\"categoria1\\\", \\\"categoria2\\\", \\\"categoria3\\\"]],\\n left_on=\\\"producto_id\\\",\\n right_on=\\\"id\\\",\\n ).drop([\\\"id\\\", \\\"id_prov\\\"], axis=1)\\n\\n precios.sort_index(axis=1, inplace=True)\\n\\n # obtenemos el periodo maximo inicio vs fin (asumiendo columnas ordenadas)\\n inicio, *_, fin = [c for c in precios.columns if c.startswith(\\\"precio_\\\")]\\n\\n precios[\\\"variacion\\\"] = precios[fin] - precios[inicio]\\n precios[\\\"variacion_relativa\\\"] = precios[\\\"variacion\\\"] / precios[inicio] * 100\\n return precios\",\n \"def prepare_data(self):\",\n \"def _importInDjango(self):\\n\\n with open(settings.DATA_PATH, 'r', encoding='latin-1') as csv_file:\\n reader = csv.DictReader(csv_file, delimiter=';')\\n for raw in reader:\\n\\n # Créer ou mettre à jour la division\\n division, created = Division.objects.get_or_create(\\n nom=raw['Division']\\n )\\n if created:\\n self.stdout.write(\\n 'Divion {} ajoutée'.format(division.nom)\\n )\\n\\n # Créer ou mettre à jour les équipes\\n equipeDom, created = Equipe.objects.get_or_create(\\n nom=raw['Equipe 1'],\\n division=division\\n )\\n if created:\\n self.stdout.write(\\n 'Equipe {} ajoutée'.format(equipeDom.nom)\\n )\\n\\n equipeExt, created = Equipe.objects.get_or_create(\\n nom=raw['Equipe 2'],\\n division=division\\n )\\n if created:\\n self.stdout.write(\\n 'Equipe {} ajoutée'.format(equipeExt.nom)\\n )\\n\\n # Créer ou mettre à jour la rencontre\\n scoreDom = 0 if raw['Score 1'] == '' else int(raw['Score 1'])\\n scoreExt = 0 if raw['Score 2'] == '' else int(raw['Score 2'])\\n forfaitDom = True if raw['Forfait 1'] == 'true' else False\\n forfaitExt = True if raw['Forfait 2'] == 'true' else False\\n date = datetime.datetime.strptime(raw['Date de rencontre'], '%d/%m/%Y')\\n heure = datetime.datetime.strptime(raw['Heure'], '%H:%M')\\n rencontre, created = Rencontre.objects.update_or_create(\\n numero=int(raw['N° de match']),\\n equipeDom=equipeDom,\\n equipeExt=equipeExt,\\n defaults={\\n 'date': date,\\n 'heure': heure,\\n 'scoreDom': scoreDom,\\n 'scoreExt': scoreExt,\\n 'forfaitDom': forfaitDom,\\n 'forfaitExt': forfaitExt,\\n }\\n )\\n if created:\\n self.stdout.write(\\n 'Rencontre {} / {} ajoutée'.format(\\n rencontre.equipeDom,\\n rencontre.equipeExt\\n )\\n )\",\n \"def dataset()-> pd.DataFrame:\\n old= pd.read_csv(DIRECT_OLD)\\n new= pd.read_csv(DIRECT_NEW)\\n return pd.concat([old, new])\",\n \"def load_and_fix(self):\\n # Read in json\\n self.read_json()\\n\\n if self.size_to_load:\\n self.data = self.data[:self.size_to_load]\\n\\n # Add names from database given _bsn:\\n self.extend_dataframe_with_personnames()\\n\\n # Clean rows in the data_frame where the names column is empty - > thus no response from the database\\n self.clean_none_response()\\n\\n # Fix path from A09.pdf to A09.json\\n self.fix_path()\\n\\n # Get the correct names from the database response\\n self.parse_names_from_response()\\n\\n print(\\\" --- Final Shape Data ---\\\")\\n print(self.data.shape)\\n print(list(self.data))\\n\\n # Save pickled object in ./data map\\n self.save_obj(self.data, self.file_name_to_save)\",\n \"def load_data():\\r\\n #读取User数据\\r\\n users_title = ['UserID', 'Gender', 'Age', 'JobID', 'Zip-code']\\r\\n users = pd.read_csv('./ml-1m/users.dat', sep='::', header=None, names=users_title, engine = 'python')\\r\\n users = users.filter(regex='UserID|Gender|Age|JobID')\\r\\n users_orig = users.values\\r\\n #改变User数据中性别和年龄\\r\\n gender_map = {'F':0, 'M':1}\\r\\n users['Gender'] = users['Gender'].map(gender_map)\\r\\n\\r\\n age_map = {val:ii for ii,val in enumerate(set(users['Age']))}\\r\\n users['Age'] = users['Age'].map(age_map)\\r\\n\\r\\n #读取Movie数据集\\r\\n movies_title = ['MovieID', 'Title', 'Genres']\\r\\n movies = pd.read_csv('./ml-1m/movies.dat', sep='::', header=None, names=movies_title, engine = 'python')\\r\\n movies_orig = movies.values\\r\\n #将Title中的年份去掉\\r\\n pattern = re.compile(r'^(.*)\\\\((\\\\d+)\\\\)$')\\r\\n\\r\\n title_map = {val:pattern.match(val).group(1) for ii,val in enumerate(set(movies['Title']))}\\r\\n movies['Title'] = movies['Title'].map(title_map)\\r\\n\\r\\n #电影类型转数字字典\\r\\n genres_set = set()\\r\\n for val in movies['Genres'].str.split('|'):\\r\\n genres_set.update(val)\\r\\n\\r\\n genres_set.add('<PAD>')\\r\\n genres2int = {val:ii for ii, val in enumerate(genres_set)}\\r\\n\\r\\n #将电影类型转成等长数字列表，长度是18\\r\\n genres_map = {val:[genres2int[row] for row in val.split('|')] for ii,val in enumerate(set(movies['Genres']))}\\r\\n\\r\\n for key in genres_map:\\r\\n for cnt in range(max(genres2int.values()) - len(genres_map[key])):\\r\\n genres_map[key].insert(len(genres_map[key]) + cnt,genres2int['<PAD>'])\\r\\n \\r\\n movies['Genres'] = movies['Genres'].map(genres_map)\\r\\n\\r\\n #电影Title转数字字典\\r\\n title_set = set()\\r\\n for val in movies['Title'].str.split():\\r\\n title_set.update(val)\\r\\n \\r\\n title_set.add('<PAD>')\\r\\n title2int = {val:ii for ii, val in enumerate(title_set)}\\r\\n\\r\\n #将电影Title转成等长数字列表，长度是15\\r\\n title_count = 15\\r\\n title_map = {val:[title2int[row] for row in val.split()] for ii,val in enumerate(set(movies['Title']))}\\r\\n \\r\\n for key in title_map:\\r\\n for cnt in range(title_count - len(title_map[key])):\\r\\n title_map[key].insert(len(title_map[key]) + cnt,title2int['<PAD>'])\\r\\n \\r\\n movies['Title'] = movies['Title'].map(title_map)\\r\\n\\r\\n #读取评分数据集\\r\\n ratings_title = ['UserID','MovieID', 'ratings', 'timestamps']\\r\\n ratings = pd.read_csv('./ml-1m/ratings.dat', sep='::', header=None, names=ratings_title, engine = 'python')\\r\\n ratings = ratings.filter(regex='UserID|MovieID|ratings')\\r\\n\\r\\n #合并三个表\\r\\n data = pd.merge(pd.merge(ratings, users), movies)\\r\\n \\r\\n #将数据分成X和y两张表\\r\\n target_fields = ['ratings']\\r\\n features_pd, targets_pd = data.drop(target_fields, axis=1), data[target_fields]\\r\\n \\r\\n features = features_pd.values\\r\\n targets_values = targets_pd.values\\r\\n \\r\\n return title_count, title_set, genres2int, features, targets_values, ratings, users, movies, data, movies_orig, users_orig\",\n \"def demodulate_data_original(self, data):\\n bank = self.bank\\n demod = np.zeros_like(data)\\n t = np.arange(data.shape[0])\\n for n, ich in enumerate(self.readout_selection):\\n phi0 = self.phases[ich]\\n k = self.tone_bins[bank,ich]\\n m = self.fft_bins[bank,ich]\\n if m >= self.nfft / 2:\\n sign = -1.0\\n doconj = True\\n else:\\n sign = -1.0\\n doconj = False\\n nfft = self.nfft\\n ns = self.tone_nsamp\\n foffs = (k * nfft - m * ns) / float(ns)\\n demod[:, n] = np.exp(sign * 1j * (2 * np.pi * foffs * t + phi0)) * data[:, n]\\n if doconj:\\n demod[:, n] = np.conjugate(demod[:, n])\\n return demod*self.wavenorm\",\n \"def save_calib_data(self):\\r\\n \\r\\n #get data to save\\r\\n x0 = self.ui.x0.value()\\r\\n x1 = self.ui.x1.value()\\r\\n y0 = self.ui.y0.value()\\r\\n y1 = self.ui.y1.value()\\r\\n \\r\\n directory, fileName = os.path.split(self.ui.imagePath.text())\\r\\n nofpixels = (max(x0,x1)-min(x0,x1))*(max(y0,y1)-min(y0,y1))\\r\\n\\r\\n #create a list with everything to be saved \\r\\n #(fast way in Python to build a string)\\r\\n strList = [self.ui.filmNumber.text(),#indentifier\\r\\n fileName, #file name\\r\\n \\\"{:d}\\\".format(x0),#coordinates\\r\\n \\\"{:d}\\\".format(y0),\\r\\n \\\"{:d}\\\".format(x1),\\r\\n \\\"{:d}\\\".format(y1),\\r\\n \\\"{:d}\\\".format(nofpixels)] \\r\\n \\r\\n \\r\\n #save the channel data\\r\\n for channel in [0,1,2]:\\r\\n avg = np.average(self.npImg[y0:y1,x0:x1,channel])\\r\\n std = np.std(self.npImg[y0:y1,x0:x1,channel])\\r\\n strList.append(\\\"{:.3f}\\\".format(avg))\\r\\n strList.append(\\\"{:.3f}\\\".format(std))\\r\\n\\r\\n #concatenate the list, using tab as a seperator\\r\\n saveStr = '\\\\t'.join(strList)+\\\"\\\\n\\\"\\r\\n \\r\\n self.saveTablePath = self.check_save_table_path(self.ui.saveTablePath.text())\\r\\n \\r\\n if self.saveTablePath == \\\"\\\":\\r\\n logging.error(\\\"no valid file selected, nothing written\\\")\\r\\n else:\\r\\n with open(self.saveTablePath,\\\"a\\\") as saveTable:\\r\\n saveTable.write(saveStr)\\r\\n logging.info((\\\"info for \\\"+self.ui.filmNumber.text()+\\\" written to file\\\"))\",\n \"def Save(cls):\\n print(\\\"Saving data...\\\")\\n # Check if the backup directory exists\\n backup_dir = FILE_PATH + \\\"FellesLab_Backup\\\"\\n if not os.path.isdir(backup_dir):\\n os.mkdir(backup_dir)\\n with open( backup_dir + \\\"/README\\\", 'w') as f:\\n f.write(BACKUP_README)\\n\\n #cmd = \\\"python -m markdown {dir}/README > {dir}/README.html\\\".format(dir=backup_dir)\\n #call([cmd])\\n # Check if the Backup directory has a directory for \\\"today\\\"\\n day = FILE_PATH + \\\"FellesLab_Backup/\\\" + cls.dayStamp()\\n if not os.path.isdir(day):\\n os.mkdir(day)\\n\\n # Save data for all the sensors\\n # TODO: Rewrite, difficult to follow...\\n DATA = [ ] # This will become a list of lists, e.g.\\n # [ [time, ...], [Temp1, ...], [time, ...], [Temp2, ...] ]\\n for ref in cls.__refs__:\\n\\n ref().File.seek(0) # Rewind file pointer\\n\\n F = csv.reader(ref().File, delimiter=',', quotechar='|')\\n r = [[],[]]\\n for row in F:\\n for i,num in enumerate(row):\\n r[i].append(num)\\n if i > 1:\\n r[i].append(float(num))\\n\\n for j in r:\\n DATA.append(j)\\n ref().File.close()\\n\\n # Finally, write data file.\\n with open( day + \\\"/\\\" + cls.timeStamp() + '.csv', 'w') as f:\\n csv.writer(f).writerows( it.izip_longest(*DATA, fillvalue='NA') )\",\n \"def reset_data(self):\\n self.data = None\",\n \"def restore(self):\\n raise NotImplementedError\",\n \"def Dbase_to_DF(self):\\n for item in sorted(self.dbase.keys()):\\n self.dataFRAME[item]=self.dbase[item]\\n return self.dataFRAME\",\n \"def uvozi_osebe(cur):\\n cur.execute(\\\"DELETE FROM oseba;\\\")\\n with open('podatki/oseba.csv') as datoteka:\\n podatki = csv.reader(datoteka)\\n stolpci = next(podatki)\\n poizvedba = \\\"\\\"\\\"\\n INSERT INTO oseba VALUES ({})\\n \\\"\\\"\\\".format(', '.join([\\\"?\\\"] * len(stolpci)))\\n for vrstica in podatki:\\n cur.execute(poizvedba, vrstica)\",\n \"def actualizar_tabla(self):\\n self.ajustar_tabla()\\n sincronizaciones = Archivo_crontab.listar_sincronizaciones()\\n self.tablaSincronizaciones.setRowCount(len(sincronizaciones))\\n row = 0\\n for i in sincronizaciones:\\n (btn_borrar, btn_editar) = self.crear_botones_fila()\\n ruta_archivo = QTableWidgetItem(i[0])\\n servidor = QTableWidgetItem(i[2])\\n tiempo = QTableWidgetItem(' ' + i[3] + ' ' + i[4] + ' ' + i[5] + ' ' + i[6])\\n self.tablaSincronizaciones.setItem(row, 0, ruta_archivo)\\n self.tablaSincronizaciones.setItem(row, 1, servidor)\\n self.tablaSincronizaciones.setItem(row, 2, tiempo)\\n self.tablaSincronizaciones.setCellWidget(row, 3, btn_borrar)\\n self.tablaSincronizaciones.setCellWidget(row, 4, btn_editar)\\n row += 1\\n\\n self.tablaSincronizaciones.setEditTriggers(QtWidgets.QTableWidget.NoEditTriggers)\\n self.tablaSincronizaciones.resizeRowsToContents()\",\n \"def apilar(pila, dato):\\n pila.cima += 1\\n pila.datos[pila.cima] = dato\",\n \"def BorrarListas(self,evt):\\n self.l_bal = []\\n self.l_muestras = []\\n self.l_vol = []\\n self.l_den = []\\n self.l_calidad = []\\n self.idact = 0\",\n \"def actualizar_tabla(self):\\n\\n self.padre.limpiarTabla(self.tablePagos)\\n for row, cobro in enumerate(self.detalles_cobro.values()):\\n self.tablePagos.insertRow(row)\\n self.tablePagos.setItem(row,0,QtGui.QTableWidgetItem(cobro[0]))\\n self.tablePagos.setItem(row,1,QtGui.QTableWidgetItem(\\\"$\\\"+str(cobro[1])))\",\n \"def __remover_jugador(self, ip, datos):\\n if MAKELOG:\\n key = \\\"Jugador Removido %s\\\" % time.time()\\n APPEND_LOG({key: ip})\\n self.server.JUGADORES[ip]['pos'] = \\\"-,-,-\\\"\\n self.server.JUGADORES[ip]['bala'] = \\\"-,-,-\\\"\",\n \"def borrar_primero(self):\\t\\t\\n\\t\\tif self.len == 0:\\n\\t\\t\\traise ValueError(\\\"Lista vacia\\\")\\n\\t\\tdato = self.prim.dato\\n\\t\\tself.prim = self.prim.prox\\n\\t\\tself.len -= 1\\n\\t\\treturn dato\",\n \"def MergeDataframe(clip_im_dir, matting_dir):\\r\\n\\r\\n clip_dict = MergeClipData(clip_im_dir)\\r\\n mate_dict = MergeMateData(matting_dir)\\r\\n\\r\\n clip_df = pd.DataFrame.from_dict(clip_dict)\\r\\n mate_df = pd.DataFrame.from_dict(mate_dict)\\r\\n\\r\\n data_df = pd.merge(clip_df, mate_df, left_index=True, right_index=True)\\r\\n\\r\\n # save\\r\\n data_df.to_csv('H:/Projet/ia/blurry_background/data/protrait.csv', index=False)\\r\\n\\r\\n return data_df\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.57277966","0.5657513","0.5653637","0.56170994","0.54800206","0.5394856","0.5384301","0.53081447","0.5278276","0.5271337","0.52682054","0.52480567","0.5191209","0.5181496","0.5179283","0.5169548","0.5168879","0.51631016","0.51624215","0.514839","0.5126115","0.51142097","0.5096942","0.50930476","0.50922483","0.5083158","0.5076619","0.50676537","0.5048323","0.50343394","0.5019212","0.50182384","0.5002224","0.49972203","0.49962214","0.4981576","0.49691907","0.49637112","0.49594244","0.49296552","0.49215207","0.4921472","0.49148735","0.49105322","0.4910039","0.49049208","0.49036458","0.48985234","0.48975477","0.48905396","0.48859543","0.4876442","0.4867054","0.48593438","0.48568922","0.48491296","0.48437026","0.4839874","0.48397934","0.48374963","0.48352736","0.482992","0.48289433","0.4827574","0.48256904","0.48227212","0.48201117","0.481851","0.4804387","0.48027405","0.48008776","0.48007554","0.4794381","0.47887012","0.47858018","0.47758016","0.4771593","0.47616407","0.47614187","0.47583565","0.47557124","0.4753395","0.47475106","0.47371167","0.47333556","0.47276396","0.47212085","0.47171748","0.47165695","0.4711364","0.4706713","0.47059217","0.47051296","0.47025162","0.47023085","0.4701146","0.4693826","0.4693067","0.4691543","0.4687349","0.46861896"],"string":"[\n \"0.57277966\",\n \"0.5657513\",\n \"0.5653637\",\n \"0.56170994\",\n \"0.54800206\",\n \"0.5394856\",\n \"0.5384301\",\n \"0.53081447\",\n \"0.5278276\",\n \"0.5271337\",\n \"0.52682054\",\n \"0.52480567\",\n \"0.5191209\",\n \"0.5181496\",\n \"0.5179283\",\n \"0.5169548\",\n \"0.5168879\",\n \"0.51631016\",\n \"0.51624215\",\n \"0.514839\",\n \"0.5126115\",\n \"0.51142097\",\n \"0.5096942\",\n \"0.50930476\",\n \"0.50922483\",\n \"0.5083158\",\n \"0.5076619\",\n \"0.50676537\",\n \"0.5048323\",\n \"0.50343394\",\n \"0.5019212\",\n \"0.50182384\",\n \"0.5002224\",\n \"0.49972203\",\n \"0.49962214\",\n \"0.4981576\",\n \"0.49691907\",\n \"0.49637112\",\n \"0.49594244\",\n \"0.49296552\",\n \"0.49215207\",\n \"0.4921472\",\n \"0.49148735\",\n \"0.49105322\",\n \"0.4910039\",\n \"0.49049208\",\n \"0.49036458\",\n \"0.48985234\",\n \"0.48975477\",\n \"0.48905396\",\n \"0.48859543\",\n \"0.4876442\",\n \"0.4867054\",\n \"0.48593438\",\n \"0.48568922\",\n \"0.48491296\",\n \"0.48437026\",\n \"0.4839874\",\n \"0.48397934\",\n \"0.48374963\",\n \"0.48352736\",\n \"0.482992\",\n \"0.48289433\",\n \"0.4827574\",\n \"0.48256904\",\n \"0.48227212\",\n \"0.48201117\",\n \"0.481851\",\n \"0.4804387\",\n \"0.48027405\",\n \"0.48008776\",\n \"0.48007554\",\n \"0.4794381\",\n \"0.47887012\",\n \"0.47858018\",\n \"0.47758016\",\n \"0.4771593\",\n \"0.47616407\",\n \"0.47614187\",\n \"0.47583565\",\n \"0.47557124\",\n \"0.4753395\",\n \"0.47475106\",\n \"0.47371167\",\n \"0.47333556\",\n \"0.47276396\",\n \"0.47212085\",\n \"0.47171748\",\n \"0.47165695\",\n \"0.4711364\",\n \"0.4706713\",\n \"0.47059217\",\n \"0.47051296\",\n \"0.47025162\",\n \"0.47023085\",\n \"0.4701146\",\n \"0.4693826\",\n \"0.4693067\",\n \"0.4691543\",\n \"0.4687349\",\n \"0.46861896\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76671,"cells":{"query":{"kind":"string","value":"Lee los registros existentes en la base abierta y en la tabla productos"},"document":{"kind":"string","value":"def read(self):\n try:\n datos = self.base.readData()\n for i in range(len(datos)):\n self.verDatos.insert('', i+1, text = i+1, values = (datos[i][0], datos[i][1], datos[i][2]))\n except:\n showerror(\"Error\", exc_info()[1])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def buscarProd(self):\n medicamento = str(self.lineMedicamento.text())\n monodroga = str(self.lineMonodroga.text())\n data = self.getAllTabla(self.tableProductos)\n\n if medicamento != \"\":\n dataMedic = filter(lambda x: x[1].upper() == medicamento.upper(), data.values())\n else:\n dataMedic = data.values()\n if monodroga != \"\":\n dataMono = filter(lambda x: x[3].upper() == monodroga.upper(), dataMedic)\n else:\n dataMono = dataMedic\n\n for dato in data:\n self.tableProductos.setRowHidden(dato,False)\n\n for dato in data:\n if not data[dato] in dataMono:\n self.tableProductos.setRowHidden(dato,True)","def cargarProductosSinObra(self):\n\n self.limpiarTabla(self.tableProductos)\n\n ##Cnsulta para obtener todos los productos del sistema, con su correspondiente\n ##codigo de barra, monodroga, descuento, importe\n query=self.sesion.query(ProductoModel.codigo_barra,ProductoModel.id_medicamento,ProductoModel.id_presentacion,MonodrogaModel.nombre,ProductoModel.importe).\\\n join(MedicamentoModel).filter(ProductoModel.id_medicamento==MedicamentoModel.nombre_comercial).\\\n join(MonodrogaModel).filter(MedicamentoModel.id_monodroga==MonodrogaModel.nombre).\\\n filter(ProductoModel.baja==False).order_by(ProductoModel.codigo_barra)\n\n ##Se cargan los datos obtenidos en la tabla de Producto\n for n, obj in enumerate(query):\n self.tableProductos.insertRow(n)\n self.tableProductos.setItem(n, 0, QtGui.QTableWidgetItem(str(obj[0])))\n self.tableProductos.setItem(n, 1, QtGui.QTableWidgetItem(str(obj[1])))\n self.tableProductos.setItem(n, 2, QtGui.QTableWidgetItem(str(obj[2])))\n self.tableProductos.setItem(n, 3, QtGui.QTableWidgetItem(str(obj[3])))\n self.tableProductos.setItem(n, 4, QtGui.QTableWidgetItem(str(0)))\n self.tableProductos.setItem(n, 5, QtGui.QTableWidgetItem(str(obj[4])))\n\n ##Se carga la cantidad de cada producto en la tabla\n for row,producto in enumerate(ProductoModel.buscarTodos(ProductoModel.codigo_barra,self.sesion)):\n self.tableProductos.setItem(row,6,QtGui.QTableWidgetItem(str(producto.getCantidad(self.sesion))))","def cargar_productos(self, obraSocial):\n self.limpiarTabla(self.tableProductos)\n\n query=self.sesion.query(ProductoModel.codigo_barra,ProductoModel.id_medicamento,ProductoModel.id_presentacion,MonodrogaModel.nombre,DescuentoModel.descuento,ProductoModel.importe).\\\n join(MedicamentoModel).filter(ProductoModel.id_medicamento==MedicamentoModel.nombre_comercial).\\\n join(MonodrogaModel).filter(MedicamentoModel.id_monodroga==MonodrogaModel.nombre).\\\n join(DescuentoModel).filter(DescuentoModel.producto==ProductoModel.codigo_barra).\\\n filter(DescuentoModel.obra_social==obraSocial,ProductoModel.baja==False).order_by(ProductoModel.codigo_barra)\n\n for n, obj in enumerate(query):\n self.tableProductos.insertRow(n)\n for m, campo in enumerate(obj):\n self.tableProductos.setItem(n, m, QtGui.QTableWidgetItem(str(campo)))\n\n for row,producto in enumerate(ProductoModel.buscarTodos(ProductoModel.codigo_barra,self.sesion)):\n self.tableProductos.setItem(row,6,QtGui.QTableWidgetItem(str(producto.getCantidad(self.sesion))))","def agregarProducto(self):\n itemActual=self.tableFactura.currentItem()\n producto = int(self.tableFactura.item(itemActual.row(),0).text())\n descuento = DescuentoModel.buscar(DescuentoModel.obra_social,self.sesion,self.obraSocial).\\\n filter(DescuentoModel.producto==producto)[0].descuento\n cantidad = int(self.tableFactura.item(itemActual.row(), 1).text())\n importe = float(self.tableFactura.item(itemActual.row(), 2).text()) * descuento\n row = self.tableNC.rowCount()\n self.tableNC.insertRow(row)\n self.tableNC.setItem(row, 0, QtGui.QTableWidgetItem(str(producto)))\n self.tableNC.setItem(row, 1, QtGui.QTableWidgetItem(str(cantidad)))\n self.tableNC.setItem(row, 2, QtGui.QTableWidgetItem(str(importe)))\n self.detallesReintegrables.append([int(self.numeroFacturaActual),itemActual.row()+1,descuento,importe])\n self.detallesImprimibles.append([producto,cantidad,descuento,importe])\n self.tableFactura.hideRow(itemActual.row())","def obtener_productos():\n\n # Se crea la lista de objetos Producto()\n productos = [\n Producto(\"Caja chica\", 5, 100.0),\n Producto(\"Caja mediana\", 3, 185.0),\n Producto(\"Caja grande\", 1, 299.0)\n ]\n\n return productos","def product_db() -> List[Text]:\n\n return [\n \"credit\",\n \"forex\",\n \"debit\",\n \"atm\"\n ]","def product_tables(self): \r\n\r\n self.mycursor.execute('CREATE TABLE IF NOT EXISTS product(\\\r\n PROD_id BIGINT PRIMARY KEY,\\\r\n PROD_name VARCHAR(100) NOT NULL,\\\r\n PROD_grade CHAR(1) NOT NULL,\\\r\n PROD_url VARCHAR(150) NOT NULL UNIQUE)')","def limpiarVentana(self):\n\n self.productosAgregados=0\n self.lotesVentas={}\n self.facturaCobrada=False\n self.obraSocialSeleccionada=None\n self.formapago = None\n self.factura = None\n self.data = {}\n self.detallesTabla = {}\n self.lineObra.clear()\n self.lineObra.setEnabled(True)\n self.lineCuit.clear()\n self.lineCuit.setEnabled(True)\n self.tableObra.setVisible(False)\n self.rbtnObra.setChecked(False)\n self.limpiarTabla(self.tableProductos)\n self.limpiarTabla(self.tableFactura)\n self.cargarProductosSinObra()","def get_all_products(self):\n\t\tpass","def insert_products(self):\n logic = ProductLogic()\n \n try:\n # We create the list of product objects\n products = self.objects_factory.create_product_object_list()\n products = set(products)\n\n for product in products:\n logic.insert(product)\n except:\n print('Il y a eu un problème lors de la récupération des données, veuillez rééssayer')","def generarConsultasLibres(self): \n \n for idw,word in enumerate(self.key_words):\n consultasparql = self.busLibre % (word,self.limit_BL)\n self.ConsultasLibres.append(consultasparql)\n \n \n return self.ConsultasLibres","def agregarProducto(self):\n itemActual=self.tableProductos.currentItem()\n cantidad, ok = QtGui.QInputDialog.getInt(self,\"Cantidad\",\"Ingrese cantidad del producto\",1,1,2000,5)\n if not ok:\n self.showMsjEstado(\"No se ha seleccionado cantidad del producto\")\n else:\n cantidadProducto=int(self.tableProductos.item(itemActual.row(),6).text())\n if cantidad>cantidadProducto:\n QtGui.QMessageBox.information(self,\"Aviso\",\"La cantidad ingresada es mayor que la del stock\")\n else:\n if self.productosAgregados == 0 and self.factura == None:\n self.factura=FacturaModel(FacturaModel.generarNumero(self.sesion))\n self.factura.guardar(self.sesion)\n self.productosAgregados+=1\n rowItemActual=itemActual.row()\n rows=self.tableFactura.rowCount()\n self.tableFactura.insertRow(rows)\n\n #--Carga de items en la tabla--*\n producto = int(self.tableProductos.item(rowItemActual,0).text())\n importeActual=float(self.tableProductos.item(rowItemActual,5).text())\n descuentoActual=float(self.tableProductos.item(rowItemActual,4).text())\n subtotal=importeActual*(1-descuentoActual)\n ####-------------------------#####\n detalleFactura=DetalleFacturaModel(self.factura.numero,producto,cantidad,\n subtotal*cantidad,descuentoActual,self.productosAgregados\n )\n self.descontarCantidad(detalleFactura,producto,cantidad)\n self.tableFactura.setItem(rows,0,QtGui.QTableWidgetItem(str(detalleFactura.producto)))\n self.tableFactura.setItem(rows,1,QtGui.QTableWidgetItem(str(detalleFactura.cantidad)))\n self.tableFactura.setItem(rows, 2, QtGui.QTableWidgetItem(str(\"%.2f\"%(subtotal*cantidad))))\n\n detalleFactura.guardar(self.sesion)\n self.detallesTabla[rows] = detalleFactura\n\n self.data[rows] = [\n producto, cantidad, subtotal*cantidad, descuentoActual\n ]\n\n self.actualizar()\n self.objectModified.emit()","def fill_data_product(self):\n self.product.fill_data_product(self.list_products, self.mycursor, self.my_database)","def fill_cache_table():\n products = []\n for query in ['bread', 'milk', 'rice']:\n grocery = grocery_search(query)\n products += get_all_products_from_grocery_search(grocery)\n\n orm = ORM()\n for product in products:\n orm.add_cache(**product)","def consultar_todos_DB(self):\n registros = db.session.query(ModelConcurso).all()\n for registro in registros:\n print(registro)","def get_data(self):\n products_list = []\n for category in CATEGORIES:\n json_data = self.url_to_json(category)\n pages_nb = self.retrieve_cat_pages_nb(json_data)\n for page in range(pages_nb):\n page_json_data = self.page_to_json(category, page+1)\n products = page_json_data[\"products\"]\n for p in products:\n params = {\n 'brands': \"\",\n 'product_name_fr': \"\",\n 'nutrition_grades': \"\",\n 'stores': \"\",\n 'url': \"\",\n 'categories': \"\"\n }\n for key in params:\n try:\n params[key] = p[key]\n except KeyError:\n continue\n if params['product_name_fr'] != \"\" and params['nutrition_grades'] != \"\" and params['url'] != \"\" and params['categories'] != \"\":\n product = Product(brand=params['brands'],\n name=params['product_name_fr'],\n nutrition_grade=params['nutrition_grades'],\n stores=params['stores'], url=params['url'],\n category=params['categories'])\n products_list.append(product)\n try:\n self.manager.save_all(self.clean_data(products_list))\n print(f\"\\n La base de données |{DB_NAME}| a été peuplée \\n\")\n except:\n print(\"\\n Une erreur s'est produite lors \"\n \"du peuplement de la base de données \\n\")","def generarConsultasConexion(self):\n for parRecursos in self.CombiConsultaLibre:\n parRecursosL0=self.limpiaRecursos(parRecursos[0])\n parRecursosL1=self.limpiaRecursos(parRecursos[1])\n \n if self.nivel_profundidad>=1:\n consultasparql = self.busConex1 % (parRecursosL0,parRecursosL1,self.limit_BC)\n print consultasparql;\n resultoCC=self.consulta(consultasparql)\n for resul in resultoCC['results']['bindings']:\n triple = parRecursos[0]+\"-|\"+parRecursos[1]+\"-|\"+resul['p1']['value']\n self.ResultConsultasConexion.append(triple) \n \n if self.nivel_profundidad>=2:\n consultasparql = self.busConex2 % (parRecursosL0,parRecursosL1,self.limit_BC)\n resultoCC=self.consulta(consultasparql)\n for resul in resultoCC['results']['bindings']:\n o1=resul['o1']['value']\n o1=o1.replace('http://dbpedia.org/resource/','')\n triple1 = parRecursos[0]+\"-|\"+o1+\"*-|\"+resul['p1']['value']\n triple2 = parRecursos[1]+\"-|\"+o1+\"*-|\"+resul['p2']['value']\n self.ResultConsultasConexion.append(triple1) \n self.ResultConsultasConexion.append(triple2) \n \n if self.nivel_profundidad>=3:\n consultasparql = self.busConex3_1 % (parRecursosL0,parRecursosL1,self.limit_BC)\n resultoCC=self.consulta(consultasparql)\n for resul in resultoCC['results']['bindings']:\n o1=resul['o1']['value']\n o1=o1.replace('http://dbpedia.org/resource/','')\n o2=resul['o2']['value']\n o2=o1.replace('http://dbpedia.org/resource/','')\n triple1 = parRecursos[0]+\"-|\"+o1+\"*-|\"+resul['p1']['value']\n triple2 = parRecursos[1]+\"-|\"+o2+\"*-|\"+resul['p2']['value']\n triple3 = o1+\"*-|\"+o2+\"*-|\"+resul['p3']['value'] \n self.ResultConsultasConexion.append(triple1) \n self.ResultConsultasConexion.append(triple2) \n self.ResultConsultasConexion.append(triple3) \n\n consultasparql = self.busConex3_2 % (parRecursosL0,parRecursosL1,self.limit_BC)\n resultoCC=self.consulta(consultasparql)\n for resul in resultoCC['results']['bindings']:\n o1=resul['o1']['value']\n o1=o1.replace('http://dbpedia.org/resource/','')\n o2=resul['o2']['value']\n o2=o1.replace('http://dbpedia.org/resource/','')\n triple1 = parRecursos[0]+\"-|\"+o1+\"*-|\"+resul['p1']['value']\n triple2 = parRecursos[1]+\"-|\"+o2+\"*-|\"+resul['p2']['value']\n triple3 = o2+\"*-|\"+o1+\"*-|\"+resul['p3']['value'] \n self.ResultConsultasConexion.append(triple1) \n self.ResultConsultasConexion.append(triple2) \n self.ResultConsultasConexion.append(triple3)","def buscarOs(self):\n\n if self.lineRazon.isEnabled():\n self.filtrarObra()\n\n elif not self.lineRazon.isEnabled() and (self.tableNC.rowCount() != 0 or self.tableFactura.rowCount() != 0):\n QtGui.QMessageBox.information(self,\"Aviso\",\"Imposible cambiar de Obra Social. Ya se ha seleccionado\\\n una\")\n else:\n self.gbNotaCredito.setEnabled(False)\n self.gbFactura.setEnabled(False)\n self.lineRazon.clear()\n self.lineRazon.setEnabled(True)\n self.lineCuit.clear()\n self.lineCuit.setEnabled(True)\n self.tableOs.setEnabled(True)","def listall():\n # I like to define the query separately.\n query = db.product\n\n # List of additional links.\n links = []\n \n links.append(\n dict(header = \"Profit\",\n body = lambda row : produce_profit(row)\n )\n )\n links.append(\n dict(header = \"\",\n body = lambda row : produce_star_btn(row.id)\n )\n )\n links.append(\n dict(header = \"\",\n body = lambda row : produce_pls_minus_btn(row)\n )\n )\n links.append(\n dict(header='',\n body = lambda row : produce_poster_btns(row.id)\n \n )\n )\n \n # Let's get rid of some fields in the add form.\n if len(request.args) > 0 and request.args[0] == 'new':\n db.product.prod_poster.readable = False\n db.product.prod_post_time.writable = False\n db.product.prod_sold.writable = False\n db.product.prod_starred.readable, db.product.prod_starred.writable =False, False\n # Grid definition.\n grid = SQLFORM.grid(\n query, \n field_id = db.product.id, # Useful, not mandatory.\n fields = [db.product.id, db.product.prod_name,\n db.product.prod_in_stock, db.product.prod_sold,\n db.product.prod_price, db.product.prod_cost], \n headers = {'product.prod_name': 'Product Name',\n 'product.prod_in_stock':'In Stock',\n 'product.prod_sold':'Sold', \n 'product.prod_price':'Price', \n 'product.prod_cost':'Cost'},\n links = links,\n # And now some generic defaults.\n details=False,\n create=True, editable=False, deletable=False,\n csv=False, \n user_signature=True, # We don't need it as one cannot take actions directly from the form.\n )\n return dict(grid=grid)","def main():\n\n parser = argparse.ArgumentParser(description='MODIS product table')\n parser.add_argument('--product', help='MODIS product ID', metavar='')\n parser.add_argument('--resolution', help='Filter for pixel size', type=str, metavar='')\n parser.add_argument('--vampc', help='Filter for VAM product code', metavar='')\n args = parser.parse_args()\n\n # Load product table\n this_dir = Path(__file__).parent\n with open(this_dir.parent.joinpath('data', 'MODIS_V6_PT.pkl').as_posix(), 'rb') as table_raw:\n product_table = pickle.load(table_raw)\n\n tbl = pd.DataFrame(product_table).T\n print('\\n')\n\n # Subset table by product\n if args.product:\n try:\n tbl = tbl.loc[args.product.upper()]\n except:\n raise SystemExit('Product ID not recognized!')\n # Subset table by resolution\n if args.resolution and not args.product:\n try:\n tbl = tbl[tbl['pixel_size'] == args.resolution]\n except:\n raise ValueError('Resolution not valid! (possible: 250,500,1000,5600)')\n # Subset table by parameter\n if args.vampc and not args.product:\n helper_dict = dict(zip(['VIM', 'VEM', 'LTD', 'LTN'],\n ['Vegetation Indices',\n 'Vegetation Indices',\n 'Temperature, Emissivity',\n 'Temperature, Emissivity']))\n try:\n tbl = tbl[tbl['product'] == helper_dict[args.vampc.upper()]]\n except:\n raise ValueError('VAM product code not valid! (possible: VIM, VEM, LTD, LTN)')\n\n # print results\n with pd.option_context('display.max_rows', None, 'display.max_columns', 4):\n print(tbl)\n print('\\n')","def getProductos(self, codigo, desc):\n if codigo != \"\" and desc != \"\":\n return self.conexion.ejecutarSQL(\"select codigo, descripcion, cantidad_disponible, porcentaje_IVA, precio_venta, fecha_modificacion, hora_modificacion, usuario_colaborador \\\n from productos \\\n where codigo like '%%%s%%' and descripcion like '%%%s%%' \\\n order by codigo\"%(codigo,desc))\n elif codigo != \"\":\n return self.conexion.ejecutarSQL(\"select codigo, descripcion, cantidad_disponible, porcentaje_IVA, precio_venta, fecha_modificacion, hora_modificacion, usuario_colaborador \\\n from productos \\\n where codigo like '%%%s%%' \\\n order by codigo\"%(codigo))\n elif desc != \"\":\n return self.conexion.ejecutarSQL(\"select codigo, descripcion, cantidad_disponible, porcentaje_IVA, precio_venta, fecha_modificacion, hora_modificacion, usuario_colaborador \\\n from productos \\\n where descripcion like '%%%s%%' \\\n order by codigo\"%(desc))\n else:\n return self.conexion.ejecutarSQL(\"select codigo, descripcion, cantidad_disponible, porcentaje_IVA, precio_venta, fecha_modificacion, hora_modificacion, usuario_colaborador \\\n from productos \\\n order by codigo\")","def generarCombinaciones(self):\n combi = [list(x) for x in itertools.combinations(self.ResultConsultaLibre, 2)]\n self.CombiConsultaLibre=combi\n #print(self.CombiConsultaLibre)","def show_all_products():\n\n data = cur.execute(\"\"\"SELECT productid, productname, unitcost, stock FROM catalogue\"\"\").fetchall()\n\n print(tabulate(data, headers=[\"Product ID\", \"Name\", \"Cost\", \"Stock\"]))","def retrieve_product_infos(self):\n\n # PRODUCT NAME\n try:\n product_name = self.product['product_name'].capitalize()\n except KeyError:\n product_name = None\n\n # PRODUCT CODE\n try:\n product_code = self.product['code'].capitalize()\n except KeyError:\n product_code = None\n\n # URL\n try:\n product_url = self.product['url'].lower()\n except KeyError:\n product_url = None\n\n # IMAGE URL\n try:\n image_url = self.product['image_url'].lower()\n except KeyError:\n image_url = None\n\n # QUANTITY\n try:\n quantity = self.product['quantity'].capitalize()\n except KeyError:\n quantity = None\n\n # INGREDIENTS\n try:\n ingredients = self.product['ingredients_text_fr'].capitalize()\n except KeyError:\n ingredients = None\n\n # BRAND\n brands = []\n try:\n for brand in self.product['brands'].split(','):\n brand = brand.strip().capitalize()\n if (\n brand != ''\n and brand not in brands\n ):\n brands.append(brand)\n except KeyError:\n pass\n\n # STORES\n stores = []\n try:\n for store in self.product['stores'].split(','):\n store = store.strip().capitalize()\n if (\n store != ''\n and store not in stores\n ):\n stores.append(store)\n except KeyError:\n pass\n\n # COUNTRY\n try:\n countries = self.product['countries'].capitalize()\n except KeyError:\n countries = None\n if 'France' in countries:\n countries = 'France'\n else:\n countries = None\n\n # COMPARE TO CATEGORY\n try:\n compare_to = self.product['compared_to_category'].capitalize().split(':')[1]\n except KeyError:\n compare_to = None\n try:\n Categories.objects.get(\n name=compare_to\n )\n except Categories.DoesNotExist:\n compare_to = None\n except:\n importable = False\n\n # CATEGORIES HIERARCHY\n try:\n categories_hierarchy = [\n category.split(':')[1] for category in self.product['categories_hierarchy']\n ]\n except KeyError:\n categories_hierarchy = None\n\n # NUTRISCORE GRADE\n nutriscore_labels = [\n 'nutrition_grade_fr',\n 'nutriscore_grade'\n ]\n nutriscore = 'F'\n i = 0\n while (\n i < len(nutriscore_labels)\n and nutriscore == 'F'\n ):\n try:\n nutriscore = self.product[nutriscore_labels[i]].upper()\n except KeyError:\n i += 1\n\n product_infos = {\n 'product_name': product_name,\n 'product_code': product_code,\n 'product_url': product_url,\n 'image_url': image_url,\n 'quantity': quantity,\n 'ingredients': ingredients,\n 'brands': brands,\n 'stores': stores,\n 'countries': countries,\n 'compare_to': compare_to,\n 'categories_hierarchy': categories_hierarchy,\n 'nutriscore': nutriscore\n }\n\n nutriments = self.product['nutriments']\n for nutriment in self.list_nutriments:\n try:\n product_infos[nutriment] = float(nutriments[nutriment])\n except KeyError:\n product_infos[nutriment] = 0\n\n return product_infos","def fill_products(self):\n cursor = self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n categories = dict()\n for page in range(1, 2):\n result = requests.get(\n 'https://fr.openfoodfacts.org/cgi/search.pl?page_size=1000&page={}&action=process&json=1'.format(\n page)).json()\n for element in result['products']:\n try:\n cursor.execute(\n \"INSERT INTO product (name, store, nutrition_grade, url) VALUES (%s, %s, %s, %s) RETURNING \"\n \"id, name\",\n (element[\"product_name\"], element[\"stores\"], element[\"nutrition_grade_fr\"], element[\"url\"]))\n # un except pour éviter les erreurs de clés\n query_result = cursor.fetchone()\n for category in element[\"categories_tags\"]:\n try:\n cursor.execute(\"INSERT INTO product_category(product_id, category_id) VALUES (%s, %s)\",\n (query_result[0], self.categories[category]))\n except KeyError:\n print(\"Categorie insertion failed\")\n\n print(element[\"product_name\"])\n except KeyError:\n print(f'product insertion failed:')\n\n self.conn.commit()\n cursor.close()","def __print_from_database(self, product: dict, procedure_result: list):\n\n # procedure_result[1] = p_product_id\n # procedure_result[2] = p_exist_substitutes\n # procedure_result[3] = p_researched_subsitutes\n\n print('Produit déjà présent dans la base de données.')\n # if product doesn't have substitutes in database\n if not procedure_result[2] and not procedure_result[3]:\n # get substitutes of the current product\n # from the openfoodfacts API\n substitutes = self.api_operator.get_substitutes(\n product['categories_tags'][-1],\n product.get('nutrition_grades', 'e'))\n self.database_manager.save_substitutes_sql_database(\n procedure_result[1], substitutes)\n operateur_result = []\n self.database_manager.fill_list_with_product_and_substitutes(\n procedure_result[1], operateur_result)\n self.printer(operateur_result)\n self.ask_with_input('Ok ? (y) ', -1, ('y',))","def search_product(self):\n cat = []\n product = open_products()\n radio = self.radiobutton_check()\n search = self.lineEdit_search.text()\n _translate = QtCore.QCoreApplication.translate\n __sortingEnabled = self.tableWidget.isSortingEnabled()\n self.tableWidget.setSortingEnabled(False)\n o=0\n if len(self.lineEdit_search.text()) == 0:\n self.show_product()\n else:\n for r in range(0, len(product)):\n if search.upper() in str(product[r][radio]).upper():\n cat.append(product[r])\n for i in range(0, len(cat)):\n for c in range(0, 5):\n item = self.tableWidget.item(i, c)\n item.setText(_translate(\"MainWindow\", str(cat[i][c])))\n o+=1\n else:\n for c in range(0, 5):\n item = self.tableWidget.item(r, c)\n item.setText(_translate(\"MainWindow\", \"\"))\n if o == 0:\n self.frame_3.show()\n self.label_16.setText('PRODUCT NOT FOUND!')","def habilitarObras(self):\n if self.productosAgregados != 0:\n QtGui.QMessageBox.information(self,\"Aviso\",\"Ya se han agregado productos a la factura\")\n else:\n if not self.rbtnObra.isChecked():\n self.btnBuscar.setEnabled(True)\n self.lineObra.setEnabled(True)\n self.lineCuit.setEnabled(True)\n self.tableObra.setVisible(True)\n else:\n self.lineObra.clear()\n self.lineCuit.clear()\n self.btnBuscar.setEnabled(False)\n self.lineObra.setEnabled(False)\n self.lineCuit.setEnabled(False)\n self.tableObra.setVisible(False)\n self.obraSocialSeleccionada=None\n self.cargarProductosSinObra()","def geneRegLleg(fech, product, cantProd):\n listaF = []\n linea1 = \"Registro de llegada de articulo:\\n\"\n linea2 = f\"Fecha de recepecion: {fech}\\n\"\n if product == 0:\n pro = \"Disney Cruella De Vil Diamond Funko Pop Exclusivo\"\n elif product == 1:\n pro = \"Funko Pop Board Games Silver Mr. Monopoly - Figura de vinilo\"\n elif product == 2:\n pro = \"Funko Pop Daenerys Targaryen 59 Game of Thrones\"\n elif product == 3:\n pro = \"Funko Pop Flocked Iceman 504 Marvel 80 Years\"\n elif product == 4:\n pro = \"Funko Pop Glowing Mr Burns 1162 The Simpsons\"\n elif product == 5:\n pro = \"Funko Pop Grogu Con Galleta 464 Star Wars The Mandalorian\"\n elif product == 6:\n pro = \"Funko Pop Keychain Pogo The Umbrella Academy\"\n elif product == 7:\n pro = \"Funko Pop Keychain The Flash DC The Flash\"\n elif product == 8:\n pro = \"Funko Pop Marvel: Avengers - Wanda Maximoff Exclusivo Disney Collector Zone\"\n elif product == 9:\n pro = \"Funko Pop Venom 888 Marvel Venom\"\n elif product == 10:\n pro = \"Llavero Funko Pop Zoey Fortnite\"\n elif product == 11:\n pro = \"Lucky The Leprechaun Edición Limitada\"\n linea3 = f\"Nombre del articulo: {pro}\\n\"\n linea4 = f\"Cantidad: {cantProd}\\n\"\n listaF.append(linea1)\n listaF.append(linea2)\n listaF.append(linea3)\n listaF.append(linea4)\n with open(\"registroLA.txt\",\"a\") as regProdAlmacen:\n regProdAlmacen.writelines(listaF)","def substitution(self, data, pro):\n\n produitid = self.category_id\n nutri = str(self.nutri_score)\n data.req(\"SELECT * from produit where category_id = '\" + produitid +\n \"' and nutri_score < \" + nutri + \" \")\n row = data.req_return\n maxi = len(row)\n list_product = []\n i = 0\n nb_dispo = maxi\n print(\"nombre de produits correspondant heo:\", nb_dispo)\n if nb_dispo > 0:\n while i < maxi:\n product = Produit(row[i][0], row[i][1],\n row[i][2], row[i][3],\n row[i][4], row[i][5])\n print(i + 1, product.nom)\n list_product.append(product)\n i += 1\n while True:\n try:\n choix_produit = int(input(\"Veuillez entrer un \"\n \"nombre : \"))\n if maxi >= choix_produit > 0:\n break\n except ValueError:\n print(\"\")\n\n print(\"produit selectionner\", \"\\n\")\n choix_produit = int(choix_produit - 1)\n print(\"nom:\", list_product[choix_produit].nom)\n print(\"description:\", list_product[choix_produit].description)\n print(\"magasin:\")\n tab = data.getMagasin(list_product[choix_produit].id, list_product[choix_produit].category_id)\n cpt = 0\n max = len(tab)\n while cpt < max:\n store = data.getStore(tab[cpt])\n print(store.nom)\n cpt += 1\n print(\"url:\", list_product[choix_produit].url)\n print(\"nutriScrore:\", list_product[choix_produit].nutri_score)\n print(\"1 - trouver un substitue de meilleur \"\n \"qualité ?\")\n print(\"2 - Sauvegarder alliment.\")\n print(\"3 - Nouvelle recherche.\")\n while True:\n try:\n choix = int(input(\"Veuillez entrer un nombre : \"))\n if maxi >= choix_produit > 0:\n break\n except ValueError:\n print(\"\")\n if choix == 1:\n pro = list_product[choix_produit]\n list_product[choix_produit].substitution(data, pro)\n elif choix == 2:\n list_product[choix_produit].save(data, pro)\n return 1\n else:\n # data.close()\n return 1\n else:\n print(\"il n'existe pas mieux! \")\n print(\"1 - Sauvegarder alliment.\")\n print(\"2 - Nouvelle recherche.\")\n while True:\n try:\n choix_t = int(input(\"Veuillez entrer un nombre : \"))\n if choix_t <= 2 and choix_t > 0:\n break\n except ValueError:\n print(\"\")\n if choix_t == 1:\n produit = Produit(self.id, self.nom,\n self.category_id,\n self.description,\n self.magasin, self.url,\n self.nutri_score)\n produit.save(data)\n return 1\n\n else:\n # data.close()\n return 1","def get_exists(self):\n self.exist_products = {}\n limit = 100\n with sa.create_engine(dsn).connect() as dbcon:\n count = [x for x in dbcon.execute(Product.count())][0][0]\n for i in range(count//limit+1):\n sql = sa.select([Product.c.id, Product.c.title]).limit(limit).offset(limit*i)\n part = {hash(x[1]): x[0] for x in dbcon.execute(sql)}\n self.exist_products.update(part)","def buscarFactura(self):\n\n if not self.lineNumero.isEnabled() and self.facturaSeleccionada != None:\n QtGui.QMessageBox.information(self,\"Aviso\",\"Ya se ha seleccionado una factura\")\n elif not self.lineNumero.isEnabled():\n self.lineNumero.setEnabled(True)\n self.lineNumero.clear()\n self.limpiarTabla(self.tableFactura)\n else:\n self.numeroFacturaActual=str(self.lineNumero.text())\n if len(self.numeroFacturaActual)==0:\n QtGui.QMessageBox.information(self,\"Aviso\",QtCore.QString.fromUtf8(\"No se ha ingresado número de factura\"))\n else:\n self.facturaSeleccionada=FacturaModel.existeFactura(int(self.numeroFacturaActual),self.sesion)\n if self.facturaSeleccionada==None:\n QtGui.QMessageBox.warning(self,\"Aviso\",\"La factura seleccionada no existe\")\n elif self.facturaSeleccionada.getNC()!=None:\n QtGui.QMessageBox.information(self,\"Aviso\",QtCore.QString.fromUtf8(\"La factura ya ha posee una Nota de Crédito\"))\n self.facturaSeleccionada = None\n elif self.facturaSeleccionada.getFechaEmision()+timedelta(days=int(self.plazo))<date.today():\n QtGui.QMessageBox.information(self,\"Aviso\",QtCore.QString.fromUtf8(\"El tiempo permitido para la devolución ha expirado\"))\n elif self.facturaSeleccionada.estaLiquidada(self.sesion):\n print self.facturaSeleccionada.estaLiquidada(self.sesion)\n QtGui.QMessageBox.information(self,\"Aviso\",\"La factura se encuentra liquidada a la Obra Social\")\n else:\n self.lineNumero.setEnabled(False)\n self.cargarObjetos(self.tableFactura,self.facturaSeleccionada.getDetalles(self.sesion),\n [\"nro_linea\",\"producto\",\"cantidad\",\"importe\"])","def cargarObra(self):\n rowActual=self.tableObra.currentItem().row()\n self.lineObra.setText(str(self.tableObra.item(rowActual,0).text()))\n self.lineCuit.setText(str(self.tableObra.item(rowActual,1).text()))\n self.tableObra.hide()\n self.lineObra.setEnabled(False)\n self.lineCuit.setEnabled(False)\n self.obraSocialSeleccionada = str(self.lineObra.text())\n self.cargar_productos(self.obraSocialSeleccionada)\n self.gbProducto.setVisible(True)","def load_products():\n\n print \"Loading Products\"\n\n for i, row in enumerate(open(\"data/mock_product_data.csv\")):\n row = row.rstrip()\n title, price, inventory = row.split(\",\")\n\n product = Product(title=title,\n price=price,\n available_inventory=inventory)\n\n db.session.add(product)\n\n db.session.commit()","def buscarFactura(self):\n if not self.lineNumeroFac.isEnabled() and self.tableNC.rowCount() != 0:\n QtGui.QMessageBox.information(self,\"Aviso\",\"Ya se ha seleccionado una factura\")\n elif not self.lineNumeroFac.isEnabled():\n self.lineNumeroFac.setEnabled(True)\n self.lineNumeroFac.clear()\n self.limpiarTabla(self.tableFactura)\n else:\n self.numeroFacturaActual=str(self.lineNumeroFac.text())\n if len(self.numeroFacturaActual)==0:\n self.showMsjEstado(\"No se ha ingresado numero de factura\")\n else:\n self.facturaSeleccionada=FacturaModel.existeFactura(int(self.numeroFacturaActual),self.sesion)\n if self.facturaSeleccionada==None:\n QtGui.QMessageBox.information(self,\"Aviso\",\"La factura seleccionada no existe\")\n elif self.facturaSeleccionada.getObra() != None and self.facturaSeleccionada.getObra() != self.obraSocial:\n QtGui.QMessageBox.information(self,\"Aviso\",\"La Obra Social seleccionada no corresponde con la factura\")\n elif self.facturaSeleccionada.getFechaEmision()+timedelta(days=int(self.plazo))<date.today():\n QtGui.QMessageBox.information(self,\"Aviso\",\"El tiempo permitido para el reintegro ha expirado\")\n elif self.facturaSeleccionada.estaLiquidada(self.sesion):\n QtGui.QMessageBox.information(self,\"Aviso\",\"La factura se encuentra liquidada a la Obra Social\")\n elif self.facturaSeleccionada.getNC()!=None:\n QtGui.QMessageBox.information(self,\"Aviso\",\"La factura ya posee una Nota de Crédito\")\n else:\n self.lineNumeroFac.setEnabled(False)\n if self.facturaSeleccionada.getObra() == None:\n self.cargarObjetos(self.tableFactura,self.facturaSeleccionada.getDetalles(self.obraSocial, self.sesion),\n [\"producto\",\"cantidad\",\"importe\"])\n else:\n self.cargarObjetos(self.tableFactura,self.facturaSeleccionada.getDetallesSinDescuento(self.sesion),\n [\"producto\",\"cantidad\",\"importe\"])","def products(self):\n return list(Product.select())","def read_precios(path=\".\"):\n\n here = Path(path)\n sucursales = sucursales_prov(here)\n productos = pd.read_csv(here / \"productos.csv\")\n precios = [read_precio(f, sucursales) for f in Path(path).glob(\"precios_*.csv\")]\n\n # aplico merges de todos los precios\n def merge(left, right):\n return (\n pd.merge(left, right, on=[\"producto_id\", \"id_prov\", \"provincia\"], how=\"inner\").dropna()\n # .drop([\"sucursal_id_x\", \"sucursal_id_y\"], axis=1)\n )\n\n precios = functools.reduce(merge, precios)\n # eliminar columnas repetidas\n precios = precios.loc[:, ~precios.columns.duplicated()]\n precios = precios.rename(columns={\"cadena_x\": \"cadena\"})\n\n # elimina columnas innecesarias.\n # basicamente sucursal_* y cadena_y (queda cadena, renombrada antes)\n for c in set(precios.columns):\n if c.startswith((\"sucursal\", \"cadena_\")):\n precios.drop(c, axis=1, inplace=True)\n\n # cruzamos datos con productos\n precios = pd.merge(\n precios,\n productos[[\"id\", \"marca\", \"nombre\", \"categoria1\", \"categoria2\", \"categoria3\"]],\n left_on=\"producto_id\",\n right_on=\"id\",\n ).drop([\"id\", \"id_prov\"], axis=1)\n\n precios.sort_index(axis=1, inplace=True)\n\n # obtenemos el periodo maximo inicio vs fin (asumiendo columnas ordenadas)\n inicio, *_, fin = [c for c in precios.columns if c.startswith(\"precio_\")]\n\n precios[\"variacion\"] = precios[fin] - precios[inicio]\n precios[\"variacion_relativa\"] = precios[\"variacion\"] / precios[inicio] * 100\n return precios","def __init__(self):\r\n super(Sistema, self).__init__()\r\n\r\n self.datos = []\r\n\r\n self.productos = [\"Harina de Soya - Hi Pro/Pellet de Soya\", \"Harina de Soya - Full Fat\", \"Torta de Soya\",\r\n \"Torta de Girasol\", \"Aceite de Soya\", \"Grano de Soya\",\r\n \"Azucar\", \"Fierro\", \"Contenedor 20\", \"Contenedor 40\"]\r\n\r\n self.colas_espera_transbordo = {\r\n \"Harina de Soya - Hi Pro/Pellet de Soya\":\r\n {\"Descarga\": [], \"Carga\": []},\r\n \"Harina de Soya - Full Fat\":\r\n {\"Descarga\": [], \"Carga\": []},\r\n \"Torta de Soya\":\r\n {\"Descarga\": [], \"Carga\": []},\r\n \"Torta de Girasol\":\r\n {\"Descarga\": [], \"Carga\": []},\r\n \"Grano de Soya\":\r\n {\"Descarga\": [], \"Carga\": []},\r\n \"Azucar\":\r\n {\"Descarga\": [], \"Carga\": []},\r\n \"Fierro\":\r\n {\"Descarga\": [], \"Carga\": []}\r\n }\r\n\r\n self.camiones_en_sistema = []\r\n self.capacidad_sistema = 20\r\n\r\n # Definición de Recursos de Atención\r\n horarios = {\r\n \"horario 1\": {\"L-V\": {\"Ingreso\": 7.5, \"I. Descanso\": 13.0, \"F. Descanso\": 14.0, \"Salida\": 16.5},\r\n \"SAB\": {\"Ingreso\": 8.5, \"Salida\": 12.5}},\r\n \"horario 2\": {\"L-V\": {\"Ingreso\": 8.0, \"I. Descanso\": 12.0, \"F. Descanso\": 13.0, \"Salida\": 17.0},\r\n \"SAB\": {\"Ingreso\": 8.5, \"Salida\": 12.5}}\r\n }\r\n\r\n self.recursos_atencion = {\r\n \"Ventanilla Recepcion\":\r\n Recurso(self, \"Ventanilla Recepcion\",\r\n horarios[\"horario 1\"],\r\n capacity=1),\r\n \"Ventanilla Despacho\":\r\n Recurso(self, \"Ventanilla Despacho\",\r\n horarios[\"horario 1\"],\r\n capacity=1),\r\n \"Balanza 2\":\r\n Recurso(self, \"Balanza 2\",\r\n horarios[\"horario 1\"],\r\n capacity=1),\r\n \"Estacion Volcadora\":\r\n Recurso(self, \"Estacion Volcadora\",\r\n horarios[\"horario 2\"],\r\n capacity=1),\r\n \"Estacion Tolva/Balanza 3\":\r\n Recurso(self, \"Estacion Tolva/Balanza 3\",\r\n horarios[\"horario 2\"],\r\n capacity=1),\r\n \"Pala Mecanica\":\r\n Recurso(self, \"Pala Mecanica\",\r\n horarios[\"horario 2\"],\r\n capacity=1),\r\n \"Cuadrilla de Estibaje\":\r\n Recurso(self, \"Cuadrillas de Estibaje\",\r\n horarios[\"horario 2\"],\r\n capacity=3),\r\n \"Cabina de Recepcion - T1\":\r\n Recurso(self, \"Cabina de Recepcion - T1\",\r\n horarios[\"horario 2\"],\r\n capacity=1),\r\n \"Cabina de Despacho - T1\":\r\n Recurso(self, \"Cabina de Despacho - T1\",\r\n horarios[\"horario 2\"],\r\n capacity=1),\r\n \"Cabina de Recepcion - T2\":\r\n Recurso(self, \"Cabina de Recepcion - T2\",\r\n horarios[\"horario 2\"],\r\n capacity=1),\r\n \"Cabina de Despacho - T2\":\r\n Recurso(self, \"Cabina de Despacho - T2\",\r\n horarios[\"horario 2\"],\r\n capacity=1),\r\n \"Grua\":\r\n Recurso(self, \"Grua\",\r\n horarios[\"horario 2\"],\r\n capacity=1)}\r\n\r\n # Definición de medios de almacenamiento\r\n niv_tolva = {\r\n \"Harina de Soya - Hi Pro/Pellet de Soya\": 0\r\n }\r\n niv_almacen_1 = {\r\n \"Harina de Soya - Hi Pro/Pellet de Soya\": 500\r\n }\r\n niv_almacen_2 = {\r\n \"Harina de Soya - Full Fat\": 100,\r\n \"Torta de Soya\": 100,\r\n \"Torta de Girasol\": 100,\r\n \"Azucar\": 100\r\n }\r\n niv_almacen_ext = {\r\n \"Grano de Soya\": 0\r\n }\r\n niv_tanque_1 = {\r\n \"Aceite de Soya\": 0\r\n }\r\n niv_tanque_2 = {\r\n \"Aceite de Soya\": 0\r\n }\r\n niv_patio_cont = {\r\n \"Contenedor 20\": 0,\r\n \"Contenedor 40\": 0\r\n }\r\n self.medios_almacenamiento = {\r\n \"Tolva\":\r\n MedioDeAlmacenamiento(self, \"Tolva\", 2, niv_tolva, 400),\r\n \"Almacen 1\":\r\n MedioDeAlmacenamiento(self, \"Almacen 1\", 3, niv_almacen_1, 2500),\r\n \"Almacen 2\":\r\n MedioDeAlmacenamiento(self, \"Almacen 2\", 1, niv_almacen_2, 1500),\r\n \"Almacen Ext\":\r\n MedioDeAlmacenamiento(self, \"Almacen Ext\", 3, niv_almacen_ext, 1500),\r\n \"Tanque 1\":\r\n MedioDeAlmacenamiento(self, \"Tanque 1\", 2, niv_tanque_1, 400),\r\n \"Tanque 2\":\r\n MedioDeAlmacenamiento(self, \"Tanque 2\", 2, niv_tanque_2, 500),\r\n \"Patio de Contenedores\":\r\n MedioDeAlmacenamiento(self, \"Patio de Contenedores\", 1, niv_patio_cont, 2500)}\r\n\r\n # Definicion de Operaciones # TODO Ingresar datos reales\r\n operaciones_manipuleo = {\r\n \"Descarga con volcadora\":\r\n OperacionManipuleo(\"Descarga con volcadora\",\r\n self.recursos_atencion[\"Estacion Volcadora\"],\r\n \"uniforme\", [14, 20],\r\n self.datos),\r\n \"Descarga a pulso - Sacos\":\r\n OperacionManipuleo(\"Descarga a pulso - Sacos\",\r\n self.recursos_atencion[\"Cuadrilla de Estibaje\"],\r\n \"uniforme\", [30, 45],\r\n self.datos),\r\n \"Descarga a pulso - Granos\":\r\n OperacionManipuleo(\"Descarga a pulso - Granos\",\r\n self.recursos_atencion[\"Cuadrilla de Estibaje\"],\r\n \"uniforme\", [40, 60],\r\n self.datos),\r\n \"Descarga con bombas electricas - T1\":\r\n OperacionManipuleo(\"Descarga con bombas electricas\",\r\n self.recursos_atencion[\"Cabina de Recepcion - T1\"],\r\n \"uniforme\", [40, 50],\r\n self.datos),\r\n \"Descarga con bombas electricas - T2\":\r\n OperacionManipuleo(\"Descarga con bombas electricas\",\r\n self.recursos_atencion[\"Cabina de Recepcion - T2\"],\r\n \"uniforme\", [40, 50],\r\n self.datos),\r\n \"Descarga con grua\":\r\n OperacionManipuleo(\"Descarga con grua\",\r\n self.recursos_atencion[\"Grua\"],\r\n \"uniforme\", [15, 20],\r\n self.datos),\r\n \"Carga con tolva\":\r\n OperacionManipuleo(\"Carga con tolva\",\r\n self.recursos_atencion[\"Estacion Tolva/Balanza 3\"],\r\n \"uniforme\", [14, 20],\r\n self.datos),\r\n \"Carga con pala mecanica\":\r\n OperacionManipuleo(\"Carga con pala mecanica\",\r\n self.recursos_atencion[\"Pala Mecanica\"],\r\n \"uniforme\", [18, 30],\r\n self.datos),\r\n \"Carga a pulso - Sacos\":\r\n OperacionManipuleo(\"Carga a pulso - Sacos\",\r\n self.recursos_atencion[\"Cuadrilla de Estibaje\"],\r\n \"uniforme\", [45, 70],\r\n self.datos),\r\n \"Carga a pulso - Granos\":\r\n OperacionManipuleo(\"Carga a pulso - Granos\",\r\n self.recursos_atencion[\"Cuadrilla de Estibaje\"],\r\n \"uniforme\", [60, 90],\r\n self.datos),\r\n \"Carga con bombas electricas - T1\":\r\n OperacionManipuleo(\"Carga con bombas electricas\",\r\n self.recursos_atencion[\"Cabina de Despacho - T1\"],\r\n \"uniforme\", [45, 60],\r\n self.datos),\r\n \"Carga con bombas electricas - T2\":\r\n OperacionManipuleo(\"Carga con bombas electricas\",\r\n self.recursos_atencion[\"Cabina de Despacho - T2\"],\r\n \"uniforme\", [45, 60],\r\n self.datos),\r\n \"Carga con grua\":\r\n OperacionManipuleo(\"Carga con grua\",\r\n self.recursos_atencion[\"Grua\"],\r\n \"uniforme\", [15, 22],\r\n self.datos),\r\n \"Transbordo en sistema mecanizado (D)\":\r\n OperacionManipuleo(\"Transbordo en sistema mecanizado (D)\",\r\n self.recursos_atencion[\"Estacion Volcadora\"],\r\n \"uniforme\", [14, 25],\r\n self.datos),\r\n \"Transbordo en sistema mecanizado (C)\":\r\n OperacionManipuleo(\"Transbordo en sistema mecanizado (C)\",\r\n self.recursos_atencion[\"Estacion Tolva/Balanza 3\"],\r\n \"uniforme\", [14, 25],\r\n self.datos),\r\n \"Transbordo a pulso - Sacos\":\r\n OperacionManipuleo(\"Transbordo a pulso - Sacos\",\r\n self.recursos_atencion[\"Cuadrilla de Estibaje\"],\r\n \"uniforme\", [40, 60],\r\n self.datos),\r\n \"Transbordo a pulso - Granos\":\r\n OperacionManipuleo(\"Transbordo a pulso - Sacos\",\r\n self.recursos_atencion[\"Cuadrilla de Estibaje\"],\r\n \"uniforme\", [45, 65],\r\n self.datos),\r\n \"Transbordo con grua\":\r\n OperacionManipuleo(\"Transbordo con grua\",\r\n self.recursos_atencion[\"Grua\"],\r\n \"uniforme\", [15, 20],\r\n self.datos)}\r\n\r\n operaciones_complementarias = {\r\n \"Atencion recepcion 1\":\r\n Operacion(\"Atencion recepcion 1\",\r\n self.recursos_atencion[\"Ventanilla Recepcion\"],\r\n \"uniforme\", [2, 10],\r\n self.datos),\r\n \"Atencion despacho 1\":\r\n Operacion(\"Atencion despacho 1\",\r\n self.recursos_atencion[\"Ventanilla Despacho\"],\r\n \"uniforme\", [2, 10],\r\n self.datos),\r\n \"Primer pesaje\":\r\n Operacion(\"Primer pesaje\",\r\n self.recursos_atencion[\"Balanza 2\"],\r\n \"uniforme\", [3, 6],\r\n self.datos),\r\n \"Segundo pesaje\":\r\n Operacion(\"Segundo pesaje\",\r\n self.recursos_atencion[\"Balanza 2\"],\r\n \"uniforme\", [3, 6],\r\n self.datos),\r\n \"Primer pesaje - B3\":\r\n Operacion(\"Primer pesaje - B3\",\r\n self.recursos_atencion[\"Estacion Tolva/Balanza 3\"],\r\n \"uniforme\", [3, 6],\r\n self.datos),\r\n \"Segundo pesaje - B3\":\r\n Operacion(\"Segundo pesaje -B3\",\r\n self.recursos_atencion[\"Estacion Tolva/Balanza 3\"],\r\n \"uniforme\", [3, 6],\r\n self.datos),\r\n \"Atencion recepcion 2\":\r\n Operacion(\"Atencion recepcion 2\",\r\n self.recursos_atencion[\"Ventanilla Recepcion\"],\r\n \"uniforme\", [4, 8],\r\n self.datos),\r\n \"Atencion despacho 2\":\r\n Operacion(\"Atencion despacho 2\",\r\n self.recursos_atencion[\"Ventanilla Despacho\"],\r\n \"uniforme\", [2, 5],\r\n self.datos)}\r\n\r\n # Diccionario general de operaciones\r\n self.operaciones = {\r\n \"Operaciones manipuleo\":\r\n operaciones_manipuleo,\r\n \"Operaciones complementarias\":\r\n operaciones_complementarias}","def import_brands(self):\n\n brands = self.product_infos['brands']\n\n for product_brand in brands:\n try:\n brand = Brands.objects.get(\n name=product_brand\n )\n except Brands.DoesNotExist:\n super().new_entry()\n brand = Brands.objects.create(\n name=product_brand\n )\n except:\n pass\n try:\n ProdBrand.objects.get(\n product=self.product_object,\n brand=brand\n )\n except ProdBrand.DoesNotExist:\n super().new_entry()\n ProdBrand.objects.create(\n product=self.product_object,\n brand=brand\n )\n except:\n pass\n\n return brands","def get_substitutable_products(self):\n\n while True:\n clean_terminal()\n products = self.database_manager.get_substitutable_products()\n\n if not products:\n cprint('Aucun resultat.', 'red')\n return\n\n print('Choisir un produit :')\n\n range_param = self.print_products_line(products)\n\n reply = self.ask_with_input('Choisir un numéro'\n ' (tapez \"quit\" pour quitter) : ',\n range_param, ('quit',))\n\n if reply == 'quit':\n break\n\n product_number = int(reply)\n product_number -= 1\n product = products[product_number]\n\n operateur_result = []\n self.database_manager.fill_list_with_product_and_substitutes(\n product.get('id'), operateur_result)\n # print product and his substitutes in the terminal\n self.printer(operateur_result)\n\n self.ask_with_input('Ok ? (y) ', -1, ('y',))","def cargarObras(self):\n self.cargarObjetos(self.tableOs,\n ObraSocialModel.buscarTodos(\"razon_social\", self.sesion).all(),\n (\"razon_social\", \"cuit\", \"direccion\")\n )","def create_products():\n try:\n # Instantiate the class and separate objects into two lists\n challenge = Challenge()\n # Get all products\n product_base = challenge.get_products(\"product_groups.json\")\n # Divide the products into independent (no parent) and dependent (with parents)\n independent, dependent = challenge.filter_products(product_base)\n if not challenge.save_independent_products(independent):\n Exception(\"Function save_independent_products() couldn't complete\")\n\n if not challenge.save_dependent_products(\n dependent, product_base, len(independent)\n ):\n raise Exception(\"Function save_dependent_products() couldn't complete\")\n\n except Exception as err:\n logging.error(f\"[ERROR] While processing the objects. Traceback: {err}\")\n return False\n else:\n return True","def select_todos_registros(nome_tabela: str) -> list:\n query = f'SELECT * FROM {nome_tabela};'\n\n lista_registros = banco_operacoes(query)\n\n return lista_registros","def return_items(self):\n cur = self.cursor\n cur.execute(f\"SELECT * FROM {self.product_name}\")\n products = cur.fetchall()\n return products","def geneLRB(fech, emplead, product, cantPro):\n listaF = []\n linea1 = \"\\nRegistro de venta:\\n\"\n linea2 = f\"Fecha de ventas: {fech}\\n\"\n linea3 = f\"Empleado: {emplead}\\n\"\n if product == 0:\n pro = \"Disney Cruella De Vil Diamond Funko Pop Exclusivo\"\n elif product == 1:\n pro = \"Funko Pop Board Games Silver Mr. Monopoly - Figura de vinilo\"\n elif product == 2:\n pro = \"Funko Pop Daenerys Targaryen 59 Game of Thrones\"\n elif product == 3:\n pro = \"Funko Pop Flocked Iceman 504 Marvel 80 Years\"\n elif product == 4:\n pro = \"Funko Pop Glowing Mr Burns 1162 The Simpsons\"\n elif product == 5:\n pro = \"Funko Pop Grogu Con Galleta 464 Star Wars The Mandalorian\"\n elif product == 6:\n pro = \"Funko Pop Keychain Pogo The Umbrella Academy\"\n elif product == 7:\n pro = \"Funko Pop Keychain The Flash DC The Flash\"\n elif product == 8:\n pro = \"Funko Pop Marvel: Avengers - Wanda Maximoff Exclusivo Disney Collector Zone\"\n elif product == 9:\n pro = \"Funko Pop Venom 888 Marvel Venom\"\n elif product == 10:\n pro = \"Llavero Funko Pop Zoey Fortnite\"\n elif product == 11:\n pro = \"Lucky The Leprechaun Edición Limitada\"\n linea4 = f\"Producto vendido(s): {pro}\\n\"\n linea5 = f\"Cantidad: {cantPro}\\n\"\n listaF.append(linea1)\n listaF.append(linea2)\n listaF.append(linea3)\n listaF.append(linea4)\n listaF.append(linea5)\n with open(\"registroVentas.txt\", \"a\") as regVen:\n regVen.writelines(listaF)","def __iter__(self):\n ids_productos = self.carro.keys()\n #obtiene los objetos producto y los agrega al carro\n productos = Producto.objects.filter(id__in=ids_productos)\n for producto in productos:\n self.carro[str(producto.id)]['producto'] = producto\n\n for item in self.carro.values():\n item['precio']=Decimal(item['precio'])\n item['precio_total'] = item['precio']*item['cantidad']\n yield item","def modificarProductosVenta(self, listaProductos):\n\t#modificar -- [idventa, codProducto, numeroitem, valorUnitario, cantidad, IVA]\n\tmodificar = listaProductos[0]\n\t#eliminar -- [idventa, idProducto, numeroitem]\n\teliminar = listaProductos[1]\n\t# primero se modifican los productos\n\tfor i in modificar:\n\t try:\n self.conexion.ejecutarSQL(\"\"\"update ProductosXVentas\n set valor_unitario=%s,\n cantidad=%s,\n IVA=%s,\n valor_total=%s*%s\n where id_Venta=%s\n and codigo_Producto = '%s'\n and numero_item=%s\"\"\"%(i[3], i[4], i[5], i[4], i[3], i[0], i[1], i[2]))\n\t except Exception, e:\n\t\tprint \"modificarProductosVenta excepcion: \", e\n\t\tself.conexion.rollback()\n\t\treturn False\n\t# Ahora se eliminan los Productos\n\tfor i in eliminar:\n\t try:\n\t\tself.conexion.ejecutarSQL(\"delete from ProductosXVentas where id_Venta=%s and codigo_Producto = '%s' and numero_item=%s\"%(i[0],i[1],i[2]))\n\t except Exception, e:\n\t\tprint \"modificarProductosVenta --> Eliminar excepcion: \", e\n\t\tself.conexion.rollback()\n\t\treturn False\n\t \n\t#Se actualiza el kardex para las ventas modificadas como para las ventas eliminadas.\n\t\n\t#Para las ventas modificadas\n\t# PENDIENTE REVISAR SI HUBO CAMBIO DE VALOR UNITARIO\n\ttry:\n\t #modificar -- [idventa,idProducto,numeroitem,cantidad,cantidad_anterior]\n\t for i in modificar:\n\t\tcantActual = float(i[3])\n\t\tcantAnterior = float(i[4])\n\t\tif cantActual == cantAnterior: #S'olo se actualiza el kardex si hubo cambio\n\t\t continue\n\t\telse:\n\t\t codigoProducto = i[1]\n\t\t info = self.getKardexProducto(codigoProducto)\n\t\t #info --> saldo_valor, saldo_cantidad, costo_unitario, valor_total, detalle\n\t\t info = info[-1]\n\t\t if cantActual < cantAnterior: # Si hizo una devoluci'on en la venta (el cliente me devolvi'o cant productos)\n\t\t\tcant = cantAnterior - cantActual\n\t\t\tdetalle = 'Compra'\n\t\t\tvalorUnitario = float(info[2])\n\t\t\tvalorTotal = valorUnitario*cant\n\t\t\tsaldoCantidad = float(info[1])+cant\n\t\t\tsaldoValor = float(info[0])+valorTotal\n\t\t else:# Si hizo una adici'on a la venta (le tengo que vender cant productos al cliente)\n\t\t\tcant = cantActual - cantAnterior\n\t\t\tdetalle = 'Venta'\n\t\t\tvalorUnitario = float(info[2])\n\t\t\tvalorTotal = valorUnitario*cant\n\t\t\tsaldoCantidad = float(info[1])-cant\n\t\t\tsaldoValor = float(info[0])-valorTotal\n\t\t costoUnitario = saldoValor/float(saldoCantidad)\n\t\t self.conexion.ejecutarSQL(\"\"\"\n\t\t insert into Kardex (codigo_Producto, fecha, hora, detalle,cantidad, valor_total,saldo_cantidad, \n\t\t saldo_valor, valor_unitario,costo_unitario) values \n\t\t ('%s',DATE('now','localtime'),TIME('now','localtime'),'%s',%s,%s,%s,%s,%s,%s )\"\"\"%(codigoProducto,\n\t\t detalle,cant,valorTotal,saldoCantidad,\n\t\t saldoValor,valorUnitario,costoUnitario))\n\t self.conexion.commit()\n\t return True\n\texcept:\n\t print \"modificarProductosVenta --> Actualizar kardex (ventas modificadas) excepcion: \", e\n\t self.conexion.rollback()\n\t return False","def cargar_obras(self):\n self.cargarObjetos(self.tableObra,\n ObraSocialModel.buscarTodos(\"razon_social\", self.sesion).all(),\n (\"razon_social\", \"cuit\", \"direccion\")\n )","def get_all_product():\r\n\r\n with mysql.db_session() as session:\r\n product = session.query(Product).all()\r\n\r\n if not product:\r\n return response.create_not_found_response()\r\n\r\n response_data = [each.to_dict() for each in product]\r\n\r\n return response.Response(message=response_data)","def search_prod(self):\n\n # Connection to the SQL database\n self.db_connect = pymysql.connect(\"localhost\",\"root\",\"\",\"mydb\")\n db = self.db_connect.cursor()\n \n # Category selection display loop\n choice = 0\n while choice is 0:\n choice = input(\"\\nVoulez vous afficher la liste de catégories ? O/n \")\n if choice.upper() == \"N\":\n print(\"\\nRetour au menu principal\")\n break\n elif choice.upper() == \"O\":\n choice_cat = True\n print(\"\\n Liste des catégories:\\n\")\n # Defining the query to return the list of categories\n show_list_cat = \"SELECT category_id, category_name \\\n FROM `mydb`.`Categories`\"\n # Command to execute the request\n db.execute(show_list_cat)\n # Variable assignment loop\n for ligne in db.fetchall():\n list_id_cat = ligne[0]\n list_name_cat = ligne[1]\n # View category list\n print(list_id_cat,'-', list_name_cat)\n\n # Product Selection Loop\n choice_cat = 0\n while choice_cat == 0:\n choice_cat = input(\"\\nSélectionner une catégorie de produit (Q pour annuler): \")\n\n if choice_cat.upper() == \"Q\":\n choice = False\n break\n\n elif choice_cat.isdigit() == False or int(choice_cat) >= 29 \\\n or int(choice_cat) == 0:\n print(\"\\nMerci de bien vouloir entrer un chiffre compris entre 1 et 28\\n\")\n time.sleep(2)\n choice_cat = 0\n \n # Definition of the request to return \n # the list of products of the selected category\n show_list_prod_select = f\"SELECT product_id, \\\n product_name, \\\n product_nutri \\\n FROM `mydb`.`Products` \\\n WHERE Categories_category_id = {choice_cat}\"\n \n # Execution command of the SQL query\n db.execute(show_list_prod_select)\n for ligne in db.fetchall():\n list_id_prod = ligne[0]\n list_name_prod = ligne[1]\n nutriscore_prod = ligne[2]\n # Uppercase display\n nutriscore_prod = nutriscore_prod.upper()\n # Display of the list of products and nutriscores\n print(list_id_prod,\"-\", list_name_prod, \\\n \"- Nutriscore:\",nutriscore_prod)\n \n # SQL query to determine the maximum and minimum value\n # the product ID for the selected category\n vmax = f\"SELECT MAX(product_id) \\\n FROM `mydb`.`Products` \\\n WHERE Categories_category_id = {choice_cat}\"\n db.execute(vmax)\n for maxv in db.fetchall():\n max_id_prod = maxv[0]\n\n vmin = f\"SELECT MIN(product_id) \\\n FROM `mydb`.`Products` \\\n WHERE Categories_category_id = {choice_cat}\"\n db.execute(vmin)\n for minv in db.fetchall():\n min_id_prod = minv[0]\n \n # Display loop for selecting the product to substitute\n choice_sub = 0\n while choice_sub == 0:\n choice_sub = input(\"\\nSélectionner un produit à substituer (Q pour annuler): \")\n\n if choice_sub.upper() == \"Q\":\n choice = 0\n break\n # Selects the product selection choice for the selected category\n elif choice_sub.isdigit() == False or \\\n int(choice_sub) > max_id_prod or \\\n int(choice_sub) < min_id_prod or int(choice_sub) == 0:\n print(\"\\nMerci de bien vouloir entrer un chiffre compris dans la liste des produits\")\n time.sleep(2)\n choice_sub = 0\n\n # Definition of the request to return the list of\n # Substitute products of the selected category\n show_list_sub_select = f\"SELECT * FROM `mydb`.`Products`\\\n WHERE product_id = {choice_sub}\"\n \n # Execution command of the SQL query\n db.execute(show_list_sub_select)\n for ligne_sub in db.fetchall():\n sub_name = ligne_sub[1]\n sub_nutri = ligne_sub[3]\n # Uppercase display\n sub_nutri = sub_nutri.upper()\n \n # Definition of the request to return the product \n # with the highest nutriscores of the selected category\n list_best_nutri = f\"SELECT * FROM `mydb`.`Products`\\\n WHERE Categories_category_id = {choice_cat} \\\n AND product_nutri <= '{sub_nutri}' \\\n ORDER BY product_nutri ASC\"\n \n db.execute(list_best_nutri)\n result = db.fetchall()\n best = (result)[0]\n bestnutri = (best)[3]\n # Uppercase display\n bestnutri = bestnutri.upper()\n # Case where the user chooses one of the products \n # with the highest nutriscore of the category\n if sub_nutri == bestnutri:\n print(\"\\nLe produit sélectionné a le meilleur nutriscore de cette catégorie\")\n print(\"Veuillez sélectionner un autre produit dans la liste\")\n choice_sub = 0\n # Case where the user selects a substitutable product \n else:\n best_id = (best)[0]\n best_name = (best)[1]\n best_nutriscore = (best)[3]\n best_nutriscore = best_nutriscore.upper()\n best_shop = (best)[2]\n best_url = (best)[4]\n # Returns the selected product and its substitute\n print(\"\\nProduit sélectionné:\",sub_name,\\\n \". Nutriscore:\",sub_nutri)\n print(\"Substitue:\",best_name,\". Nutriscore:\",\\\n best_nutriscore)\n print(\"Magasin(s):\",best_shop)\n print(\"Infos produit:\",best_url)\n # Backup loop in the SQL database\n save_choice1 = 0\n while save_choice1 is 0:\n save_choice1 = input(\"\\nSouhaitez-vous sauvegarder le produit dans la base ? O/n \")\n if save_choice1.upper() == \"N\":\n print(\"\\nRetour au menu principal\")\n time.sleep(2)\n break\n elif save_choice1.upper() == \"O\":\n # Insertion request in the \"Substitute\" table\n save_sub = \"INSERT IGNORE INTO \\\n `mydb`.`Substitute` \\\n (save_product_id, save_product_sub_name) \\\n VALUES ('{}','{}')\".format(best_id, sub_name)\n db.execute(save_sub)\n self.db_connect.commit()\n print(\"\\nLe produit a bien été sauvegardé dans vos favoris\")\n print(\"Retour au menu principal\")\n time.sleep(2)\n break","def get_table(prod):\n pgconn = get_dbconn(\"mesosite\")\n cursor = pgconn.cursor()\n xref = [1.0e20] * 256\n cursor.execute(\n \"SELECT id, filename_template, units, cf_long_name \"\n \"from iemrasters where name = %s\",\n (prod,),\n )\n (rid, template, units, long_name) = cursor.fetchone()\n cursor.execute(\n \"\"\"\n SELECT coloridx, value from iemrasters_lookup\n WHERE iemraster_id = %s and value is not null\n ORDER by coloridx ASC\n \"\"\",\n (rid,),\n )\n for row in cursor:\n xref[row[0]] = row[1]\n return np.array(xref), template, units, long_name","def load_products():\n\n for i, row in enumerate(open(\"seed_data/category.product\")):\n row = row.rstrip()\n name = row.split(\"|\")\n product_category = ProductCategoryModel(name=name)\n db.session.add(product_category)\n\n for i, row in enumerate(open(\"seed_data/product.product\")):\n row = row.rstrip()\n name, short_description, long_description, product_category_id, img_path_xs, img_path_sm, img_path_md, img_path_lg = row.split(\"|\")\n product = ProductModel(name=name,\n short_description=short_description,\n long_description=long_description,\n product_category_id=product_category_id,\n img_path_xs=img_path_xs,\n img_path_sm=img_path_sm,\n img_path_md=img_path_md,\n img_path_lg=img_path_lg)\n db.session.add(product)\n\n for i, row in enumerate(open(\"seed_data/location.product\")):\n row = row.rstrip()\n name, description, address1, address2, city, state, zip_code, country, latitude, longitude, direction_url = row.split(\"|\")\n location = LocationModel(name=name,\n description=description,\n address1=address1,\n address2=address2,\n city=city,\n state=state,\n zip_code=zip_code,\n country=country,\n latitude=latitude,\n longitude=longitude,\n direction_url=direction_url)\n db.session.add(location)\n\n for i, row in enumerate(open(\"seed_data/location_product.product\")):\n row = row.rstrip()\n location_id, product_id, price, num_available = row.split(\"|\")\n location_product = LocationProductModel(location_id=location_id,\n product_id=product_id,\n price=price,\n num_available=num_available)\n db.session.add(location_product)\n\n db.session.commit()","def test_Product(self):\n self.assertEquals(self.prod_1.pk, 1)\n self.assertEquals(self.prod_1.ean, '3350033118072')\n self.assertEquals(self.prod_1.name, 'test 1')\n self.assertEquals(self.prod_1.nutriscore, 'u')\n self.assertEquals(self.prod_1.category, 'cat 1')","def create_base_testes(dbsession):\n # print('Iniciando criação da base #############################')\n # print('dbsession', dbsession)\n base1 = BaseOrigem('alimentos_e_esportes')\n dbsession.add(base1)\n dbsession.commit()\n risco1 = PadraoRisco('perigo', base1)\n dbsession.add(risco1)\n dbsession.commit()\n param1 = ParametroRisco('alimento', 'teste1', risco1)\n param2 = ParametroRisco('esporte', 'teste2', risco1)\n dbsession.add(param1)\n dbsession.add(param2)\n dbsession.commit()\n valor1 = ValorParametro('bacon', Filtro.igual, param1)\n valor2 = ValorParametro('base jump', Filtro.igual, param2)\n dbsession.add(valor1)\n dbsession.add(valor2)\n dbsession.commit()\n visao1 = Visao('viagens', base1.id)\n dbsession.add(visao1)\n dbsession.commit()\n tabela1 = Tabela('viagens', 'viagem', '', 0, visao1.id)\n tabela2 = Tabela('alimentos', 'alimento', 'viagem', tabela1.id, visao1.id)\n tabela3 = Tabela('esportes', 'esporte', 'viagem', tabela1.id, visao1.id)\n dbsession.add(tabela1)\n dbsession.add(tabela2)\n dbsession.add(tabela3)\n dbsession.commit()\n # print('Base criada! ########################')","def get_label():\r\n\r\n user = check_auth(request.headers, __name__)\r\n if user != True:\r\n return user\r\n user = authorize.get(request.headers.get('UserToken'))\r\n\r\n vozvrat = {}\r\n try:\r\n database = Database(config)\r\n except TypeError:\r\n vozvrat[\"messageError\"] = \"Нет подключения к БД\"\r\n return jsonify(vozvrat)\r\n\r\n vozvrat = []\r\n\r\n fields = [\r\n \"u.firstname\",\r\n \"u.lastname\",\r\n \"up.id\",\r\n \"up.name\",\r\n \"up.photo\",\r\n \"up.type\",\r\n \"up.method\",\r\n \"up.sale\",\r\n \"up.price\",\r\n \"c.name\",\r\n \"up.weight\",\r\n \"u2.name\",\r\n \"fp.id\",\r\n \"a.country\",\r\n \"a.city\",\r\n \"a.address\",\r\n \"a.lat\",\r\n \"a.lng\"\r\n ]\r\n\r\n query = sql.SQL(\"SELECT {} FROM users u \\\r\n RIGHT JOIN users_product up on u.id = up.user_id\\\r\n LEFT JOIN units u2 on up.unit_id = u2.id\\\r\n LEFT JOIN currencys c on up.currency_id = c.id\\\r\n LEFT JOIN favorit_products fp on u.id = fp.user_id\\\r\n LEFT JOIN address a on up.address_id = a.id\").format(\r\n sql.SQL(\",\").join(sql.Identifier(\r\n i.split('.')[0], i.split('.')[1]) for i in fields)\r\n )\r\n execute = database.select_data(query)\r\n if type(execute) != list:\r\n return execute\r\n\r\n data_append = {}\r\n for row in execute:\r\n for i in range(len(fields)):\r\n value = row[i]\r\n\r\n if fields[i] == \"up.id\":\r\n fields[i] = \"up.users_product_id\"\r\n if fields[i] == \"c.name\":\r\n fields[i] = \"c.currency\"\r\n if fields[i] == \"u2.name\":\r\n fields[i] = \"u2.unit\"\r\n if fields[i] == \"fp.id\":\r\n fields[i] = \"fp.is_favorit\"\r\n value = True if value != None else False\r\n\r\n data_append[fields[i].split('.')[1]] = value\r\n vozvrat.append(data_append)\r\n\r\n return jsonify(vozvrat)","def get_products(self):\n\n lst = []\n for product in self.products.findall('product'):\n id = product.find('id').text\n name = product.find('name').text\n dispensary_id = product.find('dispensary_id').text\n dispensary_name = product.find('dispensary_name').text\n canabis_brand = product.find('canabis_brand').text\n canabis_strain = product.find('canabis_strain').text\n category = product.find('category').text\n subcategory = product.find('subcategory').text\n thc_level = product.find('thc_level').text\n cbd_level = product.find('cbd_level').text\n cbn_level = product.find('cbn_level').text\n thc_level_type = product.find('thc_level_type').text\n cbd_level_type = product.find('cbd_level_type').text\n cbn_level_type = product.find('cbn_level_type').text\n\n description = product.find('description').text\n created_at = product.find('created_at').text\n updated_at = product.find('updated_at').text\n\n prices = []\n urls = []\n images = []\n\n for child in product:\n if child.tag == 'prices':\n for cost in child.findall('cost'):\n prices.append(Price(cost.attrib['unit'], cost.text))\n\n if child.tag == 'urls':\n admin = child.find('admin').text\n public = child.find('public').text\n urls.append(UrlInfo(admin, public))\n\n if child.tag == 'images':\n for image in child.findall('image'):\n images.append(Image(image.attrib['main'], image.text,))\n\n lst.append(Product(id, name, dispensary_id, dispensary_name,\n canabis_brand, canabis_strain,\n category, subcategory, thc_level, cbd_level,\n cbn_level, thc_level_type, cbd_level_type,\n cbn_level_type, prices, urls, images,\n description, created_at, updated_at))\n\n return lst","def existeProducto(self, cod):\n return self.conexion.ejecutarSQL(\"select codigo from productos where codigo='%s'\"%(cod)) != []","def _check_product(self):\n\n self.importable = False\n abcde = string.ascii_uppercase[:5]\n product_infos = self.retrieve_product_infos()\n\n if product_infos['product_code'] is not None:\n try:\n Products.objects.get(\n code=product_infos['product_code']\n )\n except Products.DoesNotExist:\n if (\n product_infos['product_name'] is not None\n and product_infos['product_code'] not in ProductImportation.codes\n and product_infos['product_code'] is not None\n and product_infos['product_url'] is not None\n and product_infos['image_url'] is not None\n and product_infos['quantity'] is not None\n and product_infos['ingredients'] is not None\n and product_infos['brands'] != []\n and product_infos['stores'] != []\n and product_infos['countries'] is not None\n and product_infos['compare_to'] is not None\n and product_infos['categories_hierarchy'] is not None\n and product_infos['nutriscore'] in abcde\n and all([product_infos[nutriment] >= 0 for nutriment in self.list_nutriments])\n and Categories.objects.filter(name=product_infos['compare_to']).count() > 0\n ):\n self.name = product_infos['product_name']\n self.product_infos = product_infos\n self.code = product_infos['product_code']\n ProductImportation.codes.append(self.code)\n self.importable = True\n\n return self.importable","def create_tables(self):\n\n # Uses methods from class DbAuth\n cursor = self.connect.create_cursor()\n cursor.execute(\"USE `dbPurBeurre`\")\n\n cursor.execute(\n \"CREATE TABLE IF NOT EXISTS `dbPurBeurre`.`Categories` (\"\n \" `num` INT UNSIGNED AUTO_INCREMENT,\"\n \" `id` VARCHAR(80) NOT NULL UNIQUE,\"\n \" `name` VARCHAR(80) NOT NULL,\"\n \" `url` VARCHAR(255) NOT NULL,\"\n \" `products` INT NULL,\"\n \" PRIMARY KEY (`num`))\"\n \" ENGINE = InnoDB\"\n )\n\n cursor.execute(\n \"CREATE TABLE IF NOT EXISTS `dbPurBeurre`.`Produits` (\"\n \" `num` INT UNSIGNED AUTO_INCREMENT,\"\n \" `id` VARCHAR(80) NOT NULL UNIQUE,\"\n \" `product_name` VARCHAR(80) NOT NULL,\"\n \" `nutrition_grade_fr` CHAR(1) NOT NULL,\"\n \" `brands` VARCHAR(80) NULL,\"\n \" `stores` VARCHAR(80) NOT NULL,\"\n \" `url` VARCHAR(255) NOT NULL,\"\n \" `watchlist` DATE NULL,\"\n \" PRIMARY KEY (`num`, `id`))\"\n \" ENGINE = InnoDB\"\n )\n\n cursor.execute(\n \"CREATE TABLE IF NOT EXISTS `dbPurBeurre`.`Asso_Prod_Cat` (\"\n \" `id_categories` VARCHAR(80) NOT NULL,\"\n \" `id_produits` VARCHAR(80) NOT NULL,\"\n \" PRIMARY KEY (`id_categories`, `id_produits`),\"\n \" CONSTRAINT `fk_id_categories`\"\n \" FOREIGN KEY (`id_categories`)\"\n \" REFERENCES `Categories` (`id`),\"\n \" CONSTRAINT `fk_id_produits`\"\n \" FOREIGN KEY (`id_produits`)\"\n \" REFERENCES `Produits` (`id`))\"\n \" ENGINE = InnoDB\"\n )","def ingresarVenta(self, total, tipoPago, idCliente, usuarioColaborador, listaProductos):\n self._lockIngresarVenta.acquire()\n try:\n # agrupar totales vendidos de cada cod_barras\n numVendidosXcod = dict()\n # calcular subtotal, totalIVA basado en IVA y valorTotal de cada producto en lista. Subtotal=total-totalIVA\n totalIVA = 0\n for (cod_barras, desc, cantidad, valorUnitario, IVA, valorTotal) in listaProductos:\n totalIVA += float(valorTotal) * float(IVA) / 100\n if numVendidosXcod.has_key(cod_barras):\n numVendidosXcod[cod_barras] += float(cantidad)\n else:\n numVendidosXcod[cod_barras] = float(cantidad)\n # almacena subtotal en variable\n subtotal = total - totalIVA \n # revisar si existen las cantidades disponibles de cada codigo\n for cod, cant in numVendidosXcod.iteritems():\n cantidadDisponible = self.getInfoProducto(cod)[0][2]\n if cant > cantidadDisponible:\n return (False,\"Cantidades insuficientes del producto %s. Cantidad disponible: %s y Cantidad en Venta: %s\"%(cod,cantidadDisponible,cant),-1)\n # obtener id_TipoPago dependiendo del tipo de pago\n id_TipoPago = self.getIdTipoPago(tipoPago)\n # obtener estado de la venta dependiendo del tipo de pago\n estado = self.estadoVentaDadoTipoPago(id_TipoPago)\n # ingresar la venta en Ventas \n if estado == __PAGADA__:\n self.conexion.ejecutarSQL(\"insert into Ventas (fecha,hora,subtotal,totalIVA,total,estado,usuario_Colaborador,id_Cliente,id_TipoPago,fechaPagoTotal,horaPagoTotal) values (DATE('now','localtime'),TIME('now','localtime'),%s,%s,%s,'%s','%s','%s',%s,DATE('now','localtime'),TIME('now','localtime'))\"%(0,0,0,estado,usuarioColaborador,idCliente,id_TipoPago))\n else:\n self.conexion.ejecutarSQL(\"insert into Ventas (fecha,hora,subtotal,totalIVA,total,estado,usuario_Colaborador,id_Cliente,id_TipoPago) values (DATE('now','localtime'),TIME('now','localtime'),%s,%s,%s,'%s','%s','%s',%s)\"%(0,0,0,estado,usuarioColaborador,idCliente,id_TipoPago))\n # obtener id de última venta\n id_Venta = self.getIdUltimaVenta()\n # ingresar todos los productos de listaProductos en ProductosXVentas\n numItem = 1\n for (cod_barras, desc, cantidad, valorUnitario, IVA, valorTotal) in listaProductos:\n self.conexion.ejecutarSQL(\"insert into ProductosXVentas (numero_item,codigo_producto,id_venta,usuario_Colaborador,cantidad,valor_unitario,IVA,valor_total) values (%s,'%s',%s,'%s',%s,%s,%s,%s)\"%(numItem,cod_barras,id_Venta,usuarioColaborador,cantidad,valorUnitario,IVA,valorTotal))\n\n\t\t#Por cada producto vendido, se debe actualizar la tabla kardex.\n\t\tcantidadKardex = cantidad\n\t\tidProductoKardex = cod_barras\n\t\tsaldos = self.conexion.ejecutarSQL(\"\"\"select saldo_cantidad, saldo_valor, costo_unitario from kardex\n where codigo_Producto='%s'\n order by fecha and hora\"\"\"%(idProductoKardex))\n\t\tif len(saldos) == 0:\n\t\t v_unitarioKardex = valorUnitario\n\t\t valor_TotalKardex = float(cantidadKardex)*float(v_unitarioKardex)\n\t\t saldo_cantidadKardex = cantidadKardex\n\t\t saldo_valorKardex = valor_TotalKardex\n\t\telse:\n\t\t v_unitarioKardex = saldos[len(saldos)-1][2]\n\t\t valor_TotalKardex = float(cantidadKardex)*float(v_unitarioKardex)\n\t\t saldo_cantidadKardex = saldos[len(saldos)-1][0]-float(cantidadKardex)\n\t\t saldo_valorKardex = saldos[len(saldos)-1][1]-valor_TotalKardex\n\t\tif float(saldo_cantidadKardex) != 0:\n costo_unitarioKardex = saldo_valorKardex/float(saldo_cantidadKardex)\n else:\n costo_unitarioKardex = saldo_valorKardex\n\t\ttry:\n\t\t self.conexion.ejecutarSQL(\"\"\"insert into Kardex (codigo_Producto, fecha, hora, detalle,cantidad, valor_total,saldo_cantidad, saldo_valor, valor_unitario,costo_unitario)\n values ('%s',DATE('now','localtime'),TIME('now','localtime'),'Venta',%s,%s,%s,%s,%s,%s )\"\"\"\n %(idProductoKardex, cantidadKardex, valor_TotalKardex, saldo_cantidadKardex,\n saldo_valorKardex, v_unitarioKardex, costo_unitarioKardex))\n\t\texcept Exception, e:\n\t\t print \"Kardex Venta: \", e\n\t\t self.conexion.rollback()\n\t\t return (False,str(e),-1)\n\t \n numItem += 1 \n # comprometer\n\t self.conexion.commit()\n return (True,\"\",id_Venta)\n except Exception, e:\n print \"ingresarVenta excepcion: \", e\n self.conexion.rollback()\n return (False,str(e),-1)\n finally:\n self._lockIngresarVenta.release()","def get_all_2(conn) -> str:\n with conn.cursor() as cursor:\n cursor.execute(\"\"\"select products.id, \n products.name, \n products.price, \n products.image, \n products.category_id, \n product_categories.name\n from products\n inner join product_categories\n on products.category_id=product_categories.id\n where products.deleted=false order by id\"\"\")\n try:\n return cursor.fetchall()\n except TypeError:\n raise errors.StoreError","def test_show_available(self):\n database.import_data('csvs', 'product_data.csv', 'customer_data.csv', 'rentals_data.csv')\n actual_available = database.show_available_products()\n expected_available = {'prd001': {'description': 'TV', 'product_type': 'livingroom',\n 'quantity_available': '3'},\n 'prd002': {'description': 'Couch', 'product_type': 'livingroom',\n 'quantity_available': '1'}}\n self.assertEqual(actual_available, expected_available)\n database.delete_database()\n\n database.import_data('csvs', 'produc_data.csv', 'customer_data.csv', 'rentals_data.csv')\n database.delete_database()","def get_all(self):\n id_proyecto = unicode(request.url.split(\"/\")[-3])\n fase_table_filler.init(\"\", id_proyecto)\n tmpl_context.widget = self.table\n value = self.fase_filler.get_value()\n d = dict(value_list = value, model = \"Fases\", accion = \"./buscar\")\n d[\"direccion_anterior\"] = \"../..\"\n return d","def scrapeProducts(conn):\n query = \"\"\"SELECT c.name, f1.item, f1.product_name, f1.manufacturer,\n f1.supplier, c.id\n FROM form1_row AS f1\n INNER JOIN country AS c\n ON f1.country=c.id\"\"\"\n c = conn.cursor()\n c.execute(query)\n results = []\n for row in c:\n result={}\n result['country'] = row[0]\n result['formulation'] = row[1].replace('*', '')\n result['product'] = row[2]\n result['manufacturer'] = row[3]\n result['supplier'] = row[4] or None\n result['country_id'] = row[5]\n results.append(result)\n return results","def get_products(self) -> dict:\n\t\tproducts = dict()\n\n\t\tdb = Database()\n\t\tdb.create_connection(self._file_path)\n\t\trows = db.get_products()\n\t\tdb.close_connection()\n\n\t\tfor row in rows:\n\t\t\tif row[0] not in products:\n\t\t\t\ttry:\n\t\t\t\t\tproducts[row[0]] = Product(row[0], row[1], row[2], row[3]) # code, price, lastupdate, currency\n\t\t\t\texcept Exception as e: \n\t\t\t\t\t# IF the database was not correct parsed, the item will be discarted, \n\t\t\t\t\t# the event will be logged in the log file and the program will continue\n\t\t\t\t\tlogging.error(str(datetime.now())+': ' + e)\n\t\t\t\t\tcontinue\n\n\t\treturn products","def check_products(self, adi):\r\n results = []\r\n products = self.get_products(adi)\r\n for product in products[\"data\"][\"products\"]:\r\n print(\"Checking product '{}'... \".format(product[\"name\"]), end='')\r\n detail = self.get_product_detail(adi, product_id=product[\"productId\"], product_name=product[\"name\"])\r\n if self.rf.valid_product_detail(detail):\r\n print(\"Valid.\")\r\n result = \"Available\"\r\n else:\r\n print(\"INVALID.\")\r\n result = \"Not available\"\r\n results.append([product[\"name\"], result])\r\n return results","def setup(self):\n print(\"INIT DATA\")\n\n self.nutella = Product.objects.create(name=\"nutella\", nutriscore=\"e\")","def save_product_bdd(self):\n for element in config.CATEGORIES:\n category = ClearData(element)\n category.get_data_api()\n category.generate_products_list()\n self.product_table.save_product(\n category.products,\n config.CATEGORIES.index(element)+1)","def get_all_genres(self):\n self.cursor.execute(\"select * from genres\")\n self.connection.commit()\n return self.cursor.fetchall()","def get_product_available(self, cr, uid, ids, context=None):\n if context is None:\n context = {}\n\n location_obj = self.pool.get('stock.location')\n warehouse_obj = self.pool.get('stock.warehouse')\n shop_obj = self.pool.get('sale.shop')\n\n user_obj = self.pool.get('res.users').browse(cr, 1, uid)\n\n states = context.get('states',[])\n what = context.get('what',())\n if not ids:\n ids = self.search(cr, uid, [])\n res = {}.fromkeys(ids, 0.0)\n if not ids:\n return res\n\n if context.get('shop', False) and context['shop']:\n warehouse_id = shop_obj.read(cr, 1, int(context['shop']), ['warehouse_id'])['warehouse_id'][0]\n if warehouse_id:\n context['warehouse'] = warehouse_id\n\n if context.get('warehouse', False) and context['warehouse']:\n lot_id = warehouse_obj.read(cr, 1, int(context['warehouse']), ['lot_stock_id'])['lot_stock_id'][0]\n if lot_id:\n context['location'] = lot_id\n\n if context.get('location', False) and context['location']:\n if type(context['location']) == type(1):\n location_ids = [context['location']]\n elif type(context['location']) in (type(''), type(u'')):\n location_ids = location_obj.search(cr, 1, [('name','ilike',context['location'])], context=context)\n else:\n location_ids = context['location']\n else:\n location_ids = []\n #wids = warehouse_obj.search(cr, uid, [], context=context)\n #for w in warehouse_obj.browse(cr, uid, wids, context=context):\n # location_ids.append(w.lot_stock_id.id)\n lids = location_obj.search(cr, 1, [])\n #print(lids, 'todas os locais', user_obj.company_id.id)\n for lo in location_obj.browse(cr, 1, lids, context=context):\n #print(lo.id, lo.company_id, lo.company_ids, user_obj.company_id.id)\n if lo.company_id and user_obj.company_id.id == lo.company_id.id:\n location_ids.append(lo.id)\n else:\n for co in lo.company_ids:\n if user_obj.company_id.id == co.id:\n location_ids.append(lo.id)\n\n # build the list of ids of children of the location given by id\n if context.get('compute_child', True):\n if len(location_ids) == 0:\n raise osv.except_osv(u'Atenção!', u'Não há local de estoque definido para a empresa/unidade!')\n\n child_location_ids = location_obj.search(cr, 1, [('location_id', 'child_of', location_ids)])\n location_ids = child_location_ids or location_ids\n\n # this will be a dictionary of the UoM resources we need for conversion purposes, by UoM id\n uoms_o = {}\n # this will be a dictionary of the product UoM by product id\n product2uom = {}\n for product in self.browse(cr, 1, ids, context=context):\n product2uom[product.id] = product.uom_id.id\n uoms_o[product.uom_id.id] = product.uom_id\n\n results = []\n results2 = []\n\n from_date = context.get('from_date',False)\n to_date = context.get('to_date',False)\n date_str = False\n date_values = False\n where = [tuple(location_ids),tuple(location_ids),tuple(ids),tuple(states)]\n if from_date and to_date:\n date_str = \"date>=%s and date<=%s\"\n where.append(tuple([from_date]))\n where.append(tuple([to_date]))\n elif from_date:\n date_str = \"date>=%s\"\n date_values = [from_date]\n elif to_date:\n date_str = \"date<=%s\"\n date_values = [to_date]\n if date_values:\n where.append(tuple(date_values))\n\n prodlot_id = context.get('prodlot_id', False)\n prodlot_clause = ''\n if prodlot_id:\n prodlot_clause = ' and prodlot_id = %s '\n where += [prodlot_id]\n elif 'prodlot_id' in context and not prodlot_id:\n prodlot_clause = 'and prodlot_id is null '\n\n # TODO: perhaps merge in one query.\n if 'in' in what:\n # all moves from a location out of the set to a location in the set\n cr.execute(\n 'select sum(product_qty), product_id, product_uom '\\\n 'from stock_move '\\\n 'where location_id NOT IN %s '\\\n 'and location_dest_id IN %s '\\\n 'and product_id IN %s '\\\n 'and state IN %s ' + (date_str and 'and '+date_str+' ' or '') +' '\\\n + prodlot_clause +\n 'group by product_id,product_uom',tuple(where))\n results = cr.fetchall()\n if 'out' in what:\n # all moves from a location in the set to a location out of the set\n cr.execute(\n 'select sum(product_qty), product_id, product_uom '\\\n 'from stock_move '\\\n 'where location_id IN %s '\\\n 'and location_dest_id NOT IN %s '\\\n 'and product_id IN %s '\\\n 'and state in %s ' + (date_str and 'and '+date_str+' ' or '') + ' '\\\n + prodlot_clause +\n 'group by product_id,product_uom',tuple(where))\n results2 = cr.fetchall()\n\n # Get the missing UoM resources\n uom_obj = self.pool.get('product.uom')\n uoms = map(lambda x: x[2], results) + map(lambda x: x[2], results2)\n if context.get('uom', False):\n uoms += [context['uom']]\n uoms = filter(lambda x: x not in uoms_o.keys(), uoms)\n if uoms:\n uoms = uom_obj.browse(cr, 1, list(set(uoms)), context=context)\n for o in uoms:\n uoms_o[o.id] = o\n\n #TOCHECK: before change uom of product, stock move line are in old uom.\n context.update({'raise-exception': False})\n # Count the incoming quantities\n for amount, prod_id, prod_uom in results:\n amount = uom_obj._compute_qty_obj(cr, 1, uoms_o[prod_uom], amount,\n uoms_o[context.get('uom', False) or product2uom[prod_id]], context=context)\n res[prod_id] += amount\n # Count the outgoing quantities\n for amount, prod_id, prod_uom in results2:\n amount = uom_obj._compute_qty_obj(cr, 1, uoms_o[prod_uom], amount,\n uoms_o[context.get('uom', False) or product2uom[prod_id]], context=context)\n res[prod_id] -= amount\n\n for prod_id in res:\n if isinstance(res[prod_id], D):\n res[prod_id] = float(res[prod_id])\n return res","def anidarCompras(self):\n\n comprasFiltradas = []\n dicFechas = []\n for compra in self.compras:\n for producto in compra[\"products\"]:\n if producto[\"name\"] in comprasFiltradas:\n for dicFecha in dicFechas:\n if dicFecha[\"producto\"]==producto[\"name\"]:\n dicFecha[\"fechas\"].append(compra[\"date\"])\n else:\n comprasFiltradas.append(producto[\"name\"])\n dicFechas.append({\"producto\":producto[\"name\"], \"fechas\":[compra[\"date\"]]})\n return dicFechas","def show_available_products(): # {{{\n products_available = {}\n try:\n with MONGO:\n product_collection = MONGO.connection.assignment_07[\"product\"].find(\n )\n\n for product in product_collection:\n if int(product[\"quantity_available\"]) > 0:\n products_available[product[\"product_id\"]] = {\n \"description\": product[\"description\"],\n \"product_type\": product[\"product_type\"],\n \"quantity_available\": product[\"quantity_available\"],\n }\n except TypeError as excep:\n LOGGER.warning(\"Error looking up available products\")\n LOGGER.warning(excep)\n else:\n if not products_available:\n LOGGER.info('No products found')\n else:\n LOGGER.info(\"Available products retrieved successfully.\")\n return products_available # }}}","def _get_prix_tarif(self,cout,pricelist):\n cr = self._cr\n product=cout.name\n prix_tarif=0\n date=time.strftime('%Y-%m-%d') # Date du jour\n if pricelist:\n #Convertion du lot_mini de US vers UA\n min_quantity = self.env['product.uom']._compute_qty(cout.name.uom_id.id, cout.name.lot_mini, cout.name.uom_po_id.id)\n #TODO : Pour contourner un bug d'arrondi (le 31/01/2017)\n min_quantity=min_quantity+0.00000000001\n #TODO en utilisant la fonction repr à la place de str, cela ne tronque pas les décimales\n SQL=\"\"\"\n select ppi.price_surcharge\n from product_pricelist_version ppv inner join product_pricelist_item ppi on ppv.id=ppi.price_version_id\n where ppv.pricelist_id=\"\"\"+str(pricelist.id)+ \"\"\" \n and min_quantity<=\"\"\"+repr(min_quantity)+\"\"\"\n and (ppv.date_start <= '\"\"\"+date+\"\"\"' or ppv.date_start is null)\n and (ppv.date_end >= '\"\"\"+date+\"\"\"' or ppv.date_end is null)\n\n and ppi.product_id=\"\"\"+str(product.id)+ \"\"\" \n and (ppi.date_start <= '\"\"\"+date+\"\"\"' or ppi.date_start is null)\n and (ppi.date_end >= '\"\"\"+date+\"\"\"' or ppi.date_end is null)\n order by ppi.sequence\n limit 1\n \"\"\"\n cr.execute(SQL)\n result = cr.fetchall()\n for row in result:\n coef=1\n if min_quantity:\n coef=cout.name.lot_mini/min_quantity\n prix_tarif=row[0]/coef\n\n\n\n return prix_tarif","def setUp(self):\n self._next_lid_nr = 100001\n\n self.rayon_1 = Rayon.objects.get(rayon_nr=1)\n self.rayon_2 = Rayon.objects.get(rayon_nr=2)\n self.regio_101 = Regio.objects.get(regio_nr=101)\n\n # maak een test vereniging\n ver = Vereniging(\n ver_nr=\"1000\",\n naam=\"Grote Club\",\n regio=self.regio_101)\n ver.save()\n self._ver = ver\n\n # maak functie HWL aan voor deze vereniging\n self.functie_hwl = maak_functie(\"HWL Vereniging %s\" % ver.ver_nr, \"HWL\")\n self.functie_hwl.vereniging = ver\n self.functie_hwl.save()\n\n # maak test leden aan die we kunnen koppelen aan beheerders functies\n self.account_bko = self._prep_beheerder_lid('BKO')\n self.account_rko = self._prep_beheerder_lid('RKO')\n self.account_rcl = self._prep_beheerder_lid('RCL')\n self.account_schutter = self._prep_beheerder_lid('Schutter')\n\n # creëer een competitie met regiocompetities\n competities_aanmaken(jaar=2019)\n # nu in fase A\n\n self.comp_18 = Competitie.objects.get(afstand='18')\n self.comp_25 = Competitie.objects.get(afstand='25')\n\n for deelkamp in Kampioenschap.objects.filter(deel=DEEL_BK).all():\n deelkamp.functie.accounts.add(self.account_bko)\n # for\n\n for deelkamp in Kampioenschap.objects.filter(deel=DEEL_RK, rayon=self.rayon_2).all():\n deelkamp.functie.accounts.add(self.account_rko)\n # for\n\n for deelcomp in Regiocompetitie.objects.filter(regio=self.regio_101).all():\n deelcomp.functie.accounts.add(self.account_rcl)\n # for\n\n # maak nog een test vereniging, zonder HWL functie\n ver = Vereniging(\n naam=\"Kleine Club\",\n ver_nr=1100,\n regio=self.regio_101)\n ver.save()\n self._ver2 = ver\n\n # maak functie HWL aan voor deze vereniging\n hwl = maak_functie(\"HWL Vereniging %s\" % ver.ver_nr, \"HWL\")\n hwl.vereniging = ver\n hwl.save()","def put_it_in_tables(self):\n my_connection = mysql.connector.connect(user=self.user, password=self.password, database='openfoodfacts')\n cursor = my_connection.cursor(buffered=True)\n for i in self.my_data:\n prod_name = i['product_name']\n try:\n add_aliment = (\"INSERT INTO aliment \"\n \"(product_name, product_description, barcode, nutritional_score, stores, product_category) \"\n \"VALUES (%s, %s, %s, %s, %s, %s)\")\n data_aliment = (i['product_name'].replace(\"'\", \"''\"), i['product_description'].replace(\"'\", \"''\"), i['barcode'].replace(\"'\", \"''\"), i['nutritional_score'].replace(\"'\", \"''\"), i['stores'].replace(\"'\", \"''\"), i['product_category'].replace(\"'\", \"''\"))\n cursor.execute(add_aliment, data_aliment)\n except mysql.connector.IntegrityError:\n pass \n my_connection.commit()\n cursor.close()\n my_connection.close()\n print(\"ok c'est fait\")","def regenone(ctx, product):\n\n global products\n\n c = ctx.obj['client']\n if not c.login:\n return False\n\n product_guid = products[product]\n\n r = requests.request(\"GET\", urljoin(c.BASE_URL, '/apiproxy/JobService.js'), params={'accesskey': c.login, 'method': 'GenerateAndPopulateAllSearchesForProduct', 'productGuid': product_guid})\n print(r.status_code, r.text)\n\n if r.status_code == 200:\n return True\n else:\n return False","def aplicarConsultasLibres(self):\n \n for consultaCL in self.ConsultasLibres:\n print(consultaCL)\n resultoCL=self.consulta(consultaCL)\n for resul in resultoCL['results']['bindings']:\n uri = resul['s']['value']\n uri=uri.replace('http://dbpedia.org/resource/','')\n self.ResultConsultaLibre.append(uri)","def lista_ventas(self,tipo,lista,filtro):\n self.lista=self.builder.get_object(lista)\n self.lista.clear()#Limpia la lista\n busqueda = \"\"\n\n if tipo==\"\":\n print(\"Llego a buscar ventas en BD\")\n #result=self.db.execute('SELECT * FROM Venta')\n busqueda = self.db.execute('SELECT ventaID ,fechaVenta, fechaInicio, fechaFin, C.nombre, P.nombre FROM Cliente C, Paquete P, Venta V WHERE V.IdCli = C.clienteID AND V.IdPaq = P.paqueteID')\n elif tipo == \"Cliente\":\n print(\"Busco venta por nombre del cliente\")\n busqueda = self.db.execute(\"SELECT ventaID ,fechaVenta, fechaInicio, fechaFin, C.nombre, P.nombre FROM Cliente C, Paquete P, Venta V WHERE V.IdCli = C.clienteID AND V.IdPaq = P.paqueteID AND C.nombre LIKE '%\"+filtro+\"%'\")\n elif tipo == \"Viaje\":\n print(\"Busco venta por nombre del paquete\")\n busqueda = self.db.execute(\"SELECT ventaID ,fechaVenta, fechaInicio, fechaFin, C.nombre, P.nombre FROM Cliente C, Paquete P, Venta V WHERE V.IdCli = C.clienteID AND V.IdPaq = P.paqueteID AND P.nombre LIKE '%\"+filtro+\"%'\")\n elif tipo == \"Fecha de inicio\":\n print(\"Busco venta por fecha de inicio\")\n busqueda = self.db.execute(\"SELECT ventaID ,fechaVenta, fechaInicio, fechaFin, C.nombre, P.nombre FROM Cliente C, Paquete P, Venta V WHERE V.IdCli = C.clienteID AND V.IdPaq = P.paqueteID AND fechaInicio LIKE '%\"+filtro+\"%'\")\n elif tipo == \"Fecha de fin\":\n print(\"Busco venta por fecha de fin\")\n busqueda = self.db.execute(\"SELECT ventaID ,fechaVenta, fechaInicio, fechaFin, C.nombre, P.nombre FROM Cliente C, Paquete P, Venta V WHERE V.IdCli = C.clienteID AND V.IdPaq = P.paqueteID AND fechaFin LIKE '%\"+filtro+\"%'\")\n \n for row in busqueda: \n #Empieza por la [1] porque el ID es la [0]\n # self.lista.append([row[4],row[5],row[1],row[2],row[3]])\n self.lista.append([row[1],row[2],row[3],row[4],row[5],row[0]])\n print(\"Listo ventas en tabla\")","def obtener_detalle_compra(self):\n\n compra = [producto.nombre_modelo for producto in self.productos]\n orden_de_compra = [(nombre_modelo, compra.count(nombre_modelo)) for nombre_modelo in set(compra)]\n return self.imprimir_orden_de_compra(orden_de_compra)","def test_wiki_products(self):\n\n prod_vals = (\n (ProductFactory(slug='b2g'), 0),\n (ProductFactory(slug='mobile'), 1),\n (ProductFactory(slug='desktop'), 2),\n )\n\n for prod, total in prod_vals:\n for i in range(total):\n doc = DocumentFactory(locale=u'en-US', category=10)\n doc.products.add(prod)\n RevisionFactory(document=doc, is_approved=True)\n\n self.refresh()\n\n qs = {'a': 1, 'w': 1, 'format': 'json'}\n\n for prod, total in prod_vals:\n qs.update({'product': prod.slug})\n response = self.client.get(reverse('search.advanced'), qs)\n eq_(total, json.loads(response.content)['total'])","def confirmarOperacion(self):\n\n if self.tableNC.rowCount() == 0 :\n QtGui.QMessageBox.information(self,\"Aviso\",QtCore.QString.fromUtf8(\"No se han agregado productos a la Nota de Crédito\"))\n\n else:\n ok = QtGui.QMessageBox.information(self,QtCore.QString.fromUtf8(\"Confirmación\"),\\\n QtCore.QString.fromUtf8(\"¿Desea generar la Nota Crédito?\"),\\\n QtGui.QMessageBox.Cancel, QtGui.QMessageBox.Accepted)\n\n if (ok==1):\n notaCredito = NotaCreditoModel(NotaCredito.generarNumero(self.sesion))\n notaCredito.guardar(self.sesion)\n for lineaNC, data in enumerate(self.detallesReintegrables):\n detalleNC = DetalleNCModel(notaCredito.numero, lineaNC+1, data[0], data[1])\n detalleNC.setImporte(data[3])\n detalleNC.setDescuento(data[2])\n detalleNC.guardar(self.sesion)\n QtGui.QMessageBox.information(self,\"Aviso\",QtCore.QString.fromUtf8(\"La Nota de Crédito ha sido generada con éxito\"))\n self.facturaSeleccionada.setNC(notaCredito.numero)\n self.facturaSeleccionada.modificar(self.sesion)\n\n #Se genera un diccionario con los datos necesarios para imprimir la nota de credito\n data = {}\n data[\"numero\"] = notaCredito.numero\n data[\"fecha\"] = notaCredito.fecha_emision\n data[\"detalles\"] = self.detallesImprimibles\n generarNotaCredito(data)\n self.limpiarVentana()\n\n else:\n QtGui.QMessageBox.information(self,\"Aviso\",QtCore.QString.fromUtf8(\"La Nota de Crédito no ha sido generada\"))","def get_products(self):\n con = dbcon()\n cur = con.cursor()\n cur.execute(\"SELECT * FROM products;\")\n res = cur.fetchall()\n if res:\n prdcts=[]\n for prodct_item in res:\n picked_prdct = {\n 'product_id':prodct_item[0],\n 'product_name':prodct_item[1],\n 'price':prodct_item[2],\n 'quantity':prodct_item[3]\n }\n prdcts.append(picked_prdct)\n return jsonify({\"Products\": prdcts}), 200\n return jsonify({\"message\":\"No products in store\"})","def product_list(id):\r\n\r\n db = get_db()\r\n product_list = db.execute(\r\n \"SELECT product_id, product_name, quantity FROM product WHERE for_business = ? AND quantity > 0\",\r\n (id,),\r\n ).fetchall()\r\n return product_list","def fetch_substitutes(self, cat_choice): \n category = self.categories[cat_choice]\n for i in range(1, 4):\n self.cur.execute(\"SELECT id FROM Product WHERE nova = %s AND category = %s\", (i, category))\n response = self.cur.fetchall()\n if response != []:\n self.sub_list.append(response[random.randint(0, len(response)-1)][0])\n\n print (\"Voici les substituts trouvés pour:\")\n self.display_product_from_id(self.product_choice_id)\n for element in self.sub_list:\n self.display_product_from_id(element)\n\n print (\"Voulez-vous enregistrer votre recherche dans vos favoris? Oui tapez 1 Non tapez 0\")\n\n logging = self.secure_input(0, 1)\n\n if logging == 0:\n pass\n elif logging == 1:\n self.log_in_DB()\n self.con.commit()","def migrar(self):\r\n start = time.time()\r\n if not os.path.exists('output'):\r\n os.makedirs('output')\r\n # seleciona as tabelas\r\n res = self.cur_origem.execute(\r\n \"select rdb$relation_name from rdb$relations where rdb$view_blr is null and (rdb$system_flag is null or rdb$system_flag = 0) %s;\" % self.filtro)\r\n\r\n # para cada tabela\r\n for row, in res.fetchall():\r\n row = row.strip()\r\n\r\n # conta os registros\r\n countsql = self.cur_origem.execute(\r\n \"select count(*) as total from %s \" % row)\r\n count, = countsql.fetchall()[0]\r\n start_time = time.time()\r\n start_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(\"MIGRANDO: %s\\n NRO REGISTROS: %s registros\\n INICIO: %s\" % (\r\n row, count, start_datetime))\r\n\r\n # gera o create table e trunca a tabela ( se ja existir )\r\n create, tipos = self.ddl_table(row)\r\n self.cur_destino.execute(create)\r\n self.cur_destino.execute(\"TRUNCATE TABLE %s\" % row)\r\n\r\n # busca os dados\r\n self.cur_origem.execute(\"select * from %s \" % (row))\r\n\r\n # grava os dados no TXT\r\n with open(\"output/%s.txt\" % row, \"wb\") as f:\r\n writer = csv.writer(f, delimiter='|')\r\n writer.writerows(self.cur_origem.fetchall())\r\n\r\n # le o arquivo gravado e copia para o banco destino\r\n with open(\"output/%s.txt\" % row, \"r\") as f:\r\n try:\r\n self.cur_destino.copy_expert(\r\n \"\"\"COPY %s FROM STDIN WITH QUOTE '\"' DELIMITER '|' NULL '' CSV \"\"\" % row, f)\r\n except Exception as e:\r\n self.erros.append([\"%s\" % row, e])\r\n end_time = time.time()\r\n end_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(\" FIM: %s\\n TEMPO: %ss\\n TABELA COM ERRO %s\" %\r\n (end_datetime, round(end_time-start_time, 0), e))\r\n else:\r\n end_time = time.time()\r\n end_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\r\n print(\" FIM: %s\\n TEMPO: %ss\\n OK\" %\r\n (end_datetime, round(end_time-start_time, 0)))\r\n end = time.time()\r\n print(\"TEMPO GASTO: %s s\" % (end-start))","def crear_vista_base(self, master, nombre_base=False, nombre_tabla=False, campo_referencia=False, dato_busqueda=False):\n if not nombre_base:\n print(\"No se ha especificado la base de datos a acceder.\\n\")\n return False\n else:\n if not nombre_tabla:\n print(\"La base de datos especificada no contiene tablas.\\n\")\n self.tree[\"columns\"] = (1,2,3)\n for row in self.tree.get_children():\n self.tree.delete(row)\n return False\n\n contador_campos = 0\n nombres = []\n for campo in base.retornar_esquema(nombre_base, nombre_tabla):\n nombre = campo[1] # escruta el nombre de la columna\n contador_campos += 1\n nombres.append(nombre)\n\n self.tree[\"columns\"] = tuple([x for x in range(1,contador_campos+1)] if contador_campos>=7 else [x for x in range(1,8)])\n\n if len(nombres) <= 6:\n for i in range(1,len(self.tree[\"columns\"])+1):\n if i > 6:\n self.tree.column(f\"#{i}\",\n width=886-(140*6),\n stretch=tk.NO)\n self.tree.heading(f\"#{i}\", \n text=f\"\")\n break\n elif i <= 6 and i > len(nombres):\n self.tree.column(f\"#{i}\",\n width=140,\n stretch=tk.NO)\n self.tree.heading(f\"#{i}\", \n text=f\"\")\n else:\n self.tree.column(f\"#{i}\",\n width=140,\n stretch=tk.NO,\n anchor='center')\n self.tree.heading(f\"#{i}\", \n text=f\"{nombres[i-1].title() if nombres[i-1] not in ('id','ID') else nombres[i-1]}\",\n anchor=\"center\")\n else:\n for i in range(1,len(self.tree[\"columns\"])+1):\n self.tree.column(f\"#{i}\",\n width=140,\n stretch=tk.NO,\n anchor='center')\n self.tree.heading(f\"#{i}\", \n text=f\"{nombres[i-1].title() if nombres[i-1] not in ('id','ID') else nombres[i-1]}\",\n anchor=\"center\")\n\n if campo_referencia:\n if dato_busqueda:\n if isinstance(dato_busqueda, (str,int,float,bool)):\n for row in self.tree.get_children():\n self.tree.delete(row)\n filas = base.traer_datos(nombre_base, nombre_tabla, campo_referencia, dato_busqueda)\n for fila in filas:\n self.tree.insert(\"\", tk.END, values=fila)\n elif isinstance(dato_busqueda, (tuple,list,dict)):\n for row in self.tree.get_children():\n self.tree.delete(row)\n for valor in dato_busqueda:\n filas = base.traer_datos(nombre_base, nombre_tabla, campo_referencia, valor)\n for fila in filas:\n self.tree.insert(\"\", tk.END, values=fila)\n else:\n for row in self.tree.get_children():\n self.tree.delete(row)\n filas = base.retornar_datos(nombre_base, nombre_tabla)\n for fila in filas:\n self.tree.insert(\"\", tk.END, values=fila)\n else:\n for row in self.tree.get_children():\n self.tree.delete(row)\n filas = base.retornar_datos_segun_tiempo(nombre_base, nombre_tabla)\n for fila in filas:\n self.tree.insert(\"\", tk.END, values=fila)","def call(self):\r\n clean_products = []\r\n\r\n for category in CATEGORIES:\r\n print(f\"Chargement des produits de type {category}\")\r\n api_url = SEARCH_API_URL + \\\r\n (f\"?search_terms={category}\"\r\n \"&search_tag=category&sort_by=unique_scans_n\"\r\n \"&page_size=1000&json=1\")\r\n json_response = requests.get(api_url).json()\r\n products = json_response[\"products\"]\r\n\r\n for product in products:\r\n clean_product = {\r\n k: v for k, v in product.items()\r\n if k in FIELD_NEEDED and v != ''}\r\n clean_products.append(clean_product)\r\n\r\n return clean_products","def generateProducts(self):\r\n\r\n # Creates items in each category\r\n for i in range(self.num_of_items):\r\n self.ID_DICT[i+self.num_of_items] = random.randint(1, 10)\r\n self.ID_DICT[i+self.num_of_items*2] = random.randint(1, 10)\r\n self.ID_DICT[i+self.num_of_items*3] = random.randint(1, 10)\r\n self.ID_DICT[i+self.num_of_items*4] = random.randint(1, 10)\r\n self.ID_DICT[i+self.num_of_items*5] = random.randint(1, 10)\r\n self.ID_DICT[i+self.num_of_items*6] = random.randint(1, 10)\r\n\r\n\r\n # Sort for easy selection\r\n sorted(self.ID_DICT)\r\n\r\n for product in self.ID_DICT.keys():\r\n temp_int = self.ID_DICT[product]\r\n self.c.execute(\"INSERT INTO Products (ProductID, Price) VALUES (?, ?)\", (product, self.ID_DICT[product]))\r\n self.conn.commit()\r\n\r\n if self.print_items:\r\n print(\"\\nAll items in store:\")\r\n print(self.ID_DICT)\r\n print()","def crearTabla(self):\n mensaje = self.base.createTable()\n showinfo('Resultado', mensaje)","def factura(num_factura):\n cursor_factura = connectiondb.cursor()\n sql_factura = \"\"\"SELECT * FROM Factura\n WHERE num_factura = '%s'\"\"\" % num_factura\n cursor_factura.execute(sql_factura)\n factura = cursor_factura.fetchone()\n cursor_factura.close()\n\n cursor_items_factura = connectiondb.cursor()\n sql_items_factura = \"\"\"SELECT * FROM ItemsFactura\n WHERE num_factura = '%s'\"\"\" % num_factura\n cursor_items_factura.execute(sql_items_factura)\n items_factura = cursor_items_factura.fetchall()\n cursor_items_factura.close()\n\n cursor_parametros = connectiondb.cursor()\n sql_parametros = \"\"\"SELECT * FROM ParametrosFactura\"\"\"\n cursor_parametros.execute(sql_parametros)\n parametros = cursor_parametros.fetchone()\n cursor_parametros.close()\n\n # Formatea el numero de factura para la resolución\n cero = '0'\n param_num_fac_ini = str(parametros['num_fac_ini'])\n param_num_fac_fin = str(parametros['num_fac_fin'])\n while len(param_num_fac_ini) < len(param_num_fac_fin):\n param_num_fac_ini = cero[:] + param_num_fac_ini\n\n # Formatea el numero de factura\n numero_factura = str(factura['num_factura'])\n ultm_consecutivo = str(parametros['num_fac_fin'])\n\n while len(numero_factura) < len(ultm_consecutivo):\n numero_factura = cero[:] + numero_factura\n\n # Datos del cliente\n cursor_datos_cliente = connectiondb.cursor()\n sql_datos_cliente = \"\"\"SELECT Factura.num_factura, Cliente.nombre_cliente,\n Cliente.identificacion_cliente, Cliente.direccion,\n Cliente.ciudad, Cliente.telefono\n FROM Factura\n INNER JOIN Cliente\n ON Factura.identificacion_cliente = Cliente.identificacion_cliente\n WHERE Factura.num_factura = '{}'\n ORDER BY Factura.num_factura DESC;\"\"\".format(factura['num_factura'])\n cursor_datos_cliente.execute(sql_datos_cliente)\n datos_cliente = cursor_datos_cliente.fetchone()\n cursor_datos_cliente.close()\n\n return render_template('factura.html',\n parametros=parametros,\n param_num_fac_ini=param_num_fac_ini,\n datos_cliente=datos_cliente,\n factura=factura,\n numero_factura=numero_factura,\n items_factura=items_factura,\n name=current_user.username)","def add(self, producto, cantidad = 1, actualizar_cantidad = False):\n id_producto = str(producto.id)\n if id_producto not in self.carro:\n self.carro[id_producto] = {\"cantidad\":0,\"precio\":str(producto.precio)}\n if actualizar_cantidad:\n self.carro[id_producto][\"cantidad\"]= cantidad\n else:\n self.carro[id_producto][\"cantidad\"] += cantidad\n self.save()","def __print_from_api(self, product: dict):\n\n substitutes = self.api_operator.get_substitutes(\n product['categories_tags'][-1],\n product.get('nutrition_grades', 'e'))\n\n # deepcopy for a isolate change\n operateur_result = [deepcopy(product)]\n\n if substitutes:\n operateur_result.extend(deepcopy(substitutes))\n\n self.adapter_for_terminal(operateur_result)\n\n # print product and his subsitutes in the terminal\n self.printer(operateur_result)\n\n save_choice = self.ask_with_input(\n 'Enregistrer dans la base de données ?'\n ' (y, n)', -1, ('y', 'n'))\n\n if save_choice == 'y':\n # save product and his substitutes\n self.database_manager.save_product(product, substitutes)\n cprint('Produit enregistré dans la base de données.', 'red')","def save_product(cls, product: dict, substitutes: (tuple, None) = None,\n user=None):\n\n nutriments = product.get('nutriments', {})\n with transaction.atomic():\n product_db, created = Product.objects.update_or_create(\n bar_code=product['code'],\n defaults={\n 'name': product.get('product_name', None),\n 'generic_name': product.get('generic_name', None),\n 'nutrition_grades': product.get('nutrition_grades', None),\n 'fat': str(nutriments.get('fat_100g', None)),\n 'saturated_fat': str(nutriments.get('saturated-fat_100g',\n None)),\n 'sugars': str(nutriments.get('sugars_100g', None)),\n 'salt': str(nutriments.get('salt_100g', None)),\n 'image_url': product.get('image_url', None)\n }\n )\n\n if created:\n categories = []\n for category in reversed(\n product.get('categories_hierarchy', ())):\n category_db, created = Category.objects.get_or_create(\n name=category)\n categories.append(category_db)\n ProductCategory.objects.bulk_create(\n [ProductCategory(product=product_db, category=category,\n hierarchy=i)\n for i, category in enumerate(categories, start=1)]\n )\n\n ingredients = []\n iteration_ingredients = ()\n if product.get('ingredients_text_fr', ()):\n iteration_ingredients = product.get('ingredients_text_fr')\n elif product.get('ingredients', ()):\n iteration_ingredients = product.get('ingredients')\n\n for ingredient in iteration_ingredients:\n ingredient = ingredient[:200]\n ingredient_db, created = Ingredient.objects.get_or_create(\n name=ingredient)\n ingredients.append(ingredient_db)\n\n product_db.ingredients.add(*ingredients)\n\n brands = []\n for brand in product.get('brands_tags', ()):\n brand_db, created = Brand.objects.get_or_create(name=brand)\n brands.append(brand_db)\n product_db.brands.add(*brands)\n\n stores = []\n for store in product.get('stores_tags', ()):\n store_db, created = Store.objects.get_or_create(name=store)\n stores.append(store_db)\n product_db.stores.add(*stores)\n\n if substitutes is not None and user is not None:\n for substitute in substitutes:\n substitute_db = cls.save_product(substitute)\n cls.save_link_p_s_p(user, product_db, substitute_db)\n\n return product_db","def devices_by_product(self):\n\n # Order by product first...\n by_product = self.devices.all().order_by(\"product_id\")\n\n def pid(device):\n return device.product_id\n\n # Already grouped by product, so we can just do a groupby() immediately\n return [{\n \"product\": Product.objects.get(pk=product_id),\n \"devices\": devices\n } for product_id, devices in groupby(by_product, pid)]","def dispone_producto_espacio_sistema(self, sistema):\r\n camiones_comparables_a_cargar = 0\r\n camiones_comparables_a_descargar = 0\r\n\r\n if self.tipo == \"Carga\":\r\n camiones_comparables_a_cargar += \\\r\n sum(1 for c in sistema.camiones_en_sistema\r\n if c.carga == self.carga and c.tipo == \"Carga\" and not c.manipulado.triggered)\r\n camiones_comparables_a_descargar += \\\r\n sum(1 for c in sistema.camiones_en_sistema\r\n if c.carga == self.carga and c.tipo == \"Descarga\" and not c.manipulado.triggered)\r\n if not self.manipulado.triggered and self in sistema.camiones_en_sistema:\r\n camiones_comparables_a_cargar -= 1\r\n else:\r\n camiones_comparables_a_cargar += \\\r\n sum(1 for c in sistema.camiones_en_sistema\r\n if self.medios_almacenamiento_destino(sistema) == c.medios_almacenamiento_destino(sistema) and\r\n c.tipo == \"Carga\" and not c.manipulado.triggered)\r\n camiones_comparables_a_descargar += \\\r\n sum(1 for c in sistema.camiones_en_sistema\r\n if self.medios_almacenamiento_destino(sistema) == c.medios_almacenamiento_destino(sistema) and\r\n c.tipo == \"Descarga\" and not c.manipulado.triggered)\r\n if not self.manipulado.triggered and self in sistema.camiones_en_sistema:\r\n camiones_comparables_a_descargar -= 1\r\n\r\n producto_espacio_disponible = 0\r\n\r\n for almacen in self.medios_almacenamiento_destino(sistema):\r\n\r\n if self.tipo == \"Carga\":\r\n producto_espacio_disponible += almacen.niveles[self.carga]\r\n else:\r\n producto_espacio_disponible += almacen.espacio\r\n\r\n if self.tipo == \"Carga\":\r\n\r\n if producto_espacio_disponible + 28 * (camiones_comparables_a_descargar - camiones_comparables_a_cargar) \\\r\n >= 28:\r\n return True\r\n else:\r\n return False\r\n else:\r\n if self.carga == \"Fierro\":\r\n return True\r\n elif producto_espacio_disponible + 28 * (camiones_comparables_a_cargar - camiones_comparables_a_descargar) \\\r\n >= 28:\r\n return True\r\n else:\r\n return False","def show_available_products():\n\n mongo = MongoDBConnection()\n result = {}\n\n with mongo:\n db = mongo.connection.HPNorton\n productcollection = db[\"products\"]\n for document in productcollection.find({\"quantity_available\": {\"$gt\": \"0\"}}):\n key = document['product_id']\n\n result[key] = {\n 'description': document['description'],\n 'product_type': document['product_type'],\n 'quantity_available': document['quantity_available']\n }\n\n return result","def poster_list_products(products):\r\n print('\\n Choisir un produit : ')\r\n dict_product = {}\r\n index = 1\r\n\r\n for i in products:\r\n poster_products = cl.Food(i, index)\r\n dict_product[poster_products.index] = poster_products.name\r\n print(index, \" : \", poster_products.name)\r\n index += 1\r\n return dict_product","def poster_product_list(product):\r\n print('\\n Séléctionner un product : ')\r\n dict_produit = {}\r\n index = 1\r\n for i in product:\r\n poster_product = cl.Food(i, index)\r\n dict_produit[poster_product.index] = poster_product.name\r\n print(index, \" : \", poster_product.name)\r\n index += 1\r\n return dict_produit","def return_products():\n with MY_CONNECTION as connection:\n cursor = connection.cursor()\n cursor.execute(\n \"\"\"\n SELECT id_product, product_name, product_price, in_stock, description\n FROM Products\n \"\"\")\n return cursor.fetchall()","def _product_available(self, cr, uid, ids, field_names=None, arg=False, context=None):\n if not field_names:\n field_names = []\n if context is None:\n context = {}\n res = {}\n \n for product_loc in self.browse(cr, uid, ids):\n c = context.copy()\n c.update({ 'states': ('done',), 'what': ('in', 'out'), 'location': product_loc.location_id.id})\n stock = self.pool.get('product.product').get_product_available(cr, uid, [product_loc.product_id.id], context=c)\n res[product_loc.id] = stock.get(product_loc.product_id.id, 0.0)\n return res","def pruebas(self):\n self.gestor_pca.pruebas()\n return None"],"string":"[\n \"def buscarProd(self):\\n medicamento = str(self.lineMedicamento.text())\\n monodroga = str(self.lineMonodroga.text())\\n data = self.getAllTabla(self.tableProductos)\\n\\n if medicamento != \\\"\\\":\\n dataMedic = filter(lambda x: x[1].upper() == medicamento.upper(), data.values())\\n else:\\n dataMedic = data.values()\\n if monodroga != \\\"\\\":\\n dataMono = filter(lambda x: x[3].upper() == monodroga.upper(), dataMedic)\\n else:\\n dataMono = dataMedic\\n\\n for dato in data:\\n self.tableProductos.setRowHidden(dato,False)\\n\\n for dato in data:\\n if not data[dato] in dataMono:\\n self.tableProductos.setRowHidden(dato,True)\",\n \"def cargarProductosSinObra(self):\\n\\n self.limpiarTabla(self.tableProductos)\\n\\n ##Cnsulta para obtener todos los productos del sistema, con su correspondiente\\n ##codigo de barra, monodroga, descuento, importe\\n query=self.sesion.query(ProductoModel.codigo_barra,ProductoModel.id_medicamento,ProductoModel.id_presentacion,MonodrogaModel.nombre,ProductoModel.importe).\\\\\\n join(MedicamentoModel).filter(ProductoModel.id_medicamento==MedicamentoModel.nombre_comercial).\\\\\\n join(MonodrogaModel).filter(MedicamentoModel.id_monodroga==MonodrogaModel.nombre).\\\\\\n filter(ProductoModel.baja==False).order_by(ProductoModel.codigo_barra)\\n\\n ##Se cargan los datos obtenidos en la tabla de Producto\\n for n, obj in enumerate(query):\\n self.tableProductos.insertRow(n)\\n self.tableProductos.setItem(n, 0, QtGui.QTableWidgetItem(str(obj[0])))\\n self.tableProductos.setItem(n, 1, QtGui.QTableWidgetItem(str(obj[1])))\\n self.tableProductos.setItem(n, 2, QtGui.QTableWidgetItem(str(obj[2])))\\n self.tableProductos.setItem(n, 3, QtGui.QTableWidgetItem(str(obj[3])))\\n self.tableProductos.setItem(n, 4, QtGui.QTableWidgetItem(str(0)))\\n self.tableProductos.setItem(n, 5, QtGui.QTableWidgetItem(str(obj[4])))\\n\\n ##Se carga la cantidad de cada producto en la tabla\\n for row,producto in enumerate(ProductoModel.buscarTodos(ProductoModel.codigo_barra,self.sesion)):\\n self.tableProductos.setItem(row,6,QtGui.QTableWidgetItem(str(producto.getCantidad(self.sesion))))\",\n \"def cargar_productos(self, obraSocial):\\n self.limpiarTabla(self.tableProductos)\\n\\n query=self.sesion.query(ProductoModel.codigo_barra,ProductoModel.id_medicamento,ProductoModel.id_presentacion,MonodrogaModel.nombre,DescuentoModel.descuento,ProductoModel.importe).\\\\\\n join(MedicamentoModel).filter(ProductoModel.id_medicamento==MedicamentoModel.nombre_comercial).\\\\\\n join(MonodrogaModel).filter(MedicamentoModel.id_monodroga==MonodrogaModel.nombre).\\\\\\n join(DescuentoModel).filter(DescuentoModel.producto==ProductoModel.codigo_barra).\\\\\\n filter(DescuentoModel.obra_social==obraSocial,ProductoModel.baja==False).order_by(ProductoModel.codigo_barra)\\n\\n for n, obj in enumerate(query):\\n self.tableProductos.insertRow(n)\\n for m, campo in enumerate(obj):\\n self.tableProductos.setItem(n, m, QtGui.QTableWidgetItem(str(campo)))\\n\\n for row,producto in enumerate(ProductoModel.buscarTodos(ProductoModel.codigo_barra,self.sesion)):\\n self.tableProductos.setItem(row,6,QtGui.QTableWidgetItem(str(producto.getCantidad(self.sesion))))\",\n \"def agregarProducto(self):\\n itemActual=self.tableFactura.currentItem()\\n producto = int(self.tableFactura.item(itemActual.row(),0).text())\\n descuento = DescuentoModel.buscar(DescuentoModel.obra_social,self.sesion,self.obraSocial).\\\\\\n filter(DescuentoModel.producto==producto)[0].descuento\\n cantidad = int(self.tableFactura.item(itemActual.row(), 1).text())\\n importe = float(self.tableFactura.item(itemActual.row(), 2).text()) * descuento\\n row = self.tableNC.rowCount()\\n self.tableNC.insertRow(row)\\n self.tableNC.setItem(row, 0, QtGui.QTableWidgetItem(str(producto)))\\n self.tableNC.setItem(row, 1, QtGui.QTableWidgetItem(str(cantidad)))\\n self.tableNC.setItem(row, 2, QtGui.QTableWidgetItem(str(importe)))\\n self.detallesReintegrables.append([int(self.numeroFacturaActual),itemActual.row()+1,descuento,importe])\\n self.detallesImprimibles.append([producto,cantidad,descuento,importe])\\n self.tableFactura.hideRow(itemActual.row())\",\n \"def obtener_productos():\\n\\n # Se crea la lista de objetos Producto()\\n productos = [\\n Producto(\\\"Caja chica\\\", 5, 100.0),\\n Producto(\\\"Caja mediana\\\", 3, 185.0),\\n Producto(\\\"Caja grande\\\", 1, 299.0)\\n ]\\n\\n return productos\",\n \"def product_db() -> List[Text]:\\n\\n return [\\n \\\"credit\\\",\\n \\\"forex\\\",\\n \\\"debit\\\",\\n \\\"atm\\\"\\n ]\",\n \"def product_tables(self): \\r\\n\\r\\n self.mycursor.execute('CREATE TABLE IF NOT EXISTS product(\\\\\\r\\n PROD_id BIGINT PRIMARY KEY,\\\\\\r\\n PROD_name VARCHAR(100) NOT NULL,\\\\\\r\\n PROD_grade CHAR(1) NOT NULL,\\\\\\r\\n PROD_url VARCHAR(150) NOT NULL UNIQUE)')\",\n \"def limpiarVentana(self):\\n\\n self.productosAgregados=0\\n self.lotesVentas={}\\n self.facturaCobrada=False\\n self.obraSocialSeleccionada=None\\n self.formapago = None\\n self.factura = None\\n self.data = {}\\n self.detallesTabla = {}\\n self.lineObra.clear()\\n self.lineObra.setEnabled(True)\\n self.lineCuit.clear()\\n self.lineCuit.setEnabled(True)\\n self.tableObra.setVisible(False)\\n self.rbtnObra.setChecked(False)\\n self.limpiarTabla(self.tableProductos)\\n self.limpiarTabla(self.tableFactura)\\n self.cargarProductosSinObra()\",\n \"def get_all_products(self):\\n\\t\\tpass\",\n \"def insert_products(self):\\n logic = ProductLogic()\\n \\n try:\\n # We create the list of product objects\\n products = self.objects_factory.create_product_object_list()\\n products = set(products)\\n\\n for product in products:\\n logic.insert(product)\\n except:\\n print('Il y a eu un problème lors de la récupération des données, veuillez rééssayer')\",\n \"def generarConsultasLibres(self): \\n \\n for idw,word in enumerate(self.key_words):\\n consultasparql = self.busLibre % (word,self.limit_BL)\\n self.ConsultasLibres.append(consultasparql)\\n \\n \\n return self.ConsultasLibres\",\n \"def agregarProducto(self):\\n itemActual=self.tableProductos.currentItem()\\n cantidad, ok = QtGui.QInputDialog.getInt(self,\\\"Cantidad\\\",\\\"Ingrese cantidad del producto\\\",1,1,2000,5)\\n if not ok:\\n self.showMsjEstado(\\\"No se ha seleccionado cantidad del producto\\\")\\n else:\\n cantidadProducto=int(self.tableProductos.item(itemActual.row(),6).text())\\n if cantidad>cantidadProducto:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"La cantidad ingresada es mayor que la del stock\\\")\\n else:\\n if self.productosAgregados == 0 and self.factura == None:\\n self.factura=FacturaModel(FacturaModel.generarNumero(self.sesion))\\n self.factura.guardar(self.sesion)\\n self.productosAgregados+=1\\n rowItemActual=itemActual.row()\\n rows=self.tableFactura.rowCount()\\n self.tableFactura.insertRow(rows)\\n\\n #--Carga de items en la tabla--*\\n producto = int(self.tableProductos.item(rowItemActual,0).text())\\n importeActual=float(self.tableProductos.item(rowItemActual,5).text())\\n descuentoActual=float(self.tableProductos.item(rowItemActual,4).text())\\n subtotal=importeActual*(1-descuentoActual)\\n ####-------------------------#####\\n detalleFactura=DetalleFacturaModel(self.factura.numero,producto,cantidad,\\n subtotal*cantidad,descuentoActual,self.productosAgregados\\n )\\n self.descontarCantidad(detalleFactura,producto,cantidad)\\n self.tableFactura.setItem(rows,0,QtGui.QTableWidgetItem(str(detalleFactura.producto)))\\n self.tableFactura.setItem(rows,1,QtGui.QTableWidgetItem(str(detalleFactura.cantidad)))\\n self.tableFactura.setItem(rows, 2, QtGui.QTableWidgetItem(str(\\\"%.2f\\\"%(subtotal*cantidad))))\\n\\n detalleFactura.guardar(self.sesion)\\n self.detallesTabla[rows] = detalleFactura\\n\\n self.data[rows] = [\\n producto, cantidad, subtotal*cantidad, descuentoActual\\n ]\\n\\n self.actualizar()\\n self.objectModified.emit()\",\n \"def fill_data_product(self):\\n self.product.fill_data_product(self.list_products, self.mycursor, self.my_database)\",\n \"def fill_cache_table():\\n products = []\\n for query in ['bread', 'milk', 'rice']:\\n grocery = grocery_search(query)\\n products += get_all_products_from_grocery_search(grocery)\\n\\n orm = ORM()\\n for product in products:\\n orm.add_cache(**product)\",\n \"def consultar_todos_DB(self):\\n registros = db.session.query(ModelConcurso).all()\\n for registro in registros:\\n print(registro)\",\n \"def get_data(self):\\n products_list = []\\n for category in CATEGORIES:\\n json_data = self.url_to_json(category)\\n pages_nb = self.retrieve_cat_pages_nb(json_data)\\n for page in range(pages_nb):\\n page_json_data = self.page_to_json(category, page+1)\\n products = page_json_data[\\\"products\\\"]\\n for p in products:\\n params = {\\n 'brands': \\\"\\\",\\n 'product_name_fr': \\\"\\\",\\n 'nutrition_grades': \\\"\\\",\\n 'stores': \\\"\\\",\\n 'url': \\\"\\\",\\n 'categories': \\\"\\\"\\n }\\n for key in params:\\n try:\\n params[key] = p[key]\\n except KeyError:\\n continue\\n if params['product_name_fr'] != \\\"\\\" and params['nutrition_grades'] != \\\"\\\" and params['url'] != \\\"\\\" and params['categories'] != \\\"\\\":\\n product = Product(brand=params['brands'],\\n name=params['product_name_fr'],\\n nutrition_grade=params['nutrition_grades'],\\n stores=params['stores'], url=params['url'],\\n category=params['categories'])\\n products_list.append(product)\\n try:\\n self.manager.save_all(self.clean_data(products_list))\\n print(f\\\"\\\\n La base de données |{DB_NAME}| a été peuplée \\\\n\\\")\\n except:\\n print(\\\"\\\\n Une erreur s'est produite lors \\\"\\n \\\"du peuplement de la base de données \\\\n\\\")\",\n \"def generarConsultasConexion(self):\\n for parRecursos in self.CombiConsultaLibre:\\n parRecursosL0=self.limpiaRecursos(parRecursos[0])\\n parRecursosL1=self.limpiaRecursos(parRecursos[1])\\n \\n if self.nivel_profundidad>=1:\\n consultasparql = self.busConex1 % (parRecursosL0,parRecursosL1,self.limit_BC)\\n print consultasparql;\\n resultoCC=self.consulta(consultasparql)\\n for resul in resultoCC['results']['bindings']:\\n triple = parRecursos[0]+\\\"-|\\\"+parRecursos[1]+\\\"-|\\\"+resul['p1']['value']\\n self.ResultConsultasConexion.append(triple) \\n \\n if self.nivel_profundidad>=2:\\n consultasparql = self.busConex2 % (parRecursosL0,parRecursosL1,self.limit_BC)\\n resultoCC=self.consulta(consultasparql)\\n for resul in resultoCC['results']['bindings']:\\n o1=resul['o1']['value']\\n o1=o1.replace('http://dbpedia.org/resource/','')\\n triple1 = parRecursos[0]+\\\"-|\\\"+o1+\\\"*-|\\\"+resul['p1']['value']\\n triple2 = parRecursos[1]+\\\"-|\\\"+o1+\\\"*-|\\\"+resul['p2']['value']\\n self.ResultConsultasConexion.append(triple1) \\n self.ResultConsultasConexion.append(triple2) \\n \\n if self.nivel_profundidad>=3:\\n consultasparql = self.busConex3_1 % (parRecursosL0,parRecursosL1,self.limit_BC)\\n resultoCC=self.consulta(consultasparql)\\n for resul in resultoCC['results']['bindings']:\\n o1=resul['o1']['value']\\n o1=o1.replace('http://dbpedia.org/resource/','')\\n o2=resul['o2']['value']\\n o2=o1.replace('http://dbpedia.org/resource/','')\\n triple1 = parRecursos[0]+\\\"-|\\\"+o1+\\\"*-|\\\"+resul['p1']['value']\\n triple2 = parRecursos[1]+\\\"-|\\\"+o2+\\\"*-|\\\"+resul['p2']['value']\\n triple3 = o1+\\\"*-|\\\"+o2+\\\"*-|\\\"+resul['p3']['value'] \\n self.ResultConsultasConexion.append(triple1) \\n self.ResultConsultasConexion.append(triple2) \\n self.ResultConsultasConexion.append(triple3) \\n\\n consultasparql = self.busConex3_2 % (parRecursosL0,parRecursosL1,self.limit_BC)\\n resultoCC=self.consulta(consultasparql)\\n for resul in resultoCC['results']['bindings']:\\n o1=resul['o1']['value']\\n o1=o1.replace('http://dbpedia.org/resource/','')\\n o2=resul['o2']['value']\\n o2=o1.replace('http://dbpedia.org/resource/','')\\n triple1 = parRecursos[0]+\\\"-|\\\"+o1+\\\"*-|\\\"+resul['p1']['value']\\n triple2 = parRecursos[1]+\\\"-|\\\"+o2+\\\"*-|\\\"+resul['p2']['value']\\n triple3 = o2+\\\"*-|\\\"+o1+\\\"*-|\\\"+resul['p3']['value'] \\n self.ResultConsultasConexion.append(triple1) \\n self.ResultConsultasConexion.append(triple2) \\n self.ResultConsultasConexion.append(triple3)\",\n \"def buscarOs(self):\\n\\n if self.lineRazon.isEnabled():\\n self.filtrarObra()\\n\\n elif not self.lineRazon.isEnabled() and (self.tableNC.rowCount() != 0 or self.tableFactura.rowCount() != 0):\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"Imposible cambiar de Obra Social. Ya se ha seleccionado\\\\\\n una\\\")\\n else:\\n self.gbNotaCredito.setEnabled(False)\\n self.gbFactura.setEnabled(False)\\n self.lineRazon.clear()\\n self.lineRazon.setEnabled(True)\\n self.lineCuit.clear()\\n self.lineCuit.setEnabled(True)\\n self.tableOs.setEnabled(True)\",\n \"def listall():\\n # I like to define the query separately.\\n query = db.product\\n\\n # List of additional links.\\n links = []\\n \\n links.append(\\n dict(header = \\\"Profit\\\",\\n body = lambda row : produce_profit(row)\\n )\\n )\\n links.append(\\n dict(header = \\\"\\\",\\n body = lambda row : produce_star_btn(row.id)\\n )\\n )\\n links.append(\\n dict(header = \\\"\\\",\\n body = lambda row : produce_pls_minus_btn(row)\\n )\\n )\\n links.append(\\n dict(header='',\\n body = lambda row : produce_poster_btns(row.id)\\n \\n )\\n )\\n \\n # Let's get rid of some fields in the add form.\\n if len(request.args) > 0 and request.args[0] == 'new':\\n db.product.prod_poster.readable = False\\n db.product.prod_post_time.writable = False\\n db.product.prod_sold.writable = False\\n db.product.prod_starred.readable, db.product.prod_starred.writable =False, False\\n # Grid definition.\\n grid = SQLFORM.grid(\\n query, \\n field_id = db.product.id, # Useful, not mandatory.\\n fields = [db.product.id, db.product.prod_name,\\n db.product.prod_in_stock, db.product.prod_sold,\\n db.product.prod_price, db.product.prod_cost], \\n headers = {'product.prod_name': 'Product Name',\\n 'product.prod_in_stock':'In Stock',\\n 'product.prod_sold':'Sold', \\n 'product.prod_price':'Price', \\n 'product.prod_cost':'Cost'},\\n links = links,\\n # And now some generic defaults.\\n details=False,\\n create=True, editable=False, deletable=False,\\n csv=False, \\n user_signature=True, # We don't need it as one cannot take actions directly from the form.\\n )\\n return dict(grid=grid)\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(description='MODIS product table')\\n parser.add_argument('--product', help='MODIS product ID', metavar='')\\n parser.add_argument('--resolution', help='Filter for pixel size', type=str, metavar='')\\n parser.add_argument('--vampc', help='Filter for VAM product code', metavar='')\\n args = parser.parse_args()\\n\\n # Load product table\\n this_dir = Path(__file__).parent\\n with open(this_dir.parent.joinpath('data', 'MODIS_V6_PT.pkl').as_posix(), 'rb') as table_raw:\\n product_table = pickle.load(table_raw)\\n\\n tbl = pd.DataFrame(product_table).T\\n print('\\\\n')\\n\\n # Subset table by product\\n if args.product:\\n try:\\n tbl = tbl.loc[args.product.upper()]\\n except:\\n raise SystemExit('Product ID not recognized!')\\n # Subset table by resolution\\n if args.resolution and not args.product:\\n try:\\n tbl = tbl[tbl['pixel_size'] == args.resolution]\\n except:\\n raise ValueError('Resolution not valid! (possible: 250,500,1000,5600)')\\n # Subset table by parameter\\n if args.vampc and not args.product:\\n helper_dict = dict(zip(['VIM', 'VEM', 'LTD', 'LTN'],\\n ['Vegetation Indices',\\n 'Vegetation Indices',\\n 'Temperature, Emissivity',\\n 'Temperature, Emissivity']))\\n try:\\n tbl = tbl[tbl['product'] == helper_dict[args.vampc.upper()]]\\n except:\\n raise ValueError('VAM product code not valid! (possible: VIM, VEM, LTD, LTN)')\\n\\n # print results\\n with pd.option_context('display.max_rows', None, 'display.max_columns', 4):\\n print(tbl)\\n print('\\\\n')\",\n \"def getProductos(self, codigo, desc):\\n if codigo != \\\"\\\" and desc != \\\"\\\":\\n return self.conexion.ejecutarSQL(\\\"select codigo, descripcion, cantidad_disponible, porcentaje_IVA, precio_venta, fecha_modificacion, hora_modificacion, usuario_colaborador \\\\\\n from productos \\\\\\n where codigo like '%%%s%%' and descripcion like '%%%s%%' \\\\\\n order by codigo\\\"%(codigo,desc))\\n elif codigo != \\\"\\\":\\n return self.conexion.ejecutarSQL(\\\"select codigo, descripcion, cantidad_disponible, porcentaje_IVA, precio_venta, fecha_modificacion, hora_modificacion, usuario_colaborador \\\\\\n from productos \\\\\\n where codigo like '%%%s%%' \\\\\\n order by codigo\\\"%(codigo))\\n elif desc != \\\"\\\":\\n return self.conexion.ejecutarSQL(\\\"select codigo, descripcion, cantidad_disponible, porcentaje_IVA, precio_venta, fecha_modificacion, hora_modificacion, usuario_colaborador \\\\\\n from productos \\\\\\n where descripcion like '%%%s%%' \\\\\\n order by codigo\\\"%(desc))\\n else:\\n return self.conexion.ejecutarSQL(\\\"select codigo, descripcion, cantidad_disponible, porcentaje_IVA, precio_venta, fecha_modificacion, hora_modificacion, usuario_colaborador \\\\\\n from productos \\\\\\n order by codigo\\\")\",\n \"def generarCombinaciones(self):\\n combi = [list(x) for x in itertools.combinations(self.ResultConsultaLibre, 2)]\\n self.CombiConsultaLibre=combi\\n #print(self.CombiConsultaLibre)\",\n \"def show_all_products():\\n\\n data = cur.execute(\\\"\\\"\\\"SELECT productid, productname, unitcost, stock FROM catalogue\\\"\\\"\\\").fetchall()\\n\\n print(tabulate(data, headers=[\\\"Product ID\\\", \\\"Name\\\", \\\"Cost\\\", \\\"Stock\\\"]))\",\n \"def retrieve_product_infos(self):\\n\\n # PRODUCT NAME\\n try:\\n product_name = self.product['product_name'].capitalize()\\n except KeyError:\\n product_name = None\\n\\n # PRODUCT CODE\\n try:\\n product_code = self.product['code'].capitalize()\\n except KeyError:\\n product_code = None\\n\\n # URL\\n try:\\n product_url = self.product['url'].lower()\\n except KeyError:\\n product_url = None\\n\\n # IMAGE URL\\n try:\\n image_url = self.product['image_url'].lower()\\n except KeyError:\\n image_url = None\\n\\n # QUANTITY\\n try:\\n quantity = self.product['quantity'].capitalize()\\n except KeyError:\\n quantity = None\\n\\n # INGREDIENTS\\n try:\\n ingredients = self.product['ingredients_text_fr'].capitalize()\\n except KeyError:\\n ingredients = None\\n\\n # BRAND\\n brands = []\\n try:\\n for brand in self.product['brands'].split(','):\\n brand = brand.strip().capitalize()\\n if (\\n brand != ''\\n and brand not in brands\\n ):\\n brands.append(brand)\\n except KeyError:\\n pass\\n\\n # STORES\\n stores = []\\n try:\\n for store in self.product['stores'].split(','):\\n store = store.strip().capitalize()\\n if (\\n store != ''\\n and store not in stores\\n ):\\n stores.append(store)\\n except KeyError:\\n pass\\n\\n # COUNTRY\\n try:\\n countries = self.product['countries'].capitalize()\\n except KeyError:\\n countries = None\\n if 'France' in countries:\\n countries = 'France'\\n else:\\n countries = None\\n\\n # COMPARE TO CATEGORY\\n try:\\n compare_to = self.product['compared_to_category'].capitalize().split(':')[1]\\n except KeyError:\\n compare_to = None\\n try:\\n Categories.objects.get(\\n name=compare_to\\n )\\n except Categories.DoesNotExist:\\n compare_to = None\\n except:\\n importable = False\\n\\n # CATEGORIES HIERARCHY\\n try:\\n categories_hierarchy = [\\n category.split(':')[1] for category in self.product['categories_hierarchy']\\n ]\\n except KeyError:\\n categories_hierarchy = None\\n\\n # NUTRISCORE GRADE\\n nutriscore_labels = [\\n 'nutrition_grade_fr',\\n 'nutriscore_grade'\\n ]\\n nutriscore = 'F'\\n i = 0\\n while (\\n i < len(nutriscore_labels)\\n and nutriscore == 'F'\\n ):\\n try:\\n nutriscore = self.product[nutriscore_labels[i]].upper()\\n except KeyError:\\n i += 1\\n\\n product_infos = {\\n 'product_name': product_name,\\n 'product_code': product_code,\\n 'product_url': product_url,\\n 'image_url': image_url,\\n 'quantity': quantity,\\n 'ingredients': ingredients,\\n 'brands': brands,\\n 'stores': stores,\\n 'countries': countries,\\n 'compare_to': compare_to,\\n 'categories_hierarchy': categories_hierarchy,\\n 'nutriscore': nutriscore\\n }\\n\\n nutriments = self.product['nutriments']\\n for nutriment in self.list_nutriments:\\n try:\\n product_infos[nutriment] = float(nutriments[nutriment])\\n except KeyError:\\n product_infos[nutriment] = 0\\n\\n return product_infos\",\n \"def fill_products(self):\\n cursor = self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\\n categories = dict()\\n for page in range(1, 2):\\n result = requests.get(\\n 'https://fr.openfoodfacts.org/cgi/search.pl?page_size=1000&page={}&action=process&json=1'.format(\\n page)).json()\\n for element in result['products']:\\n try:\\n cursor.execute(\\n \\\"INSERT INTO product (name, store, nutrition_grade, url) VALUES (%s, %s, %s, %s) RETURNING \\\"\\n \\\"id, name\\\",\\n (element[\\\"product_name\\\"], element[\\\"stores\\\"], element[\\\"nutrition_grade_fr\\\"], element[\\\"url\\\"]))\\n # un except pour éviter les erreurs de clés\\n query_result = cursor.fetchone()\\n for category in element[\\\"categories_tags\\\"]:\\n try:\\n cursor.execute(\\\"INSERT INTO product_category(product_id, category_id) VALUES (%s, %s)\\\",\\n (query_result[0], self.categories[category]))\\n except KeyError:\\n print(\\\"Categorie insertion failed\\\")\\n\\n print(element[\\\"product_name\\\"])\\n except KeyError:\\n print(f'product insertion failed:')\\n\\n self.conn.commit()\\n cursor.close()\",\n \"def __print_from_database(self, product: dict, procedure_result: list):\\n\\n # procedure_result[1] = p_product_id\\n # procedure_result[2] = p_exist_substitutes\\n # procedure_result[3] = p_researched_subsitutes\\n\\n print('Produit déjà présent dans la base de données.')\\n # if product doesn't have substitutes in database\\n if not procedure_result[2] and not procedure_result[3]:\\n # get substitutes of the current product\\n # from the openfoodfacts API\\n substitutes = self.api_operator.get_substitutes(\\n product['categories_tags'][-1],\\n product.get('nutrition_grades', 'e'))\\n self.database_manager.save_substitutes_sql_database(\\n procedure_result[1], substitutes)\\n operateur_result = []\\n self.database_manager.fill_list_with_product_and_substitutes(\\n procedure_result[1], operateur_result)\\n self.printer(operateur_result)\\n self.ask_with_input('Ok ? (y) ', -1, ('y',))\",\n \"def search_product(self):\\n cat = []\\n product = open_products()\\n radio = self.radiobutton_check()\\n search = self.lineEdit_search.text()\\n _translate = QtCore.QCoreApplication.translate\\n __sortingEnabled = self.tableWidget.isSortingEnabled()\\n self.tableWidget.setSortingEnabled(False)\\n o=0\\n if len(self.lineEdit_search.text()) == 0:\\n self.show_product()\\n else:\\n for r in range(0, len(product)):\\n if search.upper() in str(product[r][radio]).upper():\\n cat.append(product[r])\\n for i in range(0, len(cat)):\\n for c in range(0, 5):\\n item = self.tableWidget.item(i, c)\\n item.setText(_translate(\\\"MainWindow\\\", str(cat[i][c])))\\n o+=1\\n else:\\n for c in range(0, 5):\\n item = self.tableWidget.item(r, c)\\n item.setText(_translate(\\\"MainWindow\\\", \\\"\\\"))\\n if o == 0:\\n self.frame_3.show()\\n self.label_16.setText('PRODUCT NOT FOUND!')\",\n \"def habilitarObras(self):\\n if self.productosAgregados != 0:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"Ya se han agregado productos a la factura\\\")\\n else:\\n if not self.rbtnObra.isChecked():\\n self.btnBuscar.setEnabled(True)\\n self.lineObra.setEnabled(True)\\n self.lineCuit.setEnabled(True)\\n self.tableObra.setVisible(True)\\n else:\\n self.lineObra.clear()\\n self.lineCuit.clear()\\n self.btnBuscar.setEnabled(False)\\n self.lineObra.setEnabled(False)\\n self.lineCuit.setEnabled(False)\\n self.tableObra.setVisible(False)\\n self.obraSocialSeleccionada=None\\n self.cargarProductosSinObra()\",\n \"def geneRegLleg(fech, product, cantProd):\\n listaF = []\\n linea1 = \\\"Registro de llegada de articulo:\\\\n\\\"\\n linea2 = f\\\"Fecha de recepecion: {fech}\\\\n\\\"\\n if product == 0:\\n pro = \\\"Disney Cruella De Vil Diamond Funko Pop Exclusivo\\\"\\n elif product == 1:\\n pro = \\\"Funko Pop Board Games Silver Mr. Monopoly - Figura de vinilo\\\"\\n elif product == 2:\\n pro = \\\"Funko Pop Daenerys Targaryen 59 Game of Thrones\\\"\\n elif product == 3:\\n pro = \\\"Funko Pop Flocked Iceman 504 Marvel 80 Years\\\"\\n elif product == 4:\\n pro = \\\"Funko Pop Glowing Mr Burns 1162 The Simpsons\\\"\\n elif product == 5:\\n pro = \\\"Funko Pop Grogu Con Galleta 464 Star Wars The Mandalorian\\\"\\n elif product == 6:\\n pro = \\\"Funko Pop Keychain Pogo The Umbrella Academy\\\"\\n elif product == 7:\\n pro = \\\"Funko Pop Keychain The Flash DC The Flash\\\"\\n elif product == 8:\\n pro = \\\"Funko Pop Marvel: Avengers - Wanda Maximoff Exclusivo Disney Collector Zone\\\"\\n elif product == 9:\\n pro = \\\"Funko Pop Venom 888 Marvel Venom\\\"\\n elif product == 10:\\n pro = \\\"Llavero Funko Pop Zoey Fortnite\\\"\\n elif product == 11:\\n pro = \\\"Lucky The Leprechaun Edición Limitada\\\"\\n linea3 = f\\\"Nombre del articulo: {pro}\\\\n\\\"\\n linea4 = f\\\"Cantidad: {cantProd}\\\\n\\\"\\n listaF.append(linea1)\\n listaF.append(linea2)\\n listaF.append(linea3)\\n listaF.append(linea4)\\n with open(\\\"registroLA.txt\\\",\\\"a\\\") as regProdAlmacen:\\n regProdAlmacen.writelines(listaF)\",\n \"def substitution(self, data, pro):\\n\\n produitid = self.category_id\\n nutri = str(self.nutri_score)\\n data.req(\\\"SELECT * from produit where category_id = '\\\" + produitid +\\n \\\"' and nutri_score < \\\" + nutri + \\\" \\\")\\n row = data.req_return\\n maxi = len(row)\\n list_product = []\\n i = 0\\n nb_dispo = maxi\\n print(\\\"nombre de produits correspondant heo:\\\", nb_dispo)\\n if nb_dispo > 0:\\n while i < maxi:\\n product = Produit(row[i][0], row[i][1],\\n row[i][2], row[i][3],\\n row[i][4], row[i][5])\\n print(i + 1, product.nom)\\n list_product.append(product)\\n i += 1\\n while True:\\n try:\\n choix_produit = int(input(\\\"Veuillez entrer un \\\"\\n \\\"nombre : \\\"))\\n if maxi >= choix_produit > 0:\\n break\\n except ValueError:\\n print(\\\"\\\")\\n\\n print(\\\"produit selectionner\\\", \\\"\\\\n\\\")\\n choix_produit = int(choix_produit - 1)\\n print(\\\"nom:\\\", list_product[choix_produit].nom)\\n print(\\\"description:\\\", list_product[choix_produit].description)\\n print(\\\"magasin:\\\")\\n tab = data.getMagasin(list_product[choix_produit].id, list_product[choix_produit].category_id)\\n cpt = 0\\n max = len(tab)\\n while cpt < max:\\n store = data.getStore(tab[cpt])\\n print(store.nom)\\n cpt += 1\\n print(\\\"url:\\\", list_product[choix_produit].url)\\n print(\\\"nutriScrore:\\\", list_product[choix_produit].nutri_score)\\n print(\\\"1 - trouver un substitue de meilleur \\\"\\n \\\"qualité ?\\\")\\n print(\\\"2 - Sauvegarder alliment.\\\")\\n print(\\\"3 - Nouvelle recherche.\\\")\\n while True:\\n try:\\n choix = int(input(\\\"Veuillez entrer un nombre : \\\"))\\n if maxi >= choix_produit > 0:\\n break\\n except ValueError:\\n print(\\\"\\\")\\n if choix == 1:\\n pro = list_product[choix_produit]\\n list_product[choix_produit].substitution(data, pro)\\n elif choix == 2:\\n list_product[choix_produit].save(data, pro)\\n return 1\\n else:\\n # data.close()\\n return 1\\n else:\\n print(\\\"il n'existe pas mieux! \\\")\\n print(\\\"1 - Sauvegarder alliment.\\\")\\n print(\\\"2 - Nouvelle recherche.\\\")\\n while True:\\n try:\\n choix_t = int(input(\\\"Veuillez entrer un nombre : \\\"))\\n if choix_t <= 2 and choix_t > 0:\\n break\\n except ValueError:\\n print(\\\"\\\")\\n if choix_t == 1:\\n produit = Produit(self.id, self.nom,\\n self.category_id,\\n self.description,\\n self.magasin, self.url,\\n self.nutri_score)\\n produit.save(data)\\n return 1\\n\\n else:\\n # data.close()\\n return 1\",\n \"def get_exists(self):\\n self.exist_products = {}\\n limit = 100\\n with sa.create_engine(dsn).connect() as dbcon:\\n count = [x for x in dbcon.execute(Product.count())][0][0]\\n for i in range(count//limit+1):\\n sql = sa.select([Product.c.id, Product.c.title]).limit(limit).offset(limit*i)\\n part = {hash(x[1]): x[0] for x in dbcon.execute(sql)}\\n self.exist_products.update(part)\",\n \"def buscarFactura(self):\\n\\n if not self.lineNumero.isEnabled() and self.facturaSeleccionada != None:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"Ya se ha seleccionado una factura\\\")\\n elif not self.lineNumero.isEnabled():\\n self.lineNumero.setEnabled(True)\\n self.lineNumero.clear()\\n self.limpiarTabla(self.tableFactura)\\n else:\\n self.numeroFacturaActual=str(self.lineNumero.text())\\n if len(self.numeroFacturaActual)==0:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",QtCore.QString.fromUtf8(\\\"No se ha ingresado número de factura\\\"))\\n else:\\n self.facturaSeleccionada=FacturaModel.existeFactura(int(self.numeroFacturaActual),self.sesion)\\n if self.facturaSeleccionada==None:\\n QtGui.QMessageBox.warning(self,\\\"Aviso\\\",\\\"La factura seleccionada no existe\\\")\\n elif self.facturaSeleccionada.getNC()!=None:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",QtCore.QString.fromUtf8(\\\"La factura ya ha posee una Nota de Crédito\\\"))\\n self.facturaSeleccionada = None\\n elif self.facturaSeleccionada.getFechaEmision()+timedelta(days=int(self.plazo))<date.today():\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",QtCore.QString.fromUtf8(\\\"El tiempo permitido para la devolución ha expirado\\\"))\\n elif self.facturaSeleccionada.estaLiquidada(self.sesion):\\n print self.facturaSeleccionada.estaLiquidada(self.sesion)\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"La factura se encuentra liquidada a la Obra Social\\\")\\n else:\\n self.lineNumero.setEnabled(False)\\n self.cargarObjetos(self.tableFactura,self.facturaSeleccionada.getDetalles(self.sesion),\\n [\\\"nro_linea\\\",\\\"producto\\\",\\\"cantidad\\\",\\\"importe\\\"])\",\n \"def cargarObra(self):\\n rowActual=self.tableObra.currentItem().row()\\n self.lineObra.setText(str(self.tableObra.item(rowActual,0).text()))\\n self.lineCuit.setText(str(self.tableObra.item(rowActual,1).text()))\\n self.tableObra.hide()\\n self.lineObra.setEnabled(False)\\n self.lineCuit.setEnabled(False)\\n self.obraSocialSeleccionada = str(self.lineObra.text())\\n self.cargar_productos(self.obraSocialSeleccionada)\\n self.gbProducto.setVisible(True)\",\n \"def load_products():\\n\\n print \\\"Loading Products\\\"\\n\\n for i, row in enumerate(open(\\\"data/mock_product_data.csv\\\")):\\n row = row.rstrip()\\n title, price, inventory = row.split(\\\",\\\")\\n\\n product = Product(title=title,\\n price=price,\\n available_inventory=inventory)\\n\\n db.session.add(product)\\n\\n db.session.commit()\",\n \"def buscarFactura(self):\\n if not self.lineNumeroFac.isEnabled() and self.tableNC.rowCount() != 0:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"Ya se ha seleccionado una factura\\\")\\n elif not self.lineNumeroFac.isEnabled():\\n self.lineNumeroFac.setEnabled(True)\\n self.lineNumeroFac.clear()\\n self.limpiarTabla(self.tableFactura)\\n else:\\n self.numeroFacturaActual=str(self.lineNumeroFac.text())\\n if len(self.numeroFacturaActual)==0:\\n self.showMsjEstado(\\\"No se ha ingresado numero de factura\\\")\\n else:\\n self.facturaSeleccionada=FacturaModel.existeFactura(int(self.numeroFacturaActual),self.sesion)\\n if self.facturaSeleccionada==None:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"La factura seleccionada no existe\\\")\\n elif self.facturaSeleccionada.getObra() != None and self.facturaSeleccionada.getObra() != self.obraSocial:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"La Obra Social seleccionada no corresponde con la factura\\\")\\n elif self.facturaSeleccionada.getFechaEmision()+timedelta(days=int(self.plazo))<date.today():\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"El tiempo permitido para el reintegro ha expirado\\\")\\n elif self.facturaSeleccionada.estaLiquidada(self.sesion):\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"La factura se encuentra liquidada a la Obra Social\\\")\\n elif self.facturaSeleccionada.getNC()!=None:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",\\\"La factura ya posee una Nota de Crédito\\\")\\n else:\\n self.lineNumeroFac.setEnabled(False)\\n if self.facturaSeleccionada.getObra() == None:\\n self.cargarObjetos(self.tableFactura,self.facturaSeleccionada.getDetalles(self.obraSocial, self.sesion),\\n [\\\"producto\\\",\\\"cantidad\\\",\\\"importe\\\"])\\n else:\\n self.cargarObjetos(self.tableFactura,self.facturaSeleccionada.getDetallesSinDescuento(self.sesion),\\n [\\\"producto\\\",\\\"cantidad\\\",\\\"importe\\\"])\",\n \"def products(self):\\n return list(Product.select())\",\n \"def read_precios(path=\\\".\\\"):\\n\\n here = Path(path)\\n sucursales = sucursales_prov(here)\\n productos = pd.read_csv(here / \\\"productos.csv\\\")\\n precios = [read_precio(f, sucursales) for f in Path(path).glob(\\\"precios_*.csv\\\")]\\n\\n # aplico merges de todos los precios\\n def merge(left, right):\\n return (\\n pd.merge(left, right, on=[\\\"producto_id\\\", \\\"id_prov\\\", \\\"provincia\\\"], how=\\\"inner\\\").dropna()\\n # .drop([\\\"sucursal_id_x\\\", \\\"sucursal_id_y\\\"], axis=1)\\n )\\n\\n precios = functools.reduce(merge, precios)\\n # eliminar columnas repetidas\\n precios = precios.loc[:, ~precios.columns.duplicated()]\\n precios = precios.rename(columns={\\\"cadena_x\\\": \\\"cadena\\\"})\\n\\n # elimina columnas innecesarias.\\n # basicamente sucursal_* y cadena_y (queda cadena, renombrada antes)\\n for c in set(precios.columns):\\n if c.startswith((\\\"sucursal\\\", \\\"cadena_\\\")):\\n precios.drop(c, axis=1, inplace=True)\\n\\n # cruzamos datos con productos\\n precios = pd.merge(\\n precios,\\n productos[[\\\"id\\\", \\\"marca\\\", \\\"nombre\\\", \\\"categoria1\\\", \\\"categoria2\\\", \\\"categoria3\\\"]],\\n left_on=\\\"producto_id\\\",\\n right_on=\\\"id\\\",\\n ).drop([\\\"id\\\", \\\"id_prov\\\"], axis=1)\\n\\n precios.sort_index(axis=1, inplace=True)\\n\\n # obtenemos el periodo maximo inicio vs fin (asumiendo columnas ordenadas)\\n inicio, *_, fin = [c for c in precios.columns if c.startswith(\\\"precio_\\\")]\\n\\n precios[\\\"variacion\\\"] = precios[fin] - precios[inicio]\\n precios[\\\"variacion_relativa\\\"] = precios[\\\"variacion\\\"] / precios[inicio] * 100\\n return precios\",\n \"def __init__(self):\\r\\n super(Sistema, self).__init__()\\r\\n\\r\\n self.datos = []\\r\\n\\r\\n self.productos = [\\\"Harina de Soya - Hi Pro/Pellet de Soya\\\", \\\"Harina de Soya - Full Fat\\\", \\\"Torta de Soya\\\",\\r\\n \\\"Torta de Girasol\\\", \\\"Aceite de Soya\\\", \\\"Grano de Soya\\\",\\r\\n \\\"Azucar\\\", \\\"Fierro\\\", \\\"Contenedor 20\\\", \\\"Contenedor 40\\\"]\\r\\n\\r\\n self.colas_espera_transbordo = {\\r\\n \\\"Harina de Soya - Hi Pro/Pellet de Soya\\\":\\r\\n {\\\"Descarga\\\": [], \\\"Carga\\\": []},\\r\\n \\\"Harina de Soya - Full Fat\\\":\\r\\n {\\\"Descarga\\\": [], \\\"Carga\\\": []},\\r\\n \\\"Torta de Soya\\\":\\r\\n {\\\"Descarga\\\": [], \\\"Carga\\\": []},\\r\\n \\\"Torta de Girasol\\\":\\r\\n {\\\"Descarga\\\": [], \\\"Carga\\\": []},\\r\\n \\\"Grano de Soya\\\":\\r\\n {\\\"Descarga\\\": [], \\\"Carga\\\": []},\\r\\n \\\"Azucar\\\":\\r\\n {\\\"Descarga\\\": [], \\\"Carga\\\": []},\\r\\n \\\"Fierro\\\":\\r\\n {\\\"Descarga\\\": [], \\\"Carga\\\": []}\\r\\n }\\r\\n\\r\\n self.camiones_en_sistema = []\\r\\n self.capacidad_sistema = 20\\r\\n\\r\\n # Definición de Recursos de Atención\\r\\n horarios = {\\r\\n \\\"horario 1\\\": {\\\"L-V\\\": {\\\"Ingreso\\\": 7.5, \\\"I. Descanso\\\": 13.0, \\\"F. Descanso\\\": 14.0, \\\"Salida\\\": 16.5},\\r\\n \\\"SAB\\\": {\\\"Ingreso\\\": 8.5, \\\"Salida\\\": 12.5}},\\r\\n \\\"horario 2\\\": {\\\"L-V\\\": {\\\"Ingreso\\\": 8.0, \\\"I. Descanso\\\": 12.0, \\\"F. Descanso\\\": 13.0, \\\"Salida\\\": 17.0},\\r\\n \\\"SAB\\\": {\\\"Ingreso\\\": 8.5, \\\"Salida\\\": 12.5}}\\r\\n }\\r\\n\\r\\n self.recursos_atencion = {\\r\\n \\\"Ventanilla Recepcion\\\":\\r\\n Recurso(self, \\\"Ventanilla Recepcion\\\",\\r\\n horarios[\\\"horario 1\\\"],\\r\\n capacity=1),\\r\\n \\\"Ventanilla Despacho\\\":\\r\\n Recurso(self, \\\"Ventanilla Despacho\\\",\\r\\n horarios[\\\"horario 1\\\"],\\r\\n capacity=1),\\r\\n \\\"Balanza 2\\\":\\r\\n Recurso(self, \\\"Balanza 2\\\",\\r\\n horarios[\\\"horario 1\\\"],\\r\\n capacity=1),\\r\\n \\\"Estacion Volcadora\\\":\\r\\n Recurso(self, \\\"Estacion Volcadora\\\",\\r\\n horarios[\\\"horario 2\\\"],\\r\\n capacity=1),\\r\\n \\\"Estacion Tolva/Balanza 3\\\":\\r\\n Recurso(self, \\\"Estacion Tolva/Balanza 3\\\",\\r\\n horarios[\\\"horario 2\\\"],\\r\\n capacity=1),\\r\\n \\\"Pala Mecanica\\\":\\r\\n Recurso(self, \\\"Pala Mecanica\\\",\\r\\n horarios[\\\"horario 2\\\"],\\r\\n capacity=1),\\r\\n \\\"Cuadrilla de Estibaje\\\":\\r\\n Recurso(self, \\\"Cuadrillas de Estibaje\\\",\\r\\n horarios[\\\"horario 2\\\"],\\r\\n capacity=3),\\r\\n \\\"Cabina de Recepcion - T1\\\":\\r\\n Recurso(self, \\\"Cabina de Recepcion - T1\\\",\\r\\n horarios[\\\"horario 2\\\"],\\r\\n capacity=1),\\r\\n \\\"Cabina de Despacho - T1\\\":\\r\\n Recurso(self, \\\"Cabina de Despacho - T1\\\",\\r\\n horarios[\\\"horario 2\\\"],\\r\\n capacity=1),\\r\\n \\\"Cabina de Recepcion - T2\\\":\\r\\n Recurso(self, \\\"Cabina de Recepcion - T2\\\",\\r\\n horarios[\\\"horario 2\\\"],\\r\\n capacity=1),\\r\\n \\\"Cabina de Despacho - T2\\\":\\r\\n Recurso(self, \\\"Cabina de Despacho - T2\\\",\\r\\n horarios[\\\"horario 2\\\"],\\r\\n capacity=1),\\r\\n \\\"Grua\\\":\\r\\n Recurso(self, \\\"Grua\\\",\\r\\n horarios[\\\"horario 2\\\"],\\r\\n capacity=1)}\\r\\n\\r\\n # Definición de medios de almacenamiento\\r\\n niv_tolva = {\\r\\n \\\"Harina de Soya - Hi Pro/Pellet de Soya\\\": 0\\r\\n }\\r\\n niv_almacen_1 = {\\r\\n \\\"Harina de Soya - Hi Pro/Pellet de Soya\\\": 500\\r\\n }\\r\\n niv_almacen_2 = {\\r\\n \\\"Harina de Soya - Full Fat\\\": 100,\\r\\n \\\"Torta de Soya\\\": 100,\\r\\n \\\"Torta de Girasol\\\": 100,\\r\\n \\\"Azucar\\\": 100\\r\\n }\\r\\n niv_almacen_ext = {\\r\\n \\\"Grano de Soya\\\": 0\\r\\n }\\r\\n niv_tanque_1 = {\\r\\n \\\"Aceite de Soya\\\": 0\\r\\n }\\r\\n niv_tanque_2 = {\\r\\n \\\"Aceite de Soya\\\": 0\\r\\n }\\r\\n niv_patio_cont = {\\r\\n \\\"Contenedor 20\\\": 0,\\r\\n \\\"Contenedor 40\\\": 0\\r\\n }\\r\\n self.medios_almacenamiento = {\\r\\n \\\"Tolva\\\":\\r\\n MedioDeAlmacenamiento(self, \\\"Tolva\\\", 2, niv_tolva, 400),\\r\\n \\\"Almacen 1\\\":\\r\\n MedioDeAlmacenamiento(self, \\\"Almacen 1\\\", 3, niv_almacen_1, 2500),\\r\\n \\\"Almacen 2\\\":\\r\\n MedioDeAlmacenamiento(self, \\\"Almacen 2\\\", 1, niv_almacen_2, 1500),\\r\\n \\\"Almacen Ext\\\":\\r\\n MedioDeAlmacenamiento(self, \\\"Almacen Ext\\\", 3, niv_almacen_ext, 1500),\\r\\n \\\"Tanque 1\\\":\\r\\n MedioDeAlmacenamiento(self, \\\"Tanque 1\\\", 2, niv_tanque_1, 400),\\r\\n \\\"Tanque 2\\\":\\r\\n MedioDeAlmacenamiento(self, \\\"Tanque 2\\\", 2, niv_tanque_2, 500),\\r\\n \\\"Patio de Contenedores\\\":\\r\\n MedioDeAlmacenamiento(self, \\\"Patio de Contenedores\\\", 1, niv_patio_cont, 2500)}\\r\\n\\r\\n # Definicion de Operaciones # TODO Ingresar datos reales\\r\\n operaciones_manipuleo = {\\r\\n \\\"Descarga con volcadora\\\":\\r\\n OperacionManipuleo(\\\"Descarga con volcadora\\\",\\r\\n self.recursos_atencion[\\\"Estacion Volcadora\\\"],\\r\\n \\\"uniforme\\\", [14, 20],\\r\\n self.datos),\\r\\n \\\"Descarga a pulso - Sacos\\\":\\r\\n OperacionManipuleo(\\\"Descarga a pulso - Sacos\\\",\\r\\n self.recursos_atencion[\\\"Cuadrilla de Estibaje\\\"],\\r\\n \\\"uniforme\\\", [30, 45],\\r\\n self.datos),\\r\\n \\\"Descarga a pulso - Granos\\\":\\r\\n OperacionManipuleo(\\\"Descarga a pulso - Granos\\\",\\r\\n self.recursos_atencion[\\\"Cuadrilla de Estibaje\\\"],\\r\\n \\\"uniforme\\\", [40, 60],\\r\\n self.datos),\\r\\n \\\"Descarga con bombas electricas - T1\\\":\\r\\n OperacionManipuleo(\\\"Descarga con bombas electricas\\\",\\r\\n self.recursos_atencion[\\\"Cabina de Recepcion - T1\\\"],\\r\\n \\\"uniforme\\\", [40, 50],\\r\\n self.datos),\\r\\n \\\"Descarga con bombas electricas - T2\\\":\\r\\n OperacionManipuleo(\\\"Descarga con bombas electricas\\\",\\r\\n self.recursos_atencion[\\\"Cabina de Recepcion - T2\\\"],\\r\\n \\\"uniforme\\\", [40, 50],\\r\\n self.datos),\\r\\n \\\"Descarga con grua\\\":\\r\\n OperacionManipuleo(\\\"Descarga con grua\\\",\\r\\n self.recursos_atencion[\\\"Grua\\\"],\\r\\n \\\"uniforme\\\", [15, 20],\\r\\n self.datos),\\r\\n \\\"Carga con tolva\\\":\\r\\n OperacionManipuleo(\\\"Carga con tolva\\\",\\r\\n self.recursos_atencion[\\\"Estacion Tolva/Balanza 3\\\"],\\r\\n \\\"uniforme\\\", [14, 20],\\r\\n self.datos),\\r\\n \\\"Carga con pala mecanica\\\":\\r\\n OperacionManipuleo(\\\"Carga con pala mecanica\\\",\\r\\n self.recursos_atencion[\\\"Pala Mecanica\\\"],\\r\\n \\\"uniforme\\\", [18, 30],\\r\\n self.datos),\\r\\n \\\"Carga a pulso - Sacos\\\":\\r\\n OperacionManipuleo(\\\"Carga a pulso - Sacos\\\",\\r\\n self.recursos_atencion[\\\"Cuadrilla de Estibaje\\\"],\\r\\n \\\"uniforme\\\", [45, 70],\\r\\n self.datos),\\r\\n \\\"Carga a pulso - Granos\\\":\\r\\n OperacionManipuleo(\\\"Carga a pulso - Granos\\\",\\r\\n self.recursos_atencion[\\\"Cuadrilla de Estibaje\\\"],\\r\\n \\\"uniforme\\\", [60, 90],\\r\\n self.datos),\\r\\n \\\"Carga con bombas electricas - T1\\\":\\r\\n OperacionManipuleo(\\\"Carga con bombas electricas\\\",\\r\\n self.recursos_atencion[\\\"Cabina de Despacho - T1\\\"],\\r\\n \\\"uniforme\\\", [45, 60],\\r\\n self.datos),\\r\\n \\\"Carga con bombas electricas - T2\\\":\\r\\n OperacionManipuleo(\\\"Carga con bombas electricas\\\",\\r\\n self.recursos_atencion[\\\"Cabina de Despacho - T2\\\"],\\r\\n \\\"uniforme\\\", [45, 60],\\r\\n self.datos),\\r\\n \\\"Carga con grua\\\":\\r\\n OperacionManipuleo(\\\"Carga con grua\\\",\\r\\n self.recursos_atencion[\\\"Grua\\\"],\\r\\n \\\"uniforme\\\", [15, 22],\\r\\n self.datos),\\r\\n \\\"Transbordo en sistema mecanizado (D)\\\":\\r\\n OperacionManipuleo(\\\"Transbordo en sistema mecanizado (D)\\\",\\r\\n self.recursos_atencion[\\\"Estacion Volcadora\\\"],\\r\\n \\\"uniforme\\\", [14, 25],\\r\\n self.datos),\\r\\n \\\"Transbordo en sistema mecanizado (C)\\\":\\r\\n OperacionManipuleo(\\\"Transbordo en sistema mecanizado (C)\\\",\\r\\n self.recursos_atencion[\\\"Estacion Tolva/Balanza 3\\\"],\\r\\n \\\"uniforme\\\", [14, 25],\\r\\n self.datos),\\r\\n \\\"Transbordo a pulso - Sacos\\\":\\r\\n OperacionManipuleo(\\\"Transbordo a pulso - Sacos\\\",\\r\\n self.recursos_atencion[\\\"Cuadrilla de Estibaje\\\"],\\r\\n \\\"uniforme\\\", [40, 60],\\r\\n self.datos),\\r\\n \\\"Transbordo a pulso - Granos\\\":\\r\\n OperacionManipuleo(\\\"Transbordo a pulso - Sacos\\\",\\r\\n self.recursos_atencion[\\\"Cuadrilla de Estibaje\\\"],\\r\\n \\\"uniforme\\\", [45, 65],\\r\\n self.datos),\\r\\n \\\"Transbordo con grua\\\":\\r\\n OperacionManipuleo(\\\"Transbordo con grua\\\",\\r\\n self.recursos_atencion[\\\"Grua\\\"],\\r\\n \\\"uniforme\\\", [15, 20],\\r\\n self.datos)}\\r\\n\\r\\n operaciones_complementarias = {\\r\\n \\\"Atencion recepcion 1\\\":\\r\\n Operacion(\\\"Atencion recepcion 1\\\",\\r\\n self.recursos_atencion[\\\"Ventanilla Recepcion\\\"],\\r\\n \\\"uniforme\\\", [2, 10],\\r\\n self.datos),\\r\\n \\\"Atencion despacho 1\\\":\\r\\n Operacion(\\\"Atencion despacho 1\\\",\\r\\n self.recursos_atencion[\\\"Ventanilla Despacho\\\"],\\r\\n \\\"uniforme\\\", [2, 10],\\r\\n self.datos),\\r\\n \\\"Primer pesaje\\\":\\r\\n Operacion(\\\"Primer pesaje\\\",\\r\\n self.recursos_atencion[\\\"Balanza 2\\\"],\\r\\n \\\"uniforme\\\", [3, 6],\\r\\n self.datos),\\r\\n \\\"Segundo pesaje\\\":\\r\\n Operacion(\\\"Segundo pesaje\\\",\\r\\n self.recursos_atencion[\\\"Balanza 2\\\"],\\r\\n \\\"uniforme\\\", [3, 6],\\r\\n self.datos),\\r\\n \\\"Primer pesaje - B3\\\":\\r\\n Operacion(\\\"Primer pesaje - B3\\\",\\r\\n self.recursos_atencion[\\\"Estacion Tolva/Balanza 3\\\"],\\r\\n \\\"uniforme\\\", [3, 6],\\r\\n self.datos),\\r\\n \\\"Segundo pesaje - B3\\\":\\r\\n Operacion(\\\"Segundo pesaje -B3\\\",\\r\\n self.recursos_atencion[\\\"Estacion Tolva/Balanza 3\\\"],\\r\\n \\\"uniforme\\\", [3, 6],\\r\\n self.datos),\\r\\n \\\"Atencion recepcion 2\\\":\\r\\n Operacion(\\\"Atencion recepcion 2\\\",\\r\\n self.recursos_atencion[\\\"Ventanilla Recepcion\\\"],\\r\\n \\\"uniforme\\\", [4, 8],\\r\\n self.datos),\\r\\n \\\"Atencion despacho 2\\\":\\r\\n Operacion(\\\"Atencion despacho 2\\\",\\r\\n self.recursos_atencion[\\\"Ventanilla Despacho\\\"],\\r\\n \\\"uniforme\\\", [2, 5],\\r\\n self.datos)}\\r\\n\\r\\n # Diccionario general de operaciones\\r\\n self.operaciones = {\\r\\n \\\"Operaciones manipuleo\\\":\\r\\n operaciones_manipuleo,\\r\\n \\\"Operaciones complementarias\\\":\\r\\n operaciones_complementarias}\",\n \"def import_brands(self):\\n\\n brands = self.product_infos['brands']\\n\\n for product_brand in brands:\\n try:\\n brand = Brands.objects.get(\\n name=product_brand\\n )\\n except Brands.DoesNotExist:\\n super().new_entry()\\n brand = Brands.objects.create(\\n name=product_brand\\n )\\n except:\\n pass\\n try:\\n ProdBrand.objects.get(\\n product=self.product_object,\\n brand=brand\\n )\\n except ProdBrand.DoesNotExist:\\n super().new_entry()\\n ProdBrand.objects.create(\\n product=self.product_object,\\n brand=brand\\n )\\n except:\\n pass\\n\\n return brands\",\n \"def get_substitutable_products(self):\\n\\n while True:\\n clean_terminal()\\n products = self.database_manager.get_substitutable_products()\\n\\n if not products:\\n cprint('Aucun resultat.', 'red')\\n return\\n\\n print('Choisir un produit :')\\n\\n range_param = self.print_products_line(products)\\n\\n reply = self.ask_with_input('Choisir un numéro'\\n ' (tapez \\\"quit\\\" pour quitter) : ',\\n range_param, ('quit',))\\n\\n if reply == 'quit':\\n break\\n\\n product_number = int(reply)\\n product_number -= 1\\n product = products[product_number]\\n\\n operateur_result = []\\n self.database_manager.fill_list_with_product_and_substitutes(\\n product.get('id'), operateur_result)\\n # print product and his substitutes in the terminal\\n self.printer(operateur_result)\\n\\n self.ask_with_input('Ok ? (y) ', -1, ('y',))\",\n \"def cargarObras(self):\\n self.cargarObjetos(self.tableOs,\\n ObraSocialModel.buscarTodos(\\\"razon_social\\\", self.sesion).all(),\\n (\\\"razon_social\\\", \\\"cuit\\\", \\\"direccion\\\")\\n )\",\n \"def create_products():\\n try:\\n # Instantiate the class and separate objects into two lists\\n challenge = Challenge()\\n # Get all products\\n product_base = challenge.get_products(\\\"product_groups.json\\\")\\n # Divide the products into independent (no parent) and dependent (with parents)\\n independent, dependent = challenge.filter_products(product_base)\\n if not challenge.save_independent_products(independent):\\n Exception(\\\"Function save_independent_products() couldn't complete\\\")\\n\\n if not challenge.save_dependent_products(\\n dependent, product_base, len(independent)\\n ):\\n raise Exception(\\\"Function save_dependent_products() couldn't complete\\\")\\n\\n except Exception as err:\\n logging.error(f\\\"[ERROR] While processing the objects. Traceback: {err}\\\")\\n return False\\n else:\\n return True\",\n \"def select_todos_registros(nome_tabela: str) -> list:\\n query = f'SELECT * FROM {nome_tabela};'\\n\\n lista_registros = banco_operacoes(query)\\n\\n return lista_registros\",\n \"def return_items(self):\\n cur = self.cursor\\n cur.execute(f\\\"SELECT * FROM {self.product_name}\\\")\\n products = cur.fetchall()\\n return products\",\n \"def geneLRB(fech, emplead, product, cantPro):\\n listaF = []\\n linea1 = \\\"\\\\nRegistro de venta:\\\\n\\\"\\n linea2 = f\\\"Fecha de ventas: {fech}\\\\n\\\"\\n linea3 = f\\\"Empleado: {emplead}\\\\n\\\"\\n if product == 0:\\n pro = \\\"Disney Cruella De Vil Diamond Funko Pop Exclusivo\\\"\\n elif product == 1:\\n pro = \\\"Funko Pop Board Games Silver Mr. Monopoly - Figura de vinilo\\\"\\n elif product == 2:\\n pro = \\\"Funko Pop Daenerys Targaryen 59 Game of Thrones\\\"\\n elif product == 3:\\n pro = \\\"Funko Pop Flocked Iceman 504 Marvel 80 Years\\\"\\n elif product == 4:\\n pro = \\\"Funko Pop Glowing Mr Burns 1162 The Simpsons\\\"\\n elif product == 5:\\n pro = \\\"Funko Pop Grogu Con Galleta 464 Star Wars The Mandalorian\\\"\\n elif product == 6:\\n pro = \\\"Funko Pop Keychain Pogo The Umbrella Academy\\\"\\n elif product == 7:\\n pro = \\\"Funko Pop Keychain The Flash DC The Flash\\\"\\n elif product == 8:\\n pro = \\\"Funko Pop Marvel: Avengers - Wanda Maximoff Exclusivo Disney Collector Zone\\\"\\n elif product == 9:\\n pro = \\\"Funko Pop Venom 888 Marvel Venom\\\"\\n elif product == 10:\\n pro = \\\"Llavero Funko Pop Zoey Fortnite\\\"\\n elif product == 11:\\n pro = \\\"Lucky The Leprechaun Edición Limitada\\\"\\n linea4 = f\\\"Producto vendido(s): {pro}\\\\n\\\"\\n linea5 = f\\\"Cantidad: {cantPro}\\\\n\\\"\\n listaF.append(linea1)\\n listaF.append(linea2)\\n listaF.append(linea3)\\n listaF.append(linea4)\\n listaF.append(linea5)\\n with open(\\\"registroVentas.txt\\\", \\\"a\\\") as regVen:\\n regVen.writelines(listaF)\",\n \"def __iter__(self):\\n ids_productos = self.carro.keys()\\n #obtiene los objetos producto y los agrega al carro\\n productos = Producto.objects.filter(id__in=ids_productos)\\n for producto in productos:\\n self.carro[str(producto.id)]['producto'] = producto\\n\\n for item in self.carro.values():\\n item['precio']=Decimal(item['precio'])\\n item['precio_total'] = item['precio']*item['cantidad']\\n yield item\",\n \"def modificarProductosVenta(self, listaProductos):\\n\\t#modificar -- [idventa, codProducto, numeroitem, valorUnitario, cantidad, IVA]\\n\\tmodificar = listaProductos[0]\\n\\t#eliminar -- [idventa, idProducto, numeroitem]\\n\\teliminar = listaProductos[1]\\n\\t# primero se modifican los productos\\n\\tfor i in modificar:\\n\\t try:\\n self.conexion.ejecutarSQL(\\\"\\\"\\\"update ProductosXVentas\\n set valor_unitario=%s,\\n cantidad=%s,\\n IVA=%s,\\n valor_total=%s*%s\\n where id_Venta=%s\\n and codigo_Producto = '%s'\\n and numero_item=%s\\\"\\\"\\\"%(i[3], i[4], i[5], i[4], i[3], i[0], i[1], i[2]))\\n\\t except Exception, e:\\n\\t\\tprint \\\"modificarProductosVenta excepcion: \\\", e\\n\\t\\tself.conexion.rollback()\\n\\t\\treturn False\\n\\t# Ahora se eliminan los Productos\\n\\tfor i in eliminar:\\n\\t try:\\n\\t\\tself.conexion.ejecutarSQL(\\\"delete from ProductosXVentas where id_Venta=%s and codigo_Producto = '%s' and numero_item=%s\\\"%(i[0],i[1],i[2]))\\n\\t except Exception, e:\\n\\t\\tprint \\\"modificarProductosVenta --> Eliminar excepcion: \\\", e\\n\\t\\tself.conexion.rollback()\\n\\t\\treturn False\\n\\t \\n\\t#Se actualiza el kardex para las ventas modificadas como para las ventas eliminadas.\\n\\t\\n\\t#Para las ventas modificadas\\n\\t# PENDIENTE REVISAR SI HUBO CAMBIO DE VALOR UNITARIO\\n\\ttry:\\n\\t #modificar -- [idventa,idProducto,numeroitem,cantidad,cantidad_anterior]\\n\\t for i in modificar:\\n\\t\\tcantActual = float(i[3])\\n\\t\\tcantAnterior = float(i[4])\\n\\t\\tif cantActual == cantAnterior: #S'olo se actualiza el kardex si hubo cambio\\n\\t\\t continue\\n\\t\\telse:\\n\\t\\t codigoProducto = i[1]\\n\\t\\t info = self.getKardexProducto(codigoProducto)\\n\\t\\t #info --> saldo_valor, saldo_cantidad, costo_unitario, valor_total, detalle\\n\\t\\t info = info[-1]\\n\\t\\t if cantActual < cantAnterior: # Si hizo una devoluci'on en la venta (el cliente me devolvi'o cant productos)\\n\\t\\t\\tcant = cantAnterior - cantActual\\n\\t\\t\\tdetalle = 'Compra'\\n\\t\\t\\tvalorUnitario = float(info[2])\\n\\t\\t\\tvalorTotal = valorUnitario*cant\\n\\t\\t\\tsaldoCantidad = float(info[1])+cant\\n\\t\\t\\tsaldoValor = float(info[0])+valorTotal\\n\\t\\t else:# Si hizo una adici'on a la venta (le tengo que vender cant productos al cliente)\\n\\t\\t\\tcant = cantActual - cantAnterior\\n\\t\\t\\tdetalle = 'Venta'\\n\\t\\t\\tvalorUnitario = float(info[2])\\n\\t\\t\\tvalorTotal = valorUnitario*cant\\n\\t\\t\\tsaldoCantidad = float(info[1])-cant\\n\\t\\t\\tsaldoValor = float(info[0])-valorTotal\\n\\t\\t costoUnitario = saldoValor/float(saldoCantidad)\\n\\t\\t self.conexion.ejecutarSQL(\\\"\\\"\\\"\\n\\t\\t insert into Kardex (codigo_Producto, fecha, hora, detalle,cantidad, valor_total,saldo_cantidad, \\n\\t\\t saldo_valor, valor_unitario,costo_unitario) values \\n\\t\\t ('%s',DATE('now','localtime'),TIME('now','localtime'),'%s',%s,%s,%s,%s,%s,%s )\\\"\\\"\\\"%(codigoProducto,\\n\\t\\t detalle,cant,valorTotal,saldoCantidad,\\n\\t\\t saldoValor,valorUnitario,costoUnitario))\\n\\t self.conexion.commit()\\n\\t return True\\n\\texcept:\\n\\t print \\\"modificarProductosVenta --> Actualizar kardex (ventas modificadas) excepcion: \\\", e\\n\\t self.conexion.rollback()\\n\\t return False\",\n \"def cargar_obras(self):\\n self.cargarObjetos(self.tableObra,\\n ObraSocialModel.buscarTodos(\\\"razon_social\\\", self.sesion).all(),\\n (\\\"razon_social\\\", \\\"cuit\\\", \\\"direccion\\\")\\n )\",\n \"def get_all_product():\\r\\n\\r\\n with mysql.db_session() as session:\\r\\n product = session.query(Product).all()\\r\\n\\r\\n if not product:\\r\\n return response.create_not_found_response()\\r\\n\\r\\n response_data = [each.to_dict() for each in product]\\r\\n\\r\\n return response.Response(message=response_data)\",\n \"def search_prod(self):\\n\\n # Connection to the SQL database\\n self.db_connect = pymysql.connect(\\\"localhost\\\",\\\"root\\\",\\\"\\\",\\\"mydb\\\")\\n db = self.db_connect.cursor()\\n \\n # Category selection display loop\\n choice = 0\\n while choice is 0:\\n choice = input(\\\"\\\\nVoulez vous afficher la liste de catégories ? O/n \\\")\\n if choice.upper() == \\\"N\\\":\\n print(\\\"\\\\nRetour au menu principal\\\")\\n break\\n elif choice.upper() == \\\"O\\\":\\n choice_cat = True\\n print(\\\"\\\\n Liste des catégories:\\\\n\\\")\\n # Defining the query to return the list of categories\\n show_list_cat = \\\"SELECT category_id, category_name \\\\\\n FROM `mydb`.`Categories`\\\"\\n # Command to execute the request\\n db.execute(show_list_cat)\\n # Variable assignment loop\\n for ligne in db.fetchall():\\n list_id_cat = ligne[0]\\n list_name_cat = ligne[1]\\n # View category list\\n print(list_id_cat,'-', list_name_cat)\\n\\n # Product Selection Loop\\n choice_cat = 0\\n while choice_cat == 0:\\n choice_cat = input(\\\"\\\\nSélectionner une catégorie de produit (Q pour annuler): \\\")\\n\\n if choice_cat.upper() == \\\"Q\\\":\\n choice = False\\n break\\n\\n elif choice_cat.isdigit() == False or int(choice_cat) >= 29 \\\\\\n or int(choice_cat) == 0:\\n print(\\\"\\\\nMerci de bien vouloir entrer un chiffre compris entre 1 et 28\\\\n\\\")\\n time.sleep(2)\\n choice_cat = 0\\n \\n # Definition of the request to return \\n # the list of products of the selected category\\n show_list_prod_select = f\\\"SELECT product_id, \\\\\\n product_name, \\\\\\n product_nutri \\\\\\n FROM `mydb`.`Products` \\\\\\n WHERE Categories_category_id = {choice_cat}\\\"\\n \\n # Execution command of the SQL query\\n db.execute(show_list_prod_select)\\n for ligne in db.fetchall():\\n list_id_prod = ligne[0]\\n list_name_prod = ligne[1]\\n nutriscore_prod = ligne[2]\\n # Uppercase display\\n nutriscore_prod = nutriscore_prod.upper()\\n # Display of the list of products and nutriscores\\n print(list_id_prod,\\\"-\\\", list_name_prod, \\\\\\n \\\"- Nutriscore:\\\",nutriscore_prod)\\n \\n # SQL query to determine the maximum and minimum value\\n # the product ID for the selected category\\n vmax = f\\\"SELECT MAX(product_id) \\\\\\n FROM `mydb`.`Products` \\\\\\n WHERE Categories_category_id = {choice_cat}\\\"\\n db.execute(vmax)\\n for maxv in db.fetchall():\\n max_id_prod = maxv[0]\\n\\n vmin = f\\\"SELECT MIN(product_id) \\\\\\n FROM `mydb`.`Products` \\\\\\n WHERE Categories_category_id = {choice_cat}\\\"\\n db.execute(vmin)\\n for minv in db.fetchall():\\n min_id_prod = minv[0]\\n \\n # Display loop for selecting the product to substitute\\n choice_sub = 0\\n while choice_sub == 0:\\n choice_sub = input(\\\"\\\\nSélectionner un produit à substituer (Q pour annuler): \\\")\\n\\n if choice_sub.upper() == \\\"Q\\\":\\n choice = 0\\n break\\n # Selects the product selection choice for the selected category\\n elif choice_sub.isdigit() == False or \\\\\\n int(choice_sub) > max_id_prod or \\\\\\n int(choice_sub) < min_id_prod or int(choice_sub) == 0:\\n print(\\\"\\\\nMerci de bien vouloir entrer un chiffre compris dans la liste des produits\\\")\\n time.sleep(2)\\n choice_sub = 0\\n\\n # Definition of the request to return the list of\\n # Substitute products of the selected category\\n show_list_sub_select = f\\\"SELECT * FROM `mydb`.`Products`\\\\\\n WHERE product_id = {choice_sub}\\\"\\n \\n # Execution command of the SQL query\\n db.execute(show_list_sub_select)\\n for ligne_sub in db.fetchall():\\n sub_name = ligne_sub[1]\\n sub_nutri = ligne_sub[3]\\n # Uppercase display\\n sub_nutri = sub_nutri.upper()\\n \\n # Definition of the request to return the product \\n # with the highest nutriscores of the selected category\\n list_best_nutri = f\\\"SELECT * FROM `mydb`.`Products`\\\\\\n WHERE Categories_category_id = {choice_cat} \\\\\\n AND product_nutri <= '{sub_nutri}' \\\\\\n ORDER BY product_nutri ASC\\\"\\n \\n db.execute(list_best_nutri)\\n result = db.fetchall()\\n best = (result)[0]\\n bestnutri = (best)[3]\\n # Uppercase display\\n bestnutri = bestnutri.upper()\\n # Case where the user chooses one of the products \\n # with the highest nutriscore of the category\\n if sub_nutri == bestnutri:\\n print(\\\"\\\\nLe produit sélectionné a le meilleur nutriscore de cette catégorie\\\")\\n print(\\\"Veuillez sélectionner un autre produit dans la liste\\\")\\n choice_sub = 0\\n # Case where the user selects a substitutable product \\n else:\\n best_id = (best)[0]\\n best_name = (best)[1]\\n best_nutriscore = (best)[3]\\n best_nutriscore = best_nutriscore.upper()\\n best_shop = (best)[2]\\n best_url = (best)[4]\\n # Returns the selected product and its substitute\\n print(\\\"\\\\nProduit sélectionné:\\\",sub_name,\\\\\\n \\\". Nutriscore:\\\",sub_nutri)\\n print(\\\"Substitue:\\\",best_name,\\\". Nutriscore:\\\",\\\\\\n best_nutriscore)\\n print(\\\"Magasin(s):\\\",best_shop)\\n print(\\\"Infos produit:\\\",best_url)\\n # Backup loop in the SQL database\\n save_choice1 = 0\\n while save_choice1 is 0:\\n save_choice1 = input(\\\"\\\\nSouhaitez-vous sauvegarder le produit dans la base ? O/n \\\")\\n if save_choice1.upper() == \\\"N\\\":\\n print(\\\"\\\\nRetour au menu principal\\\")\\n time.sleep(2)\\n break\\n elif save_choice1.upper() == \\\"O\\\":\\n # Insertion request in the \\\"Substitute\\\" table\\n save_sub = \\\"INSERT IGNORE INTO \\\\\\n `mydb`.`Substitute` \\\\\\n (save_product_id, save_product_sub_name) \\\\\\n VALUES ('{}','{}')\\\".format(best_id, sub_name)\\n db.execute(save_sub)\\n self.db_connect.commit()\\n print(\\\"\\\\nLe produit a bien été sauvegardé dans vos favoris\\\")\\n print(\\\"Retour au menu principal\\\")\\n time.sleep(2)\\n break\",\n \"def get_table(prod):\\n pgconn = get_dbconn(\\\"mesosite\\\")\\n cursor = pgconn.cursor()\\n xref = [1.0e20] * 256\\n cursor.execute(\\n \\\"SELECT id, filename_template, units, cf_long_name \\\"\\n \\\"from iemrasters where name = %s\\\",\\n (prod,),\\n )\\n (rid, template, units, long_name) = cursor.fetchone()\\n cursor.execute(\\n \\\"\\\"\\\"\\n SELECT coloridx, value from iemrasters_lookup\\n WHERE iemraster_id = %s and value is not null\\n ORDER by coloridx ASC\\n \\\"\\\"\\\",\\n (rid,),\\n )\\n for row in cursor:\\n xref[row[0]] = row[1]\\n return np.array(xref), template, units, long_name\",\n \"def load_products():\\n\\n for i, row in enumerate(open(\\\"seed_data/category.product\\\")):\\n row = row.rstrip()\\n name = row.split(\\\"|\\\")\\n product_category = ProductCategoryModel(name=name)\\n db.session.add(product_category)\\n\\n for i, row in enumerate(open(\\\"seed_data/product.product\\\")):\\n row = row.rstrip()\\n name, short_description, long_description, product_category_id, img_path_xs, img_path_sm, img_path_md, img_path_lg = row.split(\\\"|\\\")\\n product = ProductModel(name=name,\\n short_description=short_description,\\n long_description=long_description,\\n product_category_id=product_category_id,\\n img_path_xs=img_path_xs,\\n img_path_sm=img_path_sm,\\n img_path_md=img_path_md,\\n img_path_lg=img_path_lg)\\n db.session.add(product)\\n\\n for i, row in enumerate(open(\\\"seed_data/location.product\\\")):\\n row = row.rstrip()\\n name, description, address1, address2, city, state, zip_code, country, latitude, longitude, direction_url = row.split(\\\"|\\\")\\n location = LocationModel(name=name,\\n description=description,\\n address1=address1,\\n address2=address2,\\n city=city,\\n state=state,\\n zip_code=zip_code,\\n country=country,\\n latitude=latitude,\\n longitude=longitude,\\n direction_url=direction_url)\\n db.session.add(location)\\n\\n for i, row in enumerate(open(\\\"seed_data/location_product.product\\\")):\\n row = row.rstrip()\\n location_id, product_id, price, num_available = row.split(\\\"|\\\")\\n location_product = LocationProductModel(location_id=location_id,\\n product_id=product_id,\\n price=price,\\n num_available=num_available)\\n db.session.add(location_product)\\n\\n db.session.commit()\",\n \"def test_Product(self):\\n self.assertEquals(self.prod_1.pk, 1)\\n self.assertEquals(self.prod_1.ean, '3350033118072')\\n self.assertEquals(self.prod_1.name, 'test 1')\\n self.assertEquals(self.prod_1.nutriscore, 'u')\\n self.assertEquals(self.prod_1.category, 'cat 1')\",\n \"def create_base_testes(dbsession):\\n # print('Iniciando criação da base #############################')\\n # print('dbsession', dbsession)\\n base1 = BaseOrigem('alimentos_e_esportes')\\n dbsession.add(base1)\\n dbsession.commit()\\n risco1 = PadraoRisco('perigo', base1)\\n dbsession.add(risco1)\\n dbsession.commit()\\n param1 = ParametroRisco('alimento', 'teste1', risco1)\\n param2 = ParametroRisco('esporte', 'teste2', risco1)\\n dbsession.add(param1)\\n dbsession.add(param2)\\n dbsession.commit()\\n valor1 = ValorParametro('bacon', Filtro.igual, param1)\\n valor2 = ValorParametro('base jump', Filtro.igual, param2)\\n dbsession.add(valor1)\\n dbsession.add(valor2)\\n dbsession.commit()\\n visao1 = Visao('viagens', base1.id)\\n dbsession.add(visao1)\\n dbsession.commit()\\n tabela1 = Tabela('viagens', 'viagem', '', 0, visao1.id)\\n tabela2 = Tabela('alimentos', 'alimento', 'viagem', tabela1.id, visao1.id)\\n tabela3 = Tabela('esportes', 'esporte', 'viagem', tabela1.id, visao1.id)\\n dbsession.add(tabela1)\\n dbsession.add(tabela2)\\n dbsession.add(tabela3)\\n dbsession.commit()\\n # print('Base criada! ########################')\",\n \"def get_label():\\r\\n\\r\\n user = check_auth(request.headers, __name__)\\r\\n if user != True:\\r\\n return user\\r\\n user = authorize.get(request.headers.get('UserToken'))\\r\\n\\r\\n vozvrat = {}\\r\\n try:\\r\\n database = Database(config)\\r\\n except TypeError:\\r\\n vozvrat[\\\"messageError\\\"] = \\\"Нет подключения к БД\\\"\\r\\n return jsonify(vozvrat)\\r\\n\\r\\n vozvrat = []\\r\\n\\r\\n fields = [\\r\\n \\\"u.firstname\\\",\\r\\n \\\"u.lastname\\\",\\r\\n \\\"up.id\\\",\\r\\n \\\"up.name\\\",\\r\\n \\\"up.photo\\\",\\r\\n \\\"up.type\\\",\\r\\n \\\"up.method\\\",\\r\\n \\\"up.sale\\\",\\r\\n \\\"up.price\\\",\\r\\n \\\"c.name\\\",\\r\\n \\\"up.weight\\\",\\r\\n \\\"u2.name\\\",\\r\\n \\\"fp.id\\\",\\r\\n \\\"a.country\\\",\\r\\n \\\"a.city\\\",\\r\\n \\\"a.address\\\",\\r\\n \\\"a.lat\\\",\\r\\n \\\"a.lng\\\"\\r\\n ]\\r\\n\\r\\n query = sql.SQL(\\\"SELECT {} FROM users u \\\\\\r\\n RIGHT JOIN users_product up on u.id = up.user_id\\\\\\r\\n LEFT JOIN units u2 on up.unit_id = u2.id\\\\\\r\\n LEFT JOIN currencys c on up.currency_id = c.id\\\\\\r\\n LEFT JOIN favorit_products fp on u.id = fp.user_id\\\\\\r\\n LEFT JOIN address a on up.address_id = a.id\\\").format(\\r\\n sql.SQL(\\\",\\\").join(sql.Identifier(\\r\\n i.split('.')[0], i.split('.')[1]) for i in fields)\\r\\n )\\r\\n execute = database.select_data(query)\\r\\n if type(execute) != list:\\r\\n return execute\\r\\n\\r\\n data_append = {}\\r\\n for row in execute:\\r\\n for i in range(len(fields)):\\r\\n value = row[i]\\r\\n\\r\\n if fields[i] == \\\"up.id\\\":\\r\\n fields[i] = \\\"up.users_product_id\\\"\\r\\n if fields[i] == \\\"c.name\\\":\\r\\n fields[i] = \\\"c.currency\\\"\\r\\n if fields[i] == \\\"u2.name\\\":\\r\\n fields[i] = \\\"u2.unit\\\"\\r\\n if fields[i] == \\\"fp.id\\\":\\r\\n fields[i] = \\\"fp.is_favorit\\\"\\r\\n value = True if value != None else False\\r\\n\\r\\n data_append[fields[i].split('.')[1]] = value\\r\\n vozvrat.append(data_append)\\r\\n\\r\\n return jsonify(vozvrat)\",\n \"def get_products(self):\\n\\n lst = []\\n for product in self.products.findall('product'):\\n id = product.find('id').text\\n name = product.find('name').text\\n dispensary_id = product.find('dispensary_id').text\\n dispensary_name = product.find('dispensary_name').text\\n canabis_brand = product.find('canabis_brand').text\\n canabis_strain = product.find('canabis_strain').text\\n category = product.find('category').text\\n subcategory = product.find('subcategory').text\\n thc_level = product.find('thc_level').text\\n cbd_level = product.find('cbd_level').text\\n cbn_level = product.find('cbn_level').text\\n thc_level_type = product.find('thc_level_type').text\\n cbd_level_type = product.find('cbd_level_type').text\\n cbn_level_type = product.find('cbn_level_type').text\\n\\n description = product.find('description').text\\n created_at = product.find('created_at').text\\n updated_at = product.find('updated_at').text\\n\\n prices = []\\n urls = []\\n images = []\\n\\n for child in product:\\n if child.tag == 'prices':\\n for cost in child.findall('cost'):\\n prices.append(Price(cost.attrib['unit'], cost.text))\\n\\n if child.tag == 'urls':\\n admin = child.find('admin').text\\n public = child.find('public').text\\n urls.append(UrlInfo(admin, public))\\n\\n if child.tag == 'images':\\n for image in child.findall('image'):\\n images.append(Image(image.attrib['main'], image.text,))\\n\\n lst.append(Product(id, name, dispensary_id, dispensary_name,\\n canabis_brand, canabis_strain,\\n category, subcategory, thc_level, cbd_level,\\n cbn_level, thc_level_type, cbd_level_type,\\n cbn_level_type, prices, urls, images,\\n description, created_at, updated_at))\\n\\n return lst\",\n \"def existeProducto(self, cod):\\n return self.conexion.ejecutarSQL(\\\"select codigo from productos where codigo='%s'\\\"%(cod)) != []\",\n \"def _check_product(self):\\n\\n self.importable = False\\n abcde = string.ascii_uppercase[:5]\\n product_infos = self.retrieve_product_infos()\\n\\n if product_infos['product_code'] is not None:\\n try:\\n Products.objects.get(\\n code=product_infos['product_code']\\n )\\n except Products.DoesNotExist:\\n if (\\n product_infos['product_name'] is not None\\n and product_infos['product_code'] not in ProductImportation.codes\\n and product_infos['product_code'] is not None\\n and product_infos['product_url'] is not None\\n and product_infos['image_url'] is not None\\n and product_infos['quantity'] is not None\\n and product_infos['ingredients'] is not None\\n and product_infos['brands'] != []\\n and product_infos['stores'] != []\\n and product_infos['countries'] is not None\\n and product_infos['compare_to'] is not None\\n and product_infos['categories_hierarchy'] is not None\\n and product_infos['nutriscore'] in abcde\\n and all([product_infos[nutriment] >= 0 for nutriment in self.list_nutriments])\\n and Categories.objects.filter(name=product_infos['compare_to']).count() > 0\\n ):\\n self.name = product_infos['product_name']\\n self.product_infos = product_infos\\n self.code = product_infos['product_code']\\n ProductImportation.codes.append(self.code)\\n self.importable = True\\n\\n return self.importable\",\n \"def create_tables(self):\\n\\n # Uses methods from class DbAuth\\n cursor = self.connect.create_cursor()\\n cursor.execute(\\\"USE `dbPurBeurre`\\\")\\n\\n cursor.execute(\\n \\\"CREATE TABLE IF NOT EXISTS `dbPurBeurre`.`Categories` (\\\"\\n \\\" `num` INT UNSIGNED AUTO_INCREMENT,\\\"\\n \\\" `id` VARCHAR(80) NOT NULL UNIQUE,\\\"\\n \\\" `name` VARCHAR(80) NOT NULL,\\\"\\n \\\" `url` VARCHAR(255) NOT NULL,\\\"\\n \\\" `products` INT NULL,\\\"\\n \\\" PRIMARY KEY (`num`))\\\"\\n \\\" ENGINE = InnoDB\\\"\\n )\\n\\n cursor.execute(\\n \\\"CREATE TABLE IF NOT EXISTS `dbPurBeurre`.`Produits` (\\\"\\n \\\" `num` INT UNSIGNED AUTO_INCREMENT,\\\"\\n \\\" `id` VARCHAR(80) NOT NULL UNIQUE,\\\"\\n \\\" `product_name` VARCHAR(80) NOT NULL,\\\"\\n \\\" `nutrition_grade_fr` CHAR(1) NOT NULL,\\\"\\n \\\" `brands` VARCHAR(80) NULL,\\\"\\n \\\" `stores` VARCHAR(80) NOT NULL,\\\"\\n \\\" `url` VARCHAR(255) NOT NULL,\\\"\\n \\\" `watchlist` DATE NULL,\\\"\\n \\\" PRIMARY KEY (`num`, `id`))\\\"\\n \\\" ENGINE = InnoDB\\\"\\n )\\n\\n cursor.execute(\\n \\\"CREATE TABLE IF NOT EXISTS `dbPurBeurre`.`Asso_Prod_Cat` (\\\"\\n \\\" `id_categories` VARCHAR(80) NOT NULL,\\\"\\n \\\" `id_produits` VARCHAR(80) NOT NULL,\\\"\\n \\\" PRIMARY KEY (`id_categories`, `id_produits`),\\\"\\n \\\" CONSTRAINT `fk_id_categories`\\\"\\n \\\" FOREIGN KEY (`id_categories`)\\\"\\n \\\" REFERENCES `Categories` (`id`),\\\"\\n \\\" CONSTRAINT `fk_id_produits`\\\"\\n \\\" FOREIGN KEY (`id_produits`)\\\"\\n \\\" REFERENCES `Produits` (`id`))\\\"\\n \\\" ENGINE = InnoDB\\\"\\n )\",\n \"def ingresarVenta(self, total, tipoPago, idCliente, usuarioColaborador, listaProductos):\\n self._lockIngresarVenta.acquire()\\n try:\\n # agrupar totales vendidos de cada cod_barras\\n numVendidosXcod = dict()\\n # calcular subtotal, totalIVA basado en IVA y valorTotal de cada producto en lista. Subtotal=total-totalIVA\\n totalIVA = 0\\n for (cod_barras, desc, cantidad, valorUnitario, IVA, valorTotal) in listaProductos:\\n totalIVA += float(valorTotal) * float(IVA) / 100\\n if numVendidosXcod.has_key(cod_barras):\\n numVendidosXcod[cod_barras] += float(cantidad)\\n else:\\n numVendidosXcod[cod_barras] = float(cantidad)\\n # almacena subtotal en variable\\n subtotal = total - totalIVA \\n # revisar si existen las cantidades disponibles de cada codigo\\n for cod, cant in numVendidosXcod.iteritems():\\n cantidadDisponible = self.getInfoProducto(cod)[0][2]\\n if cant > cantidadDisponible:\\n return (False,\\\"Cantidades insuficientes del producto %s. Cantidad disponible: %s y Cantidad en Venta: %s\\\"%(cod,cantidadDisponible,cant),-1)\\n # obtener id_TipoPago dependiendo del tipo de pago\\n id_TipoPago = self.getIdTipoPago(tipoPago)\\n # obtener estado de la venta dependiendo del tipo de pago\\n estado = self.estadoVentaDadoTipoPago(id_TipoPago)\\n # ingresar la venta en Ventas \\n if estado == __PAGADA__:\\n self.conexion.ejecutarSQL(\\\"insert into Ventas (fecha,hora,subtotal,totalIVA,total,estado,usuario_Colaborador,id_Cliente,id_TipoPago,fechaPagoTotal,horaPagoTotal) values (DATE('now','localtime'),TIME('now','localtime'),%s,%s,%s,'%s','%s','%s',%s,DATE('now','localtime'),TIME('now','localtime'))\\\"%(0,0,0,estado,usuarioColaborador,idCliente,id_TipoPago))\\n else:\\n self.conexion.ejecutarSQL(\\\"insert into Ventas (fecha,hora,subtotal,totalIVA,total,estado,usuario_Colaborador,id_Cliente,id_TipoPago) values (DATE('now','localtime'),TIME('now','localtime'),%s,%s,%s,'%s','%s','%s',%s)\\\"%(0,0,0,estado,usuarioColaborador,idCliente,id_TipoPago))\\n # obtener id de última venta\\n id_Venta = self.getIdUltimaVenta()\\n # ingresar todos los productos de listaProductos en ProductosXVentas\\n numItem = 1\\n for (cod_barras, desc, cantidad, valorUnitario, IVA, valorTotal) in listaProductos:\\n self.conexion.ejecutarSQL(\\\"insert into ProductosXVentas (numero_item,codigo_producto,id_venta,usuario_Colaborador,cantidad,valor_unitario,IVA,valor_total) values (%s,'%s',%s,'%s',%s,%s,%s,%s)\\\"%(numItem,cod_barras,id_Venta,usuarioColaborador,cantidad,valorUnitario,IVA,valorTotal))\\n\\n\\t\\t#Por cada producto vendido, se debe actualizar la tabla kardex.\\n\\t\\tcantidadKardex = cantidad\\n\\t\\tidProductoKardex = cod_barras\\n\\t\\tsaldos = self.conexion.ejecutarSQL(\\\"\\\"\\\"select saldo_cantidad, saldo_valor, costo_unitario from kardex\\n where codigo_Producto='%s'\\n order by fecha and hora\\\"\\\"\\\"%(idProductoKardex))\\n\\t\\tif len(saldos) == 0:\\n\\t\\t v_unitarioKardex = valorUnitario\\n\\t\\t valor_TotalKardex = float(cantidadKardex)*float(v_unitarioKardex)\\n\\t\\t saldo_cantidadKardex = cantidadKardex\\n\\t\\t saldo_valorKardex = valor_TotalKardex\\n\\t\\telse:\\n\\t\\t v_unitarioKardex = saldos[len(saldos)-1][2]\\n\\t\\t valor_TotalKardex = float(cantidadKardex)*float(v_unitarioKardex)\\n\\t\\t saldo_cantidadKardex = saldos[len(saldos)-1][0]-float(cantidadKardex)\\n\\t\\t saldo_valorKardex = saldos[len(saldos)-1][1]-valor_TotalKardex\\n\\t\\tif float(saldo_cantidadKardex) != 0:\\n costo_unitarioKardex = saldo_valorKardex/float(saldo_cantidadKardex)\\n else:\\n costo_unitarioKardex = saldo_valorKardex\\n\\t\\ttry:\\n\\t\\t self.conexion.ejecutarSQL(\\\"\\\"\\\"insert into Kardex (codigo_Producto, fecha, hora, detalle,cantidad, valor_total,saldo_cantidad, saldo_valor, valor_unitario,costo_unitario)\\n values ('%s',DATE('now','localtime'),TIME('now','localtime'),'Venta',%s,%s,%s,%s,%s,%s )\\\"\\\"\\\"\\n %(idProductoKardex, cantidadKardex, valor_TotalKardex, saldo_cantidadKardex,\\n saldo_valorKardex, v_unitarioKardex, costo_unitarioKardex))\\n\\t\\texcept Exception, e:\\n\\t\\t print \\\"Kardex Venta: \\\", e\\n\\t\\t self.conexion.rollback()\\n\\t\\t return (False,str(e),-1)\\n\\t \\n numItem += 1 \\n # comprometer\\n\\t self.conexion.commit()\\n return (True,\\\"\\\",id_Venta)\\n except Exception, e:\\n print \\\"ingresarVenta excepcion: \\\", e\\n self.conexion.rollback()\\n return (False,str(e),-1)\\n finally:\\n self._lockIngresarVenta.release()\",\n \"def get_all_2(conn) -> str:\\n with conn.cursor() as cursor:\\n cursor.execute(\\\"\\\"\\\"select products.id, \\n products.name, \\n products.price, \\n products.image, \\n products.category_id, \\n product_categories.name\\n from products\\n inner join product_categories\\n on products.category_id=product_categories.id\\n where products.deleted=false order by id\\\"\\\"\\\")\\n try:\\n return cursor.fetchall()\\n except TypeError:\\n raise errors.StoreError\",\n \"def test_show_available(self):\\n database.import_data('csvs', 'product_data.csv', 'customer_data.csv', 'rentals_data.csv')\\n actual_available = database.show_available_products()\\n expected_available = {'prd001': {'description': 'TV', 'product_type': 'livingroom',\\n 'quantity_available': '3'},\\n 'prd002': {'description': 'Couch', 'product_type': 'livingroom',\\n 'quantity_available': '1'}}\\n self.assertEqual(actual_available, expected_available)\\n database.delete_database()\\n\\n database.import_data('csvs', 'produc_data.csv', 'customer_data.csv', 'rentals_data.csv')\\n database.delete_database()\",\n \"def get_all(self):\\n id_proyecto = unicode(request.url.split(\\\"/\\\")[-3])\\n fase_table_filler.init(\\\"\\\", id_proyecto)\\n tmpl_context.widget = self.table\\n value = self.fase_filler.get_value()\\n d = dict(value_list = value, model = \\\"Fases\\\", accion = \\\"./buscar\\\")\\n d[\\\"direccion_anterior\\\"] = \\\"../..\\\"\\n return d\",\n \"def scrapeProducts(conn):\\n query = \\\"\\\"\\\"SELECT c.name, f1.item, f1.product_name, f1.manufacturer,\\n f1.supplier, c.id\\n FROM form1_row AS f1\\n INNER JOIN country AS c\\n ON f1.country=c.id\\\"\\\"\\\"\\n c = conn.cursor()\\n c.execute(query)\\n results = []\\n for row in c:\\n result={}\\n result['country'] = row[0]\\n result['formulation'] = row[1].replace('*', '')\\n result['product'] = row[2]\\n result['manufacturer'] = row[3]\\n result['supplier'] = row[4] or None\\n result['country_id'] = row[5]\\n results.append(result)\\n return results\",\n \"def get_products(self) -> dict:\\n\\t\\tproducts = dict()\\n\\n\\t\\tdb = Database()\\n\\t\\tdb.create_connection(self._file_path)\\n\\t\\trows = db.get_products()\\n\\t\\tdb.close_connection()\\n\\n\\t\\tfor row in rows:\\n\\t\\t\\tif row[0] not in products:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tproducts[row[0]] = Product(row[0], row[1], row[2], row[3]) # code, price, lastupdate, currency\\n\\t\\t\\t\\texcept Exception as e: \\n\\t\\t\\t\\t\\t# IF the database was not correct parsed, the item will be discarted, \\n\\t\\t\\t\\t\\t# the event will be logged in the log file and the program will continue\\n\\t\\t\\t\\t\\tlogging.error(str(datetime.now())+': ' + e)\\n\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\treturn products\",\n \"def check_products(self, adi):\\r\\n results = []\\r\\n products = self.get_products(adi)\\r\\n for product in products[\\\"data\\\"][\\\"products\\\"]:\\r\\n print(\\\"Checking product '{}'... \\\".format(product[\\\"name\\\"]), end='')\\r\\n detail = self.get_product_detail(adi, product_id=product[\\\"productId\\\"], product_name=product[\\\"name\\\"])\\r\\n if self.rf.valid_product_detail(detail):\\r\\n print(\\\"Valid.\\\")\\r\\n result = \\\"Available\\\"\\r\\n else:\\r\\n print(\\\"INVALID.\\\")\\r\\n result = \\\"Not available\\\"\\r\\n results.append([product[\\\"name\\\"], result])\\r\\n return results\",\n \"def setup(self):\\n print(\\\"INIT DATA\\\")\\n\\n self.nutella = Product.objects.create(name=\\\"nutella\\\", nutriscore=\\\"e\\\")\",\n \"def save_product_bdd(self):\\n for element in config.CATEGORIES:\\n category = ClearData(element)\\n category.get_data_api()\\n category.generate_products_list()\\n self.product_table.save_product(\\n category.products,\\n config.CATEGORIES.index(element)+1)\",\n \"def get_all_genres(self):\\n self.cursor.execute(\\\"select * from genres\\\")\\n self.connection.commit()\\n return self.cursor.fetchall()\",\n \"def get_product_available(self, cr, uid, ids, context=None):\\n if context is None:\\n context = {}\\n\\n location_obj = self.pool.get('stock.location')\\n warehouse_obj = self.pool.get('stock.warehouse')\\n shop_obj = self.pool.get('sale.shop')\\n\\n user_obj = self.pool.get('res.users').browse(cr, 1, uid)\\n\\n states = context.get('states',[])\\n what = context.get('what',())\\n if not ids:\\n ids = self.search(cr, uid, [])\\n res = {}.fromkeys(ids, 0.0)\\n if not ids:\\n return res\\n\\n if context.get('shop', False) and context['shop']:\\n warehouse_id = shop_obj.read(cr, 1, int(context['shop']), ['warehouse_id'])['warehouse_id'][0]\\n if warehouse_id:\\n context['warehouse'] = warehouse_id\\n\\n if context.get('warehouse', False) and context['warehouse']:\\n lot_id = warehouse_obj.read(cr, 1, int(context['warehouse']), ['lot_stock_id'])['lot_stock_id'][0]\\n if lot_id:\\n context['location'] = lot_id\\n\\n if context.get('location', False) and context['location']:\\n if type(context['location']) == type(1):\\n location_ids = [context['location']]\\n elif type(context['location']) in (type(''), type(u'')):\\n location_ids = location_obj.search(cr, 1, [('name','ilike',context['location'])], context=context)\\n else:\\n location_ids = context['location']\\n else:\\n location_ids = []\\n #wids = warehouse_obj.search(cr, uid, [], context=context)\\n #for w in warehouse_obj.browse(cr, uid, wids, context=context):\\n # location_ids.append(w.lot_stock_id.id)\\n lids = location_obj.search(cr, 1, [])\\n #print(lids, 'todas os locais', user_obj.company_id.id)\\n for lo in location_obj.browse(cr, 1, lids, context=context):\\n #print(lo.id, lo.company_id, lo.company_ids, user_obj.company_id.id)\\n if lo.company_id and user_obj.company_id.id == lo.company_id.id:\\n location_ids.append(lo.id)\\n else:\\n for co in lo.company_ids:\\n if user_obj.company_id.id == co.id:\\n location_ids.append(lo.id)\\n\\n # build the list of ids of children of the location given by id\\n if context.get('compute_child', True):\\n if len(location_ids) == 0:\\n raise osv.except_osv(u'Atenção!', u'Não há local de estoque definido para a empresa/unidade!')\\n\\n child_location_ids = location_obj.search(cr, 1, [('location_id', 'child_of', location_ids)])\\n location_ids = child_location_ids or location_ids\\n\\n # this will be a dictionary of the UoM resources we need for conversion purposes, by UoM id\\n uoms_o = {}\\n # this will be a dictionary of the product UoM by product id\\n product2uom = {}\\n for product in self.browse(cr, 1, ids, context=context):\\n product2uom[product.id] = product.uom_id.id\\n uoms_o[product.uom_id.id] = product.uom_id\\n\\n results = []\\n results2 = []\\n\\n from_date = context.get('from_date',False)\\n to_date = context.get('to_date',False)\\n date_str = False\\n date_values = False\\n where = [tuple(location_ids),tuple(location_ids),tuple(ids),tuple(states)]\\n if from_date and to_date:\\n date_str = \\\"date>=%s and date<=%s\\\"\\n where.append(tuple([from_date]))\\n where.append(tuple([to_date]))\\n elif from_date:\\n date_str = \\\"date>=%s\\\"\\n date_values = [from_date]\\n elif to_date:\\n date_str = \\\"date<=%s\\\"\\n date_values = [to_date]\\n if date_values:\\n where.append(tuple(date_values))\\n\\n prodlot_id = context.get('prodlot_id', False)\\n prodlot_clause = ''\\n if prodlot_id:\\n prodlot_clause = ' and prodlot_id = %s '\\n where += [prodlot_id]\\n elif 'prodlot_id' in context and not prodlot_id:\\n prodlot_clause = 'and prodlot_id is null '\\n\\n # TODO: perhaps merge in one query.\\n if 'in' in what:\\n # all moves from a location out of the set to a location in the set\\n cr.execute(\\n 'select sum(product_qty), product_id, product_uom '\\\\\\n 'from stock_move '\\\\\\n 'where location_id NOT IN %s '\\\\\\n 'and location_dest_id IN %s '\\\\\\n 'and product_id IN %s '\\\\\\n 'and state IN %s ' + (date_str and 'and '+date_str+' ' or '') +' '\\\\\\n + prodlot_clause +\\n 'group by product_id,product_uom',tuple(where))\\n results = cr.fetchall()\\n if 'out' in what:\\n # all moves from a location in the set to a location out of the set\\n cr.execute(\\n 'select sum(product_qty), product_id, product_uom '\\\\\\n 'from stock_move '\\\\\\n 'where location_id IN %s '\\\\\\n 'and location_dest_id NOT IN %s '\\\\\\n 'and product_id IN %s '\\\\\\n 'and state in %s ' + (date_str and 'and '+date_str+' ' or '') + ' '\\\\\\n + prodlot_clause +\\n 'group by product_id,product_uom',tuple(where))\\n results2 = cr.fetchall()\\n\\n # Get the missing UoM resources\\n uom_obj = self.pool.get('product.uom')\\n uoms = map(lambda x: x[2], results) + map(lambda x: x[2], results2)\\n if context.get('uom', False):\\n uoms += [context['uom']]\\n uoms = filter(lambda x: x not in uoms_o.keys(), uoms)\\n if uoms:\\n uoms = uom_obj.browse(cr, 1, list(set(uoms)), context=context)\\n for o in uoms:\\n uoms_o[o.id] = o\\n\\n #TOCHECK: before change uom of product, stock move line are in old uom.\\n context.update({'raise-exception': False})\\n # Count the incoming quantities\\n for amount, prod_id, prod_uom in results:\\n amount = uom_obj._compute_qty_obj(cr, 1, uoms_o[prod_uom], amount,\\n uoms_o[context.get('uom', False) or product2uom[prod_id]], context=context)\\n res[prod_id] += amount\\n # Count the outgoing quantities\\n for amount, prod_id, prod_uom in results2:\\n amount = uom_obj._compute_qty_obj(cr, 1, uoms_o[prod_uom], amount,\\n uoms_o[context.get('uom', False) or product2uom[prod_id]], context=context)\\n res[prod_id] -= amount\\n\\n for prod_id in res:\\n if isinstance(res[prod_id], D):\\n res[prod_id] = float(res[prod_id])\\n return res\",\n \"def anidarCompras(self):\\n\\n comprasFiltradas = []\\n dicFechas = []\\n for compra in self.compras:\\n for producto in compra[\\\"products\\\"]:\\n if producto[\\\"name\\\"] in comprasFiltradas:\\n for dicFecha in dicFechas:\\n if dicFecha[\\\"producto\\\"]==producto[\\\"name\\\"]:\\n dicFecha[\\\"fechas\\\"].append(compra[\\\"date\\\"])\\n else:\\n comprasFiltradas.append(producto[\\\"name\\\"])\\n dicFechas.append({\\\"producto\\\":producto[\\\"name\\\"], \\\"fechas\\\":[compra[\\\"date\\\"]]})\\n return dicFechas\",\n \"def show_available_products(): # {{{\\n products_available = {}\\n try:\\n with MONGO:\\n product_collection = MONGO.connection.assignment_07[\\\"product\\\"].find(\\n )\\n\\n for product in product_collection:\\n if int(product[\\\"quantity_available\\\"]) > 0:\\n products_available[product[\\\"product_id\\\"]] = {\\n \\\"description\\\": product[\\\"description\\\"],\\n \\\"product_type\\\": product[\\\"product_type\\\"],\\n \\\"quantity_available\\\": product[\\\"quantity_available\\\"],\\n }\\n except TypeError as excep:\\n LOGGER.warning(\\\"Error looking up available products\\\")\\n LOGGER.warning(excep)\\n else:\\n if not products_available:\\n LOGGER.info('No products found')\\n else:\\n LOGGER.info(\\\"Available products retrieved successfully.\\\")\\n return products_available # }}}\",\n \"def _get_prix_tarif(self,cout,pricelist):\\n cr = self._cr\\n product=cout.name\\n prix_tarif=0\\n date=time.strftime('%Y-%m-%d') # Date du jour\\n if pricelist:\\n #Convertion du lot_mini de US vers UA\\n min_quantity = self.env['product.uom']._compute_qty(cout.name.uom_id.id, cout.name.lot_mini, cout.name.uom_po_id.id)\\n #TODO : Pour contourner un bug d'arrondi (le 31/01/2017)\\n min_quantity=min_quantity+0.00000000001\\n #TODO en utilisant la fonction repr à la place de str, cela ne tronque pas les décimales\\n SQL=\\\"\\\"\\\"\\n select ppi.price_surcharge\\n from product_pricelist_version ppv inner join product_pricelist_item ppi on ppv.id=ppi.price_version_id\\n where ppv.pricelist_id=\\\"\\\"\\\"+str(pricelist.id)+ \\\"\\\"\\\" \\n and min_quantity<=\\\"\\\"\\\"+repr(min_quantity)+\\\"\\\"\\\"\\n and (ppv.date_start <= '\\\"\\\"\\\"+date+\\\"\\\"\\\"' or ppv.date_start is null)\\n and (ppv.date_end >= '\\\"\\\"\\\"+date+\\\"\\\"\\\"' or ppv.date_end is null)\\n\\n and ppi.product_id=\\\"\\\"\\\"+str(product.id)+ \\\"\\\"\\\" \\n and (ppi.date_start <= '\\\"\\\"\\\"+date+\\\"\\\"\\\"' or ppi.date_start is null)\\n and (ppi.date_end >= '\\\"\\\"\\\"+date+\\\"\\\"\\\"' or ppi.date_end is null)\\n order by ppi.sequence\\n limit 1\\n \\\"\\\"\\\"\\n cr.execute(SQL)\\n result = cr.fetchall()\\n for row in result:\\n coef=1\\n if min_quantity:\\n coef=cout.name.lot_mini/min_quantity\\n prix_tarif=row[0]/coef\\n\\n\\n\\n return prix_tarif\",\n \"def setUp(self):\\n self._next_lid_nr = 100001\\n\\n self.rayon_1 = Rayon.objects.get(rayon_nr=1)\\n self.rayon_2 = Rayon.objects.get(rayon_nr=2)\\n self.regio_101 = Regio.objects.get(regio_nr=101)\\n\\n # maak een test vereniging\\n ver = Vereniging(\\n ver_nr=\\\"1000\\\",\\n naam=\\\"Grote Club\\\",\\n regio=self.regio_101)\\n ver.save()\\n self._ver = ver\\n\\n # maak functie HWL aan voor deze vereniging\\n self.functie_hwl = maak_functie(\\\"HWL Vereniging %s\\\" % ver.ver_nr, \\\"HWL\\\")\\n self.functie_hwl.vereniging = ver\\n self.functie_hwl.save()\\n\\n # maak test leden aan die we kunnen koppelen aan beheerders functies\\n self.account_bko = self._prep_beheerder_lid('BKO')\\n self.account_rko = self._prep_beheerder_lid('RKO')\\n self.account_rcl = self._prep_beheerder_lid('RCL')\\n self.account_schutter = self._prep_beheerder_lid('Schutter')\\n\\n # creëer een competitie met regiocompetities\\n competities_aanmaken(jaar=2019)\\n # nu in fase A\\n\\n self.comp_18 = Competitie.objects.get(afstand='18')\\n self.comp_25 = Competitie.objects.get(afstand='25')\\n\\n for deelkamp in Kampioenschap.objects.filter(deel=DEEL_BK).all():\\n deelkamp.functie.accounts.add(self.account_bko)\\n # for\\n\\n for deelkamp in Kampioenschap.objects.filter(deel=DEEL_RK, rayon=self.rayon_2).all():\\n deelkamp.functie.accounts.add(self.account_rko)\\n # for\\n\\n for deelcomp in Regiocompetitie.objects.filter(regio=self.regio_101).all():\\n deelcomp.functie.accounts.add(self.account_rcl)\\n # for\\n\\n # maak nog een test vereniging, zonder HWL functie\\n ver = Vereniging(\\n naam=\\\"Kleine Club\\\",\\n ver_nr=1100,\\n regio=self.regio_101)\\n ver.save()\\n self._ver2 = ver\\n\\n # maak functie HWL aan voor deze vereniging\\n hwl = maak_functie(\\\"HWL Vereniging %s\\\" % ver.ver_nr, \\\"HWL\\\")\\n hwl.vereniging = ver\\n hwl.save()\",\n \"def put_it_in_tables(self):\\n my_connection = mysql.connector.connect(user=self.user, password=self.password, database='openfoodfacts')\\n cursor = my_connection.cursor(buffered=True)\\n for i in self.my_data:\\n prod_name = i['product_name']\\n try:\\n add_aliment = (\\\"INSERT INTO aliment \\\"\\n \\\"(product_name, product_description, barcode, nutritional_score, stores, product_category) \\\"\\n \\\"VALUES (%s, %s, %s, %s, %s, %s)\\\")\\n data_aliment = (i['product_name'].replace(\\\"'\\\", \\\"''\\\"), i['product_description'].replace(\\\"'\\\", \\\"''\\\"), i['barcode'].replace(\\\"'\\\", \\\"''\\\"), i['nutritional_score'].replace(\\\"'\\\", \\\"''\\\"), i['stores'].replace(\\\"'\\\", \\\"''\\\"), i['product_category'].replace(\\\"'\\\", \\\"''\\\"))\\n cursor.execute(add_aliment, data_aliment)\\n except mysql.connector.IntegrityError:\\n pass \\n my_connection.commit()\\n cursor.close()\\n my_connection.close()\\n print(\\\"ok c'est fait\\\")\",\n \"def regenone(ctx, product):\\n\\n global products\\n\\n c = ctx.obj['client']\\n if not c.login:\\n return False\\n\\n product_guid = products[product]\\n\\n r = requests.request(\\\"GET\\\", urljoin(c.BASE_URL, '/apiproxy/JobService.js'), params={'accesskey': c.login, 'method': 'GenerateAndPopulateAllSearchesForProduct', 'productGuid': product_guid})\\n print(r.status_code, r.text)\\n\\n if r.status_code == 200:\\n return True\\n else:\\n return False\",\n \"def aplicarConsultasLibres(self):\\n \\n for consultaCL in self.ConsultasLibres:\\n print(consultaCL)\\n resultoCL=self.consulta(consultaCL)\\n for resul in resultoCL['results']['bindings']:\\n uri = resul['s']['value']\\n uri=uri.replace('http://dbpedia.org/resource/','')\\n self.ResultConsultaLibre.append(uri)\",\n \"def lista_ventas(self,tipo,lista,filtro):\\n self.lista=self.builder.get_object(lista)\\n self.lista.clear()#Limpia la lista\\n busqueda = \\\"\\\"\\n\\n if tipo==\\\"\\\":\\n print(\\\"Llego a buscar ventas en BD\\\")\\n #result=self.db.execute('SELECT * FROM Venta')\\n busqueda = self.db.execute('SELECT ventaID ,fechaVenta, fechaInicio, fechaFin, C.nombre, P.nombre FROM Cliente C, Paquete P, Venta V WHERE V.IdCli = C.clienteID AND V.IdPaq = P.paqueteID')\\n elif tipo == \\\"Cliente\\\":\\n print(\\\"Busco venta por nombre del cliente\\\")\\n busqueda = self.db.execute(\\\"SELECT ventaID ,fechaVenta, fechaInicio, fechaFin, C.nombre, P.nombre FROM Cliente C, Paquete P, Venta V WHERE V.IdCli = C.clienteID AND V.IdPaq = P.paqueteID AND C.nombre LIKE '%\\\"+filtro+\\\"%'\\\")\\n elif tipo == \\\"Viaje\\\":\\n print(\\\"Busco venta por nombre del paquete\\\")\\n busqueda = self.db.execute(\\\"SELECT ventaID ,fechaVenta, fechaInicio, fechaFin, C.nombre, P.nombre FROM Cliente C, Paquete P, Venta V WHERE V.IdCli = C.clienteID AND V.IdPaq = P.paqueteID AND P.nombre LIKE '%\\\"+filtro+\\\"%'\\\")\\n elif tipo == \\\"Fecha de inicio\\\":\\n print(\\\"Busco venta por fecha de inicio\\\")\\n busqueda = self.db.execute(\\\"SELECT ventaID ,fechaVenta, fechaInicio, fechaFin, C.nombre, P.nombre FROM Cliente C, Paquete P, Venta V WHERE V.IdCli = C.clienteID AND V.IdPaq = P.paqueteID AND fechaInicio LIKE '%\\\"+filtro+\\\"%'\\\")\\n elif tipo == \\\"Fecha de fin\\\":\\n print(\\\"Busco venta por fecha de fin\\\")\\n busqueda = self.db.execute(\\\"SELECT ventaID ,fechaVenta, fechaInicio, fechaFin, C.nombre, P.nombre FROM Cliente C, Paquete P, Venta V WHERE V.IdCli = C.clienteID AND V.IdPaq = P.paqueteID AND fechaFin LIKE '%\\\"+filtro+\\\"%'\\\")\\n \\n for row in busqueda: \\n #Empieza por la [1] porque el ID es la [0]\\n # self.lista.append([row[4],row[5],row[1],row[2],row[3]])\\n self.lista.append([row[1],row[2],row[3],row[4],row[5],row[0]])\\n print(\\\"Listo ventas en tabla\\\")\",\n \"def obtener_detalle_compra(self):\\n\\n compra = [producto.nombre_modelo for producto in self.productos]\\n orden_de_compra = [(nombre_modelo, compra.count(nombre_modelo)) for nombre_modelo in set(compra)]\\n return self.imprimir_orden_de_compra(orden_de_compra)\",\n \"def test_wiki_products(self):\\n\\n prod_vals = (\\n (ProductFactory(slug='b2g'), 0),\\n (ProductFactory(slug='mobile'), 1),\\n (ProductFactory(slug='desktop'), 2),\\n )\\n\\n for prod, total in prod_vals:\\n for i in range(total):\\n doc = DocumentFactory(locale=u'en-US', category=10)\\n doc.products.add(prod)\\n RevisionFactory(document=doc, is_approved=True)\\n\\n self.refresh()\\n\\n qs = {'a': 1, 'w': 1, 'format': 'json'}\\n\\n for prod, total in prod_vals:\\n qs.update({'product': prod.slug})\\n response = self.client.get(reverse('search.advanced'), qs)\\n eq_(total, json.loads(response.content)['total'])\",\n \"def confirmarOperacion(self):\\n\\n if self.tableNC.rowCount() == 0 :\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",QtCore.QString.fromUtf8(\\\"No se han agregado productos a la Nota de Crédito\\\"))\\n\\n else:\\n ok = QtGui.QMessageBox.information(self,QtCore.QString.fromUtf8(\\\"Confirmación\\\"),\\\\\\n QtCore.QString.fromUtf8(\\\"¿Desea generar la Nota Crédito?\\\"),\\\\\\n QtGui.QMessageBox.Cancel, QtGui.QMessageBox.Accepted)\\n\\n if (ok==1):\\n notaCredito = NotaCreditoModel(NotaCredito.generarNumero(self.sesion))\\n notaCredito.guardar(self.sesion)\\n for lineaNC, data in enumerate(self.detallesReintegrables):\\n detalleNC = DetalleNCModel(notaCredito.numero, lineaNC+1, data[0], data[1])\\n detalleNC.setImporte(data[3])\\n detalleNC.setDescuento(data[2])\\n detalleNC.guardar(self.sesion)\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",QtCore.QString.fromUtf8(\\\"La Nota de Crédito ha sido generada con éxito\\\"))\\n self.facturaSeleccionada.setNC(notaCredito.numero)\\n self.facturaSeleccionada.modificar(self.sesion)\\n\\n #Se genera un diccionario con los datos necesarios para imprimir la nota de credito\\n data = {}\\n data[\\\"numero\\\"] = notaCredito.numero\\n data[\\\"fecha\\\"] = notaCredito.fecha_emision\\n data[\\\"detalles\\\"] = self.detallesImprimibles\\n generarNotaCredito(data)\\n self.limpiarVentana()\\n\\n else:\\n QtGui.QMessageBox.information(self,\\\"Aviso\\\",QtCore.QString.fromUtf8(\\\"La Nota de Crédito no ha sido generada\\\"))\",\n \"def get_products(self):\\n con = dbcon()\\n cur = con.cursor()\\n cur.execute(\\\"SELECT * FROM products;\\\")\\n res = cur.fetchall()\\n if res:\\n prdcts=[]\\n for prodct_item in res:\\n picked_prdct = {\\n 'product_id':prodct_item[0],\\n 'product_name':prodct_item[1],\\n 'price':prodct_item[2],\\n 'quantity':prodct_item[3]\\n }\\n prdcts.append(picked_prdct)\\n return jsonify({\\\"Products\\\": prdcts}), 200\\n return jsonify({\\\"message\\\":\\\"No products in store\\\"})\",\n \"def product_list(id):\\r\\n\\r\\n db = get_db()\\r\\n product_list = db.execute(\\r\\n \\\"SELECT product_id, product_name, quantity FROM product WHERE for_business = ? AND quantity > 0\\\",\\r\\n (id,),\\r\\n ).fetchall()\\r\\n return product_list\",\n \"def fetch_substitutes(self, cat_choice): \\n category = self.categories[cat_choice]\\n for i in range(1, 4):\\n self.cur.execute(\\\"SELECT id FROM Product WHERE nova = %s AND category = %s\\\", (i, category))\\n response = self.cur.fetchall()\\n if response != []:\\n self.sub_list.append(response[random.randint(0, len(response)-1)][0])\\n\\n print (\\\"Voici les substituts trouvés pour:\\\")\\n self.display_product_from_id(self.product_choice_id)\\n for element in self.sub_list:\\n self.display_product_from_id(element)\\n\\n print (\\\"Voulez-vous enregistrer votre recherche dans vos favoris? Oui tapez 1 Non tapez 0\\\")\\n\\n logging = self.secure_input(0, 1)\\n\\n if logging == 0:\\n pass\\n elif logging == 1:\\n self.log_in_DB()\\n self.con.commit()\",\n \"def migrar(self):\\r\\n start = time.time()\\r\\n if not os.path.exists('output'):\\r\\n os.makedirs('output')\\r\\n # seleciona as tabelas\\r\\n res = self.cur_origem.execute(\\r\\n \\\"select rdb$relation_name from rdb$relations where rdb$view_blr is null and (rdb$system_flag is null or rdb$system_flag = 0) %s;\\\" % self.filtro)\\r\\n\\r\\n # para cada tabela\\r\\n for row, in res.fetchall():\\r\\n row = row.strip()\\r\\n\\r\\n # conta os registros\\r\\n countsql = self.cur_origem.execute(\\r\\n \\\"select count(*) as total from %s \\\" % row)\\r\\n count, = countsql.fetchall()[0]\\r\\n start_time = time.time()\\r\\n start_datetime = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n print(\\\"MIGRANDO: %s\\\\n NRO REGISTROS: %s registros\\\\n INICIO: %s\\\" % (\\r\\n row, count, start_datetime))\\r\\n\\r\\n # gera o create table e trunca a tabela ( se ja existir )\\r\\n create, tipos = self.ddl_table(row)\\r\\n self.cur_destino.execute(create)\\r\\n self.cur_destino.execute(\\\"TRUNCATE TABLE %s\\\" % row)\\r\\n\\r\\n # busca os dados\\r\\n self.cur_origem.execute(\\\"select * from %s \\\" % (row))\\r\\n\\r\\n # grava os dados no TXT\\r\\n with open(\\\"output/%s.txt\\\" % row, \\\"wb\\\") as f:\\r\\n writer = csv.writer(f, delimiter='|')\\r\\n writer.writerows(self.cur_origem.fetchall())\\r\\n\\r\\n # le o arquivo gravado e copia para o banco destino\\r\\n with open(\\\"output/%s.txt\\\" % row, \\\"r\\\") as f:\\r\\n try:\\r\\n self.cur_destino.copy_expert(\\r\\n \\\"\\\"\\\"COPY %s FROM STDIN WITH QUOTE '\\\"' DELIMITER '|' NULL '' CSV \\\"\\\"\\\" % row, f)\\r\\n except Exception as e:\\r\\n self.erros.append([\\\"%s\\\" % row, e])\\r\\n end_time = time.time()\\r\\n end_datetime = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n print(\\\" FIM: %s\\\\n TEMPO: %ss\\\\n TABELA COM ERRO %s\\\" %\\r\\n (end_datetime, round(end_time-start_time, 0), e))\\r\\n else:\\r\\n end_time = time.time()\\r\\n end_datetime = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n print(\\\" FIM: %s\\\\n TEMPO: %ss\\\\n OK\\\" %\\r\\n (end_datetime, round(end_time-start_time, 0)))\\r\\n end = time.time()\\r\\n print(\\\"TEMPO GASTO: %s s\\\" % (end-start))\",\n \"def crear_vista_base(self, master, nombre_base=False, nombre_tabla=False, campo_referencia=False, dato_busqueda=False):\\n if not nombre_base:\\n print(\\\"No se ha especificado la base de datos a acceder.\\\\n\\\")\\n return False\\n else:\\n if not nombre_tabla:\\n print(\\\"La base de datos especificada no contiene tablas.\\\\n\\\")\\n self.tree[\\\"columns\\\"] = (1,2,3)\\n for row in self.tree.get_children():\\n self.tree.delete(row)\\n return False\\n\\n contador_campos = 0\\n nombres = []\\n for campo in base.retornar_esquema(nombre_base, nombre_tabla):\\n nombre = campo[1] # escruta el nombre de la columna\\n contador_campos += 1\\n nombres.append(nombre)\\n\\n self.tree[\\\"columns\\\"] = tuple([x for x in range(1,contador_campos+1)] if contador_campos>=7 else [x for x in range(1,8)])\\n\\n if len(nombres) <= 6:\\n for i in range(1,len(self.tree[\\\"columns\\\"])+1):\\n if i > 6:\\n self.tree.column(f\\\"#{i}\\\",\\n width=886-(140*6),\\n stretch=tk.NO)\\n self.tree.heading(f\\\"#{i}\\\", \\n text=f\\\"\\\")\\n break\\n elif i <= 6 and i > len(nombres):\\n self.tree.column(f\\\"#{i}\\\",\\n width=140,\\n stretch=tk.NO)\\n self.tree.heading(f\\\"#{i}\\\", \\n text=f\\\"\\\")\\n else:\\n self.tree.column(f\\\"#{i}\\\",\\n width=140,\\n stretch=tk.NO,\\n anchor='center')\\n self.tree.heading(f\\\"#{i}\\\", \\n text=f\\\"{nombres[i-1].title() if nombres[i-1] not in ('id','ID') else nombres[i-1]}\\\",\\n anchor=\\\"center\\\")\\n else:\\n for i in range(1,len(self.tree[\\\"columns\\\"])+1):\\n self.tree.column(f\\\"#{i}\\\",\\n width=140,\\n stretch=tk.NO,\\n anchor='center')\\n self.tree.heading(f\\\"#{i}\\\", \\n text=f\\\"{nombres[i-1].title() if nombres[i-1] not in ('id','ID') else nombres[i-1]}\\\",\\n anchor=\\\"center\\\")\\n\\n if campo_referencia:\\n if dato_busqueda:\\n if isinstance(dato_busqueda, (str,int,float,bool)):\\n for row in self.tree.get_children():\\n self.tree.delete(row)\\n filas = base.traer_datos(nombre_base, nombre_tabla, campo_referencia, dato_busqueda)\\n for fila in filas:\\n self.tree.insert(\\\"\\\", tk.END, values=fila)\\n elif isinstance(dato_busqueda, (tuple,list,dict)):\\n for row in self.tree.get_children():\\n self.tree.delete(row)\\n for valor in dato_busqueda:\\n filas = base.traer_datos(nombre_base, nombre_tabla, campo_referencia, valor)\\n for fila in filas:\\n self.tree.insert(\\\"\\\", tk.END, values=fila)\\n else:\\n for row in self.tree.get_children():\\n self.tree.delete(row)\\n filas = base.retornar_datos(nombre_base, nombre_tabla)\\n for fila in filas:\\n self.tree.insert(\\\"\\\", tk.END, values=fila)\\n else:\\n for row in self.tree.get_children():\\n self.tree.delete(row)\\n filas = base.retornar_datos_segun_tiempo(nombre_base, nombre_tabla)\\n for fila in filas:\\n self.tree.insert(\\\"\\\", tk.END, values=fila)\",\n \"def call(self):\\r\\n clean_products = []\\r\\n\\r\\n for category in CATEGORIES:\\r\\n print(f\\\"Chargement des produits de type {category}\\\")\\r\\n api_url = SEARCH_API_URL + \\\\\\r\\n (f\\\"?search_terms={category}\\\"\\r\\n \\\"&search_tag=category&sort_by=unique_scans_n\\\"\\r\\n \\\"&page_size=1000&json=1\\\")\\r\\n json_response = requests.get(api_url).json()\\r\\n products = json_response[\\\"products\\\"]\\r\\n\\r\\n for product in products:\\r\\n clean_product = {\\r\\n k: v for k, v in product.items()\\r\\n if k in FIELD_NEEDED and v != ''}\\r\\n clean_products.append(clean_product)\\r\\n\\r\\n return clean_products\",\n \"def generateProducts(self):\\r\\n\\r\\n # Creates items in each category\\r\\n for i in range(self.num_of_items):\\r\\n self.ID_DICT[i+self.num_of_items] = random.randint(1, 10)\\r\\n self.ID_DICT[i+self.num_of_items*2] = random.randint(1, 10)\\r\\n self.ID_DICT[i+self.num_of_items*3] = random.randint(1, 10)\\r\\n self.ID_DICT[i+self.num_of_items*4] = random.randint(1, 10)\\r\\n self.ID_DICT[i+self.num_of_items*5] = random.randint(1, 10)\\r\\n self.ID_DICT[i+self.num_of_items*6] = random.randint(1, 10)\\r\\n\\r\\n\\r\\n # Sort for easy selection\\r\\n sorted(self.ID_DICT)\\r\\n\\r\\n for product in self.ID_DICT.keys():\\r\\n temp_int = self.ID_DICT[product]\\r\\n self.c.execute(\\\"INSERT INTO Products (ProductID, Price) VALUES (?, ?)\\\", (product, self.ID_DICT[product]))\\r\\n self.conn.commit()\\r\\n\\r\\n if self.print_items:\\r\\n print(\\\"\\\\nAll items in store:\\\")\\r\\n print(self.ID_DICT)\\r\\n print()\",\n \"def crearTabla(self):\\n mensaje = self.base.createTable()\\n showinfo('Resultado', mensaje)\",\n \"def factura(num_factura):\\n cursor_factura = connectiondb.cursor()\\n sql_factura = \\\"\\\"\\\"SELECT * FROM Factura\\n WHERE num_factura = '%s'\\\"\\\"\\\" % num_factura\\n cursor_factura.execute(sql_factura)\\n factura = cursor_factura.fetchone()\\n cursor_factura.close()\\n\\n cursor_items_factura = connectiondb.cursor()\\n sql_items_factura = \\\"\\\"\\\"SELECT * FROM ItemsFactura\\n WHERE num_factura = '%s'\\\"\\\"\\\" % num_factura\\n cursor_items_factura.execute(sql_items_factura)\\n items_factura = cursor_items_factura.fetchall()\\n cursor_items_factura.close()\\n\\n cursor_parametros = connectiondb.cursor()\\n sql_parametros = \\\"\\\"\\\"SELECT * FROM ParametrosFactura\\\"\\\"\\\"\\n cursor_parametros.execute(sql_parametros)\\n parametros = cursor_parametros.fetchone()\\n cursor_parametros.close()\\n\\n # Formatea el numero de factura para la resolución\\n cero = '0'\\n param_num_fac_ini = str(parametros['num_fac_ini'])\\n param_num_fac_fin = str(parametros['num_fac_fin'])\\n while len(param_num_fac_ini) < len(param_num_fac_fin):\\n param_num_fac_ini = cero[:] + param_num_fac_ini\\n\\n # Formatea el numero de factura\\n numero_factura = str(factura['num_factura'])\\n ultm_consecutivo = str(parametros['num_fac_fin'])\\n\\n while len(numero_factura) < len(ultm_consecutivo):\\n numero_factura = cero[:] + numero_factura\\n\\n # Datos del cliente\\n cursor_datos_cliente = connectiondb.cursor()\\n sql_datos_cliente = \\\"\\\"\\\"SELECT Factura.num_factura, Cliente.nombre_cliente,\\n Cliente.identificacion_cliente, Cliente.direccion,\\n Cliente.ciudad, Cliente.telefono\\n FROM Factura\\n INNER JOIN Cliente\\n ON Factura.identificacion_cliente = Cliente.identificacion_cliente\\n WHERE Factura.num_factura = '{}'\\n ORDER BY Factura.num_factura DESC;\\\"\\\"\\\".format(factura['num_factura'])\\n cursor_datos_cliente.execute(sql_datos_cliente)\\n datos_cliente = cursor_datos_cliente.fetchone()\\n cursor_datos_cliente.close()\\n\\n return render_template('factura.html',\\n parametros=parametros,\\n param_num_fac_ini=param_num_fac_ini,\\n datos_cliente=datos_cliente,\\n factura=factura,\\n numero_factura=numero_factura,\\n items_factura=items_factura,\\n name=current_user.username)\",\n \"def add(self, producto, cantidad = 1, actualizar_cantidad = False):\\n id_producto = str(producto.id)\\n if id_producto not in self.carro:\\n self.carro[id_producto] = {\\\"cantidad\\\":0,\\\"precio\\\":str(producto.precio)}\\n if actualizar_cantidad:\\n self.carro[id_producto][\\\"cantidad\\\"]= cantidad\\n else:\\n self.carro[id_producto][\\\"cantidad\\\"] += cantidad\\n self.save()\",\n \"def __print_from_api(self, product: dict):\\n\\n substitutes = self.api_operator.get_substitutes(\\n product['categories_tags'][-1],\\n product.get('nutrition_grades', 'e'))\\n\\n # deepcopy for a isolate change\\n operateur_result = [deepcopy(product)]\\n\\n if substitutes:\\n operateur_result.extend(deepcopy(substitutes))\\n\\n self.adapter_for_terminal(operateur_result)\\n\\n # print product and his subsitutes in the terminal\\n self.printer(operateur_result)\\n\\n save_choice = self.ask_with_input(\\n 'Enregistrer dans la base de données ?'\\n ' (y, n)', -1, ('y', 'n'))\\n\\n if save_choice == 'y':\\n # save product and his substitutes\\n self.database_manager.save_product(product, substitutes)\\n cprint('Produit enregistré dans la base de données.', 'red')\",\n \"def save_product(cls, product: dict, substitutes: (tuple, None) = None,\\n user=None):\\n\\n nutriments = product.get('nutriments', {})\\n with transaction.atomic():\\n product_db, created = Product.objects.update_or_create(\\n bar_code=product['code'],\\n defaults={\\n 'name': product.get('product_name', None),\\n 'generic_name': product.get('generic_name', None),\\n 'nutrition_grades': product.get('nutrition_grades', None),\\n 'fat': str(nutriments.get('fat_100g', None)),\\n 'saturated_fat': str(nutriments.get('saturated-fat_100g',\\n None)),\\n 'sugars': str(nutriments.get('sugars_100g', None)),\\n 'salt': str(nutriments.get('salt_100g', None)),\\n 'image_url': product.get('image_url', None)\\n }\\n )\\n\\n if created:\\n categories = []\\n for category in reversed(\\n product.get('categories_hierarchy', ())):\\n category_db, created = Category.objects.get_or_create(\\n name=category)\\n categories.append(category_db)\\n ProductCategory.objects.bulk_create(\\n [ProductCategory(product=product_db, category=category,\\n hierarchy=i)\\n for i, category in enumerate(categories, start=1)]\\n )\\n\\n ingredients = []\\n iteration_ingredients = ()\\n if product.get('ingredients_text_fr', ()):\\n iteration_ingredients = product.get('ingredients_text_fr')\\n elif product.get('ingredients', ()):\\n iteration_ingredients = product.get('ingredients')\\n\\n for ingredient in iteration_ingredients:\\n ingredient = ingredient[:200]\\n ingredient_db, created = Ingredient.objects.get_or_create(\\n name=ingredient)\\n ingredients.append(ingredient_db)\\n\\n product_db.ingredients.add(*ingredients)\\n\\n brands = []\\n for brand in product.get('brands_tags', ()):\\n brand_db, created = Brand.objects.get_or_create(name=brand)\\n brands.append(brand_db)\\n product_db.brands.add(*brands)\\n\\n stores = []\\n for store in product.get('stores_tags', ()):\\n store_db, created = Store.objects.get_or_create(name=store)\\n stores.append(store_db)\\n product_db.stores.add(*stores)\\n\\n if substitutes is not None and user is not None:\\n for substitute in substitutes:\\n substitute_db = cls.save_product(substitute)\\n cls.save_link_p_s_p(user, product_db, substitute_db)\\n\\n return product_db\",\n \"def devices_by_product(self):\\n\\n # Order by product first...\\n by_product = self.devices.all().order_by(\\\"product_id\\\")\\n\\n def pid(device):\\n return device.product_id\\n\\n # Already grouped by product, so we can just do a groupby() immediately\\n return [{\\n \\\"product\\\": Product.objects.get(pk=product_id),\\n \\\"devices\\\": devices\\n } for product_id, devices in groupby(by_product, pid)]\",\n \"def dispone_producto_espacio_sistema(self, sistema):\\r\\n camiones_comparables_a_cargar = 0\\r\\n camiones_comparables_a_descargar = 0\\r\\n\\r\\n if self.tipo == \\\"Carga\\\":\\r\\n camiones_comparables_a_cargar += \\\\\\r\\n sum(1 for c in sistema.camiones_en_sistema\\r\\n if c.carga == self.carga and c.tipo == \\\"Carga\\\" and not c.manipulado.triggered)\\r\\n camiones_comparables_a_descargar += \\\\\\r\\n sum(1 for c in sistema.camiones_en_sistema\\r\\n if c.carga == self.carga and c.tipo == \\\"Descarga\\\" and not c.manipulado.triggered)\\r\\n if not self.manipulado.triggered and self in sistema.camiones_en_sistema:\\r\\n camiones_comparables_a_cargar -= 1\\r\\n else:\\r\\n camiones_comparables_a_cargar += \\\\\\r\\n sum(1 for c in sistema.camiones_en_sistema\\r\\n if self.medios_almacenamiento_destino(sistema) == c.medios_almacenamiento_destino(sistema) and\\r\\n c.tipo == \\\"Carga\\\" and not c.manipulado.triggered)\\r\\n camiones_comparables_a_descargar += \\\\\\r\\n sum(1 for c in sistema.camiones_en_sistema\\r\\n if self.medios_almacenamiento_destino(sistema) == c.medios_almacenamiento_destino(sistema) and\\r\\n c.tipo == \\\"Descarga\\\" and not c.manipulado.triggered)\\r\\n if not self.manipulado.triggered and self in sistema.camiones_en_sistema:\\r\\n camiones_comparables_a_descargar -= 1\\r\\n\\r\\n producto_espacio_disponible = 0\\r\\n\\r\\n for almacen in self.medios_almacenamiento_destino(sistema):\\r\\n\\r\\n if self.tipo == \\\"Carga\\\":\\r\\n producto_espacio_disponible += almacen.niveles[self.carga]\\r\\n else:\\r\\n producto_espacio_disponible += almacen.espacio\\r\\n\\r\\n if self.tipo == \\\"Carga\\\":\\r\\n\\r\\n if producto_espacio_disponible + 28 * (camiones_comparables_a_descargar - camiones_comparables_a_cargar) \\\\\\r\\n >= 28:\\r\\n return True\\r\\n else:\\r\\n return False\\r\\n else:\\r\\n if self.carga == \\\"Fierro\\\":\\r\\n return True\\r\\n elif producto_espacio_disponible + 28 * (camiones_comparables_a_cargar - camiones_comparables_a_descargar) \\\\\\r\\n >= 28:\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def show_available_products():\\n\\n mongo = MongoDBConnection()\\n result = {}\\n\\n with mongo:\\n db = mongo.connection.HPNorton\\n productcollection = db[\\\"products\\\"]\\n for document in productcollection.find({\\\"quantity_available\\\": {\\\"$gt\\\": \\\"0\\\"}}):\\n key = document['product_id']\\n\\n result[key] = {\\n 'description': document['description'],\\n 'product_type': document['product_type'],\\n 'quantity_available': document['quantity_available']\\n }\\n\\n return result\",\n \"def poster_list_products(products):\\r\\n print('\\\\n Choisir un produit : ')\\r\\n dict_product = {}\\r\\n index = 1\\r\\n\\r\\n for i in products:\\r\\n poster_products = cl.Food(i, index)\\r\\n dict_product[poster_products.index] = poster_products.name\\r\\n print(index, \\\" : \\\", poster_products.name)\\r\\n index += 1\\r\\n return dict_product\",\n \"def poster_product_list(product):\\r\\n print('\\\\n Séléctionner un product : ')\\r\\n dict_produit = {}\\r\\n index = 1\\r\\n for i in product:\\r\\n poster_product = cl.Food(i, index)\\r\\n dict_produit[poster_product.index] = poster_product.name\\r\\n print(index, \\\" : \\\", poster_product.name)\\r\\n index += 1\\r\\n return dict_produit\",\n \"def return_products():\\n with MY_CONNECTION as connection:\\n cursor = connection.cursor()\\n cursor.execute(\\n \\\"\\\"\\\"\\n SELECT id_product, product_name, product_price, in_stock, description\\n FROM Products\\n \\\"\\\"\\\")\\n return cursor.fetchall()\",\n \"def _product_available(self, cr, uid, ids, field_names=None, arg=False, context=None):\\n if not field_names:\\n field_names = []\\n if context is None:\\n context = {}\\n res = {}\\n \\n for product_loc in self.browse(cr, uid, ids):\\n c = context.copy()\\n c.update({ 'states': ('done',), 'what': ('in', 'out'), 'location': product_loc.location_id.id})\\n stock = self.pool.get('product.product').get_product_available(cr, uid, [product_loc.product_id.id], context=c)\\n res[product_loc.id] = stock.get(product_loc.product_id.id, 0.0)\\n return res\",\n \"def pruebas(self):\\n self.gestor_pca.pruebas()\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7017241","0.68887484","0.64854866","0.6218559","0.61975086","0.6082025","0.60445845","0.59041625","0.58877075","0.5804047","0.5794914","0.5783742","0.576173","0.56790864","0.56208664","0.5607758","0.5550904","0.5536961","0.548066","0.5475638","0.54541093","0.5441325","0.542163","0.5420545","0.5418675","0.54179883","0.5412239","0.5411635","0.53924996","0.5389493","0.5387512","0.5376802","0.53449327","0.5340915","0.53390473","0.53268486","0.53216004","0.5304085","0.53039414","0.52828544","0.52791494","0.5261606","0.5250629","0.5245478","0.52439153","0.52351063","0.5217513","0.52077293","0.52063984","0.5195277","0.51907736","0.5189207","0.51886237","0.5185921","0.5185726","0.51825047","0.51786387","0.51724243","0.51679194","0.515876","0.5156517","0.51467144","0.5141092","0.51344335","0.5131333","0.51305413","0.5130151","0.51056546","0.5099292","0.5096885","0.5079564","0.5073601","0.5069256","0.5065329","0.5064108","0.5057686","0.5056871","0.50563604","0.50510216","0.5042482","0.50422585","0.5041816","0.5039681","0.5032335","0.50299","0.50242084","0.5020032","0.50187963","0.501545","0.50122064","0.5008907","0.500232","0.49973568","0.49927348","0.49889305","0.49842277","0.49766892","0.49760783","0.49643642","0.49617234","0.49607033"],"string":"[\n \"0.7017241\",\n \"0.68887484\",\n \"0.64854866\",\n \"0.6218559\",\n \"0.61975086\",\n \"0.6082025\",\n \"0.60445845\",\n \"0.59041625\",\n \"0.58877075\",\n \"0.5804047\",\n \"0.5794914\",\n \"0.5783742\",\n \"0.576173\",\n \"0.56790864\",\n \"0.56208664\",\n \"0.5607758\",\n \"0.5550904\",\n \"0.5536961\",\n \"0.548066\",\n \"0.5475638\",\n \"0.54541093\",\n \"0.5441325\",\n \"0.542163\",\n \"0.5420545\",\n \"0.5418675\",\n \"0.54179883\",\n \"0.5412239\",\n \"0.5411635\",\n \"0.53924996\",\n \"0.5389493\",\n \"0.5387512\",\n \"0.5376802\",\n \"0.53449327\",\n \"0.5340915\",\n \"0.53390473\",\n \"0.53268486\",\n \"0.53216004\",\n \"0.5304085\",\n \"0.53039414\",\n \"0.52828544\",\n \"0.52791494\",\n \"0.5261606\",\n \"0.5250629\",\n \"0.5245478\",\n \"0.52439153\",\n \"0.52351063\",\n \"0.5217513\",\n \"0.52077293\",\n \"0.52063984\",\n \"0.5195277\",\n \"0.51907736\",\n \"0.5189207\",\n \"0.51886237\",\n \"0.5185921\",\n \"0.5185726\",\n \"0.51825047\",\n \"0.51786387\",\n \"0.51724243\",\n \"0.51679194\",\n \"0.515876\",\n \"0.5156517\",\n \"0.51467144\",\n \"0.5141092\",\n \"0.51344335\",\n \"0.5131333\",\n \"0.51305413\",\n \"0.5130151\",\n \"0.51056546\",\n \"0.5099292\",\n \"0.5096885\",\n \"0.5079564\",\n \"0.5073601\",\n \"0.5069256\",\n \"0.5065329\",\n \"0.5064108\",\n \"0.5057686\",\n \"0.5056871\",\n \"0.50563604\",\n \"0.50510216\",\n \"0.5042482\",\n \"0.50422585\",\n \"0.5041816\",\n \"0.5039681\",\n \"0.5032335\",\n \"0.50299\",\n \"0.50242084\",\n \"0.5020032\",\n \"0.50187963\",\n \"0.501545\",\n \"0.50122064\",\n \"0.5008907\",\n \"0.500232\",\n \"0.49973568\",\n \"0.49927348\",\n \"0.49889305\",\n \"0.49842277\",\n \"0.49766892\",\n \"0.49760783\",\n \"0.49643642\",\n \"0.49617234\",\n \"0.49607033\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76672,"cells":{"query":{"kind":"string","value":"Crea la tabla si no existe"},"document":{"kind":"string","value":"def crearTabla(self):\n mensaje = self.base.createTable()\n showinfo('Resultado', mensaje)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _create_table_if_not_exists(self) -> bigquery.Table:\n table = self.client.create_table(\n table=bigquery.Table(table_ref=self._table_ref, schema=Schema),\n exists_ok=True,\n )\n logging.info(\"table %s already exists.\", table.full_table_id)\n return table","def check_and_create_table(self) -> None:\n table_ids = [t.table_id for t in self.instance.list_tables()]\n\n if not self.table_id in table_ids:\n self.table.create()\n f = self.table.column_family(self.family_id)\n f.create()\n\n f_inc = self.table.column_family(self.incrementer_family_id,\n gc_rule=MaxVersionsGCRule(1))\n f_inc.create()\n\n f_log = self.table.column_family(self.log_family_id)\n f_log.create()\n\n f_ce = self.table.column_family(self.cross_edge_family_id,\n gc_rule=MaxVersionsGCRule(1))\n f_ce.create()\n\n print(\"Table created\")","def __createTableIfNotExists(self):\n query = \"SELECT name FROM sqlite_master WHERE type='table'\"\n results = self._cursor.execute(query)\n name_list = [name for r in results for name in r]\n\n if self.table_name not in name_list:\n # Timestamp is for the time from pythons datetime.now() func\n table = \"\"\"CREATE TABLE {}\n (media TEXT PRIMARY KEY,\n thumbnail TEXT, regular TEXT, large TEXT,\n [timestamp] timestamp)\n \"\"\".format(self.table_name)\n self._cursor.execute(table)","def table_create(name):\n query = \"\"\"CREATE TABLE if not exists `emp`.`\"\"\"+name+\"\"\"` (`Id` int(11) NOT NULL,`First name` varchar(45) NOT NULL,`Last Name` varchar(45) NOT NULL,`deparment` varchar(45) NOT NULL,`salary` int(11) NOT NULL,PRIMARY KEY (`Id`))\"\"\"\n try:\n conn = db_conection()\n cur = conn.cursor()\n cur.execute(query)\n conn.commit()\n# print (\"table Success\")\n __log(1, 'Table Create if not exists')\n except Exception as e:\n print (e)\n __log(2,'Table creation check and craete Error')","def create_table(self):\n self.db.query(f\"\"\"\n CREATE TABLE IF NOT EXISTS {self.table} (\n id INT UNSIGNED NOT NULL AUTO_INCREMENT,\n name VARCHAR(140) NOT NULL,\n PRIMARY KEY (id)\n )\n \"\"\")","def create_table(self):\n pass","def create_table():\n\tCURSOR.execute(\"\"\"CREATE TABLE IF NOT EXISTS {} (\n\t\t\t[ID] NVARCHAR NOT NULL PRIMARY KEY,\n\t\t\t[Name] NVARCHAR,\n\t\t\t[Definition] NVARCHAR)\"\"\".format(TABLE_NAME))","def create_tables():\n\tlog_msg4(\"No hay tablas para el año \" + txt_year + \". Creando\")\n\n\tcreate_table('visited')\n\tcreate_table('saved')\n\tcreate_table('actions')\n\n\tglobal new_tables_created\n\tnew_tables_created = True\n\n\tlog_msg_ok4()","def _create_table(self, thistable):\n created = True\n try:\n thistable.__table__.create(self.__engine, checkfirst=True)\n self._commit()\n return created\n except ProgrammingError:\n self._rollback()\n created = None\n raise\n except Exception:\n self._rollback()\n created = False\n raise","def __create_table_if_not_exist(self):\n query = 'create TABLE IF NOT EXISTS {} (user_name char(255), time_stamp DATE, message char(255));'.format(TABLE_NAME)\n try:\n self.cursor.execute(query)\n except sqlite3.DatabaseError as err:\n # print error message to display\n print (\"Error: \", err)\n else:\n self.db_conn.commit()","def _create_table_if_not_exists(self) -> None:\n COLUMN_DEFINITIONS = 'definitions'\n COLUMN_TYPE = 'type'\n\n KEY_REF = '$ref'\n\n TYPE_LOOKUP = {\n 'string': 'VARCHAR(255)',\n 'integer': 'INTEGER',\n 'boolean': 'BOOLEAN',\n 'number': 'INTEGER',\n }\n\n def ref_lookup(\n property: Dict[str, Any], fields: Dict[str, Any]\n ) -> Dict[str, Any]:\n ref = property[KEY_REF]\n property_lookup_name = ref[ref.rfind('/') + 1 :]\n return fields[COLUMN_DEFINITIONS][property_lookup_name]\n\n field_queries = []\n fields = json.loads(self.schema.schema_json())\n\n del fields[Keywords.Properties.value][\n Keywords.ID.value\n ] # Remove primary key field. It is handled with auto increment below.\n\n for property_name, property in fields[Keywords.Properties.value].items():\n if KEY_REF in property:\n property = ref_lookup(property, fields)\n field_queries.append(\n f'{property_name} {TYPE_LOOKUP[property[COLUMN_TYPE]]}'\n )\n table_columns = ', '.join(field_queries)\n\n with connect(**BaseModel.db_settings) as connection:\n cursor = connection.cursor()\n cursor.execute(\n f'CREATE TABLE IF NOT EXISTS {self.table_name} (ID INTEGER PRIMARY KEY AUTO_INCREMENT, {table_columns})'\n )\n self._table_created[self.table_name] = True","def _create_table_if_not_exists(self, table_name):\n if self.environments['database'].engine.dialect.has_table(self.environments['database'].engine, table_name):\n return True\n\n filename = glob(f\"{DATASET_DIR}/{DATASETS[self.dataset]['file_pattern']}\")\n with open(filename[0]) as csv_file:\n csv_reader = csv.DictReader(csv_file, delimiter=DATASETS[self.dataset]['delimiter'])\n header = []\n header.append(next(csv_reader))\n metadata = sqlalchemy.MetaData()\n data_column = []\n for col in header[0]:\n if isinstance(header[0][col], int):\n data_column.append((col, sqlalchemy.Integer))\n elif isinstance(header[0][col], str):\n data_column.append((col, sqlalchemy.String))\n elif isinstance(header[0][col], bool):\n data_column.append((col, sqlalchemy.BOOLEAN))\n elif isinstance(header[0][col], float):\n data_column.append((col, sqlalchemy.FLOAT))\n table = sqlalchemy.Table(table_name,\n metadata,\n sqlalchemy.Column('id', sqlalchemy.Integer, primary_key=True),\n *(sqlalchemy.Column(column_name, column_type(60))\n for column_name, column_type in data_column))\n table.create(self.environments['database'].engine)\n self.destination_table = table\n return False","def _create_table(self) :\n\n cur = self.con.cursor()\n delete_sql = 'DROP TABLE IF EXISTS \"%s\"' % self.name\n cur.execute(delete_sql)\n\n col_sql = ','.join(['\"%s\" %s' % (self.cols[i], self.types[i])\n for i in range(len(self.cols))])\n create_sql = 'CREATE TABLE \"%s\" ( %s );' % (self.name, col_sql)\n cur.execute(create_sql)","def creates_table(self):\n return self.statements[0].creates_table()","def create_table(self, table_name):\n if not self.open:\n print(\"Not currently connected to a DB.\")\n return False\n\n q = 'CREATE TABLE IF NOT EXISTS {tn}(ID INT PRIMARY KEY NOT NULL UNIQUE)'\n self.query = q.format(tn=table_name)\n\n try:\n self.cursor.execute(self.query)\n print(\"{} table created.\".format(table_name))\n return True\n except Exception as error:\n print(\"Unable to create '{}' table.\".format(table_name))\n print(\"SQL Query: \\n{}\\n\".format(self.query))\n print(\"Exception: \\n{}\".format(error))\n\n return False","def create_table(cls):\n if not connection.connected:\n raise Exception('Not connected to the database.')\n cursor = connection.execute(cls.create_table_sql())\n cursor.close()","def create_table():\n\n cnx = create_connection()\n cursor = cnx.cursor()\n\n print(TABLE_DESCRIPTION)\n try:\n print(\"Creating table...\")\n cursor.execute(TABLE_DESCRIPTION)\n print(\"Table created!\")\n except mysql.connector.Error as err:\n if err.errno == errorcode.ER_TABLE_EXISTS_ERROR:\n print(\"already exists.\")\n cnx.close()\n cursor.close()\n return\n else:\n print(err.msg)\n cnx.close()\n cursor.close()\n return\n else:\n print(\"OK\")\n\n cnx.close()\n cursor.close()","def _create_table(self):\n query = '''\n CREATE TABLE IF NOT EXISTS {} (\n id integer PRIMARY KEY,\n url text NOT NULL UNIQUE)\n '''.format(\n self.tablename\n )\n\n self.conn.execute(query)","def create_table_if_not_exists(tablename: str, create_query: str) -> bool:\n\n if cursor:\n cursor.execute(\"select exists(select * from information_schema.tables where table_name=%s)\", (tablename,))\n if not cursor.fetchone()[0]:\n cursor.execute(create_query)\n return True\n else:\n return False\n\n else:\n logger.error(\"Attempted to create a table while DB cursor has not been initialised\")","def create_table(self):\n Engine.create_table(self)\n self.connection.commit()","def test_create_table_successfully (self):\n\n new_table = self.wrapper.create_table(self.table, [self.bob, self.jane])\n self.assertIsNone(new_table)","def db_create_table(db_in, tablename):\n connection = db_in.connection.cursor()\n connection.execute('CREATE TABLE IF NOT EXISTS %s(id INTEGER PRIMARY KEY);' % tablename)","def create_table(self, create_table_sql):\n connection = self.__create_connection()\n try:\n c = connection.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn):\n\n try:\n c = conn.cursor()\n c.execute('''CREATE TABLE IF NOT EXISTS students\n (last_name text, first_name text, student_id text)''')\n conn.commit()\n except Error as e:\n print(e)","def create_table(create_table_sql):\n conn = DbUtil.connection\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_base_table(self, table_name):\n print('new')\n # Create table at first.\n select_stm = self.construct_base_table()\n exec_query('DROP TABLE IF EXISTS %s;' % table_name) \n sql = \"\"\"\n CREATE TABLE %s AS\n %s\n \"\"\" % (table_name, select_stm)\n exec_query(sql)","def create_table(cursor, TABLES):\n for t_name,t_desc in TABLES.items():\n try:\n print(f\"Creating table '{t_name}'...\")\n cursor.execute(t_desc)\n except mysql.connector.Error as err:\n if err.errno == errorcode.ER_TABLE_EXISTS_ERROR:\n print(f\"{t_name} already exists, moving on...\")\n pass\n else:\n print(err.msg+\".\")\n exit(1)\n else:\n print(\"OK\")\n return None","def create_table(self, conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create(self):\n self.execute(self.commands.create_table(self.name, self.primary_key_column))\n return self.commit()","def createTable(self):\n results = self.db.table_create(self.entity).run(self.r)\n time.sleep(5)\n return results","def create_table(self) -> None:\n try:\n self.cursor.execute(\"CREATE TABLE IF NOT EXISTS CAR (id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT, title TEXT NOT NULL, price real NOT NULL, url TEXT NOT NULL, location TEXT, datelisted TEXT, unique(title));\")\n except sql.OperationalError as e:\n print(e)","def create_table(conn, tbl_data):\r\n try:\r\n cur = conn.cursor()\r\n cur.execute(tbl_data)\r\n except Error as e:\r\n print(e)","def use_table(self):\n connection = self._get_connection()\n cursor = connection.cursor()\n cursor.execute(\n 'select exists(select * from information_schema.tables where table_name=%s)',\n (self.table,),\n )\n if cursor.fetchone()[0]:\n self.logger.info('Using existing table')\n else:\n try:\n cursor.execute(\n f'CREATE TABLE {self.table} ( \\\n ID VARCHAR PRIMARY KEY, \\\n DOC BYTEA);'\n )\n self.logger.info('Successfully created table')\n except (Exception, psycopg2.Error) as error:\n self.logger.error('Error while creating table!')\n connection.commit()\n self._close_connection(connection)","def create_table(self, name):\n # check read only\n if self.__read_only:\n raise IOError(\"DB is for reading only.\")\n # add new table to data\n table = Table()\n self.__tables[name] = table\n # if table previously removed - unmark it\n if name in self.__dropped_tables:\n self.__dropped_tables.remove(name)","def create_table_db(self):\n table_name = self.name_table.get()\n if len(table_name) > 0:\n self.table_db = table_name\n self.sql_database.db_name = self.db\n if self.sql_database.create_table(self.table_db):\n msg.showinfo(\n message=\"\".join([str(self.table_db), \" created\"]))\n self.name_table.delete(0, tk.END)\n self.show_table_combobox()\n else:\n msg.showinfo(message=\"Failed\")\n else:\n msg.showinfo(message=\"Write table name!\")","def createDbTables():\n logger.info(\"creating required DB table if it does not already exist\")\n try:\n with db.connect() as conn:\n conn.execute(\n \"CREATE TABLE IF NOT EXISTS birthdays \"\n \"( username VARCHAR(255) NOT NULL, birthday DATE NOT NULL, \"\n \"PRIMARY KEY (username) );\"\n )\n return OK\n except Exception as e:\n logger.info(\"Error creating table: %s\" % e)\n return ERR","def create_table(self, table_name):\n self.table_name = table_name\n conn, cursor = SQLDatabase.connect()\n self.change_database(cursor)\n success = False\n try:\n cursor.execute(\"CREATE TABLE IF NOT EXISTS {} (\\\n Text_ID int NOT NULL AUTO_INCREMENT PRIMARY KEY,\\\n Text_Path varchar(250) NOT NULL,\\\n Word_First varchar(250) NOT NULL, \\\n Word_Second varchar(250),\\\n Word_Third varchar(250),\\\n Word_Fourth varchar(250),\\\n Word_Fifth varchar(250));\\\n \".format(self.table_name))\n success = True\n except mysql.connector.errors.ProgrammingError as err:\n print(\"Failed creating table: {}\".format(err))\n finally:\n SQLDatabase.close(cursor, conn)\n\n return success","def create_table(conn, create_table_sql):\r\n try:\r\n c = conn.cursor()\r\n c.execute(create_table_sql)\r\n except Error as e:\r\n print(e)","def create_sql_table(self, query):\n table_pattern = r\"CREATE TABLE ([\\w_:]+)\"\n match = re.search(table_pattern, query, re.IGNORECASE)\n table_name = match.group(1)\n if \":\" in table_name:\n query = self.clean_table_name(query, table_name)\n\n # Check to see if tbable exists already\n try:\n self.send_sql_query(query)\n # Recreate if table already exists\n except mysql.connector.Error as err:\n if err.errno == errorcode.ER_TABLE_EXISTS_ERROR:\n table_name = table_name.replace(\":\", \"\")\n self.send_sql_query(f\"DROP TABLE {table_name}\")\n self.send_sql_query(query)","def create_table(self):\n conn = self._connect_DB()\n cur = conn.cursor()\n cur.execute(\n \"CREATE TABLE IF NOT EXISTS movie_table (movie_title, people);\"\n )\n self._close_connection(conn)","def create_table(conn, sql_create_table):\n try:\n c = conn.cursor()\n c.execute(sql_create_table)\n except Error as e:\n print(e)","def create_table(conn, sql_create_table):\n try:\n c = conn.cursor()\n c.execute(sql_create_table)\n except Error as e:\n print(e)","def create_table(conn, sql_create_table):\n try:\n c = conn.cursor()\n c.execute(sql_create_table)\n except Error as e:\n print(e)","def ctable_sansanito():\n\tcur.execute(\"\"\"\n\t\t\t\tCREATE TABLE sansanito(\n\t\t\t\tid NUMBER NOT NULL PRIMARY KEY,\n\t\t\t\tpokedex INT,\n\t\t\t\tnombre VARCHAR(40),\n\t\t\t\ttype1 VARCHAR(20),\n\t\t\t\ttype2 VARCHAR(20),\n\t\t\t\thpactual INT,\n\t\t\t\thpmax INT,\n\t\t\t\tlegendary NUMBER(1),\n\t\t\t\testado VARCHAR(30),\n\t\t\t\tingreso DATE,\n\t\t\t\tprioridad INT)\"\"\"\n\t\t\t\t)\n\n\t\n\tconnection.commit()","def create_table(self):\n logger.debug('Function create_table start')\n\n table = \"\"\"CREATE TABLE IF NOT EXISTS sensor_data (\n id integer PRIMARY KEY,\n time timestamp NOT NULL,\n img_name INTEGER,\n img_score INTEGER,\n magnetometer_z REAL,\n magnetometer_y REAL,\n magnetometer_x REAL\n );\"\"\"\n\n try:\n c = self.conn.cursor()\n\n # Create table\n c.execute(table)\n\n self.conn.commit()\n\n logger.info('Created a table')\n\n return True\n except Exception as e:\n table_exists = \"SELECT name FROM sqlite_master WHERE type='table' AND name='spwords'\"\n if self.conn.execute(table_exists).fetchone() and isinstance(e, sqlite3.OperationalError):\n # sqlite3 docs say ProgrammingError is raised when table exists, although OperationalError was raised when testing.\n logger.warning('Table already exists: {}'.format(format_exc()))\n return True\n\n logger.critical('Could not create a table: {}'.format(format_exc()))\n return False\n\n logger.debug('Function create_table end')","def create_table(drop_if_exists=True) -> None:\r\n connection = ConnectDB.connect()\r\n additional_types_query = \"\"\"DO\r\n BEGIN\r\n IF NOT EXISTS (SELECT 1 FROM pg_type WHERE typname = 'gender') THEN\r\n CREATE TYPE Gender AS ENUM ('female', 'man');\r\n END IF;\r\n IF NOT EXISTS (SELECT 1 FROM pg_type WHERE typname = 'ter') THEN\r\n CREATE TYPE ter AS ENUM ('city', 'village');\r\n END IF;\r\n END;\"\"\"\r\n\r\n ZnoData_drop_query = \"DROP TABLE IF EXISTS ZnoData;\"\r\n\r\n ZnoData_create_query = 'CREATE TABLE IF NOT EXISTS ZnoData ('\r\n fields = ''\r\n for col, t in table_columns_types.items():\r\n if fields == '':\r\n fields += (f'{col} {t}') + ' PRIMARY KEY, '\r\n else:\r\n fields += (f'{col} {t}')+ ', '\r\n ZnoData_create_query += fields[:-2] + ');'\r\n \r\n with connection.cursor() as cursor:\r\n cursor.execute(additional_types_query)\r\n if drop_if_exists:\r\n cursor.execute(ZnoData_drop_query)\r\n print('Table was droped.')\r\n cursor.execute(ZnoData_create_query)\r\n print('Table was created.')\r\n ConnectDB.disconnect(connection)","def create_table(self, conn, create_table_sql):\n try:\n # create a Cursor object and call its .execute() method to perform SQL queries\n c = conn.cursor()\n # execute SQL queries: create a table named card\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_users_table():\n try:\n print(\"Creating 'users' table..\")\n db.execute(\"CREATE TABLE users (\\\n id SERIAL PRIMARY KEY, \\\n username VARCHAR NOT NULL, \\\n password VARCHAR NOT NULL\\\n )\")\n print(\"Table 'users' created\")\n except exc.ProgrammingError as err:\n print(\"Table 'users' already exists\")\n db.commit()","def create_tables():\n db.create_all()","def create_tables():\n db.create_all()","def createTable(self, tableName: str, configuration: str):\n sql = \"CREATE TABLE IF NOT EXISTS {} ({})\".format(tableName, configuration)\n self._execute(sql)","def _create_table(self):\n query = f\"\"\"CREATE TABLE IF NOT EXISTS {TABLE}(\n member_Id INT,\n memberName VARCHAR(50),\n amount INT,\n date datetime NOT NULL,\n time datetime NOT NULL,\n status VARCHAR(20) NOT NULL DEFAULT 'Completed'\n );\"\"\"\n\n self.cursor.execute(query)\n self.conn.commit()","def _create_table(conn, table_name, columns):\n # Create the table.\n with conn:\n cur = conn.cursor()\n cur.execute('CREATE TABLE IF NOT EXISTS %s%s' % (table_name, columns))","def create_table(self):\n table = self.table\n table.create()\n return table.bind.wait()","def create_table(self):\n logging.debug('Creating new table')\n if not self._dbconnect or not self._cursor:\n raise Exception('Invalid call to Context Manager method!')\n\n self._cursor.execute(\"create table {} (date text, time text, location text, nodeID text)\".format(self._name))","def new_table(self):\n self.c.execute(\n \"\"\"CREATE TABLE IF NOT EXISTS {table} (\n id integer primary key,\n {event} integer,\n {desc} text,\n {date} text,\n {link} text)\n \"\"\".format(\n table=TABLE,\n event=EVENT,\n desc=DESC,\n date=DATE,\n link=LINK,\n )\n )","def create_table(conn, create_table_sql):\r\n try:\r\n c = conn.cursor()\r\n c.execute(create_table_sql)\r\n except Error as e:\r\n print(e)","def create_table(self, table_info, table_name):\r\n t1 = time.time()\r\n if self.database in ['redshift', 'postgres']:\r\n postgres_helper.create_table(\r\n conf=self.conf,\r\n table_info=table_info,\r\n table_name=table_name\r\n )\r\n else:\r\n raise Exception(\"database not supported yet: '{}'\"\r\n .format(self.database))\r\n t2 = time.time()\r\n t = t2 - t1\r\n print('Finished in {:.2f} seconds.'.format(t))\r\n return","def create_table():\r\n\tc = conn.cursor()\r\n\tstrsql=\"\"\"\r\n\t\tCREATE TABLE if not exists cacao (\r\n\t\t\tid INTEGER PRIMARY KEY AUTOINCREMENT,\r\n\t\t\tCompany text,\r\n\t\t\tSpecific_Bean_Origin text,\r\n\t\t\tREF\tint,\r\n\t\t\tReview\tint,\r\n\t\t\tCocoa text,\r\n\t\t\tLocation text,\r\n\t\t\tRating\tfloat,\r\n\t\t\tBean text,\r\n\t\t\tBroad_Bean_Origin text\r\n\t\t\t);\r\n\t\t\"\"\"\r\n\tc.execute(strsql)\r\n\tconn.commit()","def create_table_in_sqlite_db(self):\n with self.con:\n cur = self.con.cursor()\n cur.execute(\"\"\"DROP TABLE IF EXISTS {};\"\"\".format(self.table_name))\n base_create_query = \"\"\"CREATE TABLE {}({}, PRIMARY KEY ({}));\"\"\"\n columns = ','.join(['{} {}'.format(col, self.columns_types[col]) for col in self.table_columns])\n primary_keys = ','.join(['{}'.format(col) for col in self.table_primary_keys])\n create_query = base_create_query.format(self.table_name, columns, primary_keys)\n cur.execute(create_query)\n self.con.commit()","def create_tables(self):\n for query in table_create_sql:\n self.cursor.execute(query)\n\n self.commit()","def create_table(conn, create_table_sql):\n\ttry:\n\t\tc = conn.cursor()\n\t\tc.execute(create_table_sql)\n\texcept Error as e:\n\t\tprint(e)","def make_new_tbl(self):\n debug = False\n default_dd = getdata.get_default_db_dets()\n con, cur = default_dd.con, default_dd.cur\n oth_name_types = getdata.get_oth_name_types(self.settings_data)\n tblname = self.tblname_lst[0]\n if debug: print(f'DBE in make_new_tbl is: {default_dd.dbe}')\n getdata.make_sofa_tbl(\n con, cur, tblname, oth_name_types, headless=False)\n wx.MessageBox(\n _('Your new table has been added to the default SOFA database'))","def create_table(curs, table_name):\n curs.execute(\"\"\"CREATE TABLE {} (\n s TEXT PRIMARY KEY,\n x integer,\n y integer\n );\"\"\".format(table_name))","def create_tables():\n db.create_all()","def create_table(conn, create_table_sql):\r\n try:\r\n c = conn.cursor()\r\n c.execute(create_table_sql)\r\n conn.commit()\r\n except Error as e:\r\n print(e)","def create_new_table():\n dataset = create_dataset()\n table_id = \"{}.{}.corona_cases_table\".format(client.project, dataset.dataset_id)\n table = bigquery.Table(table_id)\n table = client.create_table(table, exists_ok=True)\n print(\n \"Created table {}.{}.{}\".format(table.project, table.dataset_id, table.table_id)\n )\n return table","def create_books_table():\n try:\n print(\"Creating 'books' table..\")\n db.execute(\"CREATE TABLE books (\\\n id SERIAL PRIMARY KEY, \\\n isbn VARCHAR NOT NULL, \\\n title VARCHAR NOT NULL, \\\n author VARCHAR NOT NULL, \\\n year INTEGER NOT NULL\\\n )\")\n print(\"Table 'books' created\")\n except exc.ProgrammingError as err:\n print(\"Table 'books' already exists\")\n db.commit()","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_table_execute(self):\n self.execute(query=self.default_template.format(self.table_name), data=None)","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n\tconn.commit()\n except Error as e:\n print(e)","def create_table(self, table_name, fields, noid=False):\n print('creating table {} ...'.format(table_name))\n sqlhead = \"CREATE TABLE %s (id INTEGER PRIMARY KEY AUTO_INCREMENT , \" % table_name\n if noid:\n sqlhead = \"CREATE TABLE '%s' (\" % table_name\n if fields is None:\n return\n if isinstance(fields[0], tuple):\n sqlmain = ', '.join([x[0] + \" \" + x[1] for x in fields])\n else:\n sqlmain = ', '.join([x + \" varchar(128)\" for x in fields])\n default_setting = \"ENGINE=InnoDB DEFAULT CHARSET=utf8;\"\n sql = sqlhead + sqlmain + \" ) \" + default_setting\n try:\n cur = self._get_cursor()\n cur.execute(sql)\n self.conn.commit()\n except Exception as err:\n self.conn.rollback()\n # if table already exists\n if err.args[0] == 1050:\n return False\n self._print_error(err)\n return True","def create_table():\n db, c = config.start_db()\n try:\n c.execute('''CREATE TABLE users (\n username TEXT PRIMARY KEY,\n pass_hash BLOB NOT NULL,\n salt BLOB NOT NULL,\n karma INT NOT NULL\n )''')\n except sqlite3.OperationalError: # Table already exists\n pass\n config.end_db(db)","def create_table(self):\n self.db.query(f\"\"\"\n CREATE TABLE IF NOT EXISTS {self.table} (\n substitut_id bigint unsigned references product(id),\n original_id bigint unsigned references product(id),\n PRIMARY KEY (substitut_id, original_id)\n )\n \"\"\")","def createFreshTable(self):\n\n # delete existing table if it exists\n try:\n self.__getTable().delete();\n time.sleep(10)\n except:\n pass;# do nothing. Maybe there was no existing table\n\n # create new table\n tableConnectionParams = parseConnectionString(\n self.tableConnString);\n return Table.create(\n tableConnectionParams['name'],\n schema = [\n HashKey('entity'),\n RangeKey('docId')\n ], throughput = {\n 'read': 1,\n 'write': 4,\n }, global_indexes = [\n GlobalAllIndex('docIdIndex', parts = [\n HashKey('docId'),\n RangeKey('entity')\n ],\n throughput = {\n 'read': 1,\n 'write': 4,\n })\n ],\n connection = getDbConnection(tableConnectionParams))","def create_table(conn, create_table_sql):\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Exception as e:\n print(e)","def createTable(self, tableName, vars):\n\n table = \"CREATE TABLE IF NOT EXISTS \"+tableName+'('\n for i in vars:\n table += i\n table += ','\n\n table[table.len()-1] = ')'\n table += ';'\n \n self.cur.execute(table)","def create_table():\n conn = psycopg2.connect(host=\"localhost\", database=\"integration\", user=\"postgres\", password=\"postgres\")\n cursor = conn.cursor()\n cursor.execute(CREATE_TABLE)\n conn.commit()\n cursor.close()","async def _create_table(self, table: TableSchema) -> None:\n try:\n await self.conn.execute(get_create_table(table))\n except PostgresError: # Only DB related exceptions\n print(f\"Failed to execute CREATE TABLE for {table['name']}\")\n raise\n # Initialize migration level (so that it can be altered in future)\n await self.conn.execute('INSERT INTO tinymud_migrations (table_name, level) VALUES ($1, $2)', table['name'], 0)","def new_table(table, db_file, schema_filename):\n \n try:\n conn, c = connect_to_db(db_file)\n with open(schema_filename, mode='r') as f:\n scriptlines = \"CREATE TABLE IF NOT EXISTS \" + safe(table) + \"\\n(\" + f.read() + \");\"\n c.executescript(scriptlines)\n conn.commit()\n conn.close()\n except Exception as e:\n print(\"Error when trying to create table \" + table + \" in\" + db_file)\n return False\n else:\n return True","def create_table(conn, create_table_sql):\n\n try:\n c = conn.cursor()\n c.execute(create_table_sql)\n except Error as e:\n print(e)","def create_tables (self):\n cursor = self.cur()\n cursor.execute('DROP TABLE IF EXISTS person')\n cursor.execute('DROP TABLE IF EXISTS room')\n cursor.execute('DROP TABLE IF EXISTS allocation')\n cursor.execute(\n 'CREATE TABLE IF NOT EXISTS person (person_id INTEGER PRIMARY KEY, name TEXT, role TEXT)WITHOUT ROWID')\n cursor.execute(\n 'CREATE TABLE IF NOT EXISTS room (name TEXT , no_of_members INTEGER, room_type TEXT)')\n cursor.execute(\n 'CREATE TABLE IF NOT EXISTS allocation (room_name TEXT , person_id INTEGER)')","def create_table(cls, cursor: sqlite3.Cursor) -> None:\n\n _get_model(cls).create_table(cursor)","def create_tables():\r\n db = connect_database()\r\n table_wait = \"waiting\"\r\n table_helped = \"helped\"\r\n table_help = \"help\"\r\n param_name = ['cus_num', 'name', 'username', 'ru_id', 'os_platform', 'description']\r\n param_type1 = ['INTEGER PRIMARY KEY AUTOINCREMENT', 'TEXT', 'TEXT', 'TEXT', 'TEXT', 'TEXT']\r\n param_type2 = ['INTEGER PRIMARY KEY', 'TEXT', 'TEXT', 'TEXT', 'TEXT', 'TEXT']\r\n with db:\r\n create_table(db, table_wait, param_name, param_type1)\r\n create_table(db, table_helped, param_name, param_type2)\r\n create_table(db, table_help, param_name, param_type2)\r\n db.close()","def create_table(self):\n self.db.query(f\"\"\"\n CREATE TABLE IF NOT EXISTS {self.table} (\n id INT UNSIGNED NOT NULL AUTO_INCREMENT,\n name VARCHAR(140) NOT NULL,\n PRIMARY KEY (id)\n )\n \"\"\")\n\n self.db.query(\"\"\"\n CREATE TABLE IF NOT EXISTS product_category (\n product_id bigint unsigned,\n category_id int unsigned,\n CONSTRAINT pfk_product_2\n FOREIGN KEY (product_id)\n REFERENCES product(id),\n CONSTRAINT pfk_category_2\n FOREIGN KEY (category_id)\n REFERENCES category(id),\n PRIMARY KEY (product_id, category_id)\n )\n \"\"\")","def create_example_test_table(conn):\n execute_sql_script(conn, \"06_create_example_test_table.sql\")"],"string":"[\n \"def _create_table_if_not_exists(self) -> bigquery.Table:\\n table = self.client.create_table(\\n table=bigquery.Table(table_ref=self._table_ref, schema=Schema),\\n exists_ok=True,\\n )\\n logging.info(\\\"table %s already exists.\\\", table.full_table_id)\\n return table\",\n \"def check_and_create_table(self) -> None:\\n table_ids = [t.table_id for t in self.instance.list_tables()]\\n\\n if not self.table_id in table_ids:\\n self.table.create()\\n f = self.table.column_family(self.family_id)\\n f.create()\\n\\n f_inc = self.table.column_family(self.incrementer_family_id,\\n gc_rule=MaxVersionsGCRule(1))\\n f_inc.create()\\n\\n f_log = self.table.column_family(self.log_family_id)\\n f_log.create()\\n\\n f_ce = self.table.column_family(self.cross_edge_family_id,\\n gc_rule=MaxVersionsGCRule(1))\\n f_ce.create()\\n\\n print(\\\"Table created\\\")\",\n \"def __createTableIfNotExists(self):\\n query = \\\"SELECT name FROM sqlite_master WHERE type='table'\\\"\\n results = self._cursor.execute(query)\\n name_list = [name for r in results for name in r]\\n\\n if self.table_name not in name_list:\\n # Timestamp is for the time from pythons datetime.now() func\\n table = \\\"\\\"\\\"CREATE TABLE {}\\n (media TEXT PRIMARY KEY,\\n thumbnail TEXT, regular TEXT, large TEXT,\\n [timestamp] timestamp)\\n \\\"\\\"\\\".format(self.table_name)\\n self._cursor.execute(table)\",\n \"def table_create(name):\\n query = \\\"\\\"\\\"CREATE TABLE if not exists `emp`.`\\\"\\\"\\\"+name+\\\"\\\"\\\"` (`Id` int(11) NOT NULL,`First name` varchar(45) NOT NULL,`Last Name` varchar(45) NOT NULL,`deparment` varchar(45) NOT NULL,`salary` int(11) NOT NULL,PRIMARY KEY (`Id`))\\\"\\\"\\\"\\n try:\\n conn = db_conection()\\n cur = conn.cursor()\\n cur.execute(query)\\n conn.commit()\\n# print (\\\"table Success\\\")\\n __log(1, 'Table Create if not exists')\\n except Exception as e:\\n print (e)\\n __log(2,'Table creation check and craete Error')\",\n \"def create_table(self):\\n self.db.query(f\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS {self.table} (\\n id INT UNSIGNED NOT NULL AUTO_INCREMENT,\\n name VARCHAR(140) NOT NULL,\\n PRIMARY KEY (id)\\n )\\n \\\"\\\"\\\")\",\n \"def create_table(self):\\n pass\",\n \"def create_table():\\n\\tCURSOR.execute(\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {} (\\n\\t\\t\\t[ID] NVARCHAR NOT NULL PRIMARY KEY,\\n\\t\\t\\t[Name] NVARCHAR,\\n\\t\\t\\t[Definition] NVARCHAR)\\\"\\\"\\\".format(TABLE_NAME))\",\n \"def create_tables():\\n\\tlog_msg4(\\\"No hay tablas para el año \\\" + txt_year + \\\". Creando\\\")\\n\\n\\tcreate_table('visited')\\n\\tcreate_table('saved')\\n\\tcreate_table('actions')\\n\\n\\tglobal new_tables_created\\n\\tnew_tables_created = True\\n\\n\\tlog_msg_ok4()\",\n \"def _create_table(self, thistable):\\n created = True\\n try:\\n thistable.__table__.create(self.__engine, checkfirst=True)\\n self._commit()\\n return created\\n except ProgrammingError:\\n self._rollback()\\n created = None\\n raise\\n except Exception:\\n self._rollback()\\n created = False\\n raise\",\n \"def __create_table_if_not_exist(self):\\n query = 'create TABLE IF NOT EXISTS {} (user_name char(255), time_stamp DATE, message char(255));'.format(TABLE_NAME)\\n try:\\n self.cursor.execute(query)\\n except sqlite3.DatabaseError as err:\\n # print error message to display\\n print (\\\"Error: \\\", err)\\n else:\\n self.db_conn.commit()\",\n \"def _create_table_if_not_exists(self) -> None:\\n COLUMN_DEFINITIONS = 'definitions'\\n COLUMN_TYPE = 'type'\\n\\n KEY_REF = '$ref'\\n\\n TYPE_LOOKUP = {\\n 'string': 'VARCHAR(255)',\\n 'integer': 'INTEGER',\\n 'boolean': 'BOOLEAN',\\n 'number': 'INTEGER',\\n }\\n\\n def ref_lookup(\\n property: Dict[str, Any], fields: Dict[str, Any]\\n ) -> Dict[str, Any]:\\n ref = property[KEY_REF]\\n property_lookup_name = ref[ref.rfind('/') + 1 :]\\n return fields[COLUMN_DEFINITIONS][property_lookup_name]\\n\\n field_queries = []\\n fields = json.loads(self.schema.schema_json())\\n\\n del fields[Keywords.Properties.value][\\n Keywords.ID.value\\n ] # Remove primary key field. It is handled with auto increment below.\\n\\n for property_name, property in fields[Keywords.Properties.value].items():\\n if KEY_REF in property:\\n property = ref_lookup(property, fields)\\n field_queries.append(\\n f'{property_name} {TYPE_LOOKUP[property[COLUMN_TYPE]]}'\\n )\\n table_columns = ', '.join(field_queries)\\n\\n with connect(**BaseModel.db_settings) as connection:\\n cursor = connection.cursor()\\n cursor.execute(\\n f'CREATE TABLE IF NOT EXISTS {self.table_name} (ID INTEGER PRIMARY KEY AUTO_INCREMENT, {table_columns})'\\n )\\n self._table_created[self.table_name] = True\",\n \"def _create_table_if_not_exists(self, table_name):\\n if self.environments['database'].engine.dialect.has_table(self.environments['database'].engine, table_name):\\n return True\\n\\n filename = glob(f\\\"{DATASET_DIR}/{DATASETS[self.dataset]['file_pattern']}\\\")\\n with open(filename[0]) as csv_file:\\n csv_reader = csv.DictReader(csv_file, delimiter=DATASETS[self.dataset]['delimiter'])\\n header = []\\n header.append(next(csv_reader))\\n metadata = sqlalchemy.MetaData()\\n data_column = []\\n for col in header[0]:\\n if isinstance(header[0][col], int):\\n data_column.append((col, sqlalchemy.Integer))\\n elif isinstance(header[0][col], str):\\n data_column.append((col, sqlalchemy.String))\\n elif isinstance(header[0][col], bool):\\n data_column.append((col, sqlalchemy.BOOLEAN))\\n elif isinstance(header[0][col], float):\\n data_column.append((col, sqlalchemy.FLOAT))\\n table = sqlalchemy.Table(table_name,\\n metadata,\\n sqlalchemy.Column('id', sqlalchemy.Integer, primary_key=True),\\n *(sqlalchemy.Column(column_name, column_type(60))\\n for column_name, column_type in data_column))\\n table.create(self.environments['database'].engine)\\n self.destination_table = table\\n return False\",\n \"def _create_table(self) :\\n\\n cur = self.con.cursor()\\n delete_sql = 'DROP TABLE IF EXISTS \\\"%s\\\"' % self.name\\n cur.execute(delete_sql)\\n\\n col_sql = ','.join(['\\\"%s\\\" %s' % (self.cols[i], self.types[i])\\n for i in range(len(self.cols))])\\n create_sql = 'CREATE TABLE \\\"%s\\\" ( %s );' % (self.name, col_sql)\\n cur.execute(create_sql)\",\n \"def creates_table(self):\\n return self.statements[0].creates_table()\",\n \"def create_table(self, table_name):\\n if not self.open:\\n print(\\\"Not currently connected to a DB.\\\")\\n return False\\n\\n q = 'CREATE TABLE IF NOT EXISTS {tn}(ID INT PRIMARY KEY NOT NULL UNIQUE)'\\n self.query = q.format(tn=table_name)\\n\\n try:\\n self.cursor.execute(self.query)\\n print(\\\"{} table created.\\\".format(table_name))\\n return True\\n except Exception as error:\\n print(\\\"Unable to create '{}' table.\\\".format(table_name))\\n print(\\\"SQL Query: \\\\n{}\\\\n\\\".format(self.query))\\n print(\\\"Exception: \\\\n{}\\\".format(error))\\n\\n return False\",\n \"def create_table(cls):\\n if not connection.connected:\\n raise Exception('Not connected to the database.')\\n cursor = connection.execute(cls.create_table_sql())\\n cursor.close()\",\n \"def create_table():\\n\\n cnx = create_connection()\\n cursor = cnx.cursor()\\n\\n print(TABLE_DESCRIPTION)\\n try:\\n print(\\\"Creating table...\\\")\\n cursor.execute(TABLE_DESCRIPTION)\\n print(\\\"Table created!\\\")\\n except mysql.connector.Error as err:\\n if err.errno == errorcode.ER_TABLE_EXISTS_ERROR:\\n print(\\\"already exists.\\\")\\n cnx.close()\\n cursor.close()\\n return\\n else:\\n print(err.msg)\\n cnx.close()\\n cursor.close()\\n return\\n else:\\n print(\\\"OK\\\")\\n\\n cnx.close()\\n cursor.close()\",\n \"def _create_table(self):\\n query = '''\\n CREATE TABLE IF NOT EXISTS {} (\\n id integer PRIMARY KEY,\\n url text NOT NULL UNIQUE)\\n '''.format(\\n self.tablename\\n )\\n\\n self.conn.execute(query)\",\n \"def create_table_if_not_exists(tablename: str, create_query: str) -> bool:\\n\\n if cursor:\\n cursor.execute(\\\"select exists(select * from information_schema.tables where table_name=%s)\\\", (tablename,))\\n if not cursor.fetchone()[0]:\\n cursor.execute(create_query)\\n return True\\n else:\\n return False\\n\\n else:\\n logger.error(\\\"Attempted to create a table while DB cursor has not been initialised\\\")\",\n \"def create_table(self):\\n Engine.create_table(self)\\n self.connection.commit()\",\n \"def test_create_table_successfully (self):\\n\\n new_table = self.wrapper.create_table(self.table, [self.bob, self.jane])\\n self.assertIsNone(new_table)\",\n \"def db_create_table(db_in, tablename):\\n connection = db_in.connection.cursor()\\n connection.execute('CREATE TABLE IF NOT EXISTS %s(id INTEGER PRIMARY KEY);' % tablename)\",\n \"def create_table(self, create_table_sql):\\n connection = self.__create_connection()\\n try:\\n c = connection.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn):\\n\\n try:\\n c = conn.cursor()\\n c.execute('''CREATE TABLE IF NOT EXISTS students\\n (last_name text, first_name text, student_id text)''')\\n conn.commit()\\n except Error as e:\\n print(e)\",\n \"def create_table(create_table_sql):\\n conn = DbUtil.connection\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_base_table(self, table_name):\\n print('new')\\n # Create table at first.\\n select_stm = self.construct_base_table()\\n exec_query('DROP TABLE IF EXISTS %s;' % table_name) \\n sql = \\\"\\\"\\\"\\n CREATE TABLE %s AS\\n %s\\n \\\"\\\"\\\" % (table_name, select_stm)\\n exec_query(sql)\",\n \"def create_table(cursor, TABLES):\\n for t_name,t_desc in TABLES.items():\\n try:\\n print(f\\\"Creating table '{t_name}'...\\\")\\n cursor.execute(t_desc)\\n except mysql.connector.Error as err:\\n if err.errno == errorcode.ER_TABLE_EXISTS_ERROR:\\n print(f\\\"{t_name} already exists, moving on...\\\")\\n pass\\n else:\\n print(err.msg+\\\".\\\")\\n exit(1)\\n else:\\n print(\\\"OK\\\")\\n return None\",\n \"def create_table(self, conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create(self):\\n self.execute(self.commands.create_table(self.name, self.primary_key_column))\\n return self.commit()\",\n \"def createTable(self):\\n results = self.db.table_create(self.entity).run(self.r)\\n time.sleep(5)\\n return results\",\n \"def create_table(self) -> None:\\n try:\\n self.cursor.execute(\\\"CREATE TABLE IF NOT EXISTS CAR (id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT, title TEXT NOT NULL, price real NOT NULL, url TEXT NOT NULL, location TEXT, datelisted TEXT, unique(title));\\\")\\n except sql.OperationalError as e:\\n print(e)\",\n \"def create_table(conn, tbl_data):\\r\\n try:\\r\\n cur = conn.cursor()\\r\\n cur.execute(tbl_data)\\r\\n except Error as e:\\r\\n print(e)\",\n \"def use_table(self):\\n connection = self._get_connection()\\n cursor = connection.cursor()\\n cursor.execute(\\n 'select exists(select * from information_schema.tables where table_name=%s)',\\n (self.table,),\\n )\\n if cursor.fetchone()[0]:\\n self.logger.info('Using existing table')\\n else:\\n try:\\n cursor.execute(\\n f'CREATE TABLE {self.table} ( \\\\\\n ID VARCHAR PRIMARY KEY, \\\\\\n DOC BYTEA);'\\n )\\n self.logger.info('Successfully created table')\\n except (Exception, psycopg2.Error) as error:\\n self.logger.error('Error while creating table!')\\n connection.commit()\\n self._close_connection(connection)\",\n \"def create_table(self, name):\\n # check read only\\n if self.__read_only:\\n raise IOError(\\\"DB is for reading only.\\\")\\n # add new table to data\\n table = Table()\\n self.__tables[name] = table\\n # if table previously removed - unmark it\\n if name in self.__dropped_tables:\\n self.__dropped_tables.remove(name)\",\n \"def create_table_db(self):\\n table_name = self.name_table.get()\\n if len(table_name) > 0:\\n self.table_db = table_name\\n self.sql_database.db_name = self.db\\n if self.sql_database.create_table(self.table_db):\\n msg.showinfo(\\n message=\\\"\\\".join([str(self.table_db), \\\" created\\\"]))\\n self.name_table.delete(0, tk.END)\\n self.show_table_combobox()\\n else:\\n msg.showinfo(message=\\\"Failed\\\")\\n else:\\n msg.showinfo(message=\\\"Write table name!\\\")\",\n \"def createDbTables():\\n logger.info(\\\"creating required DB table if it does not already exist\\\")\\n try:\\n with db.connect() as conn:\\n conn.execute(\\n \\\"CREATE TABLE IF NOT EXISTS birthdays \\\"\\n \\\"( username VARCHAR(255) NOT NULL, birthday DATE NOT NULL, \\\"\\n \\\"PRIMARY KEY (username) );\\\"\\n )\\n return OK\\n except Exception as e:\\n logger.info(\\\"Error creating table: %s\\\" % e)\\n return ERR\",\n \"def create_table(self, table_name):\\n self.table_name = table_name\\n conn, cursor = SQLDatabase.connect()\\n self.change_database(cursor)\\n success = False\\n try:\\n cursor.execute(\\\"CREATE TABLE IF NOT EXISTS {} (\\\\\\n Text_ID int NOT NULL AUTO_INCREMENT PRIMARY KEY,\\\\\\n Text_Path varchar(250) NOT NULL,\\\\\\n Word_First varchar(250) NOT NULL, \\\\\\n Word_Second varchar(250),\\\\\\n Word_Third varchar(250),\\\\\\n Word_Fourth varchar(250),\\\\\\n Word_Fifth varchar(250));\\\\\\n \\\".format(self.table_name))\\n success = True\\n except mysql.connector.errors.ProgrammingError as err:\\n print(\\\"Failed creating table: {}\\\".format(err))\\n finally:\\n SQLDatabase.close(cursor, conn)\\n\\n return success\",\n \"def create_table(conn, create_table_sql):\\r\\n try:\\r\\n c = conn.cursor()\\r\\n c.execute(create_table_sql)\\r\\n except Error as e:\\r\\n print(e)\",\n \"def create_sql_table(self, query):\\n table_pattern = r\\\"CREATE TABLE ([\\\\w_:]+)\\\"\\n match = re.search(table_pattern, query, re.IGNORECASE)\\n table_name = match.group(1)\\n if \\\":\\\" in table_name:\\n query = self.clean_table_name(query, table_name)\\n\\n # Check to see if tbable exists already\\n try:\\n self.send_sql_query(query)\\n # Recreate if table already exists\\n except mysql.connector.Error as err:\\n if err.errno == errorcode.ER_TABLE_EXISTS_ERROR:\\n table_name = table_name.replace(\\\":\\\", \\\"\\\")\\n self.send_sql_query(f\\\"DROP TABLE {table_name}\\\")\\n self.send_sql_query(query)\",\n \"def create_table(self):\\n conn = self._connect_DB()\\n cur = conn.cursor()\\n cur.execute(\\n \\\"CREATE TABLE IF NOT EXISTS movie_table (movie_title, people);\\\"\\n )\\n self._close_connection(conn)\",\n \"def create_table(conn, sql_create_table):\\n try:\\n c = conn.cursor()\\n c.execute(sql_create_table)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, sql_create_table):\\n try:\\n c = conn.cursor()\\n c.execute(sql_create_table)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, sql_create_table):\\n try:\\n c = conn.cursor()\\n c.execute(sql_create_table)\\n except Error as e:\\n print(e)\",\n \"def ctable_sansanito():\\n\\tcur.execute(\\\"\\\"\\\"\\n\\t\\t\\t\\tCREATE TABLE sansanito(\\n\\t\\t\\t\\tid NUMBER NOT NULL PRIMARY KEY,\\n\\t\\t\\t\\tpokedex INT,\\n\\t\\t\\t\\tnombre VARCHAR(40),\\n\\t\\t\\t\\ttype1 VARCHAR(20),\\n\\t\\t\\t\\ttype2 VARCHAR(20),\\n\\t\\t\\t\\thpactual INT,\\n\\t\\t\\t\\thpmax INT,\\n\\t\\t\\t\\tlegendary NUMBER(1),\\n\\t\\t\\t\\testado VARCHAR(30),\\n\\t\\t\\t\\tingreso DATE,\\n\\t\\t\\t\\tprioridad INT)\\\"\\\"\\\"\\n\\t\\t\\t\\t)\\n\\n\\t\\n\\tconnection.commit()\",\n \"def create_table(self):\\n logger.debug('Function create_table start')\\n\\n table = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS sensor_data (\\n id integer PRIMARY KEY,\\n time timestamp NOT NULL,\\n img_name INTEGER,\\n img_score INTEGER,\\n magnetometer_z REAL,\\n magnetometer_y REAL,\\n magnetometer_x REAL\\n );\\\"\\\"\\\"\\n\\n try:\\n c = self.conn.cursor()\\n\\n # Create table\\n c.execute(table)\\n\\n self.conn.commit()\\n\\n logger.info('Created a table')\\n\\n return True\\n except Exception as e:\\n table_exists = \\\"SELECT name FROM sqlite_master WHERE type='table' AND name='spwords'\\\"\\n if self.conn.execute(table_exists).fetchone() and isinstance(e, sqlite3.OperationalError):\\n # sqlite3 docs say ProgrammingError is raised when table exists, although OperationalError was raised when testing.\\n logger.warning('Table already exists: {}'.format(format_exc()))\\n return True\\n\\n logger.critical('Could not create a table: {}'.format(format_exc()))\\n return False\\n\\n logger.debug('Function create_table end')\",\n \"def create_table(drop_if_exists=True) -> None:\\r\\n connection = ConnectDB.connect()\\r\\n additional_types_query = \\\"\\\"\\\"DO\\r\\n BEGIN\\r\\n IF NOT EXISTS (SELECT 1 FROM pg_type WHERE typname = 'gender') THEN\\r\\n CREATE TYPE Gender AS ENUM ('female', 'man');\\r\\n END IF;\\r\\n IF NOT EXISTS (SELECT 1 FROM pg_type WHERE typname = 'ter') THEN\\r\\n CREATE TYPE ter AS ENUM ('city', 'village');\\r\\n END IF;\\r\\n END;\\\"\\\"\\\"\\r\\n\\r\\n ZnoData_drop_query = \\\"DROP TABLE IF EXISTS ZnoData;\\\"\\r\\n\\r\\n ZnoData_create_query = 'CREATE TABLE IF NOT EXISTS ZnoData ('\\r\\n fields = ''\\r\\n for col, t in table_columns_types.items():\\r\\n if fields == '':\\r\\n fields += (f'{col} {t}') + ' PRIMARY KEY, '\\r\\n else:\\r\\n fields += (f'{col} {t}')+ ', '\\r\\n ZnoData_create_query += fields[:-2] + ');'\\r\\n \\r\\n with connection.cursor() as cursor:\\r\\n cursor.execute(additional_types_query)\\r\\n if drop_if_exists:\\r\\n cursor.execute(ZnoData_drop_query)\\r\\n print('Table was droped.')\\r\\n cursor.execute(ZnoData_create_query)\\r\\n print('Table was created.')\\r\\n ConnectDB.disconnect(connection)\",\n \"def create_table(self, conn, create_table_sql):\\n try:\\n # create a Cursor object and call its .execute() method to perform SQL queries\\n c = conn.cursor()\\n # execute SQL queries: create a table named card\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_users_table():\\n try:\\n print(\\\"Creating 'users' table..\\\")\\n db.execute(\\\"CREATE TABLE users (\\\\\\n id SERIAL PRIMARY KEY, \\\\\\n username VARCHAR NOT NULL, \\\\\\n password VARCHAR NOT NULL\\\\\\n )\\\")\\n print(\\\"Table 'users' created\\\")\\n except exc.ProgrammingError as err:\\n print(\\\"Table 'users' already exists\\\")\\n db.commit()\",\n \"def create_tables():\\n db.create_all()\",\n \"def create_tables():\\n db.create_all()\",\n \"def createTable(self, tableName: str, configuration: str):\\n sql = \\\"CREATE TABLE IF NOT EXISTS {} ({})\\\".format(tableName, configuration)\\n self._execute(sql)\",\n \"def _create_table(self):\\n query = f\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {TABLE}(\\n member_Id INT,\\n memberName VARCHAR(50),\\n amount INT,\\n date datetime NOT NULL,\\n time datetime NOT NULL,\\n status VARCHAR(20) NOT NULL DEFAULT 'Completed'\\n );\\\"\\\"\\\"\\n\\n self.cursor.execute(query)\\n self.conn.commit()\",\n \"def _create_table(conn, table_name, columns):\\n # Create the table.\\n with conn:\\n cur = conn.cursor()\\n cur.execute('CREATE TABLE IF NOT EXISTS %s%s' % (table_name, columns))\",\n \"def create_table(self):\\n table = self.table\\n table.create()\\n return table.bind.wait()\",\n \"def create_table(self):\\n logging.debug('Creating new table')\\n if not self._dbconnect or not self._cursor:\\n raise Exception('Invalid call to Context Manager method!')\\n\\n self._cursor.execute(\\\"create table {} (date text, time text, location text, nodeID text)\\\".format(self._name))\",\n \"def new_table(self):\\n self.c.execute(\\n \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {table} (\\n id integer primary key,\\n {event} integer,\\n {desc} text,\\n {date} text,\\n {link} text)\\n \\\"\\\"\\\".format(\\n table=TABLE,\\n event=EVENT,\\n desc=DESC,\\n date=DATE,\\n link=LINK,\\n )\\n )\",\n \"def create_table(conn, create_table_sql):\\r\\n try:\\r\\n c = conn.cursor()\\r\\n c.execute(create_table_sql)\\r\\n except Error as e:\\r\\n print(e)\",\n \"def create_table(self, table_info, table_name):\\r\\n t1 = time.time()\\r\\n if self.database in ['redshift', 'postgres']:\\r\\n postgres_helper.create_table(\\r\\n conf=self.conf,\\r\\n table_info=table_info,\\r\\n table_name=table_name\\r\\n )\\r\\n else:\\r\\n raise Exception(\\\"database not supported yet: '{}'\\\"\\r\\n .format(self.database))\\r\\n t2 = time.time()\\r\\n t = t2 - t1\\r\\n print('Finished in {:.2f} seconds.'.format(t))\\r\\n return\",\n \"def create_table():\\r\\n\\tc = conn.cursor()\\r\\n\\tstrsql=\\\"\\\"\\\"\\r\\n\\t\\tCREATE TABLE if not exists cacao (\\r\\n\\t\\t\\tid INTEGER PRIMARY KEY AUTOINCREMENT,\\r\\n\\t\\t\\tCompany text,\\r\\n\\t\\t\\tSpecific_Bean_Origin text,\\r\\n\\t\\t\\tREF\\tint,\\r\\n\\t\\t\\tReview\\tint,\\r\\n\\t\\t\\tCocoa text,\\r\\n\\t\\t\\tLocation text,\\r\\n\\t\\t\\tRating\\tfloat,\\r\\n\\t\\t\\tBean text,\\r\\n\\t\\t\\tBroad_Bean_Origin text\\r\\n\\t\\t\\t);\\r\\n\\t\\t\\\"\\\"\\\"\\r\\n\\tc.execute(strsql)\\r\\n\\tconn.commit()\",\n \"def create_table_in_sqlite_db(self):\\n with self.con:\\n cur = self.con.cursor()\\n cur.execute(\\\"\\\"\\\"DROP TABLE IF EXISTS {};\\\"\\\"\\\".format(self.table_name))\\n base_create_query = \\\"\\\"\\\"CREATE TABLE {}({}, PRIMARY KEY ({}));\\\"\\\"\\\"\\n columns = ','.join(['{} {}'.format(col, self.columns_types[col]) for col in self.table_columns])\\n primary_keys = ','.join(['{}'.format(col) for col in self.table_primary_keys])\\n create_query = base_create_query.format(self.table_name, columns, primary_keys)\\n cur.execute(create_query)\\n self.con.commit()\",\n \"def create_tables(self):\\n for query in table_create_sql:\\n self.cursor.execute(query)\\n\\n self.commit()\",\n \"def create_table(conn, create_table_sql):\\n\\ttry:\\n\\t\\tc = conn.cursor()\\n\\t\\tc.execute(create_table_sql)\\n\\texcept Error as e:\\n\\t\\tprint(e)\",\n \"def make_new_tbl(self):\\n debug = False\\n default_dd = getdata.get_default_db_dets()\\n con, cur = default_dd.con, default_dd.cur\\n oth_name_types = getdata.get_oth_name_types(self.settings_data)\\n tblname = self.tblname_lst[0]\\n if debug: print(f'DBE in make_new_tbl is: {default_dd.dbe}')\\n getdata.make_sofa_tbl(\\n con, cur, tblname, oth_name_types, headless=False)\\n wx.MessageBox(\\n _('Your new table has been added to the default SOFA database'))\",\n \"def create_table(curs, table_name):\\n curs.execute(\\\"\\\"\\\"CREATE TABLE {} (\\n s TEXT PRIMARY KEY,\\n x integer,\\n y integer\\n );\\\"\\\"\\\".format(table_name))\",\n \"def create_tables():\\n db.create_all()\",\n \"def create_table(conn, create_table_sql):\\r\\n try:\\r\\n c = conn.cursor()\\r\\n c.execute(create_table_sql)\\r\\n conn.commit()\\r\\n except Error as e:\\r\\n print(e)\",\n \"def create_new_table():\\n dataset = create_dataset()\\n table_id = \\\"{}.{}.corona_cases_table\\\".format(client.project, dataset.dataset_id)\\n table = bigquery.Table(table_id)\\n table = client.create_table(table, exists_ok=True)\\n print(\\n \\\"Created table {}.{}.{}\\\".format(table.project, table.dataset_id, table.table_id)\\n )\\n return table\",\n \"def create_books_table():\\n try:\\n print(\\\"Creating 'books' table..\\\")\\n db.execute(\\\"CREATE TABLE books (\\\\\\n id SERIAL PRIMARY KEY, \\\\\\n isbn VARCHAR NOT NULL, \\\\\\n title VARCHAR NOT NULL, \\\\\\n author VARCHAR NOT NULL, \\\\\\n year INTEGER NOT NULL\\\\\\n )\\\")\\n print(\\\"Table 'books' created\\\")\\n except exc.ProgrammingError as err:\\n print(\\\"Table 'books' already exists\\\")\\n db.commit()\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_table_execute(self):\\n self.execute(query=self.default_template.format(self.table_name), data=None)\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n\\tconn.commit()\\n except Error as e:\\n print(e)\",\n \"def create_table(self, table_name, fields, noid=False):\\n print('creating table {} ...'.format(table_name))\\n sqlhead = \\\"CREATE TABLE %s (id INTEGER PRIMARY KEY AUTO_INCREMENT , \\\" % table_name\\n if noid:\\n sqlhead = \\\"CREATE TABLE '%s' (\\\" % table_name\\n if fields is None:\\n return\\n if isinstance(fields[0], tuple):\\n sqlmain = ', '.join([x[0] + \\\" \\\" + x[1] for x in fields])\\n else:\\n sqlmain = ', '.join([x + \\\" varchar(128)\\\" for x in fields])\\n default_setting = \\\"ENGINE=InnoDB DEFAULT CHARSET=utf8;\\\"\\n sql = sqlhead + sqlmain + \\\" ) \\\" + default_setting\\n try:\\n cur = self._get_cursor()\\n cur.execute(sql)\\n self.conn.commit()\\n except Exception as err:\\n self.conn.rollback()\\n # if table already exists\\n if err.args[0] == 1050:\\n return False\\n self._print_error(err)\\n return True\",\n \"def create_table():\\n db, c = config.start_db()\\n try:\\n c.execute('''CREATE TABLE users (\\n username TEXT PRIMARY KEY,\\n pass_hash BLOB NOT NULL,\\n salt BLOB NOT NULL,\\n karma INT NOT NULL\\n )''')\\n except sqlite3.OperationalError: # Table already exists\\n pass\\n config.end_db(db)\",\n \"def create_table(self):\\n self.db.query(f\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS {self.table} (\\n substitut_id bigint unsigned references product(id),\\n original_id bigint unsigned references product(id),\\n PRIMARY KEY (substitut_id, original_id)\\n )\\n \\\"\\\"\\\")\",\n \"def createFreshTable(self):\\n\\n # delete existing table if it exists\\n try:\\n self.__getTable().delete();\\n time.sleep(10)\\n except:\\n pass;# do nothing. Maybe there was no existing table\\n\\n # create new table\\n tableConnectionParams = parseConnectionString(\\n self.tableConnString);\\n return Table.create(\\n tableConnectionParams['name'],\\n schema = [\\n HashKey('entity'),\\n RangeKey('docId')\\n ], throughput = {\\n 'read': 1,\\n 'write': 4,\\n }, global_indexes = [\\n GlobalAllIndex('docIdIndex', parts = [\\n HashKey('docId'),\\n RangeKey('entity')\\n ],\\n throughput = {\\n 'read': 1,\\n 'write': 4,\\n })\\n ],\\n connection = getDbConnection(tableConnectionParams))\",\n \"def create_table(conn, create_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Exception as e:\\n print(e)\",\n \"def createTable(self, tableName, vars):\\n\\n table = \\\"CREATE TABLE IF NOT EXISTS \\\"+tableName+'('\\n for i in vars:\\n table += i\\n table += ','\\n\\n table[table.len()-1] = ')'\\n table += ';'\\n \\n self.cur.execute(table)\",\n \"def create_table():\\n conn = psycopg2.connect(host=\\\"localhost\\\", database=\\\"integration\\\", user=\\\"postgres\\\", password=\\\"postgres\\\")\\n cursor = conn.cursor()\\n cursor.execute(CREATE_TABLE)\\n conn.commit()\\n cursor.close()\",\n \"async def _create_table(self, table: TableSchema) -> None:\\n try:\\n await self.conn.execute(get_create_table(table))\\n except PostgresError: # Only DB related exceptions\\n print(f\\\"Failed to execute CREATE TABLE for {table['name']}\\\")\\n raise\\n # Initialize migration level (so that it can be altered in future)\\n await self.conn.execute('INSERT INTO tinymud_migrations (table_name, level) VALUES ($1, $2)', table['name'], 0)\",\n \"def new_table(table, db_file, schema_filename):\\n \\n try:\\n conn, c = connect_to_db(db_file)\\n with open(schema_filename, mode='r') as f:\\n scriptlines = \\\"CREATE TABLE IF NOT EXISTS \\\" + safe(table) + \\\"\\\\n(\\\" + f.read() + \\\");\\\"\\n c.executescript(scriptlines)\\n conn.commit()\\n conn.close()\\n except Exception as e:\\n print(\\\"Error when trying to create table \\\" + table + \\\" in\\\" + db_file)\\n return False\\n else:\\n return True\",\n \"def create_table(conn, create_table_sql):\\n\\n try:\\n c = conn.cursor()\\n c.execute(create_table_sql)\\n except Error as e:\\n print(e)\",\n \"def create_tables (self):\\n cursor = self.cur()\\n cursor.execute('DROP TABLE IF EXISTS person')\\n cursor.execute('DROP TABLE IF EXISTS room')\\n cursor.execute('DROP TABLE IF EXISTS allocation')\\n cursor.execute(\\n 'CREATE TABLE IF NOT EXISTS person (person_id INTEGER PRIMARY KEY, name TEXT, role TEXT)WITHOUT ROWID')\\n cursor.execute(\\n 'CREATE TABLE IF NOT EXISTS room (name TEXT , no_of_members INTEGER, room_type TEXT)')\\n cursor.execute(\\n 'CREATE TABLE IF NOT EXISTS allocation (room_name TEXT , person_id INTEGER)')\",\n \"def create_table(cls, cursor: sqlite3.Cursor) -> None:\\n\\n _get_model(cls).create_table(cursor)\",\n \"def create_tables():\\r\\n db = connect_database()\\r\\n table_wait = \\\"waiting\\\"\\r\\n table_helped = \\\"helped\\\"\\r\\n table_help = \\\"help\\\"\\r\\n param_name = ['cus_num', 'name', 'username', 'ru_id', 'os_platform', 'description']\\r\\n param_type1 = ['INTEGER PRIMARY KEY AUTOINCREMENT', 'TEXT', 'TEXT', 'TEXT', 'TEXT', 'TEXT']\\r\\n param_type2 = ['INTEGER PRIMARY KEY', 'TEXT', 'TEXT', 'TEXT', 'TEXT', 'TEXT']\\r\\n with db:\\r\\n create_table(db, table_wait, param_name, param_type1)\\r\\n create_table(db, table_helped, param_name, param_type2)\\r\\n create_table(db, table_help, param_name, param_type2)\\r\\n db.close()\",\n \"def create_table(self):\\n self.db.query(f\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS {self.table} (\\n id INT UNSIGNED NOT NULL AUTO_INCREMENT,\\n name VARCHAR(140) NOT NULL,\\n PRIMARY KEY (id)\\n )\\n \\\"\\\"\\\")\\n\\n self.db.query(\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS product_category (\\n product_id bigint unsigned,\\n category_id int unsigned,\\n CONSTRAINT pfk_product_2\\n FOREIGN KEY (product_id)\\n REFERENCES product(id),\\n CONSTRAINT pfk_category_2\\n FOREIGN KEY (category_id)\\n REFERENCES category(id),\\n PRIMARY KEY (product_id, category_id)\\n )\\n \\\"\\\"\\\")\",\n \"def create_example_test_table(conn):\\n execute_sql_script(conn, \\\"06_create_example_test_table.sql\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7952568","0.7628907","0.75765896","0.7453806","0.7421533","0.7417688","0.73947656","0.7391235","0.73709404","0.7370457","0.73420954","0.7339442","0.7294078","0.72117263","0.7173514","0.71690124","0.7127797","0.71181244","0.710121","0.70358473","0.70275396","0.7002455","0.69893026","0.69755816","0.69640595","0.6950895","0.69259787","0.6906101","0.69015795","0.69014233","0.6880621","0.68714434","0.6863276","0.6847912","0.68463373","0.684392","0.6829208","0.68147427","0.68096405","0.68048525","0.67980146","0.67980146","0.67980146","0.67959124","0.67721266","0.67615885","0.67525905","0.67419136","0.673798","0.673798","0.67307717","0.6722976","0.67082024","0.66745526","0.667276","0.66696715","0.6653413","0.66403735","0.66305125","0.6626402","0.66165555","0.6614119","0.6612596","0.6605313","0.65991384","0.6596115","0.65826374","0.6578496","0.6573835","0.6573835","0.6573835","0.6573835","0.6573835","0.6573835","0.6573835","0.6573835","0.6573835","0.6573835","0.6573835","0.6573835","0.6573835","0.6573835","0.6573835","0.6557575","0.65552795","0.6545832","0.6539004","0.6538139","0.65353113","0.6530962","0.65232164","0.6512196","0.65063584","0.65020275","0.6501106","0.6497778","0.6495943","0.64864725","0.6475071","0.6473035"],"string":"[\n \"0.7952568\",\n \"0.7628907\",\n \"0.75765896\",\n \"0.7453806\",\n \"0.7421533\",\n \"0.7417688\",\n \"0.73947656\",\n \"0.7391235\",\n \"0.73709404\",\n \"0.7370457\",\n \"0.73420954\",\n \"0.7339442\",\n \"0.7294078\",\n \"0.72117263\",\n \"0.7173514\",\n \"0.71690124\",\n \"0.7127797\",\n \"0.71181244\",\n \"0.710121\",\n \"0.70358473\",\n \"0.70275396\",\n \"0.7002455\",\n \"0.69893026\",\n \"0.69755816\",\n \"0.69640595\",\n \"0.6950895\",\n \"0.69259787\",\n \"0.6906101\",\n \"0.69015795\",\n \"0.69014233\",\n \"0.6880621\",\n \"0.68714434\",\n \"0.6863276\",\n \"0.6847912\",\n \"0.68463373\",\n \"0.684392\",\n \"0.6829208\",\n \"0.68147427\",\n \"0.68096405\",\n \"0.68048525\",\n \"0.67980146\",\n \"0.67980146\",\n \"0.67980146\",\n \"0.67959124\",\n \"0.67721266\",\n \"0.67615885\",\n \"0.67525905\",\n \"0.67419136\",\n \"0.673798\",\n \"0.673798\",\n \"0.67307717\",\n \"0.6722976\",\n \"0.67082024\",\n \"0.66745526\",\n \"0.667276\",\n \"0.66696715\",\n \"0.6653413\",\n \"0.66403735\",\n \"0.66305125\",\n \"0.6626402\",\n \"0.66165555\",\n \"0.6614119\",\n \"0.6612596\",\n \"0.6605313\",\n \"0.65991384\",\n \"0.6596115\",\n \"0.65826374\",\n \"0.6578496\",\n \"0.6573835\",\n \"0.6573835\",\n \"0.6573835\",\n \"0.6573835\",\n \"0.6573835\",\n \"0.6573835\",\n \"0.6573835\",\n \"0.6573835\",\n \"0.6573835\",\n \"0.6573835\",\n \"0.6573835\",\n \"0.6573835\",\n \"0.6573835\",\n \"0.6573835\",\n \"0.6573835\",\n \"0.6557575\",\n \"0.65552795\",\n \"0.6545832\",\n \"0.6539004\",\n \"0.6538139\",\n \"0.65353113\",\n \"0.6530962\",\n \"0.65232164\",\n \"0.6512196\",\n \"0.65063584\",\n \"0.65020275\",\n \"0.6501106\",\n \"0.6497778\",\n \"0.6495943\",\n \"0.64864725\",\n \"0.6475071\",\n \"0.6473035\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71380335"},"document_rank":{"kind":"string","value":"16"}}},{"rowIdx":76673,"cells":{"query":{"kind":"string","value":"Crea la base si no existe"},"document":{"kind":"string","value":"def crearBD(self):\n if self.base.isConnected():\n mensaje = \"Usted ya se encuentra conectado a la base \" + self.base.getDbName() + \", ¿Desea Crear una nueva?\"\n if askyesno(\"Atención\", mensaje):\n nombre = tkinter.simpledialog.askstring(\"Elija el Nombre de la Base\", prompt=\"Nombre\")\n self.base.setDbName(nombre)\n resultado = self.base.createDB()\n showinfo('Resultado', resultado)\n self.crearTabla()\n self.mostrarString.set('Mostrando Registros Existentes en ' + self.base.getDbName())\n self.tituloTree.configure(text=self.mostrarString.get())\n self.updateTree()\n else:\n try:\n mibase = mysql.connector.connect(\n host=\"localhost\",\n user=\"root\",\n password=\"\"\n )\n baseNombre['nombre'] = tkinter.simpledialog.askstring(\"Elija el Nombre de la Base\", prompt=\"Nombre\")\n buffer = open('nombreBD.json', 'w')\n buffer.write(json.dumps(baseNombre))\n buffer.close() \n micursor = mibase.cursor()\n baseSQL = f\"CREATE DATABASE {getNameDB()}\"\n micursor.execute(baseSQL)\n mensaje = f\"Se ha creado la base {getNameDB()}\"\n showinfo('BD Creada', mensaje)\n mostrarString.set('Mostrando Registros Existentes en ' + getNameDB())\n tituloTree.configure(text=mostrarString.get())\n\n if askyesno('Tabla Inexistente', '¿Desea crear una tabla?'):\n crearTabla()\n except:\n showinfo ('Error', exc_info()[1])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_base(self):\r\n self.mycursor.execute(\r\n 'CREATE DATABASE IF NOT EXISTS purbeurre CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci')\r\n self.mycursor.execute('USE purbeurre')","def create():\n\tcreate_db()","def create_db(self, path: str) -> None:\n if os.path.isfile(path):\n self.db_path = path\n print(\"DB already exists\")\n return\n\n print(path)\n\n self.db_path = path\n\n print(\"Opening the base db\")\n with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'basedb.xml'), 'r') as f:\n base = f.read()\n print(\"Reading the base as {0}\".format(base))","def create_db(self):\n creation_query = f\"CREATE DATABASE {DB_NAME} CHARACTER SET 'utf8mb4' \"\n existence_query = f\"USE {DB_NAME}\"\n try:\n self.cursor.execute(creation_query)\n self.cursor.execute(existence_query)\n print(f\"\\n La base de données |{DB_NAME}| a été créée \\n\")\n except:\n print(\"\\n Une erreur s'est produite lors \"\n \"de la création de la base de données \\n\")","def create_db(self):","def create_db(self):\n return None","def create():","def create():","def get_creation(self,DATA_PATH,db):\n\t\t\n\t\t# if the database already exists\n\t\tif os.path.isfile(DATA_PATH):\n\t\t\treturn template('home',error=\"La base de donnée est déjà existante\",bd=\"\",admin=\"\")\n\t\t# if not, if the database does not exist\n\t\telse:\n\t\t\tlistAdmin = []\n\t\t\t# all tables for the database are created\n\t\t\tgenerateDB.GenerateDB(listAdmin)\n\t\t\treturn template('home',error=\"La base a bien été créé\",bd=\"\",admin=\"True\")","def create_new_db():\n global data_base, table\n data_base = asksaveasfilename(title=\"Select file\", filetypes=((\"DATA BASE\", \"*.db\"), (\"all files\", \"*.*\")),\n defaultextension='.db')\n\n if Path(data_base).suffix == '.db':\n create_win_create_table()\n else:\n mistake_db_file()","def create():\n pass","def create(db):\n if exists(db):\n print(\"phonebook %r already exists\" % db)\n sys.exit(-1)\n else:\n database = {}\n pickle.dump(database, open(db, 'wb'))\n print(\"created phonebook %r in the current directory\" % db)","def createdb():\n if database.exists():\n raise click.UsageError(\n click.style('Database already exists at {}.'\n .format(database.url), fg='red')\n )\n else:\n database.create()\n click.echo(OK)","def create_db():\n db.create_all()\n click.echo(\"Banco de dados criado\")","def create_db():\n db.create_all()\n print('Database structure created successfully')","def _create_db(self):\n self.db = easydms.dbcore.Database(\":memory:\")\n self.db.create_db()","def __create_db(self):\n\n if not database_exists(self.db.url):\n create_database(self.db.url)\n print(\"DB Created\")\n\n return \"Does DB exist? {}\".format(database_exists(self.db.url))","def create(self, db_name):\n path = self.get_path(db_name)\n if not os.path.exists(path):\n fp = open(path, 'wb')\n fp.close()\n self.connect(db_name)\n self.make()\n else:\n self.connect(db_name)","def test_database_creation(self):\r\n not_existing_wsp = \"doesnotexist.wsp\"\r\n path = os.path.join(settings.SALMON_WHISPER_DB_PATH, not_existing_wsp)\r\n self.assertEqual(os.path.exists(path), False)\r\n graph.WhisperDatabase(path)\r\n self.assertEqual(os.path.exists(path), True)","def create_db():\n db_url = engine.url\n if not database_exists(db_url):\n create_database(db_url)\n base.metadata.create_all()","def create(name):\n\t\treturn \"CREATE DATABASE {0};\".format(name)","def create_db():\n db.create_all()\n click.echo(\"DB criado com sucesso!\")","def create_dataBase(conn, create_cmd):\n if conn:\n cursor = conn.cursor()\n cursor.execute(create_cmd)\n conn.commit()\n #print '[sql management] Table Created...'","def create_db():\n from sqlalchemy_utils import database_exists, create_database, drop_database\n if not database_exists(DB_URL):\n print('Creating database.')\n create_database(DB_URL)\n else:\n drop_database(DB_URL)\n create_database(DB_URL)\n print(\"Deleted and created new database\")\n create_tables()","def create(self) -> None:\n if not os.path.exists(self.path):\n os.mkdir(self.path)\n return None\n\n return None","def connect_dataBase(db_dir, create_cmd):\n just_created = False #flag of is the db is already exist\n \n if not os.path.isfile(db_dir):\n #create the db file in the directory\n with open(db_dir , 'w') as f:\n just_created = True\n #print 'database handler created -- ' , db_dir \n\n try:\n conn = lite.connect(db_dir)\n except lite.Error, e:\n #print \"Error %s:\" % db_dir\n sys.exit(1)\n finally:\n if just_created:\n #create the table \n create_dataBase(conn, create_cmd)\n return True","def create(self):\n if not self.exists_on_client:\n command = \"mkdir -p %s\" % self.parent_dir\n rcode, _, _ = g.run(self._host, command)\n if rcode != 0:\n return False\n command = \"cp /etc/inittab %s\" % self._fqpath\n rcode, _, _ = g.run(self._host, command)\n if rcode == 0:\n return True\n\n return False","def test_database_object_can_be_created(self):\n database = generate_database_object()","def create_base_table(self, table_name):\n print('new')\n # Create table at first.\n select_stm = self.construct_base_table()\n exec_query('DROP TABLE IF EXISTS %s;' % table_name) \n sql = \"\"\"\n CREATE TABLE %s AS\n %s\n \"\"\" % (table_name, select_stm)\n exec_query(sql)","def initDB(self): \t\t\t#ancien initBase\n\t\tif os.path.isdir(self.dirName):\n\t\t\ttry:\n\t\t\t\tshutil.rmtree(self.dirName)\n\t\t\texcept OSError:\n\t\t\t\tself.ficlog.write(\"error 1: cannot delete directory\\n\")\n\t\ttry:\n\t\t\tos.mkdir(self.dirName)\n\t\texcept OSError:\n\t\t\tself.ficlog.write(\"error 2: cannot create directory\\n\")\n\t\t\texit(1)","def create_base_testes(dbsession):\n # print('Iniciando criação da base #############################')\n # print('dbsession', dbsession)\n base1 = BaseOrigem('alimentos_e_esportes')\n dbsession.add(base1)\n dbsession.commit()\n risco1 = PadraoRisco('perigo', base1)\n dbsession.add(risco1)\n dbsession.commit()\n param1 = ParametroRisco('alimento', 'teste1', risco1)\n param2 = ParametroRisco('esporte', 'teste2', risco1)\n dbsession.add(param1)\n dbsession.add(param2)\n dbsession.commit()\n valor1 = ValorParametro('bacon', Filtro.igual, param1)\n valor2 = ValorParametro('base jump', Filtro.igual, param2)\n dbsession.add(valor1)\n dbsession.add(valor2)\n dbsession.commit()\n visao1 = Visao('viagens', base1.id)\n dbsession.add(visao1)\n dbsession.commit()\n tabela1 = Tabela('viagens', 'viagem', '', 0, visao1.id)\n tabela2 = Tabela('alimentos', 'alimento', 'viagem', tabela1.id, visao1.id)\n tabela3 = Tabela('esportes', 'esporte', 'viagem', tabela1.id, visao1.id)\n dbsession.add(tabela1)\n dbsession.add(tabela2)\n dbsession.add(tabela3)\n dbsession.commit()\n # print('Base criada! ########################')","def _create_db(db_name):\n template_conn.execute('commit')\n template_conn.execute('create database {}'.format(db_name))","def create_db():\r\n\r\n try:\r\n conn = sqlite3.connect('account.db')\r\n c = conn.cursor()\r\n c.execute('''CREATE TABLE accounts\r\n (\r\n username text,\r\n password text,\r\n access_level int\r\n )''')\r\n conn.commit()\r\n print(\"Successfully created database and table\")\r\n return True\r\n except BaseException:\r\n return False\r\n finally:\r\n if c is not None:\r\n c.close()\r\n if conn is not None:\r\n conn.close()","def create_db():\n db.create_all()\n print(\"DB Created\")","def createDB(dbName:str)->None:\n conn, cur = DBConnect()\n cur.execute(f\"CREATE DATABASE IF NOT EXISTS {dbName} CHARSET = utf8mb4 DEFAULT COLLATE = utf8mb4_unicode_ci;\")\n conn.commit()\n cur.close()","def create_db(self, db_name):\n if self.conf['database'] not in self.show_database():\n sql = 'CREATE DATABASE IF NOT EXISTS %s CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci' % db_name\n Log.debug('DB -> %s' % sql)\n self.execute(sql)","def create():\n\n from slicr.extensions import db\n\n click.echo('creating database...')\n\n db.create_all()","def create_db():\n db.create_all()\n print ('Intialized....!')","def create(self):\n pass","def create(self):\n pass","def create(self):\n pass","def create(self):","def create_db(self, dbname):\n if dbname not in self.list_dbs():\n return self.put(dbname).text\n else:\n raise ExistingDatabase(dbname)","def createNew(path):\n\n if(os.path.isfile(path)):\n log.info(\"Database file at '%s' already exists.\" % path)\n else:\n conn = sqlite3.connect(path)\n conn.close()\n\n fullpath = \"sqlite:///\" + path\n engine = sqlalchemy.create_engine(fullpath, echo=False)\n\n Base.metadata.create_all(engine)\n\n return True","def create_database():\n create_db(app)","def create_database():\r\n try:\r\n conn = sql.connect('database.db')\r\n conn.execute('CREATE TABLE IF NOT EXISTS users1(UserName TEXT PRIMARY KEY, Password TEXT, FullName TEXT, Email TEXT ,otp INT)')\r\n msg=\"Database connected.\"\r\n except:\r\n msg=\"Database Connection issue .\"\r\n\r\n finally:\r\n conn.close()\r\n return msg","def create_db():\n db.create_all()","def create_db():\n db.create_all()","def create_db():\n db.create_all()","def create_db():\n db.create_all()","def create_db():\n db.create_all()","def create_db():\n db.create_all()","def create_db():\n db.create_all()","def create_db():\n db.create_all()","def create_db():\n db.create_all()","def create_db():\n db.create_all()","def create_db():\n db.create_all()","def create_db(dbname):\r\n # Connect to the default database\r\n con = getopenconnection(dbname='postgres')\r\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\r\n cur = con.cursor()\r\n\r\n # Check if an existing database with the same name exists\r\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\'%s\\'' % (dbname,))\r\n count = cur.fetchone()[0]\r\n if count == 0:\r\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\r\n else:\r\n print 'A database named {0} already exists'.format(dbname)\r\n\r\n # Clean up\r\n cur.close()\r\n con.close()","def make_db():\n\n db.create_all()","def _create_table_if_not_exists(self) -> bigquery.Table:\n table = self.client.create_table(\n table=bigquery.Table(table_ref=self._table_ref, schema=Schema),\n exists_ok=True,\n )\n logging.info(\"table %s already exists.\", table.full_table_id)\n return table","def mkdir(base, name):\n path = os.path.join(base, name)\n if not os.path.exists(path):\n os.makedirs(path)\n return path","def __KCreateDB(self):\n if self.create_db_method == \"Create New\":\n if self.page_size == \"Default\":\n createCommand = \"CREATE DATABASE '%s' USER '%s' PASSWORD '%s'\" % (self.__dsn,\n self.user_name,\n self.user_password)\n else:\n createCommand = \"CREATE DATABASE '%s' USER '%s' PASSWORD '%s' PAGE_SIZE=%d\" % (self.__dsn,\n self.user_name,\n self.user_password,\n int(self.page_size))\n try:\n conn= kdb.create_database(createCommand, int(self.sql_dialect))\n\n conn.close()\n\n except:\n self.__result.NoteException(cause=\"Exception raised while creating database.\")\n return","def creacion_conexion_db():\n\n global bd\n bd = mysql.connector.connect(host=\"localhost\", user=\"root\", passwd=\"\")\n micursor = bd.cursor(buffered=True)\n try:\n micursor.execute(\"CREATE DATABASE entrega\")\n bd = mysql.connector.connect(\n host=\"localhost\", user=\"root\", passwd=\"\", database=\"entrega\"\n )\n micursor = bd.cursor(buffered=True)\n micursor.execute(\n \"CREATE TABLE paciente( id int(11) NOT NULL PRIMARY KEY AUTO_INCREMENT, nombre VARCHAR(128) COLLATE utf8_spanish2_ci NOT NULL, apellido varchar(128) COLLATE utf8_spanish2_ci NOT NULL, dni VARCHAR(128) COLLATE utf8_spanish2_ci NOT NULL, dia date NOT NULL, hora int(11) NOT NULL, minuto int(11) NOT NULL )\"\n )\n\n return mysql.connector.connect(\n host=\"localhost\", user=\"root\", passwd=\"\", database=\"entrega\"\n )\n\n except Exception:\n\n return mysql.connector.connect(\n host=\"localhost\", user=\"root\", passwd=\"\", database=\"entrega\"\n )","def test_create_database(self):\n self.assertTrue(self.ss.create_database(self.test_database),\n \"Create testing database\")\n self.assertTrue(os.path.exists(self.test_database))\n self.assertNotEqual(self.test_database, self.ss.data_file(),\n \"Should not connect to created database\")","def create_database():\n # Build a unique URL using todays date\n dbname = 'orthomcl_{t.year}_{t.month}_{t.day}_at_{t.hour}_{t.minute}_{t.second}'.format(t=datetime.today())\n dbhost, port, user, passwd = _get_root_credentials()\n clhost = 'odose.nl' if dbhost not in ['127.0.0.1', 'localhost'] else dbhost\n db_connection = MySQLdb.connect(host=dbhost, port=port, user=user, passwd=passwd)\n cursor = db_connection.cursor()\n cursor.execute('CREATE DATABASE ' + dbname)\n cursor.execute('GRANT ALL on {0}.* TO orthomcl@\\'{1}\\' IDENTIFIED BY \\'pass\\';'.format(dbname, clhost))\n db_connection.commit()\n cursor.close()\n db_connection.close()\n log.info('Created database %s as %s on %s', dbname, user, dbhost)\n return dbname","def create_table(self):\n pass","def create(self):\n ...","def create_database():\n try:\n db.create_all()\n print('Created tables successfully!')\n except Exception:\n print('Failed to create db. Make sure your database server is running')","def create():\n upgrade()\n populate()","def createDB(dbname='ddsassignment3'):\r\n # Connect to the default database\r\n con = getOpenConnection(dbname='postgres')\r\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\r\n cur = con.cursor()\r\n\r\n # Check if an existing database with the same name exists\r\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\'%s\\'' % (dbname,))\r\n count = cur.fetchone()[0]\r\n if count == 0:\r\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\r\n else:\r\n print 'A database named {0} already exists'.format(dbname)\r\n\r\n # Clean up\r\n cur.close()\r\n con.commit()\r\n con.close()","def create(self):\n db.create_all()","def createDataBase(mysql,dbase):\n\tsql = 'CREATE DATABASE IF NOT EXISTS '+ dbase + ';'\n\tmysql.query(sql)\n\tmysql.select_db(dbase)\n\tsql = 'DROP TABLE IF EXISTS names;'\n\tmysql.query(sql)\n\tsql = 'DROP TABLE IF EXISTS files;'\n\tmysql.query(sql)\n\tsql = 'DROP TABLE IF EXISTS linesinfile;'\n\tmysql.query(sql)\n\tsql = 'DROP TABLE IF EXISTS allfiles;'\n\tmysql.query(sql)\n\tsql = 'DROP TABLE IF EXISTS allnames;'\n\tmysql.query(sql)\n\tsql = 'CREATE TABLE names (id BIGINT NOT NULL AUTO_INCREMENT PRIMARY KEY, \\\n\tname TINYTEXT NOT NULL);'\n\tmysql.query(sql)\n\tsql = 'CREATE TABLE files (id BIGINT NOT NULL AUTO_INCREMENT PRIMARY KEY, \\\n\t\tname BIGINT NOT NULL, \\\n\t\tfilename TEXT NOT NULL \\\n\t\tREFERENCES names(id));'\n\tmysql.query(sql)\n\tsql = 'CREATE TABLE linesinfile (id BIGINT NOT NULL AUTO_INCREMENT PRIMARY KEY, \\\n\t\tfilename BIGINT NOT NULL, \\\n\t\tnumber TEXT NOT NULL, \\\n\t\tref TINYTEXT NOT NULL \\\n\t\tREFERENCES files(id));'\n\tmysql.query(sql) \n\tsql = 'CREATE TABLE allfiles (id BIGINT NOT NULL AUTO_INCREMENT PRIMARY KEY, \\\n\tfile TEXT NOT NULL);'\n\tmysql.query(sql)\n\tsql = 'CREATE TABLE allnames (id BIGINT NOT NULL AUTO_INCREMENT PRIMARY KEY, \\\n\t\tfile BIGINT NOT NULL, \\\n\t\tname TEXT NOT NULL \\\n\t\tREFERENCES allfiles(id));'\n\tmysql.query(sql)","def create_database(self, name):\n osmO5M = os.path.join(self.OSMDIR, \"%s-latest.o5m\" % name)\n osmPBF = os.path.join(self.OSMDIR, \"%s-latest.pbf\" % name)\n if self.args.ask_before_creating_a_new_db:\n answer = raw_input(\"\\n- Creo database %s tramite osmosis?[Y/n]\" % name)\n if answer in (\"n\", \"N\"):\n return False\n\n print \"\\n- converti file filtrato\"\n print \" %s\\n -->\\n %s\" % (osmO5M, osmPBF)\n call(\"osmconvert %s -o=%s\" % (osmO5M, osmPBF), shell=True)\n\n print \"\\n0- cancella eventuale database preesistente\"\n call(\"echo 'DROP DATABASE %s;'| psql\" % name, shell=True)\n\n print \"\\n1- crea database\"\n call(\"createdb %s\" % name, shell=True)\n\n print \"\\n2- aggiungi estensioni postgis al database:\"\n call(\"echo 'CREATE EXTENSION postgis;'| psql -U %s -d %s\" % (self.user, name), shell=True)\n call(\"echo 'CREATE EXTENSION hstore;'| psql -U %s -d %s\" % (self.user, name), shell=True)\n #Non si può più utilizzare con gli id in formato bigint\n #call(\"echo 'CREATE EXTENSION intarray;'| psql -U %s -d %s\" % (self.user, name), shell=True)\n\n print \"\\n3- importa schemi:\"\n call(\"psql -U %s -d %s -f /usr/share/doc/osmosis/examples/pgsnapshot_schema_0.6.sql\" % (self.user, name), shell=True)\n call(\"psql -U %s -d %s -f /usr/share/doc/osmosis/examples/pgsnapshot_schema_0.6_linestring.sql\" % (self.user, name), shell=True)\n\n print \"\\n4- importa dati OSM:\"\n #If you want to specify a temporary directory for osmosis use this prefix:\n # JAVACMD_OPTIONS='-Djava.io.tmpdir=/path/to/a/temp/dir/' osmosis...\n call(\"osmosis --rb %s --wp database=%s user=%s password=%s\" % (osmPBF, name, self.user, self.password), shell=True)\n\n #Alternative command using --write-pgsql-dump\n #pgdir = os.path.join(SCRIPTDIR, \"pgdump\")\n #call(\"osmosis --rb %s --write-pgsql-dump directory=%s enableLinestringBuilder=yes enableBboxBuilder=no\" % (osmPBF, pgdir), shell=True)\n #os.chdir(pgdir)\n #call(\"psql -U %s -d %s -f pgsnapshot_load_0.6.sql\" % (self.user, name), shell=True)\n return True","def createDB(dbname='ddsassignment3'):\n # Connect to the default database\n con = getOpenConnection(dbname='postgres')\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\n cur = con.cursor()\n\n # Check if an existing database with the same name exists\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\'%s\\'' % (dbname,))\n count = cur.fetchone()[0]\n print \"Count \",count\n if count == 0:\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\n else:\n print 'A database named {0} already exists'.format(dbname)\n\n # Clean up\n cur.close()\n con.commit()\n con.close()","def create_db():\n db_uri = app.config[\"SQLALCHEMY_DATABASE_URI\"]\n if not database_exists(db_uri):\n print(\"Creating database ...\")\n create_database(db_uri)\n # db.create_all()\n else:\n print(\"Database already exists. Nothing to create.\")","def createDB(dbname='dds_assignment1'):\n # Connect to the default database\n con = getOpenConnection(dbname='postgres')\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\n cur = con.cursor()\n\n # Check if an existing database with the same name exists\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\'%s\\'' % (dbname,))\n count = cur.fetchone()[0]\n if count == 0:\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\n else:\n print('A database named {0} already exists'.format(dbname))\n\n # Clean up\n cur.close()\n con.close()","def create_db():\n _init_db()\n db.create_all()","def create_empty_db():\r\n drop_db()\r\n database.create_tables([Customer])\r\n database.close()","def create_default(self):\n # create man-contest, if not exists\n\ttry:\n\t Contest_Type.objects.get(code=project_constants.MAN_CONTEST)\n\texcept Contest_Type.DoesNotExist:\n Contest_Type_obj = Contest_Type(\n code = project_constants.MAN_CONTEST,\n description = \"Concorso maschile\",\n status = 1,\n )\n Contest_Type_obj.save()\n logger.info(\"contest_type creato: \" + str(project_constants.MAN_CONTEST))\n\n # create woman-contest, if not exists\n\ttry:\n\t Contest_Type.objects.get(code=project_constants.WOMAN_CONTEST)\n\texcept Contest_Type.DoesNotExist:\n Contest_Type_obj = Contest_Type(\n code = project_constants.WOMAN_CONTEST,\n description = \"Concorso femminile\",\n status = 1,\n )\n Contest_Type_obj.save()\n logger.info(\"contest_type creato: \" + str(project_constants.WOMAN_CONTEST))\n\n return True","def create_db(dbname):\n # Connect to the default database\n con = getopenconnection(dbname='postgres')\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\n cur = con.cursor()\n\n # Check if an existing database with the same name exists\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\'%s\\'' % (dbname,))\n count = cur.fetchone()[0]\n if count == 0:\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\n else:\n print ('A database named {0} already exists'.format(dbname))\n\n # Clean up\n cur.close()\n con.close()","def createDB(dbname='ddsassignment3'):\n # Connect to the default database\n con = getOpenConnection(dbname='postgres')\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\n cur = con.cursor()\n\n # Check if an existing database with the same name exists\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\'%s\\'' % (dbname,))\n count = cur.fetchone()[0]\n if count == 0:\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\n else:\n print 'A database named {0} already exists'.format(dbname)\n\n # Clean up\n cur.close()\n con.commit()\n con.close()","def createDB(dbname='dds_assignment2'):\n # Connect to the default database\n con = getOpenConnection(dbname='postgres')\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\n cur = con.cursor()\n\n # Check if an existing database with the same name exists\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\'%s\\'' % (dbname,))\n count = cur.fetchone()[0]\n if count == 0:\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\n else:\n print('A database named {0} already exists'.format(dbname))\n\n # Clean up\n cur.close()\n con.commit()\n con.close()","def create_database_stock_master():\n sql = \"\"\"\n CREATE DATABASE stock_master;\n \"\"\"\n excute_sql(sql,None)","def create_empty_database(lang, extension, database, dbscheme=None):\n subprocess_run([\"codeql\", \"database\", \"init\", \"--language=\" + lang,\n \"--source-root=/tmp/empty\", \"--allow-missing-source-root\", database])\n subprocess_run([\"mkdir\", \"-p\", database + \"/src/tmp/empty\"])\n subprocess_run([\"touch\", database + \"/src/tmp/empty/empty\" + extension])\n\n finalize_cmd = [\"codeql\", \"database\", \"finalize\",\n database, \"--no-pre-finalize\"]\n if dbscheme is not None:\n for scheme in dbscheme:\n if os.path.exists(scheme):\n finalize_cmd.append(\"--dbscheme\")\n finalize_cmd.append(scheme)\n break\n\n subprocess_run(finalize_cmd)","def createDB(dbname='dds_assignment'):\n # Connect to the default database\n con = getOpenConnection(dbname='postgres')\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\n\n cur = con.cursor()\n\n # Check if an existing database with the same name exists\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\'%s\\'' % (dbname,))\n count = cur.fetchone()[0]\n if count == 0:\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\n else:\n print('A database named ' + dbname + ' already exists')\n\n # Clean up\n cur.close()\n con.close()","def create_data_base():\n\n\tscript_files = []\n\tjson_files = []\n\t\n\t# get script files list\n\tfor file in os.listdir(\"learned_objects_scripts/\"):\n\t\tif file.endswith(\".script\"):\n\t\t\tscript_files.append(file)\n\n\t# get json files list\n\tfor file in os.listdir(\"object_models/\"):\n\t\tif file.endswith(\".json\"):\n\t\t\tjson_files.append(file)\n\t\n\t# create json file for new objects\n\tmodel_created = False\n\tfor file in script_files:\n\t\tif \"{}.json\".format(file[:-7]) not in json_files:\n\t\t\twith open(\"object_models/{}.json\".format(file[:-7]), 'w') as outfile:\n\t\t\t\tobj_model = object_script_to_model(\"learned_objects_scripts/\" + file)\n\t\t\t\tjson.dump(obj_model, outfile)\n\t\t\t\tmodel_created = True\n\t\t\t\tprint(\"model created for\", file)\n\tif not model_created:\n\t\tprint(\"data base is already up to date\")","def createdb():\n db.create_all()","def createDB(dbname='dds_assignment'):\n # Connect to the default database\n con = getOpenConnection(dbname='postgres')\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\n cur = con.cursor()\n\n # Check if an existing database with the same name exists\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\'%s\\'' % (dbname,))\n count = cur.fetchone()[0]\n if count == 0:\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\n else:\n print 'A database named {0} already exists'.format(dbname)\n\n # Clean up\n cur.close()\n con.close()","def createDB(dbname='dds_assignment'):\n # Connect to the default database\n con = getOpenConnection(dbname='postgres')\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\n cur = con.cursor()\n\n # Check if an existing database with the same name exists\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\'%s\\'' % (dbname,))\n count = cur.fetchone()[0]\n if count == 0:\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\n else:\n print 'A database named {0} already exists'.format(dbname)\n\n # Clean up\n cur.close()\n con.close()","def __init__(self, name='class_num_2'):\n self.conn = self.cursor = None\n exists = os.path.exists(name)\n self.conn = sqlite3.connect(name)\n self.cursor = self.conn.cursor()\n if not exists:\n for command in self.CREATE_TABLES:\n self.conn.execute(command)\n self.conn.commit()","def init_db(db):\n\n # will create database and tables if not exist\n db.create_all()\n\n # will append a default user if none exist\n if models.User.query.count() == 0:\n default_user = models.User('admin', 'password', 'John', 'Smith')\n db.session.add(default_user)\n db.session.commit()","def create(self):\n\n pass","def create_database():\n try:\n db_conn.execute('CREATE TABLE IF NOT EXISTS Fietsenstalling (FietsNr INTEGER PRIMARY KEY NOT NULL, '\n 'Naam TEXT ,Achternaam TEXT ,Adress TEXT ,Telefoon INTEGER ,PIN INTEGER ,Gestald INTEGER,otpKEY TEXT)')\n db_conn.commit()\n print(\"Table Created\")\n\n except sqlite3.OperationalError:\n print(\"Table couldn't be Created\")","def createDB(dbname='dds_assignment'):\n # Connect to the default database\n con = getOpenConnection(dbname='postgres')\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\n cur = con.cursor()\n\n # Check if an existing database with the same name exists\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\'%s\\'' % (dbname,))\n count = cur.fetchone()[0]\n if count == 0:\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\n else:\n print 'A database named {0} already exists'.format(dbname)\n\n # Clean up\n cur.close()\n con.commit()\n con.close()","def test_create(self):\n with self.assertRaises(QiitaDBNotImplementedError):\n MetadataTemplate.create()","def begin_create(self):\n self.mdb.get().begin_create()","def test_create_already_exists():\n current_folder = os.path.dirname(os.path.realpath(__file__))\n misc_folder = os.path.join(current_folder, \"misc\")\n model_file = os.path.join(misc_folder, \"model.tar\")\n create.main(\"mlp\", \"10:12:8\", model_file)\n # TODO: Check if error was logged","def create_DB (name):\n engine = create_engine('sqlite:///%s' % name)\n Base.metadata.create_all(engine)","def create(self, database_name):\n return isempty(self._send_command(database_name, \"create\"))","def test_410_000_non_existant_db(self):\n with TDC() as temp_dir:\n file = Path(temp_dir) / 'database.db'\n self.assertFalse(file.exists(),'Database file exists pre test')\n eng = Engine(file)\n con = eng.connect()\n self.assertTrue(file.exists(), 'Database file does not exists post test')"],"string":"[\n \"def create_base(self):\\r\\n self.mycursor.execute(\\r\\n 'CREATE DATABASE IF NOT EXISTS purbeurre CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci')\\r\\n self.mycursor.execute('USE purbeurre')\",\n \"def create():\\n\\tcreate_db()\",\n \"def create_db(self, path: str) -> None:\\n if os.path.isfile(path):\\n self.db_path = path\\n print(\\\"DB already exists\\\")\\n return\\n\\n print(path)\\n\\n self.db_path = path\\n\\n print(\\\"Opening the base db\\\")\\n with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'basedb.xml'), 'r') as f:\\n base = f.read()\\n print(\\\"Reading the base as {0}\\\".format(base))\",\n \"def create_db(self):\\n creation_query = f\\\"CREATE DATABASE {DB_NAME} CHARACTER SET 'utf8mb4' \\\"\\n existence_query = f\\\"USE {DB_NAME}\\\"\\n try:\\n self.cursor.execute(creation_query)\\n self.cursor.execute(existence_query)\\n print(f\\\"\\\\n La base de données |{DB_NAME}| a été créée \\\\n\\\")\\n except:\\n print(\\\"\\\\n Une erreur s'est produite lors \\\"\\n \\\"de la création de la base de données \\\\n\\\")\",\n \"def create_db(self):\",\n \"def create_db(self):\\n return None\",\n \"def create():\",\n \"def create():\",\n \"def get_creation(self,DATA_PATH,db):\\n\\t\\t\\n\\t\\t# if the database already exists\\n\\t\\tif os.path.isfile(DATA_PATH):\\n\\t\\t\\treturn template('home',error=\\\"La base de donnée est déjà existante\\\",bd=\\\"\\\",admin=\\\"\\\")\\n\\t\\t# if not, if the database does not exist\\n\\t\\telse:\\n\\t\\t\\tlistAdmin = []\\n\\t\\t\\t# all tables for the database are created\\n\\t\\t\\tgenerateDB.GenerateDB(listAdmin)\\n\\t\\t\\treturn template('home',error=\\\"La base a bien été créé\\\",bd=\\\"\\\",admin=\\\"True\\\")\",\n \"def create_new_db():\\n global data_base, table\\n data_base = asksaveasfilename(title=\\\"Select file\\\", filetypes=((\\\"DATA BASE\\\", \\\"*.db\\\"), (\\\"all files\\\", \\\"*.*\\\")),\\n defaultextension='.db')\\n\\n if Path(data_base).suffix == '.db':\\n create_win_create_table()\\n else:\\n mistake_db_file()\",\n \"def create():\\n pass\",\n \"def create(db):\\n if exists(db):\\n print(\\\"phonebook %r already exists\\\" % db)\\n sys.exit(-1)\\n else:\\n database = {}\\n pickle.dump(database, open(db, 'wb'))\\n print(\\\"created phonebook %r in the current directory\\\" % db)\",\n \"def createdb():\\n if database.exists():\\n raise click.UsageError(\\n click.style('Database already exists at {}.'\\n .format(database.url), fg='red')\\n )\\n else:\\n database.create()\\n click.echo(OK)\",\n \"def create_db():\\n db.create_all()\\n click.echo(\\\"Banco de dados criado\\\")\",\n \"def create_db():\\n db.create_all()\\n print('Database structure created successfully')\",\n \"def _create_db(self):\\n self.db = easydms.dbcore.Database(\\\":memory:\\\")\\n self.db.create_db()\",\n \"def __create_db(self):\\n\\n if not database_exists(self.db.url):\\n create_database(self.db.url)\\n print(\\\"DB Created\\\")\\n\\n return \\\"Does DB exist? {}\\\".format(database_exists(self.db.url))\",\n \"def create(self, db_name):\\n path = self.get_path(db_name)\\n if not os.path.exists(path):\\n fp = open(path, 'wb')\\n fp.close()\\n self.connect(db_name)\\n self.make()\\n else:\\n self.connect(db_name)\",\n \"def test_database_creation(self):\\r\\n not_existing_wsp = \\\"doesnotexist.wsp\\\"\\r\\n path = os.path.join(settings.SALMON_WHISPER_DB_PATH, not_existing_wsp)\\r\\n self.assertEqual(os.path.exists(path), False)\\r\\n graph.WhisperDatabase(path)\\r\\n self.assertEqual(os.path.exists(path), True)\",\n \"def create_db():\\n db_url = engine.url\\n if not database_exists(db_url):\\n create_database(db_url)\\n base.metadata.create_all()\",\n \"def create(name):\\n\\t\\treturn \\\"CREATE DATABASE {0};\\\".format(name)\",\n \"def create_db():\\n db.create_all()\\n click.echo(\\\"DB criado com sucesso!\\\")\",\n \"def create_dataBase(conn, create_cmd):\\n if conn:\\n cursor = conn.cursor()\\n cursor.execute(create_cmd)\\n conn.commit()\\n #print '[sql management] Table Created...'\",\n \"def create_db():\\n from sqlalchemy_utils import database_exists, create_database, drop_database\\n if not database_exists(DB_URL):\\n print('Creating database.')\\n create_database(DB_URL)\\n else:\\n drop_database(DB_URL)\\n create_database(DB_URL)\\n print(\\\"Deleted and created new database\\\")\\n create_tables()\",\n \"def create(self) -> None:\\n if not os.path.exists(self.path):\\n os.mkdir(self.path)\\n return None\\n\\n return None\",\n \"def connect_dataBase(db_dir, create_cmd):\\n just_created = False #flag of is the db is already exist\\n \\n if not os.path.isfile(db_dir):\\n #create the db file in the directory\\n with open(db_dir , 'w') as f:\\n just_created = True\\n #print 'database handler created -- ' , db_dir \\n\\n try:\\n conn = lite.connect(db_dir)\\n except lite.Error, e:\\n #print \\\"Error %s:\\\" % db_dir\\n sys.exit(1)\\n finally:\\n if just_created:\\n #create the table \\n create_dataBase(conn, create_cmd)\\n return True\",\n \"def create(self):\\n if not self.exists_on_client:\\n command = \\\"mkdir -p %s\\\" % self.parent_dir\\n rcode, _, _ = g.run(self._host, command)\\n if rcode != 0:\\n return False\\n command = \\\"cp /etc/inittab %s\\\" % self._fqpath\\n rcode, _, _ = g.run(self._host, command)\\n if rcode == 0:\\n return True\\n\\n return False\",\n \"def test_database_object_can_be_created(self):\\n database = generate_database_object()\",\n \"def create_base_table(self, table_name):\\n print('new')\\n # Create table at first.\\n select_stm = self.construct_base_table()\\n exec_query('DROP TABLE IF EXISTS %s;' % table_name) \\n sql = \\\"\\\"\\\"\\n CREATE TABLE %s AS\\n %s\\n \\\"\\\"\\\" % (table_name, select_stm)\\n exec_query(sql)\",\n \"def initDB(self): \\t\\t\\t#ancien initBase\\n\\t\\tif os.path.isdir(self.dirName):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tshutil.rmtree(self.dirName)\\n\\t\\t\\texcept OSError:\\n\\t\\t\\t\\tself.ficlog.write(\\\"error 1: cannot delete directory\\\\n\\\")\\n\\t\\ttry:\\n\\t\\t\\tos.mkdir(self.dirName)\\n\\t\\texcept OSError:\\n\\t\\t\\tself.ficlog.write(\\\"error 2: cannot create directory\\\\n\\\")\\n\\t\\t\\texit(1)\",\n \"def create_base_testes(dbsession):\\n # print('Iniciando criação da base #############################')\\n # print('dbsession', dbsession)\\n base1 = BaseOrigem('alimentos_e_esportes')\\n dbsession.add(base1)\\n dbsession.commit()\\n risco1 = PadraoRisco('perigo', base1)\\n dbsession.add(risco1)\\n dbsession.commit()\\n param1 = ParametroRisco('alimento', 'teste1', risco1)\\n param2 = ParametroRisco('esporte', 'teste2', risco1)\\n dbsession.add(param1)\\n dbsession.add(param2)\\n dbsession.commit()\\n valor1 = ValorParametro('bacon', Filtro.igual, param1)\\n valor2 = ValorParametro('base jump', Filtro.igual, param2)\\n dbsession.add(valor1)\\n dbsession.add(valor2)\\n dbsession.commit()\\n visao1 = Visao('viagens', base1.id)\\n dbsession.add(visao1)\\n dbsession.commit()\\n tabela1 = Tabela('viagens', 'viagem', '', 0, visao1.id)\\n tabela2 = Tabela('alimentos', 'alimento', 'viagem', tabela1.id, visao1.id)\\n tabela3 = Tabela('esportes', 'esporte', 'viagem', tabela1.id, visao1.id)\\n dbsession.add(tabela1)\\n dbsession.add(tabela2)\\n dbsession.add(tabela3)\\n dbsession.commit()\\n # print('Base criada! ########################')\",\n \"def _create_db(db_name):\\n template_conn.execute('commit')\\n template_conn.execute('create database {}'.format(db_name))\",\n \"def create_db():\\r\\n\\r\\n try:\\r\\n conn = sqlite3.connect('account.db')\\r\\n c = conn.cursor()\\r\\n c.execute('''CREATE TABLE accounts\\r\\n (\\r\\n username text,\\r\\n password text,\\r\\n access_level int\\r\\n )''')\\r\\n conn.commit()\\r\\n print(\\\"Successfully created database and table\\\")\\r\\n return True\\r\\n except BaseException:\\r\\n return False\\r\\n finally:\\r\\n if c is not None:\\r\\n c.close()\\r\\n if conn is not None:\\r\\n conn.close()\",\n \"def create_db():\\n db.create_all()\\n print(\\\"DB Created\\\")\",\n \"def createDB(dbName:str)->None:\\n conn, cur = DBConnect()\\n cur.execute(f\\\"CREATE DATABASE IF NOT EXISTS {dbName} CHARSET = utf8mb4 DEFAULT COLLATE = utf8mb4_unicode_ci;\\\")\\n conn.commit()\\n cur.close()\",\n \"def create_db(self, db_name):\\n if self.conf['database'] not in self.show_database():\\n sql = 'CREATE DATABASE IF NOT EXISTS %s CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci' % db_name\\n Log.debug('DB -> %s' % sql)\\n self.execute(sql)\",\n \"def create():\\n\\n from slicr.extensions import db\\n\\n click.echo('creating database...')\\n\\n db.create_all()\",\n \"def create_db():\\n db.create_all()\\n print ('Intialized....!')\",\n \"def create(self):\\n pass\",\n \"def create(self):\\n pass\",\n \"def create(self):\\n pass\",\n \"def create(self):\",\n \"def create_db(self, dbname):\\n if dbname not in self.list_dbs():\\n return self.put(dbname).text\\n else:\\n raise ExistingDatabase(dbname)\",\n \"def createNew(path):\\n\\n if(os.path.isfile(path)):\\n log.info(\\\"Database file at '%s' already exists.\\\" % path)\\n else:\\n conn = sqlite3.connect(path)\\n conn.close()\\n\\n fullpath = \\\"sqlite:///\\\" + path\\n engine = sqlalchemy.create_engine(fullpath, echo=False)\\n\\n Base.metadata.create_all(engine)\\n\\n return True\",\n \"def create_database():\\n create_db(app)\",\n \"def create_database():\\r\\n try:\\r\\n conn = sql.connect('database.db')\\r\\n conn.execute('CREATE TABLE IF NOT EXISTS users1(UserName TEXT PRIMARY KEY, Password TEXT, FullName TEXT, Email TEXT ,otp INT)')\\r\\n msg=\\\"Database connected.\\\"\\r\\n except:\\r\\n msg=\\\"Database Connection issue .\\\"\\r\\n\\r\\n finally:\\r\\n conn.close()\\r\\n return msg\",\n \"def create_db():\\n db.create_all()\",\n \"def create_db():\\n db.create_all()\",\n \"def create_db():\\n db.create_all()\",\n \"def create_db():\\n db.create_all()\",\n \"def create_db():\\n db.create_all()\",\n \"def create_db():\\n db.create_all()\",\n \"def create_db():\\n db.create_all()\",\n \"def create_db():\\n db.create_all()\",\n \"def create_db():\\n db.create_all()\",\n \"def create_db():\\n db.create_all()\",\n \"def create_db():\\n db.create_all()\",\n \"def create_db(dbname):\\r\\n # Connect to the default database\\r\\n con = getopenconnection(dbname='postgres')\\r\\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\\r\\n cur = con.cursor()\\r\\n\\r\\n # Check if an existing database with the same name exists\\r\\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\\\'%s\\\\'' % (dbname,))\\r\\n count = cur.fetchone()[0]\\r\\n if count == 0:\\r\\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\\r\\n else:\\r\\n print 'A database named {0} already exists'.format(dbname)\\r\\n\\r\\n # Clean up\\r\\n cur.close()\\r\\n con.close()\",\n \"def make_db():\\n\\n db.create_all()\",\n \"def _create_table_if_not_exists(self) -> bigquery.Table:\\n table = self.client.create_table(\\n table=bigquery.Table(table_ref=self._table_ref, schema=Schema),\\n exists_ok=True,\\n )\\n logging.info(\\\"table %s already exists.\\\", table.full_table_id)\\n return table\",\n \"def mkdir(base, name):\\n path = os.path.join(base, name)\\n if not os.path.exists(path):\\n os.makedirs(path)\\n return path\",\n \"def __KCreateDB(self):\\n if self.create_db_method == \\\"Create New\\\":\\n if self.page_size == \\\"Default\\\":\\n createCommand = \\\"CREATE DATABASE '%s' USER '%s' PASSWORD '%s'\\\" % (self.__dsn,\\n self.user_name,\\n self.user_password)\\n else:\\n createCommand = \\\"CREATE DATABASE '%s' USER '%s' PASSWORD '%s' PAGE_SIZE=%d\\\" % (self.__dsn,\\n self.user_name,\\n self.user_password,\\n int(self.page_size))\\n try:\\n conn= kdb.create_database(createCommand, int(self.sql_dialect))\\n\\n conn.close()\\n\\n except:\\n self.__result.NoteException(cause=\\\"Exception raised while creating database.\\\")\\n return\",\n \"def creacion_conexion_db():\\n\\n global bd\\n bd = mysql.connector.connect(host=\\\"localhost\\\", user=\\\"root\\\", passwd=\\\"\\\")\\n micursor = bd.cursor(buffered=True)\\n try:\\n micursor.execute(\\\"CREATE DATABASE entrega\\\")\\n bd = mysql.connector.connect(\\n host=\\\"localhost\\\", user=\\\"root\\\", passwd=\\\"\\\", database=\\\"entrega\\\"\\n )\\n micursor = bd.cursor(buffered=True)\\n micursor.execute(\\n \\\"CREATE TABLE paciente( id int(11) NOT NULL PRIMARY KEY AUTO_INCREMENT, nombre VARCHAR(128) COLLATE utf8_spanish2_ci NOT NULL, apellido varchar(128) COLLATE utf8_spanish2_ci NOT NULL, dni VARCHAR(128) COLLATE utf8_spanish2_ci NOT NULL, dia date NOT NULL, hora int(11) NOT NULL, minuto int(11) NOT NULL )\\\"\\n )\\n\\n return mysql.connector.connect(\\n host=\\\"localhost\\\", user=\\\"root\\\", passwd=\\\"\\\", database=\\\"entrega\\\"\\n )\\n\\n except Exception:\\n\\n return mysql.connector.connect(\\n host=\\\"localhost\\\", user=\\\"root\\\", passwd=\\\"\\\", database=\\\"entrega\\\"\\n )\",\n \"def test_create_database(self):\\n self.assertTrue(self.ss.create_database(self.test_database),\\n \\\"Create testing database\\\")\\n self.assertTrue(os.path.exists(self.test_database))\\n self.assertNotEqual(self.test_database, self.ss.data_file(),\\n \\\"Should not connect to created database\\\")\",\n \"def create_database():\\n # Build a unique URL using todays date\\n dbname = 'orthomcl_{t.year}_{t.month}_{t.day}_at_{t.hour}_{t.minute}_{t.second}'.format(t=datetime.today())\\n dbhost, port, user, passwd = _get_root_credentials()\\n clhost = 'odose.nl' if dbhost not in ['127.0.0.1', 'localhost'] else dbhost\\n db_connection = MySQLdb.connect(host=dbhost, port=port, user=user, passwd=passwd)\\n cursor = db_connection.cursor()\\n cursor.execute('CREATE DATABASE ' + dbname)\\n cursor.execute('GRANT ALL on {0}.* TO orthomcl@\\\\'{1}\\\\' IDENTIFIED BY \\\\'pass\\\\';'.format(dbname, clhost))\\n db_connection.commit()\\n cursor.close()\\n db_connection.close()\\n log.info('Created database %s as %s on %s', dbname, user, dbhost)\\n return dbname\",\n \"def create_table(self):\\n pass\",\n \"def create(self):\\n ...\",\n \"def create_database():\\n try:\\n db.create_all()\\n print('Created tables successfully!')\\n except Exception:\\n print('Failed to create db. Make sure your database server is running')\",\n \"def create():\\n upgrade()\\n populate()\",\n \"def createDB(dbname='ddsassignment3'):\\r\\n # Connect to the default database\\r\\n con = getOpenConnection(dbname='postgres')\\r\\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\\r\\n cur = con.cursor()\\r\\n\\r\\n # Check if an existing database with the same name exists\\r\\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\\\'%s\\\\'' % (dbname,))\\r\\n count = cur.fetchone()[0]\\r\\n if count == 0:\\r\\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\\r\\n else:\\r\\n print 'A database named {0} already exists'.format(dbname)\\r\\n\\r\\n # Clean up\\r\\n cur.close()\\r\\n con.commit()\\r\\n con.close()\",\n \"def create(self):\\n db.create_all()\",\n \"def createDataBase(mysql,dbase):\\n\\tsql = 'CREATE DATABASE IF NOT EXISTS '+ dbase + ';'\\n\\tmysql.query(sql)\\n\\tmysql.select_db(dbase)\\n\\tsql = 'DROP TABLE IF EXISTS names;'\\n\\tmysql.query(sql)\\n\\tsql = 'DROP TABLE IF EXISTS files;'\\n\\tmysql.query(sql)\\n\\tsql = 'DROP TABLE IF EXISTS linesinfile;'\\n\\tmysql.query(sql)\\n\\tsql = 'DROP TABLE IF EXISTS allfiles;'\\n\\tmysql.query(sql)\\n\\tsql = 'DROP TABLE IF EXISTS allnames;'\\n\\tmysql.query(sql)\\n\\tsql = 'CREATE TABLE names (id BIGINT NOT NULL AUTO_INCREMENT PRIMARY KEY, \\\\\\n\\tname TINYTEXT NOT NULL);'\\n\\tmysql.query(sql)\\n\\tsql = 'CREATE TABLE files (id BIGINT NOT NULL AUTO_INCREMENT PRIMARY KEY, \\\\\\n\\t\\tname BIGINT NOT NULL, \\\\\\n\\t\\tfilename TEXT NOT NULL \\\\\\n\\t\\tREFERENCES names(id));'\\n\\tmysql.query(sql)\\n\\tsql = 'CREATE TABLE linesinfile (id BIGINT NOT NULL AUTO_INCREMENT PRIMARY KEY, \\\\\\n\\t\\tfilename BIGINT NOT NULL, \\\\\\n\\t\\tnumber TEXT NOT NULL, \\\\\\n\\t\\tref TINYTEXT NOT NULL \\\\\\n\\t\\tREFERENCES files(id));'\\n\\tmysql.query(sql) \\n\\tsql = 'CREATE TABLE allfiles (id BIGINT NOT NULL AUTO_INCREMENT PRIMARY KEY, \\\\\\n\\tfile TEXT NOT NULL);'\\n\\tmysql.query(sql)\\n\\tsql = 'CREATE TABLE allnames (id BIGINT NOT NULL AUTO_INCREMENT PRIMARY KEY, \\\\\\n\\t\\tfile BIGINT NOT NULL, \\\\\\n\\t\\tname TEXT NOT NULL \\\\\\n\\t\\tREFERENCES allfiles(id));'\\n\\tmysql.query(sql)\",\n \"def create_database(self, name):\\n osmO5M = os.path.join(self.OSMDIR, \\\"%s-latest.o5m\\\" % name)\\n osmPBF = os.path.join(self.OSMDIR, \\\"%s-latest.pbf\\\" % name)\\n if self.args.ask_before_creating_a_new_db:\\n answer = raw_input(\\\"\\\\n- Creo database %s tramite osmosis?[Y/n]\\\" % name)\\n if answer in (\\\"n\\\", \\\"N\\\"):\\n return False\\n\\n print \\\"\\\\n- converti file filtrato\\\"\\n print \\\" %s\\\\n -->\\\\n %s\\\" % (osmO5M, osmPBF)\\n call(\\\"osmconvert %s -o=%s\\\" % (osmO5M, osmPBF), shell=True)\\n\\n print \\\"\\\\n0- cancella eventuale database preesistente\\\"\\n call(\\\"echo 'DROP DATABASE %s;'| psql\\\" % name, shell=True)\\n\\n print \\\"\\\\n1- crea database\\\"\\n call(\\\"createdb %s\\\" % name, shell=True)\\n\\n print \\\"\\\\n2- aggiungi estensioni postgis al database:\\\"\\n call(\\\"echo 'CREATE EXTENSION postgis;'| psql -U %s -d %s\\\" % (self.user, name), shell=True)\\n call(\\\"echo 'CREATE EXTENSION hstore;'| psql -U %s -d %s\\\" % (self.user, name), shell=True)\\n #Non si può più utilizzare con gli id in formato bigint\\n #call(\\\"echo 'CREATE EXTENSION intarray;'| psql -U %s -d %s\\\" % (self.user, name), shell=True)\\n\\n print \\\"\\\\n3- importa schemi:\\\"\\n call(\\\"psql -U %s -d %s -f /usr/share/doc/osmosis/examples/pgsnapshot_schema_0.6.sql\\\" % (self.user, name), shell=True)\\n call(\\\"psql -U %s -d %s -f /usr/share/doc/osmosis/examples/pgsnapshot_schema_0.6_linestring.sql\\\" % (self.user, name), shell=True)\\n\\n print \\\"\\\\n4- importa dati OSM:\\\"\\n #If you want to specify a temporary directory for osmosis use this prefix:\\n # JAVACMD_OPTIONS='-Djava.io.tmpdir=/path/to/a/temp/dir/' osmosis...\\n call(\\\"osmosis --rb %s --wp database=%s user=%s password=%s\\\" % (osmPBF, name, self.user, self.password), shell=True)\\n\\n #Alternative command using --write-pgsql-dump\\n #pgdir = os.path.join(SCRIPTDIR, \\\"pgdump\\\")\\n #call(\\\"osmosis --rb %s --write-pgsql-dump directory=%s enableLinestringBuilder=yes enableBboxBuilder=no\\\" % (osmPBF, pgdir), shell=True)\\n #os.chdir(pgdir)\\n #call(\\\"psql -U %s -d %s -f pgsnapshot_load_0.6.sql\\\" % (self.user, name), shell=True)\\n return True\",\n \"def createDB(dbname='ddsassignment3'):\\n # Connect to the default database\\n con = getOpenConnection(dbname='postgres')\\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\\n cur = con.cursor()\\n\\n # Check if an existing database with the same name exists\\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\\\'%s\\\\'' % (dbname,))\\n count = cur.fetchone()[0]\\n print \\\"Count \\\",count\\n if count == 0:\\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\\n else:\\n print 'A database named {0} already exists'.format(dbname)\\n\\n # Clean up\\n cur.close()\\n con.commit()\\n con.close()\",\n \"def create_db():\\n db_uri = app.config[\\\"SQLALCHEMY_DATABASE_URI\\\"]\\n if not database_exists(db_uri):\\n print(\\\"Creating database ...\\\")\\n create_database(db_uri)\\n # db.create_all()\\n else:\\n print(\\\"Database already exists. Nothing to create.\\\")\",\n \"def createDB(dbname='dds_assignment1'):\\n # Connect to the default database\\n con = getOpenConnection(dbname='postgres')\\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\\n cur = con.cursor()\\n\\n # Check if an existing database with the same name exists\\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\\\'%s\\\\'' % (dbname,))\\n count = cur.fetchone()[0]\\n if count == 0:\\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\\n else:\\n print('A database named {0} already exists'.format(dbname))\\n\\n # Clean up\\n cur.close()\\n con.close()\",\n \"def create_db():\\n _init_db()\\n db.create_all()\",\n \"def create_empty_db():\\r\\n drop_db()\\r\\n database.create_tables([Customer])\\r\\n database.close()\",\n \"def create_default(self):\\n # create man-contest, if not exists\\n\\ttry:\\n\\t Contest_Type.objects.get(code=project_constants.MAN_CONTEST)\\n\\texcept Contest_Type.DoesNotExist:\\n Contest_Type_obj = Contest_Type(\\n code = project_constants.MAN_CONTEST,\\n description = \\\"Concorso maschile\\\",\\n status = 1,\\n )\\n Contest_Type_obj.save()\\n logger.info(\\\"contest_type creato: \\\" + str(project_constants.MAN_CONTEST))\\n\\n # create woman-contest, if not exists\\n\\ttry:\\n\\t Contest_Type.objects.get(code=project_constants.WOMAN_CONTEST)\\n\\texcept Contest_Type.DoesNotExist:\\n Contest_Type_obj = Contest_Type(\\n code = project_constants.WOMAN_CONTEST,\\n description = \\\"Concorso femminile\\\",\\n status = 1,\\n )\\n Contest_Type_obj.save()\\n logger.info(\\\"contest_type creato: \\\" + str(project_constants.WOMAN_CONTEST))\\n\\n return True\",\n \"def create_db(dbname):\\n # Connect to the default database\\n con = getopenconnection(dbname='postgres')\\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\\n cur = con.cursor()\\n\\n # Check if an existing database with the same name exists\\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\\\'%s\\\\'' % (dbname,))\\n count = cur.fetchone()[0]\\n if count == 0:\\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\\n else:\\n print ('A database named {0} already exists'.format(dbname))\\n\\n # Clean up\\n cur.close()\\n con.close()\",\n \"def createDB(dbname='ddsassignment3'):\\n # Connect to the default database\\n con = getOpenConnection(dbname='postgres')\\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\\n cur = con.cursor()\\n\\n # Check if an existing database with the same name exists\\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\\\'%s\\\\'' % (dbname,))\\n count = cur.fetchone()[0]\\n if count == 0:\\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\\n else:\\n print 'A database named {0} already exists'.format(dbname)\\n\\n # Clean up\\n cur.close()\\n con.commit()\\n con.close()\",\n \"def createDB(dbname='dds_assignment2'):\\n # Connect to the default database\\n con = getOpenConnection(dbname='postgres')\\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\\n cur = con.cursor()\\n\\n # Check if an existing database with the same name exists\\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\\\'%s\\\\'' % (dbname,))\\n count = cur.fetchone()[0]\\n if count == 0:\\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\\n else:\\n print('A database named {0} already exists'.format(dbname))\\n\\n # Clean up\\n cur.close()\\n con.commit()\\n con.close()\",\n \"def create_database_stock_master():\\n sql = \\\"\\\"\\\"\\n CREATE DATABASE stock_master;\\n \\\"\\\"\\\"\\n excute_sql(sql,None)\",\n \"def create_empty_database(lang, extension, database, dbscheme=None):\\n subprocess_run([\\\"codeql\\\", \\\"database\\\", \\\"init\\\", \\\"--language=\\\" + lang,\\n \\\"--source-root=/tmp/empty\\\", \\\"--allow-missing-source-root\\\", database])\\n subprocess_run([\\\"mkdir\\\", \\\"-p\\\", database + \\\"/src/tmp/empty\\\"])\\n subprocess_run([\\\"touch\\\", database + \\\"/src/tmp/empty/empty\\\" + extension])\\n\\n finalize_cmd = [\\\"codeql\\\", \\\"database\\\", \\\"finalize\\\",\\n database, \\\"--no-pre-finalize\\\"]\\n if dbscheme is not None:\\n for scheme in dbscheme:\\n if os.path.exists(scheme):\\n finalize_cmd.append(\\\"--dbscheme\\\")\\n finalize_cmd.append(scheme)\\n break\\n\\n subprocess_run(finalize_cmd)\",\n \"def createDB(dbname='dds_assignment'):\\n # Connect to the default database\\n con = getOpenConnection(dbname='postgres')\\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\\n\\n cur = con.cursor()\\n\\n # Check if an existing database with the same name exists\\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\\\'%s\\\\'' % (dbname,))\\n count = cur.fetchone()[0]\\n if count == 0:\\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\\n else:\\n print('A database named ' + dbname + ' already exists')\\n\\n # Clean up\\n cur.close()\\n con.close()\",\n \"def create_data_base():\\n\\n\\tscript_files = []\\n\\tjson_files = []\\n\\t\\n\\t# get script files list\\n\\tfor file in os.listdir(\\\"learned_objects_scripts/\\\"):\\n\\t\\tif file.endswith(\\\".script\\\"):\\n\\t\\t\\tscript_files.append(file)\\n\\n\\t# get json files list\\n\\tfor file in os.listdir(\\\"object_models/\\\"):\\n\\t\\tif file.endswith(\\\".json\\\"):\\n\\t\\t\\tjson_files.append(file)\\n\\t\\n\\t# create json file for new objects\\n\\tmodel_created = False\\n\\tfor file in script_files:\\n\\t\\tif \\\"{}.json\\\".format(file[:-7]) not in json_files:\\n\\t\\t\\twith open(\\\"object_models/{}.json\\\".format(file[:-7]), 'w') as outfile:\\n\\t\\t\\t\\tobj_model = object_script_to_model(\\\"learned_objects_scripts/\\\" + file)\\n\\t\\t\\t\\tjson.dump(obj_model, outfile)\\n\\t\\t\\t\\tmodel_created = True\\n\\t\\t\\t\\tprint(\\\"model created for\\\", file)\\n\\tif not model_created:\\n\\t\\tprint(\\\"data base is already up to date\\\")\",\n \"def createdb():\\n db.create_all()\",\n \"def createDB(dbname='dds_assignment'):\\n # Connect to the default database\\n con = getOpenConnection(dbname='postgres')\\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\\n cur = con.cursor()\\n\\n # Check if an existing database with the same name exists\\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\\\'%s\\\\'' % (dbname,))\\n count = cur.fetchone()[0]\\n if count == 0:\\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\\n else:\\n print 'A database named {0} already exists'.format(dbname)\\n\\n # Clean up\\n cur.close()\\n con.close()\",\n \"def createDB(dbname='dds_assignment'):\\n # Connect to the default database\\n con = getOpenConnection(dbname='postgres')\\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\\n cur = con.cursor()\\n\\n # Check if an existing database with the same name exists\\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\\\'%s\\\\'' % (dbname,))\\n count = cur.fetchone()[0]\\n if count == 0:\\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\\n else:\\n print 'A database named {0} already exists'.format(dbname)\\n\\n # Clean up\\n cur.close()\\n con.close()\",\n \"def __init__(self, name='class_num_2'):\\n self.conn = self.cursor = None\\n exists = os.path.exists(name)\\n self.conn = sqlite3.connect(name)\\n self.cursor = self.conn.cursor()\\n if not exists:\\n for command in self.CREATE_TABLES:\\n self.conn.execute(command)\\n self.conn.commit()\",\n \"def init_db(db):\\n\\n # will create database and tables if not exist\\n db.create_all()\\n\\n # will append a default user if none exist\\n if models.User.query.count() == 0:\\n default_user = models.User('admin', 'password', 'John', 'Smith')\\n db.session.add(default_user)\\n db.session.commit()\",\n \"def create(self):\\n\\n pass\",\n \"def create_database():\\n try:\\n db_conn.execute('CREATE TABLE IF NOT EXISTS Fietsenstalling (FietsNr INTEGER PRIMARY KEY NOT NULL, '\\n 'Naam TEXT ,Achternaam TEXT ,Adress TEXT ,Telefoon INTEGER ,PIN INTEGER ,Gestald INTEGER,otpKEY TEXT)')\\n db_conn.commit()\\n print(\\\"Table Created\\\")\\n\\n except sqlite3.OperationalError:\\n print(\\\"Table couldn't be Created\\\")\",\n \"def createDB(dbname='dds_assignment'):\\n # Connect to the default database\\n con = getOpenConnection(dbname='postgres')\\n con.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\\n cur = con.cursor()\\n\\n # Check if an existing database with the same name exists\\n cur.execute('SELECT COUNT(*) FROM pg_catalog.pg_database WHERE datname=\\\\'%s\\\\'' % (dbname,))\\n count = cur.fetchone()[0]\\n if count == 0:\\n cur.execute('CREATE DATABASE %s' % (dbname,)) # Create the database\\n else:\\n print 'A database named {0} already exists'.format(dbname)\\n\\n # Clean up\\n cur.close()\\n con.commit()\\n con.close()\",\n \"def test_create(self):\\n with self.assertRaises(QiitaDBNotImplementedError):\\n MetadataTemplate.create()\",\n \"def begin_create(self):\\n self.mdb.get().begin_create()\",\n \"def test_create_already_exists():\\n current_folder = os.path.dirname(os.path.realpath(__file__))\\n misc_folder = os.path.join(current_folder, \\\"misc\\\")\\n model_file = os.path.join(misc_folder, \\\"model.tar\\\")\\n create.main(\\\"mlp\\\", \\\"10:12:8\\\", model_file)\\n # TODO: Check if error was logged\",\n \"def create_DB (name):\\n engine = create_engine('sqlite:///%s' % name)\\n Base.metadata.create_all(engine)\",\n \"def create(self, database_name):\\n return isempty(self._send_command(database_name, \\\"create\\\"))\",\n \"def test_410_000_non_existant_db(self):\\n with TDC() as temp_dir:\\n file = Path(temp_dir) / 'database.db'\\n self.assertFalse(file.exists(),'Database file exists pre test')\\n eng = Engine(file)\\n con = eng.connect()\\n self.assertTrue(file.exists(), 'Database file does not exists post test')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7140915","0.6911187","0.67524874","0.6660668","0.6562868","0.6482472","0.64741087","0.64741087","0.6414927","0.6377228","0.6242515","0.6120511","0.61106586","0.6092203","0.6079177","0.6060785","0.604896","0.6047035","0.603988","0.6019423","0.60109794","0.60021186","0.59965533","0.5989169","0.5982497","0.5980083","0.5975511","0.59635013","0.59451616","0.59208727","0.58950365","0.5874685","0.58744586","0.5871574","0.5852513","0.58272797","0.5823168","0.58159924","0.5799392","0.5799392","0.5799392","0.5791362","0.57861996","0.57676876","0.5767504","0.57564265","0.57404214","0.57404214","0.57404214","0.57404214","0.57404214","0.57404214","0.57404214","0.57404214","0.57404214","0.57404214","0.57404214","0.5737158","0.5735036","0.57329625","0.5720137","0.57156444","0.5715059","0.5714579","0.57107556","0.5710051","0.57047427","0.56978947","0.5692607","0.5681822","0.56808746","0.56783694","0.56728464","0.5672291","0.5671715","0.56665105","0.56560045","0.5637868","0.56225747","0.5620474","0.5616117","0.5612765","0.55930865","0.55915403","0.55885184","0.55827373","0.5580569","0.5565947","0.5565947","0.5565571","0.55609876","0.55546546","0.5553772","0.55525094","0.55516005","0.5551418","0.55442286","0.55434483","0.5543252","0.5540139"],"string":"[\n \"0.7140915\",\n \"0.6911187\",\n \"0.67524874\",\n \"0.6660668\",\n \"0.6562868\",\n \"0.6482472\",\n \"0.64741087\",\n \"0.64741087\",\n \"0.6414927\",\n \"0.6377228\",\n \"0.6242515\",\n \"0.6120511\",\n \"0.61106586\",\n \"0.6092203\",\n \"0.6079177\",\n \"0.6060785\",\n \"0.604896\",\n \"0.6047035\",\n \"0.603988\",\n \"0.6019423\",\n \"0.60109794\",\n \"0.60021186\",\n \"0.59965533\",\n \"0.5989169\",\n \"0.5982497\",\n \"0.5980083\",\n \"0.5975511\",\n \"0.59635013\",\n \"0.59451616\",\n \"0.59208727\",\n \"0.58950365\",\n \"0.5874685\",\n \"0.58744586\",\n \"0.5871574\",\n \"0.5852513\",\n \"0.58272797\",\n \"0.5823168\",\n \"0.58159924\",\n \"0.5799392\",\n \"0.5799392\",\n \"0.5799392\",\n \"0.5791362\",\n \"0.57861996\",\n \"0.57676876\",\n \"0.5767504\",\n \"0.57564265\",\n \"0.57404214\",\n \"0.57404214\",\n \"0.57404214\",\n \"0.57404214\",\n \"0.57404214\",\n \"0.57404214\",\n \"0.57404214\",\n \"0.57404214\",\n \"0.57404214\",\n \"0.57404214\",\n \"0.57404214\",\n \"0.5737158\",\n \"0.5735036\",\n \"0.57329625\",\n \"0.5720137\",\n \"0.57156444\",\n \"0.5715059\",\n \"0.5714579\",\n \"0.57107556\",\n \"0.5710051\",\n \"0.57047427\",\n \"0.56978947\",\n \"0.5692607\",\n \"0.5681822\",\n \"0.56808746\",\n \"0.56783694\",\n \"0.56728464\",\n \"0.5672291\",\n \"0.5671715\",\n \"0.56665105\",\n \"0.56560045\",\n \"0.5637868\",\n \"0.56225747\",\n \"0.5620474\",\n \"0.5616117\",\n \"0.5612765\",\n \"0.55930865\",\n \"0.55915403\",\n \"0.55885184\",\n \"0.55827373\",\n \"0.5580569\",\n \"0.5565947\",\n \"0.5565947\",\n \"0.5565571\",\n \"0.55609876\",\n \"0.55546546\",\n \"0.5553772\",\n \"0.55525094\",\n \"0.55516005\",\n \"0.5551418\",\n \"0.55442286\",\n \"0.55434483\",\n \"0.5543252\",\n \"0.5540139\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62442166"},"document_rank":{"kind":"string","value":"10"}}},{"rowIdx":76674,"cells":{"query":{"kind":"string","value":"Valida cada registro segun los criterios"},"document":{"kind":"string","value":"def validarRE(self, datoAValidar):\n return validarTitulo(datoAValidar)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def validate():","def validadores(self):\n\n camposRequeridos = [getattr(self,\"lineRazon\")]\n ValidarDatos.setValidador(camposRequeridos)\n\n camposRequeridos = [getattr(self,\"lineCuit\")]\n ValidarDatos.setValidador(camposRequeridos)\n\n camposRequeridos = [getattr(self,\"lineNumeroFac\")]\n ValidarDatos.setValidador(camposRequeridos)","def validate_fields(row,fila,new_values):\n # \"Definiciones iniciales del diccionario de errores\n # y status de error.\n dict_error={}\n error_count = False\n\n \"\"\"Validaciones de cada campo segun el modelo Job.\"\"\"\n #Validate Job.id\n if row[0] == None:\n new_values['id'] =row[0]\n # dict_error['id'] = ''\n else:\n try:\n if isinstance(int(row[0]),int):\n new_values['id'] =row[0]\n # dict_error['id'] = ''\n except ValueError:\n error_count=True\n dict_error['id'] = ValueError('Error en id')\n\n #Validate Job.company_ruc\n if len(row[1])==11:\n new_values['company_ruc'] =row[1]\n # dict_error['company_ruc'] = ''\n else:\n error_count=True\n dict_error['company_ruc'] = ValidationError('Error en company_ruc')\n\n #Validate Job.company_name\n if len(row[2])<=100 and row[2] != None:\n new_values['company_name'] =row[2]\n # dict_error['company_name'] = ''\n else:\n error_count=True\n dict_error['company_name'] = ValidationError('Error en company_name')\n\n #Validate Job.title\n if len(row[3])<=100 and row[3] != None:\n new_values['title'] =row[3]\n # dict_error['title'] = ''\n else:\n error_count=True\n dict_error['title'] = ValidationError('Error en title')\n\n #Validate Job.description\n if isinstance(row[4],str) and row[4] != None:\n new_values['description'] =row[4]\n # dict_error['description'] = ''\n else:\n error_count=True\n dict_error['description'] = ValidationError('Error en description')\n\n #Validate Job.requeriments\n if isinstance(row[5],str) and row[5] != None:\n new_values['requeriments'] =row[5]\n # dict_error['requeriments'] = ''\n else:\n error_count=True\n dict_error['requeriments'] = ValidationError('Error en requeriments')\n\n #Validate Job.contact_email\n if row[6] != None:\n new_values['contact_email'] =row[6]\n # dict_error['contact_email'] = ''\n else:\n error_count=True\n dict_error['contact_email'] = ValidationError('Error en contact email')\n\n #Validate Job.location\n if len(row[7])<=50 and row[7] != None:\n new_values['location'] =row[7]\n # dict_error['location'] = ''\n else:\n error_count=True\n dict_error['location'] = ValidationError('Error en Location')\n\n #Validate Job.is_active\n if row[8] == None:\n new_values['is_active'] =row[8]\n # dict_error['is_active'] = ''\n else:\n try:\n if int(row[8])==1 or int(row[8])==0:\n new_values['is_active'] =row[8]\n # dict_error['is_active'] = ''\n except Exception as e:\n error_count=True\n dict_error['is_active'] = ValidationError('Error en el is_active')\n\n #Validate Job.is_verified\n if row[9] == None:\n new_values['is_verified'] =row[9]\n # dict_error['is_verified'] = ''\n else:\n try:\n if int(row[9])==1 or int(row[9])==0:\n new_values['is_verified'] =row[9]\n # dict_error['is_verified'] = ''\n except Exception as e:\n error_count=True\n dict_error['is_verified'] = ValidationError('Error en el is_verified')\n\n #Validate Job.is_public\n if row[10] == None:\n new_values['is_public'] =row[10]\n # dict_error['is_public'] = ''\n else:\n try:\n if int(row[10])==1 or int(row[10])==0:\n new_values['is_public'] =row[10]\n # dict_error['is_public'] = ''\n except Exception as e:\n error_count=True\n dict_error['is_public'] = ValidationError('Error en el is_public')\n\n #Validate Job.show_recruiter\n if row[11] == None:\n new_values['show_recruiter'] =row[11]\n # dict_error['show_recruiter'] = ''\n else:\n try:\n if int(row[11])==1 or int(row[11])==0:\n new_values['show_recruiter'] =row[11]\n # dict_error['show_recruiter'] = ''\n except Exception as e:\n error_count=True\n dict_error['show_recruiter'] = ValidationError('Error en el show_recruiter')\n\n #Validate Job.website_url\n if row[12] == None:\n new_values['website_url'] =row[12]\n # dict_error['website_url'] = ''\n else:\n if len(row[12])>4:\n new_values['website_url'] =row[12]\n # dict_error['website_url'] = ''\n else:\n error_count=True\n dict_error['website_url'] = ValidationError('Error en website_url')\n\n #Validate Job.benefits\n if isinstance(row[13],str) or row[13] == None:\n new_values['benefits'] =row[13]\n # dict_error['benefits'] = ''\n else:\n error_count=True\n dict_error['benefits'] = ValidationError('Error en benefits')\n\n #Validate Job.urgency\n if isinstance(row[14],str) or row[14] == None:\n new_values['urgency'] =row[14]\n # dict_error['urgency'] = ''\n else:\n error_count=True\n dict_error['urgency'] = ValidationError('Error en urgency')\n\n #Validate Job.schedule\n if isinstance(row[15],str) or row[15] == None:\n new_values['work_schedule'] =row[15]\n # dict_error['work_schedule'] = ''\n else:\n error_count=True\n dict_error['work_schedule'] = ValidationError('Error en work_schedule')\n\n #Validate Job.comment\n if isinstance(row[16],str) or row[16] == None:\n new_values['comment'] =row[16]\n # dict_error['comment'] = ''\n else:\n error_count=True\n dict_error['comment'] = ValidationError('Error en comment')\n\n #Validate Job.min_salary\n if isinstance(int(row[17]),int) or row[17] == None:\n new_values['min_salary'] =row[17]\n # dict_error['min_salary'] = ''\n else:\n error_count=True\n dict_error['min_salary'] = ValidationError('Error en min_salary')\n\n #Validate Job.max_salary\n if isinstance(int(row[18]),int) or row[18] == None:\n new_values['max_salary'] =row[18]\n # dict_error['max_salary'] = ''\n else:\n error_count=True\n dict_error['max_salary'] = ValidationError('Error en max_salary')\n\n #Validate Job.pay_range_period\n if row[19]=='annual' or row[19]=='monthly' or row[19] == None:\n new_values['pay_range_period'] =row[19]\n # dict_error['pay_range_period'] = ''\n else:\n error_count=True\n dict_error['pay_range_period'] = ValidationError('Error en pay_range_period')\n\n return {\"errors\": dict_error,\"fila\":fila,\"new_values\":new_values,\"error_status\":error_count}","def clean(self):\n cleaned_data = super().clean()\n hora_inicio = cleaned_data.get(\"hora_inicio\")\n hora_final = cleaned_data.get(\"hora_final\")\n if hora_inicio > hora_final or hora_inicio == hora_final:\n raise forms.ValidationError(\"Error. La hora de inicio debe ser menor que la hora final\")","def clean(self):\r\n cleaned_data = super(RPEventCreateForm, self).clean()\r\n self.check_risk()\r\n self.check_costs()\r\n self.check_location_or_plotroom()\r\n return cleaned_data","def validate(self, data):\r\n flight_planes = Flight.objects.filter(planes__id=data['planes'].id)\r\n\r\n if data['to'] == data['airports'].city:\r\n raise serializers.ValidationError(\"El vuelo no puede ir hacia el mismo aeropuerto\")\r\n\r\n elif len(flight_planes) and flight_planes[len(flight_planes) - 1].to == data['to']:\r\n raise serializers.ValidationError(\r\n \"Un vuelo solo puede ser generado con un avión cuyo último vuelo haya sido al Aeropuerto\")\r\n return data","def validate_usuario(self, data):\n\t\tuser = Usuario.objects.filter(usuario=data)\n\t\t# Si estoy creando (no hay instancia) comprobar si hay usuarios con ese\n\t\t# username\n\t\tif not self.instance and len(user) != 0:\n\t\t\traise ValidationError(u\"Ya existe un usuario con ese usuario\")\n\t\t# Si estoy actualizando (hay instancia) y estamos cambiando el username\n\t\t# y existen usuarios con el nuevo username\n\t\telif self.instance.usuario != data and len(user) != 0:\n\t\t\traise ValidationError(u\"Ya existe un usuario con ese usuario\")\n\t\telse:\n\t\t\treturn data","def validate(self,data):\r\n for person in data:\r\n #check the format is a letter and 3 digit e.g A002 or a002\r\n \r\n if (re.match(r'[A-Z][0-9]{3}', (person[0]).lower())):\r\n print (person[0])\r\n else:\r\n print(person[0] + \" \" + 'is incorrect ID, '\r\n ' must contains a letter and 3 digits e.g a002')\r\n \r\n #check the format is either M/F/Male/Female\r\n \r\n if (person[1] == \"M\" or (person[1]).upper() == \"F\" or\r\n person[1] == \"Male\" or person[1] == \"Female\"):\r\n print (person[1])\r\n else:\r\n print(person[1] + \" \" + 'is incorrect Gender, '\r\n ' must either be M and Male or F and Female')\r\n \r\n #check age is valid entry and match with date\r\n \r\n if (re.match(r'[0-9]{2}', person[2]) and person[2] == self.valid_age(person[6])):\r\n print (person[2])\r\n elif (person[2] != self.valid_age(person[2])):\r\n print(\"Does not match with your birthday, invalid age\")\r\n else:\r\n print(person[2] + \" \" + 'age must be an integer')\r\n \r\n #check sales is 3 interger value\r\n if (re.match(r'[0-9]{3}', person[3])):\r\n return (person[3])\r\n else:\r\n print(person[3] + \" \" + 'is incorrect sales number, '\r\n 'must be a 2 interger number')\r\n \r\n #check BMI is either Normal / Overweight / Obesity or Underweight\r\n if (re.match(r'\\b(NORMAL|OVERWEIGHT|OBESITY|UNDERWEIGHT)\\b',(person[4]).upper())):\r\n print (person[4])\r\n else:\r\n print(person[4] + \" \" ' is incorrect BMI value, '\r\n 'must select from Normal, Overweight, Obesity or Underweight')\r\n \r\n #check Income is float\r\n\r\n if (re.match(r'[0-9]{2,3}', person[5])):\r\n print (person[5])\r\n else:\r\n print(person[5] + \" \" + 'is incorrect income, '\r\n 'must be a interger number') \r\n \r\n #check birthday\r\n \r\n if (self.valid_date(person[6]) and person[2] == self.valid_age(person[6]) ):\r\n print (person[6])\r\n else:\r\n print(person[2] + \" \" + 'is incorrect date format, '\r\n 'must contain DD-MM-YYYY or DD-MM-YY and seperated by -')\r\n \r\n return readFile","def test_special_case(self):\n cases = [\n ('3467875434578764345789654', False),\n ('AAAAAAAAAAA', False),\n ('', False),\n ]\n for titulo_eleitoral, is_valid in cases:\n self.assertEqual(self.titulo_eleitoral.validate(titulo_eleitoral), is_valid)","def validate(self,data):\n ride= self.context['view'].get_object()\n user = data['passanger']\n\n \n if not ride.passangers.filter(pk=user.pk).exists():\n raise serializers.ValidationError('El pasajero no esta en este ride')\n\n if Raitings.objects.filter(ride=ride,passanger=user).exists():\n raise serializers.ValidationError('El pasajero ya dio su puntuacion')\n\n\n # si el ride esta activo \n if ride.is_active:\n raise serializers.ValidationError('El ride aún no esta finalizado') \n \n\n return data","def check_data(self):\n\n for i in range(len(self.full_ed_lines)):\n if self.full_ed_lines[i].text() != \"\":\n if self.full_ed_lines[i].hasAcceptableInput():\n continue\n else:\n if i == 1:\n self.msg2Statusbar.emit('Неправильный формат версии! Исправьте и повторите действие!')\n elif i == 5:\n self.msg2Statusbar.emit('Неправильная почта! Исправьте и повторите действие!')\n return False\n else:\n self.msg2Statusbar.emit('Не все поля заполнены! Исправьте и повторите действие!')\n return False\n return True","def validate(self, data):\n\n end_data = data.get('end') or self.instance.end\n start_data = data.get('start') or self.instance.start\n\n if end_data <= start_data:\n raise serializers.ValidationError({\n 'end': ['Data de termino deve ser maior que a data de inicio'],\n })\n\n query = Meeting.objects.filter(\n meeting_room=data.get('meeting_room') or self.instance.meeting_room.id,\n start__gte=start_data, end__lte=end_data\n )\n if self.instance:\n query = query.exclude(pk=self.instance.pk)\n\n if query.exists():\n raise serializers.ValidationError(\n 'Esta sala ja esta reservada para esse horario')\n\n return data","def validate(self):\n\n raise NotImplementedError('Ando validation is not implemented yet.')","def validate(self):","def validate(self):","def validate(cls, data, errors):","def clean(self):\n data = super().clean()\n if self.pre_user:\n pre_ci = self.pre_user.perfil.cedula_identidad\n ci = self.cleaned_data['cedula_identidad']\n\n if pre_ci == ci:\n return data\n\n cedula_identidad = self.cleaned_data['cedula_identidad']\n cedula_existente = Perfil.objects.filter(\n cedula_identidad=cedula_identidad).exists()\n if cedula_existente:\n raise forms.ValidationError(\n 'Tu cédula se encuentra registrada. Verifica los datos')\n return data","def clean(self):\n data = super().clean()\n if self.pre_user:\n pre_ci = self.pre_user.perfil.cedula_identidad\n ci = self.cleaned_data['cedula_identidad']\n\n if pre_ci == ci:\n return data\n\n cedula_identidad = self.cleaned_data['cedula_identidad']\n cedula_existente = Perfil.objects.filter(\n cedula_identidad=cedula_identidad).exists()\n if cedula_existente:\n raise forms.ValidationError(\n 'Tu cédula se encuentra registrada. Verifica los datos')\n return data","def clean(self):\n data = super().clean()\n if self.pre_user:\n pre_ci = self.pre_user.perfil.cedula_identidad\n ci = self.cleaned_data['cedula_identidad']\n\n if pre_ci == ci:\n return data\n\n cedula_identidad = self.cleaned_data['cedula_identidad']\n cedula_existente = Perfil.objects.filter(\n cedula_identidad=cedula_identidad).exists()\n if cedula_existente:\n raise forms.ValidationError(\n 'Tu cédula se encuentra registrada. Verifica los datos')\n return data","def clean(self):\n\n # Getting cleaned email and username data.\n email = self.cleaned_data.get('email')\n username = self.cleaned_data.get('username')\n\n # Get possible user objects based on email and username.\n user_email = User.objects.filter(email=email)\n user_uname = User.objects.filter(username=username)\n\n # If the user has changed his email\n # and if the email already exists.\n if email != self.user.email:\n if user_email:\n raise forms.ValidationError(\"Email address is already taken\")\n\n # If the user has changed his username\n # and if the username already exists.\n if username != self.user.username:\n if user_uname:\n raise forms.ValidationError(\"Username is already taken\")","def validate(self, data):\n if data.has_key('site'):\n if FieldSightXF.objects.filter(\n xf__id=data['xf'], is_staged=False, is_scheduled=True, site=data['site']).exists():\n raise serializers.ValidationError(\"Form Already Exists, Duplicate Forms Not Allowded\")\n elif data.has_key('project'):\n if FieldSightXF.objects.filter(\n xf__id=data['xf'], is_staged=False, is_scheduled=True, project=data['project']).exists():\n raise serializers.ValidationError(\"Form Already Exists, Duplicate Forms Not Allowded\")\n return data","def validate(self, field, row):\n raise NotImplementedError","def _checkstate(self, queryset):\n for presup in queryset.filter(estado='borrador').exclude(fecha_realizado=None):\n if ((presup.fecha_realizado + timedelta(days=21) < datetime.now().date() and presup.tipo != 'asistencia') or\n (presup.fecha_realizado + timedelta(days=60) < datetime.now().date() and presup.tipo == 'asistencia')):\n presup._toState_cancelado(obs=\"Registro automático: Presupuesto vencido\")","def clean(self):\n\n\t\tfor form in self.forms:\n\t\t\tstatus = form.cleaned_data.get('status')\n\t\t\tif not status:\n\t\t\t\traise ValidationError('Keinen Status', 'error')","def _validate_select_where(self):","def isValid(self):\n\t\treturn ValidadorChaveAcessoNFe.validar(self)","def test_validation_function(self):\n\n for data in ('tbldata', 'dihedraldata', 'rdcdata', 'danidata'):\n v = self.web.query_nodes(key=data)\n\n if not v.empty():\n self.assertTrue(validate_tbl(v.value, pcs=False))","def validate(ob):","def validate(self) -> None:\n\n if self.field not in self.model.table_fields:\n raise ValueError(f\"Value field {self.field} not present in {self.model.table}\")\n\n if self.pivot:\n if self.pivot not in self.model.table_fields:\n raise ValueError(\n f\"Pivot field {self.pivot} not present in {self.model.table}\"\n )\n\n if self.connector:\n if self.connector not in self.model.table_fields:\n raise ValueError(\n f\"Connector field {self.connector} not present in {self.model.table}\"\n )\n\n for field in self.selectors:\n if field not in self.model.table_fields:\n raise ValueError(f\"Selector field {field} not present in {self.model.table}\")","def is_valid(self, data_model: DataModel) -> bool:","def _validate(self, queryset):\n values_distinct = queryset.values(\n *self._invoice_report_common_fields\n ).distinct()\n if values_distinct.count() != 1:\n raise ValidationError(self._get_non_unique_error(queryset))\n if not all(values_distinct[0].values()):\n raise ValidationError(\"None of {} can't be empty\".format(', '.join(\n self._invoice_report_common_fields\n )))","def _check_employee(self):\n\n for record in self:\n\n if record.nik_number:\n # find duplicate nik\n employee_ids = self.search([('id', 'not in', self.ids), ('nik_number', '=', record.nik_number)])\n if employee_ids:\n error_msg = _(\"There is duplicate of Employee Identity Number.\")\n raise ValidationError(error_msg)\n\n # check nik format. it required base_indonesia\n if not record._check_nik(record):\n error_msg = _(\"NIK did not match with Company Code.\")\n raise ValidationError(error_msg)\n\n if record.identification_id:\n employee_ids = self.search([('id', 'not in', self.ids), ('identification_id', '=', record.identification_id)])\n if employee_ids:\n error_msg = _(\"There is duplicate of Identification Number.\")\n raise ValidationError(error_msg)\n\n return True","def validate(self, row):\n raise NotImplementedError","def validate(self):\n for rule in self.get_rules():\n rule.validate(self.get_val())","def check_validity(self):","def test_clean(self):\n doc = Document.objects.create()\n\n form = DocumentMergeForm()\n form.cleaned_data = {\n \"primary_document\": doc.id,\n \"rationale\": \"other\",\n \"rationale_notes\": \"\",\n }\n form.clean()\n assert len(form.errors) == 1\n\n # should not produce an error if rationale notes provided\n form = DocumentSearchForm()\n form.cleaned_data = {\n \"primary_document\": doc.id,\n \"rationale\": \"other\",\n \"rationale_notes\": \"test\",\n }\n form.clean()\n assert len(form.errors) == 0\n\n # should not produce an error if rational is \"duplicate\" or \"join\"\n form = DocumentSearchForm()\n form.cleaned_data = {\n \"primary_document\": doc.id,\n \"rationale\": \"duplicate\",\n \"rationale_notes\": \"\",\n }\n form.clean()\n assert len(form.errors) == 0","def question_new_validate():","def form_valid(self, form):\n\t self.object = form.save()\n\t registro = self.object\n\t cliente = registro.cliente\n\t cpf = cliente.cpf\n\t self.validar_registros(cpf)\n\t premios_cliente = Premios.objects.filter(cliente__cpf = cpf)\n\t return render(self.request, 'ctfidelidade/lista_premios.html', {'premios': premios_cliente})","def validate(self, registration):\n self.l.info(\"Starting registration validation\")\n\n validation_errors = []\n\n # Check if registrant_id is a valid UUID\n if not utils.is_valid_uuid(registration.registrant_id):\n validation_errors += [\"Invalid UUID registrant_id\"]\n\n # Check that required fields are provided and valid\n data_fields = registration.data.keys()\n\n if registration.reg_type == \"pmtct_prebirth\":\n validation_errors += self.check_lang(data_fields, registration)\n validation_errors += self.check_mom_dob(data_fields, registration)\n validation_errors += self.check_edd(data_fields, registration)\n validation_errors += self.check_operator_id(\n data_fields, registration)\n\n elif registration.reg_type == \"pmtct_postbirth\":\n validation_errors += self.check_lang(data_fields, registration)\n validation_errors += self.check_mom_dob(data_fields, registration)\n validation_errors += self.check_baby_dob(data_fields, registration)\n validation_errors += self.check_operator_id(\n data_fields, registration)\n\n elif registration.reg_type == \"nurseconnect\":\n validation_errors += self.check_faccode(\n data_fields, registration)\n validation_errors += self.check_operator_id(\n data_fields, registration)\n validation_errors += self.check_msisdn_registrant(\n data_fields, registration)\n validation_errors += self.check_msisdn_device(\n data_fields, registration)\n validation_errors += self.check_lang(\n data_fields, registration)\n\n elif registration.reg_type == \"momconnect_prebirth\":\n # Checks that apply to clinic, chw, public\n validation_errors += self.check_operator_id(\n data_fields, registration)\n validation_errors += self.check_msisdn_registrant(\n data_fields, registration)\n validation_errors += self.check_msisdn_device(\n data_fields, registration)\n validation_errors += self.check_lang(\n data_fields, registration)\n validation_errors += self.check_consent(\n data_fields, registration)\n\n # Checks that apply to clinic, chw\n if registration.source.authority in [\"hw_full\", \"hw_partial\"]:\n validation_errors += self.check_id(\n data_fields, registration)\n\n # Checks that apply to clinic only\n if registration.source.authority == \"hw_full\":\n validation_errors += self.check_edd(\n data_fields, registration)\n validation_errors += self.check_faccode(\n data_fields, registration)\n\n elif registration.reg_type == \"momconnect_postbirth\":\n validation_errors.append(\"Momconnect postbirth not yet supported\")\n\n elif registration.reg_type == \"loss_general\":\n validation_errors.append(\"Loss general not yet supported\")\n\n # Evaluate if there were any problems, save and return\n if len(validation_errors) == 0:\n self.l.info(\"Registration validated successfully - updating \"\n \"registration object\")\n registration.validated = True\n registration.save()\n self.l.info(\"Registration object updated.\")\n return True\n else:\n self.l.info(\"Registration validation failed - updating \"\n \"registration object\")\n registration.data[\"invalid_fields\"] = validation_errors\n registration.save()\n self.l.info(\"Registration object updated.\")\n return False","def validare(film):\n\n erori = []\n if not film.get_fid():\n erori.append(\"Trebuie introdus un ID!\")\n if not type(film.get_fid()) is int or film.get_fid() <= 0:\n erori.append(\"ID-ul filmului trebuie sa fie un numar intreg mai mare sau egal decat 1!\")\n if not film.get_titlu():\n erori.append(\"Trebuie introdus un titlu!\")\n if not film.get_desc():\n erori.append(\"Trebuie introdusa o descriere!\")\n if not film.get_gen():\n erori.append(\"Trebuie introdus un gen!\")\n\n if len(erori) > 0:\n raise exceptie_film(erori)","def __call__(self, data):\n if not self.instance or self.instance.status not in self.mapping:\n return\n\n combiner = DataCombiner(self.instance, data, model=self.instance.__class__)\n\n editable_fields = self.mapping[self.instance.status]\n for field in combiner.data:\n if field not in editable_fields and self._has_changed(field, combiner):\n raise ValidationError({field: self.message})","def validate_all_fields(self):\n\n if self.validate_byr() and \\\n self.validate_iyr() and \\\n self.validate_eyr() and \\\n self.validate_hgt() and \\\n self.validate_hcl() and \\\n self.validate_ecl() and \\\n self.validate_pid() and \\\n self.validate_cid():\n return True\n return False","def validate(self):\r\n # Check KeyError\r\n try:\r\n self.fields[\"product_name_fr\"]\r\n self.fields[\"generic_name\"]\r\n self.fields[\"url\"]\r\n self.fields[\"nutrition_grade_fr\"]\r\n self.fields[\"categories\"]\r\n self.fields[\"stores\"]\r\n self.fields[\"brands\"]\r\n except KeyError:\r\n return False\r\n\r\n # Check empty field and lenght of generic_name\r\n for key, value in self.fields.items():\r\n if value == '':\r\n return False\r\n break\r\n if key == \"generic_name\":\r\n if len(value) > 255:\r\n return False\r\n\r\n try:\r\n self.categories = ProductFromApiToDatabase.clean_tag(\r\n self.fields[\"categories\"], 100)\r\n self.stores = ProductFromApiToDatabase.clean_tag(\r\n self.fields[\"stores\"], 45)\r\n self.brands = ProductFromApiToDatabase.clean_tag(\r\n self.fields[\"brands\"], 45)\r\n self.category_index = self.categories.index(self.category)\r\n except KeyError:\r\n return False\r\n except ValueError:\r\n return False\r\n except AttributeError:\r\n self.errors += 1\r\n print(self.errors)\r\n return False","def valida_digital(id, codigo):\n id = str(id)\n codigo = str(codigo)\n sql = \"SELECT digital from eleitores WHERE id_eleitor=\" + \\\n id + \" AND digital=\" + codigo\n estado = consulta.execute(sql)\n\n if(estado):\n return True\n else:\n return False","def clean(self):\n\n self.cleaned_data = super(RoomForm, self).clean()\n\n cleaned_data = self.cleaned_data.copy()\n\n if cleaned_data.get(\"trial\") is None:\n self.cleaned_data[\"trial\"] = False\n else:\n cleaned_data.pop(\"trial\")\n\n if not all(cleaned_data.get(field) for field in cleaned_data.keys()):\n\n raise forms.ValidationError(\n message=\"You must fill in all the required fields!\",\n )","def validate_batch(self, *arg, **kwargs):\n pass","def test_domain_validator(self):\n id_stud = 1\n nume_stud = \"Mircea Augustin\"\n stud = Student(id_stud,nume_stud)\n self.assertEqual(stud.get_id_stud(),id_stud)\n self.assertEqual(stud.get_nume_stud(),nume_stud)\n self.assertEqual(str(stud),\"1 | Mircea Augustin\")\n stud2 = Student(id_stud,\"Horatiu Murdar\")\n self.assertEqual(stud,stud2)\n \"\"\"\n test validator student\n \"\"\"\n validStud = ValidatorStudent()\n stud = Student(1,\"Caine Rau\")\n validStud.valideaza_stud(stud)\n stud2 = Student(-1,\"\")\n self.assertRaisesRegex(ValidError,\"Id student invalid!Nume student invalid!\",validStud.valideaza_stud,stud2)\n \"\"\"\n test domeniu pt disc\n \"\"\"\n id_disc = 1\n nume_disc = \"Analiza\"\n nume_prof = \"Berinde Stefan\"\n disc = Disciplina(id_disc,nume_disc,nume_prof)\n self.assertEqual(disc.get_id_disc(),id_disc)\n self.assertEqual(disc.get_nume_disc(),nume_disc)\n self.assertEqual(disc.get_nume_prof(),nume_prof)\n self.assertEqual(str(disc),\"1 | Analiza | Berinde Stefan\")\n disc2 = Disciplina(id_disc,\"Logica\",\"Pop\")\n self.assertEqual(disc,disc2)\n \"\"\"\n test validare disc\n \"\"\"\n validDisc = ValidatorDisciplina()\n id_disc = 1\n nume_disc = \"Analiza\"\n nume_prof = \"Berinde Stefan\"\n disc = Disciplina(id_disc,nume_disc,nume_prof)\n validDisc.valideaza_disc(disc)\n disc_gresit = Disciplina(-1,\"\",\"\")\n self.assertRaisesRegex(ValidError,\"Id disciplina invalid!Nume disciplina invalid!Nume profesor invalid!\",validDisc.valideaza_disc,disc_gresit)\n \"\"\"\n test domeniu pt note\n \"\"\"\n id_nota = 0\n id_stud = 3\n nume_stud = \"Mircea Augustin\"\n stud = Student(id_stud,nume_stud)\n id_disc = 2\n nume_disc = \"Analiza\"\n nume_prof = \"Berinde Stefan\"\n disc = Disciplina(id_disc,nume_disc,nume_prof)\n val_nota = 9.5\n nota = Nota(id_nota,stud,disc,val_nota)\n self.assertEqual(nota.get_id_nota(),id_nota)\n self.assertEqual(nota.get_stud(),stud)\n self.assertEqual(nota.get_disc(),disc)\n self.assertEqual(nota.get_val_nota(),val_nota)\n nota2 = Nota(id_nota,Student(0,\"a\"),Disciplina(0,\"a\",\"a\"),5.4)\n self.assertEqual(nota,nota2)\n \"\"\"\n test validare pt note - BlackBox\n \"\"\"\n validNota = ValidatorNota()\n stud = Student(0,\"a\")\n disc = Disciplina(0,\"a\",\"a\")\n validNota.valideaza_nota(Nota(3,Student(3,\"Chef Scarlatescu\"),Disciplina(2,\"Analiza\",\"Berinde Stefan\"),5.4))\n validNota.valideaza_nota(Nota(0,Student(-1,\"a\"),Disciplina(0,\"\",\"a\"),1))\n validNota.valideaza_nota(Nota(0,stud,disc,10))\n self.assertRaisesRegex(ValidError,\"Id nota invalid! Valoare nota invalida!\",validNota.valideaza_nota, Nota(-1,stud,disc,0.9) )\n self.assertRaisesRegex(ValidError,\"Id nota invalid! Valoare nota invalida!\",validNota.valideaza_nota, Nota(-50,stud,disc,0) )\n self.assertRaisesRegex(ValidError,\"Valoare nota invalida!\",validNota.valideaza_nota, Nota(0,stud,disc,-1) )\n self.assertRaisesRegex(ValidError,\"Valoare nota invalida!\",validNota.valideaza_nota, Nota(0,stud,disc,10.1) )\n self.assertRaisesRegex(ValidError,\"Valoare nota invalida!\",validNota.valideaza_nota, Nota(0,stud,disc,50) )","def validate_fields(self, window, values):\n \n #Check if record id is new\n is_valid = True\n problem_field_name = \"\"\n experiment_names = GUI.api.get_experiment_names()\n if values['record_id'] in experiment_names:\n is_valid = False\n problem_field_name = \"Record ID\"\n return is_valid, problem_field_name \n \n metadata = GUI.api.get_metadata()\n enbaled_fields = filter(lambda elem: (elem['form_name']=='material_information' or elem['form_name']=='printer_information') \n and not (isinstance(window[elem['field_name']], sg.Text) or window[elem['field_name']].Disabled), metadata)#only validate enbaled fields\n for field in enbaled_fields:\n validation = field['text_validation_type_or_show_slider_number']\n value = values[field['field_name']]\n if (validation == \"number\" and value.isdigit()):\n #check if correct ranges\n if field['text_validation_max'] != \"\":\n if value > field['text_validation_max']:\n is_valid = False \n problem_field_name = field['field_label']\n return is_valid, problem_field_name \n if field['text_validation_min'] != \"\":\n if value < field['text_validation_min']:\n is_valid = False \n problem_field_name = field['field_label']\n return is_valid, problem_field_name \n elif (validation == \"number\" and not value.isdigit()):\n is_valid = False\n problem_field_name = field['field_label']\n return is_valid, problem_field_name\n return is_valid, problem_field_name","def validate(self,data):\n if self.context['request'].user!=data['offered_by']:\n raise serializer.ValidationError('Ride offered on behalf of others are not allowed')\n user = data['offered_by']\n circle = self.context['circle']\n \n try:\n membership=MemberShip.objects.get(\n user=user,\n circle=circle,\n is_active=True)\n except MemberShip.DoesNotExist:\n raise serializers.ValidationError('user is not an active member of circle')\n\n # La llegada tiene que ser despues de la salida, si la salida es mayor o igual a la llegada marca error\n if data['arrival_date']<=data['departure_date']:\n raise serializers.ValidationError('Departure date must happen offer arrival date')\n \n self.context['membership']=membership\n return data","def clean(self):\n cleaned_data = super(AddShopForm, self).clean()\n self.validate_unique_domain(cleaned_data)\n return cleaned_data","def __validate():\n # TODO: implement","def validate(self, data):\n choices = data['answer']\n question = Question.objects.get(id=data['question'])\n if question.qtype != 'select-multiple' and len(choices) > 1:\n raise serializers.ValidationError('This is a question with single choice')\n queryset = Choice.objects.filter(question_id=data['question'])\n for choice in choices:\n get_object_or_404(queryset, id=choice)\n return data","def clean(self):\n\n # Fetch cleaned email and username data.\n email = self.cleaned_data.get('email')\n username = self.cleaned_data.get('username')\n\n # Fetch possible user objects from the database\n # based on provided and email and password.\n user_email = User.objects.filter(email=email)\n user_uname = User.objects.filter(username=username)\n\n # If user exists based on the email address or username,\n # raise validation error.\n if user_email:\n self._errors[\"email\"] = \"Email address is already associated with another account\"\n\n if user_uname:\n self._errors[\"username\"] = \"Usename is already associated with another account\"","def clean_form(self):\n\n for condition in self.conditional_rules:\n\n all_conditions_passed = True\n\n # Only check if the field is required\n field_required = condition['required']\n if field_required:\n action = condition['action']\n\n for cond in condition['conditions']:\n condition_rule = cond['rule']\n condition_value = cond['value']\n condition_field_value = self.cleaned_data.get(cond['field_name'], None)\n\n all_conditions_passed = conditions_passed(condition_rule, condition_value, condition_field_value)\n if not all_conditions_passed:\n break\n\n if not all_conditions_passed and action == consts.FIELD_ACTION_SHOW:\n if condition['field_name'] in self._errors:\n del self._errors[condition['field_name']]\n elif all_conditions_passed and action == consts.FIELD_ACTION_HIDE:\n if condition['field_name'] in self._errors:\n del self._errors[condition['field_name']]\n\n return self.cleaned_data","def _validate(self):\n _models = {'hrrr', 'hrrrak', 'rap'}\n _fields = {'prs', 'sfc', 'nat', 'subh'}\n \n self.date = pd.to_datetime(self.date)\n \n if self.model == 'alaska':\n self.model == 'hrrrak'\n\n assert self.fxx in range(49), \"Forecast lead time `fxx` is too large\"\n assert self.model in _models, f\"`model` must be one of {_models}\"\n if self.model in ['hrrr', 'hrrrak']:\n assert self.field in _fields, f\"`field must be one of {_fields}\"\n else:\n # field is not needed for RAP model.\n self.field = ''\n \n if isinstance(self.priority, str):\n self.priority = [self.priority]\n \n self.priority = [i.lower() for i in self.priority]\n\n # Don't look for data from NOMADS if requested date is earlier\n # than yesterday. NOMADS doesn't keep data that old.\n if 'nomads' in self.priority:\n yesterday = datetime.utcnow() - timedelta(hours=24)\n yesterday = pd.to_datetime(f\"{yesterday:%Y-%m-%d}\")\n if self.date < yesterday:\n self.priority.remove('nomads')","def validate(self, data):\r\n budgets = Budget.objects.filter(\r\n campaign__pk=self.context['view'].kwargs['campaign_pk']\r\n )\r\n covered = budgets.values_list('start_date', 'end_date')\r\n for each in covered:\r\n sd_fail = (\r\n data['start_date'] >= each[0] and\r\n data['start_date'] <= each[1]\r\n )\r\n ed_fail = (\r\n data['end_date'] >= each[0] and\r\n data['end_date'] <= each[1]\r\n )\r\n over_fail = (\r\n each[0] >= data['start_date'] and\r\n each[1] <= data['end_date']\r\n )\r\n if sd_fail:\r\n raise serializers.ValidationError({\r\n 'start_date': \"Start date conflicts with existing budget\"\r\n })\r\n if ed_fail:\r\n raise serializers.ValidationError({\r\n 'end_date': \"End date conflicts with existing budget\"\r\n })\r\n if over_fail:\r\n raise serializers.ValidationError(\r\n \"Date conflicts with existing budget\"\r\n )\r\n return data","def validate(self):\n ...","def validate(self, value):\n # Use the parent's handling of required fields, etc.\n super().validate(value)\n for email in value:\n validate_email(email)","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def test_listar_expedientes_grado(self):\n self.assertNotEqual(self.almacen.listar_expedientes_grado(self.grado_valido), None)","def __call__(self, form, field):\n\t\tif not hasattr(self.model, self.field_name):\n\t\t\tmessage = \"Terjadi kesalahan, hubungi administrator!\"\n\t\t\tfield.errors = []\n\t\t\traise validators.StopValidation(message)\n\n\t\tif field.data and isinstance(field.data, string_types) and \\\n\t\t\tfield.data.strip():\n\t\t\t\tobj = getattr(self.model, self.field_name)\n\t\t\t\tuser = self.model.query.filter(obj == field.data).first()\n\t\t\t\tif user:\n\t\t\t\t\tfield.errors = []\n\t\t\t\t\tif self.message is None:\n\t\t\t\t\t\tmessage = \"Data {} sudah ada!\".format(self.field_name)\n\t\t\t\t\telse:\n\t\t\t\t\t\tmessage = self.message\n\t\t\t\t\traise validators.StopValidation(message)","def clean(self, *args, **kwargs):\n self.is_cleaned = True","def validate(self):\n if self.id == None:\n return False\n return self.validator.validate(self.fields)","def _check_date(self, cr, uid,ids, context=None):\n for act in self.browse(cr, uid, ids, context):\n line_date = datetime.strptime(str(act.date), \"%Y-%m-%d\")\n if int(line_date.month)!=int(act.enrich_id.month) or int(line_date.year)!=int(act.enrich_id.year):\n raise osv.except_osv(_('ValidateError'), _(\"Payment Enrich Date Must Be Within Enrich Month And Year %s - %s\")%(act.enrich_id.month,act.enrich_id.year))\n return True","def check(id_establecimiento):\n establecimientos = Establecimientos().get_establecimientos()\n datos = Establecimientos().get_establecimiento(id_establecimiento)\n datos['db_clean'] = False\n form = Establecimiento_form(**datos)\n return render_template(\n 'restaurar/check.html.jinja',\n form=form,\n establecimientos=establecimientos,\n active=int(id_establecimiento)\n )","def validate(self, name, values):\r\n \r\n pass","def aggAtributos(request,idTI):\n atributos= Atributo.objects.filter(ti_id=idTI)\n\n if validar_permiso(request.user,\"is_gerente\",atributos[0].ti.fase.id_Proyecto) or request.user.has_perm('crear_item',atributos[0].ti.fase.id_Proyecto) and validar_rol_fase('crear_item',atributos[0].ti.fase,request.user):\n print('tiene el permiso de crear_item')\n else:\n messages.error(request,\"NO SE POSEE EL PERMISO: crear_item\" + \" SOLICITE EL PERMISO CORRESPONDINTE PARA REALIZAR LA ACCION\")\n return redirect('gestion:detallesFase',atributos.ti.fase.id_Proyecto.id_proyecto)\n\n if(atributos.count() == 0):\n return HttpResponse(request,\"id de TI invalida\",status=400)\n\n if request.method == 'POST':\n itemID = Item.objects.last()\n ti = TipoItem.objects.get(id_ti=idTI)\n contador=0\n\n for c in atributos:\n print(c.id_atributo)\n contador=contador+1\n\n item=Item.objects.last()#SE OBTIENE EL ITEM CREADO RECIENTEMENTE\n list=[]\n for atributos in atributos:#SE RECORRE POR VALOR INGRESADO CONSULTANDO SI ES OBLIGARORIO Y ESTA VACIO-->MUESTRA ERROR\n ok = False\n if(atributos.tipo_dato == 'Boolean'):\n x = request.POST.getlist(atributos.tipo_dato)\n tiposAtributo = Atributo.objects.filter(ti_id=idTI, tipo_dato=atributos.tipo_dato)\n print(x)\n for ini in range(len(x)):\n if(x[ini] == '' and tiposAtributo[ini].es_obligatorio == True):\n ok=True\n nombre=tiposAtributo[ini].nombre\n if( ok == True ):\n messages.error(request, \"EL ATRIBUTO ES OBLIGATRIO FAVOR INGRESE UN VALOR PARA EL ATRIBUTO: \"+nombre)\n return redirect('gestion:aggAtributos', idTI)\n\n list=[\"Decimal\",\"Boolean\",\"File\",\"String\",\"Date\"]\n for ini in range(len(list)): #SI INGRESO VALORES CORRECTAMENTE LOS GUARDA RELACIONANDO CON EL ITEM CORRESPONDIENTE\n print('este es el valor de ini'+str(ini))\n print(list[ini])\n try:\n tiposAtributo = Atributo.objects.filter(ti_id=idTI, tipo_dato=list[ini])\n x=request.POST.getlist(list[ini])\n except:\n tiposAtributo=None\n\n if (tiposAtributo!=None):\n for valor in range(tiposAtributo.count()):\n if(list[ini]==\"File\"):\n list2 = []\n for atr in tiposAtributo:\n DOC = request.FILES.getlist(str(atr.id_atributo))\n if (DOC != list2):\n print(\"no vacio\",DOC[0])\n ruta = str(ti.fase.id_Proyecto.id_proyecto) + \"/\" + str(itemID.id_item)\n PATH = f'/{ruta}/{DOC[0]}'\n #SubirArchivo(DOC[0], PATH)\n print(\"--\",PATH)\n\n\n ##se sube archivo a dropbox en segundo plano\n t2 = Thread(\n target=SubirArchivo,\n args=(DOC[0],PATH),\n )\n t2.start()\n\n p = Atributo_Item(idAtributoTI=atr, id_item=item, valor=str(PATH))\n p.save()\n else:\n print(\"vacio\",DOC)\n p = Atributo_Item(idAtributoTI=atr, id_item=item, valor=\"Sin archivos adjuntos\")\n p.save()\n break\n else:\n p = Atributo_Item(idAtributoTI=tiposAtributo[valor],id_item=item,valor=str(x[valor]))\n p.save()\n\n itemID=Item.objects.last()\n ti=TipoItem.objects.get(id_ti=idTI)\n return redirect('gestion:relacionarItem',ti.fase.id_Proyecto.id_proyecto,itemID.id_item)\n item_id = Item.objects.last()\n contexto={\n 'atributos':atributos,\n 'true':True,\n 'false':False,\n 'id_item':item_id.id_item\n }\n return render (request,'items/aggAtributos.html',contexto)","def test_check(self):\n\t\tself.filter.set_operator('.equals')\n\t\tfor lim in [10, '10']:\n\t\t\tself.filter.set_limit(lim)\n\t\t\tfor val in [10, '10']:\n\t\t\t\tob = Object(field=val)\n\t\t\t\tself.assertTrue(self.filter.check(ob), \"Failed Filter comparison check - Fields should match!\")","def validate(self, request):\n values = {\n 'robot_match_comments':request.POST['robot_match_comments'],\n 'did_foul':'did_foul' in request.POST,\n 'did_technical_foul':'did_technical_foul' in request.POST,\n 'foul_description':request.POST['foul_description'],\n 'did_shoot':'did_shoot' in request.POST,\n 'auto_1':request.POST['auto_1'],\n 'auto_2':request.POST['auto_2'],\n 'auto_3':request.POST['auto_3'],\n 'auto_miss':request.POST['auto_miss'],\n 'teleop_1':request.POST['teleop_1'],\n 'teleop_2':request.POST['teleop_2'],\n 'teleop_3':request.POST['teleop_3'],\n 'teleop_5':request.POST['teleop_5'],\n 'teleop_miss':request.POST['teleop_miss'],\n 'shooting_description':request.POST['shooting_description'],\n 'did_climb':'did_climb' in request.POST,\n 'climb_start':request.POST['climb_start'],\n 'climb_finish':request.POST['climb_finish'],\n 'level_reached':request.POST.get('level_reached'),\n 'frisbees_dumped':request.POST['frisbees_dumped'],\n 'climbing_description':request.POST['climbing_description'],\n 'did_human_load':'did_human_load' in request.POST,\n 'did_ground_load':'did_ground_load' in request.POST,\n 'auto_frisbees_ground_loaded':\\\n request.POST['auto_frisbees_ground_loaded'],\n 'loading_description':request.POST['loading_description'],\n }\n if ((values['did_foul'] or values['did_technical_foul']) and\n not values['foul_description']):\n new_values = self.__dict__.copy()\n new_values.update(values)\n raise ValidationError(\n 'Please enter a description of the foul(s) the robot committed',\n new_values\n )\n if values['did_shoot']:\n try:\n values['auto_1'] = int(values['auto_1'])\n values['auto_2'] = int(values['auto_2'])\n values['auto_3'] = int(values['auto_3'])\n values['auto_miss'] = int(values['auto_miss'])\n values['teleop_1'] = int(values['teleop_1'])\n values['teleop_2'] = int(values['teleop_2'])\n values['teleop_3'] = int(values['teleop_3'])\n values['teleop_5'] = int(values['teleop_5'])\n values['teleop_miss'] = int(values['teleop_miss'])\n except ValueError:\n raise ValidationError(\n 'You must enter a number for all of the shooting numbers',\n self.__dict__.copy().update(values)\n )\n if values['did_climb']:\n try:\n values['climb_start'] = int(values['climb_start'])\n values['climb_finish'] = int(values['climb_finish'])\n try:\n values['level_reached'] = int(values['level_reached'])\n except TypeError:\n new_values = self.__dict__.copy()\n new_values.update(values)\n raise ValidationError(\n 'You must select a level the robot climbed too',\n new_values\n )\n values['frisbees_dumped'] = int(values['frisbees_dumped'])\n except ValueError:\n new_values = self.__dict__.copy()\n new_values.update(values)\n raise ValidationError(\n 'All climbing related numbers must be numbers',\n new_values\n )\n if values['did_ground_load']:\n try:\n values['auto_frisbees_ground_loaded'] = int(\n values['auto_frisbees_ground_loaded'])\n except ValueError:\n new_values = self.__dict__.copy()\n new_values.update(values)\n raise ValidationError(\n 'All numbers of frisbees ground loaded must be numbers',\n new_values\n )\n return values","def pre_validate(self, form):\n for item1,item2 in self.choices:\n if isinstance(item2, (list, tuple)):\n group_label = item1\n group_items = item2\n for val,label in group_items:\n if val == self.data:\n return\n else:\n val = item1\n label = item2\n if val == self.data:\n return\n raise ValueError(self.gettext('Not a valid choice!'))","def test_clean(self):\n form = DocumentSearchForm()\n form.cleaned_data = {\"q\": \"\", \"sort\": \"relevance\"}\n form.clean()\n assert len(form.errors) == 1\n\n # Otherwise should not raise an error\n form = DocumentSearchForm()\n form.cleaned_data = {\"q\": \"test\", \"sort\": \"relevance\"}\n form.clean()\n assert len(form.errors) == 0\n form = DocumentSearchForm()\n form.cleaned_data = {\"q\": \"\", \"sort\": \"scholarship_desc\"}\n form.clean()\n assert len(form.errors) == 0","def valid(self):\n pass","def validate(self, instance, value):","def validate(self, instance, value):","def clean(self):\n\n cleaned_data = self.cleaned_data\n if cleaned_data.get('order') and cleaned_data.get('stock') \\\n and cleaned_data.get('volume') and cleaned_data.get('price'):\n t = cleaned_data['trader']\n if cleaned_data['order'] == 'B': # buy order\n open_orders = Order.objects.filter(trader=t,\n order='B', completed=False)\n open_order_value = float(sum([o.volume * o.price for o in open_orders]))\n open_order_value += int(cleaned_data['volume']) * float(cleaned_data['price'])\n\n if open_order_value > t.cash:\n raise ValidationError(\"You don't have enough cash!\")\n\n elif cleaned_data['order'] == 'S': # sell order!\n open_orders = sum(Order.objects.filter(trader=t, order='S',\n stock=cleaned_data['stock'],\n completed=False).values_list('volume', flat=True))\n open_orders += cleaned_data['volume']\n\n if open_orders > t.holding_set.get(stock=cleaned_data['stock']).shares:\n raise ValidationError(\"You don't have enough shares!\")\n return cleaned_data","def clean(self):\n\n if self.dateEnd <= self.dateStart:\n raise ValidationError(\"Start date must be before end date!\")\n \n # If there are bookings in the database with:\n # - same room__id\n # - different booking__id\n # - first date at or after dateStart (but before dateEnd)\n # - last date at or before dateEnd (but after dateStart)\n # Raise ValidationError\n if Booking.objects.filter(room__id=self.room_id).exclude(pk=self.pk).filter(\n Q(dateEnd__gt=self.dateStart, dateStart__lt=self.dateEnd)\n ).exists():\n raise ValidationError(\"Overlapping dates, room has been booked.\")","def _check_active(self):\n for record in self:\n count = self.search_count([('active', '=', True), ('id', '!=', record.id)])\n if count:\n raise ValidationError(_('Multiple active records. Please Inactive other!'))","def validate(data):\n if 'value' not in data or \\\n 'category' not in data or \\\n 'classification' not in data or \\\n 'account' not in data:\n raise Exception('Missing required field.')\n classifications = ['Personal', 'Essential', 'Savings', 'Income']\n if data['classification'] not in classifications:\n raise Exception('Invalid classification.')","def clean(self):\n\n # First, check that something hasn't gone wrong already.\n if self.errors:\n return {}\n\n # Cleaning IDs\n category_name = self.cleaned_data['category']\n if category_name != u'all':\n matching_category_ids = get_all_matching_category_ids(\n category_name\n )\n self.cleaned_data['category_id'] = matching_category_ids[0]\n else:\n self.cleaned_data['category_id'] = 0\n\n location_name = self.cleaned_data['location']\n matching_location_ids = get_all_matching_location_ids(\n location_name)\n self.cleaned_data['location_id'] = matching_location_ids[0]\n location = Location.objects.get(id=matching_location_ids[0])\n\n # Cleaning date\n t = self.cleaned_data['date']\n now = datetime.now(pytz.timezone(location.timezone)).date()\n if t < now:\n raise forms.ValidationError(\"It looks like the date you searched for already happened...\")\n\n return self.cleaned_data","def _validate(self):\n pass","def AggComite(request,pk):#esta enlazado con la clase FaseForm del archivo getion/forms\n ##ESte es lo que yo(presi) le agregue\n try:\n proyectos = Proyecto.objects.get(id_proyecto=pk)\n except Proyecto.DoesNotExist:\n return HttpResponse(\"Proyecto no existe\",status=400)\n\n try:\n proyecto = User_Proyecto.objects.filter(proyecto_id=pk)\n except:\n return HttpResponse(\"Proyecto no existe\",status=400)\n\n gerente = User.objects.get(id=proyecto[0].user_id)\n print(gerente.username)\n\n proyecto_validar=Proyecto.objects.get(id_proyecto=pk)\n\n if validar_permiso(request.user, \"is_gerente\",proyecto_validar)==False: # primero se valida si es gerente en el proyecto actual)\n messages.error(request, 'No eres gerente de proyecto, por lo tanto no puedes crear el comite de cambio')\n return redirect('gestion:comite', pk)\n\n proyectos=Proyecto.objects.get(id_proyecto=pk)\n comite= Comite.objects.all()\n form = Usuario.objects.all()\n registrados = User_Proyecto.objects.all()\n\n if request.method == 'POST': #preguntamos primero si la petición Http es POST ||| revienta todo con este\n some_var=request.POST.getlist('checkbox')\n\n if ((len(some_var)+1)%2==0 or (len(some_var)+1)==1):# SE VALIDA QUE DEBE DE SER IMPAR Y MAYOR A 1\n messages.error(request,'EL NUMERO DE USUARIOS EN EL COMITE DEBE DE SER IMPAR Y MAYOR A UNO')\n return redirect('gestion:AggComite',pk)\n p=Comite(id_proyecto=pk,id_user=gerente.id)\n p.save()\n for id in some_var:###### SE GUARDAN EN USER_PROYECTOS LAS RELACIONES\n id_user =id\n usuario=User.objects.get(id=id_user)\n registrarAuditoriaProyecto(request.user,\"Añadio al comite de cambio al usuario: \"+str(usuario.username),proyectos.id_proyecto,proyectos.nombre,\"\")\n p=Comite(id_proyecto=pk,id_user=id_user)\n p.save()\n\n return redirect('gestion:comite',pk)\n else:\n list=[]\n for i in range(form.count()):\n ok = False\n if form[i].esta_aprobado == True:\n for x in range(registrados.count()):\n if registrados[x].proyecto_id == pk and registrados[x].user_id == form[i].user.id and registrados[x].activo == True:# esta en el proyecto?\n ok=True\n for z in range(comite.count()):#si ya esta en el comite no\n if form[i].user.id == comite[z].id_user and pk==comite[z].id_proyecto:\n ok=False\n if ok:\n list.append(form[i].user.id)\n\n return render(request, 'proyectos/agg_comite.html', {'form': form,'list':list,'proyectos':proyectos,'idGerente':gerente.id})","def validate(self):\n return self.validator.validate(self.fields)","def post(self, request, *args, **kwargs):\n self.object = Usuario.objects.get(pk=kwargs['pk'])\n form_class = self.get_form_class()\n form = self.get_form(form_class)\n contacto_linea_form = Contacto_LineaFormSet(self.request.POST, instance=self.object)\n direccion_linea_form = Direccion_LineaFormSet(self.request.POST, instance=self.object)\n if (form.is_valid()\n and contacto_linea_form.is_valid()\n and direccion_linea_form.is_valid()):\n return self.form_valid(form, contacto_linea_form, direccion_linea_form)\n else:\n return self.form_invalid(form, contacto_linea_form, direccion_linea_form)","def clean(self):\n return self.cleaned_data","def validate_batch(self, *args, **kwargs):\n raise NotImplementedError()","def run_validation(self, data=empty):\n # ensure Unique and UniqueTogether don't collide with a DB match\n validators = self.remove_validation_unique()\n validated_data = super().run_validation(data)\n # restore Unique or UniqueTogether\n self.restore_validation_unique(validators)\n return self.validated_data","def validate(self):\n rv = Form.validate(self)\n if not rv:\n return False\n\n cur = get_cursor()\n if email_exists(cur, self.email.data):\n self.email.errors.append('This email already exists!')\n return False\n\n if username_exists(cur, self.username.data):\n self.username.errors.append('This username already exists!')\n return False\n\n return True","def filter_form_valid(self, filter_form):\n return True","def clean(self):\n\n # count subjects with matching subject_code and parent project,\n # which don't have this pk\n try:\n Project.objects.get(id=self.project_id)\n except ObjectDoesNotExist:\n return # Project is required and will get caught by Form.is_valid()\n\n if self.subject_group is not None:\n if self.subject_group.project_id != self.project_id:\n raise ValidationError(\"Group chosen is not in this Project\")\n\n subject_duplicates = Subject.objects.filter(\n subject_code=self.subject_code,\n project=self.project).exclude(\n id=self.id)\n if subject_duplicates.count() > 0:\n raise ValidationError(\n \"Subject code already exists in this project.\"\n )","def do_validate(self, request, _object):\n\n pass","def test_validation(self):\n def test(**invalid_fields):\n with self.assertRaises(AssertionError):\n c = models.Citizen(**{'relatives': [], **invalid_fields})\n\n # Must throw errors in every case\n test(citizen_id=-1, )\n test(apartment='WRONG-STRING')\n test(town='', )\n test(street='', )\n test(building='', )\n test(name=None, )\n test(birth_date='WRONG-STRING')\n test(birth_date='31.02.2019')\n test(relatives=['a'])","def clean(self):\n cleaned_data = super().clean()\n if all(val == \"\" for val in cleaned_data.values()):\n raise ValidationError(\"You must fill at least one field!\")\n\n return cleaned_data","def _validate_data(self, vms, fw_rules):\n self._validate_vms(vms)\n self._validate_fw_rules(fw_rules)\n self._validated = True"],"string":"[\n \"def validate():\",\n \"def validadores(self):\\n\\n camposRequeridos = [getattr(self,\\\"lineRazon\\\")]\\n ValidarDatos.setValidador(camposRequeridos)\\n\\n camposRequeridos = [getattr(self,\\\"lineCuit\\\")]\\n ValidarDatos.setValidador(camposRequeridos)\\n\\n camposRequeridos = [getattr(self,\\\"lineNumeroFac\\\")]\\n ValidarDatos.setValidador(camposRequeridos)\",\n \"def validate_fields(row,fila,new_values):\\n # \\\"Definiciones iniciales del diccionario de errores\\n # y status de error.\\n dict_error={}\\n error_count = False\\n\\n \\\"\\\"\\\"Validaciones de cada campo segun el modelo Job.\\\"\\\"\\\"\\n #Validate Job.id\\n if row[0] == None:\\n new_values['id'] =row[0]\\n # dict_error['id'] = ''\\n else:\\n try:\\n if isinstance(int(row[0]),int):\\n new_values['id'] =row[0]\\n # dict_error['id'] = ''\\n except ValueError:\\n error_count=True\\n dict_error['id'] = ValueError('Error en id')\\n\\n #Validate Job.company_ruc\\n if len(row[1])==11:\\n new_values['company_ruc'] =row[1]\\n # dict_error['company_ruc'] = ''\\n else:\\n error_count=True\\n dict_error['company_ruc'] = ValidationError('Error en company_ruc')\\n\\n #Validate Job.company_name\\n if len(row[2])<=100 and row[2] != None:\\n new_values['company_name'] =row[2]\\n # dict_error['company_name'] = ''\\n else:\\n error_count=True\\n dict_error['company_name'] = ValidationError('Error en company_name')\\n\\n #Validate Job.title\\n if len(row[3])<=100 and row[3] != None:\\n new_values['title'] =row[3]\\n # dict_error['title'] = ''\\n else:\\n error_count=True\\n dict_error['title'] = ValidationError('Error en title')\\n\\n #Validate Job.description\\n if isinstance(row[4],str) and row[4] != None:\\n new_values['description'] =row[4]\\n # dict_error['description'] = ''\\n else:\\n error_count=True\\n dict_error['description'] = ValidationError('Error en description')\\n\\n #Validate Job.requeriments\\n if isinstance(row[5],str) and row[5] != None:\\n new_values['requeriments'] =row[5]\\n # dict_error['requeriments'] = ''\\n else:\\n error_count=True\\n dict_error['requeriments'] = ValidationError('Error en requeriments')\\n\\n #Validate Job.contact_email\\n if row[6] != None:\\n new_values['contact_email'] =row[6]\\n # dict_error['contact_email'] = ''\\n else:\\n error_count=True\\n dict_error['contact_email'] = ValidationError('Error en contact email')\\n\\n #Validate Job.location\\n if len(row[7])<=50 and row[7] != None:\\n new_values['location'] =row[7]\\n # dict_error['location'] = ''\\n else:\\n error_count=True\\n dict_error['location'] = ValidationError('Error en Location')\\n\\n #Validate Job.is_active\\n if row[8] == None:\\n new_values['is_active'] =row[8]\\n # dict_error['is_active'] = ''\\n else:\\n try:\\n if int(row[8])==1 or int(row[8])==0:\\n new_values['is_active'] =row[8]\\n # dict_error['is_active'] = ''\\n except Exception as e:\\n error_count=True\\n dict_error['is_active'] = ValidationError('Error en el is_active')\\n\\n #Validate Job.is_verified\\n if row[9] == None:\\n new_values['is_verified'] =row[9]\\n # dict_error['is_verified'] = ''\\n else:\\n try:\\n if int(row[9])==1 or int(row[9])==0:\\n new_values['is_verified'] =row[9]\\n # dict_error['is_verified'] = ''\\n except Exception as e:\\n error_count=True\\n dict_error['is_verified'] = ValidationError('Error en el is_verified')\\n\\n #Validate Job.is_public\\n if row[10] == None:\\n new_values['is_public'] =row[10]\\n # dict_error['is_public'] = ''\\n else:\\n try:\\n if int(row[10])==1 or int(row[10])==0:\\n new_values['is_public'] =row[10]\\n # dict_error['is_public'] = ''\\n except Exception as e:\\n error_count=True\\n dict_error['is_public'] = ValidationError('Error en el is_public')\\n\\n #Validate Job.show_recruiter\\n if row[11] == None:\\n new_values['show_recruiter'] =row[11]\\n # dict_error['show_recruiter'] = ''\\n else:\\n try:\\n if int(row[11])==1 or int(row[11])==0:\\n new_values['show_recruiter'] =row[11]\\n # dict_error['show_recruiter'] = ''\\n except Exception as e:\\n error_count=True\\n dict_error['show_recruiter'] = ValidationError('Error en el show_recruiter')\\n\\n #Validate Job.website_url\\n if row[12] == None:\\n new_values['website_url'] =row[12]\\n # dict_error['website_url'] = ''\\n else:\\n if len(row[12])>4:\\n new_values['website_url'] =row[12]\\n # dict_error['website_url'] = ''\\n else:\\n error_count=True\\n dict_error['website_url'] = ValidationError('Error en website_url')\\n\\n #Validate Job.benefits\\n if isinstance(row[13],str) or row[13] == None:\\n new_values['benefits'] =row[13]\\n # dict_error['benefits'] = ''\\n else:\\n error_count=True\\n dict_error['benefits'] = ValidationError('Error en benefits')\\n\\n #Validate Job.urgency\\n if isinstance(row[14],str) or row[14] == None:\\n new_values['urgency'] =row[14]\\n # dict_error['urgency'] = ''\\n else:\\n error_count=True\\n dict_error['urgency'] = ValidationError('Error en urgency')\\n\\n #Validate Job.schedule\\n if isinstance(row[15],str) or row[15] == None:\\n new_values['work_schedule'] =row[15]\\n # dict_error['work_schedule'] = ''\\n else:\\n error_count=True\\n dict_error['work_schedule'] = ValidationError('Error en work_schedule')\\n\\n #Validate Job.comment\\n if isinstance(row[16],str) or row[16] == None:\\n new_values['comment'] =row[16]\\n # dict_error['comment'] = ''\\n else:\\n error_count=True\\n dict_error['comment'] = ValidationError('Error en comment')\\n\\n #Validate Job.min_salary\\n if isinstance(int(row[17]),int) or row[17] == None:\\n new_values['min_salary'] =row[17]\\n # dict_error['min_salary'] = ''\\n else:\\n error_count=True\\n dict_error['min_salary'] = ValidationError('Error en min_salary')\\n\\n #Validate Job.max_salary\\n if isinstance(int(row[18]),int) or row[18] == None:\\n new_values['max_salary'] =row[18]\\n # dict_error['max_salary'] = ''\\n else:\\n error_count=True\\n dict_error['max_salary'] = ValidationError('Error en max_salary')\\n\\n #Validate Job.pay_range_period\\n if row[19]=='annual' or row[19]=='monthly' or row[19] == None:\\n new_values['pay_range_period'] =row[19]\\n # dict_error['pay_range_period'] = ''\\n else:\\n error_count=True\\n dict_error['pay_range_period'] = ValidationError('Error en pay_range_period')\\n\\n return {\\\"errors\\\": dict_error,\\\"fila\\\":fila,\\\"new_values\\\":new_values,\\\"error_status\\\":error_count}\",\n \"def clean(self):\\n cleaned_data = super().clean()\\n hora_inicio = cleaned_data.get(\\\"hora_inicio\\\")\\n hora_final = cleaned_data.get(\\\"hora_final\\\")\\n if hora_inicio > hora_final or hora_inicio == hora_final:\\n raise forms.ValidationError(\\\"Error. La hora de inicio debe ser menor que la hora final\\\")\",\n \"def clean(self):\\r\\n cleaned_data = super(RPEventCreateForm, self).clean()\\r\\n self.check_risk()\\r\\n self.check_costs()\\r\\n self.check_location_or_plotroom()\\r\\n return cleaned_data\",\n \"def validate(self, data):\\r\\n flight_planes = Flight.objects.filter(planes__id=data['planes'].id)\\r\\n\\r\\n if data['to'] == data['airports'].city:\\r\\n raise serializers.ValidationError(\\\"El vuelo no puede ir hacia el mismo aeropuerto\\\")\\r\\n\\r\\n elif len(flight_planes) and flight_planes[len(flight_planes) - 1].to == data['to']:\\r\\n raise serializers.ValidationError(\\r\\n \\\"Un vuelo solo puede ser generado con un avión cuyo último vuelo haya sido al Aeropuerto\\\")\\r\\n return data\",\n \"def validate_usuario(self, data):\\n\\t\\tuser = Usuario.objects.filter(usuario=data)\\n\\t\\t# Si estoy creando (no hay instancia) comprobar si hay usuarios con ese\\n\\t\\t# username\\n\\t\\tif not self.instance and len(user) != 0:\\n\\t\\t\\traise ValidationError(u\\\"Ya existe un usuario con ese usuario\\\")\\n\\t\\t# Si estoy actualizando (hay instancia) y estamos cambiando el username\\n\\t\\t# y existen usuarios con el nuevo username\\n\\t\\telif self.instance.usuario != data and len(user) != 0:\\n\\t\\t\\traise ValidationError(u\\\"Ya existe un usuario con ese usuario\\\")\\n\\t\\telse:\\n\\t\\t\\treturn data\",\n \"def validate(self,data):\\r\\n for person in data:\\r\\n #check the format is a letter and 3 digit e.g A002 or a002\\r\\n \\r\\n if (re.match(r'[A-Z][0-9]{3}', (person[0]).lower())):\\r\\n print (person[0])\\r\\n else:\\r\\n print(person[0] + \\\" \\\" + 'is incorrect ID, '\\r\\n ' must contains a letter and 3 digits e.g a002')\\r\\n \\r\\n #check the format is either M/F/Male/Female\\r\\n \\r\\n if (person[1] == \\\"M\\\" or (person[1]).upper() == \\\"F\\\" or\\r\\n person[1] == \\\"Male\\\" or person[1] == \\\"Female\\\"):\\r\\n print (person[1])\\r\\n else:\\r\\n print(person[1] + \\\" \\\" + 'is incorrect Gender, '\\r\\n ' must either be M and Male or F and Female')\\r\\n \\r\\n #check age is valid entry and match with date\\r\\n \\r\\n if (re.match(r'[0-9]{2}', person[2]) and person[2] == self.valid_age(person[6])):\\r\\n print (person[2])\\r\\n elif (person[2] != self.valid_age(person[2])):\\r\\n print(\\\"Does not match with your birthday, invalid age\\\")\\r\\n else:\\r\\n print(person[2] + \\\" \\\" + 'age must be an integer')\\r\\n \\r\\n #check sales is 3 interger value\\r\\n if (re.match(r'[0-9]{3}', person[3])):\\r\\n return (person[3])\\r\\n else:\\r\\n print(person[3] + \\\" \\\" + 'is incorrect sales number, '\\r\\n 'must be a 2 interger number')\\r\\n \\r\\n #check BMI is either Normal / Overweight / Obesity or Underweight\\r\\n if (re.match(r'\\\\b(NORMAL|OVERWEIGHT|OBESITY|UNDERWEIGHT)\\\\b',(person[4]).upper())):\\r\\n print (person[4])\\r\\n else:\\r\\n print(person[4] + \\\" \\\" ' is incorrect BMI value, '\\r\\n 'must select from Normal, Overweight, Obesity or Underweight')\\r\\n \\r\\n #check Income is float\\r\\n\\r\\n if (re.match(r'[0-9]{2,3}', person[5])):\\r\\n print (person[5])\\r\\n else:\\r\\n print(person[5] + \\\" \\\" + 'is incorrect income, '\\r\\n 'must be a interger number') \\r\\n \\r\\n #check birthday\\r\\n \\r\\n if (self.valid_date(person[6]) and person[2] == self.valid_age(person[6]) ):\\r\\n print (person[6])\\r\\n else:\\r\\n print(person[2] + \\\" \\\" + 'is incorrect date format, '\\r\\n 'must contain DD-MM-YYYY or DD-MM-YY and seperated by -')\\r\\n \\r\\n return readFile\",\n \"def test_special_case(self):\\n cases = [\\n ('3467875434578764345789654', False),\\n ('AAAAAAAAAAA', False),\\n ('', False),\\n ]\\n for titulo_eleitoral, is_valid in cases:\\n self.assertEqual(self.titulo_eleitoral.validate(titulo_eleitoral), is_valid)\",\n \"def validate(self,data):\\n ride= self.context['view'].get_object()\\n user = data['passanger']\\n\\n \\n if not ride.passangers.filter(pk=user.pk).exists():\\n raise serializers.ValidationError('El pasajero no esta en este ride')\\n\\n if Raitings.objects.filter(ride=ride,passanger=user).exists():\\n raise serializers.ValidationError('El pasajero ya dio su puntuacion')\\n\\n\\n # si el ride esta activo \\n if ride.is_active:\\n raise serializers.ValidationError('El ride aún no esta finalizado') \\n \\n\\n return data\",\n \"def check_data(self):\\n\\n for i in range(len(self.full_ed_lines)):\\n if self.full_ed_lines[i].text() != \\\"\\\":\\n if self.full_ed_lines[i].hasAcceptableInput():\\n continue\\n else:\\n if i == 1:\\n self.msg2Statusbar.emit('Неправильный формат версии! Исправьте и повторите действие!')\\n elif i == 5:\\n self.msg2Statusbar.emit('Неправильная почта! Исправьте и повторите действие!')\\n return False\\n else:\\n self.msg2Statusbar.emit('Не все поля заполнены! Исправьте и повторите действие!')\\n return False\\n return True\",\n \"def validate(self, data):\\n\\n end_data = data.get('end') or self.instance.end\\n start_data = data.get('start') or self.instance.start\\n\\n if end_data <= start_data:\\n raise serializers.ValidationError({\\n 'end': ['Data de termino deve ser maior que a data de inicio'],\\n })\\n\\n query = Meeting.objects.filter(\\n meeting_room=data.get('meeting_room') or self.instance.meeting_room.id,\\n start__gte=start_data, end__lte=end_data\\n )\\n if self.instance:\\n query = query.exclude(pk=self.instance.pk)\\n\\n if query.exists():\\n raise serializers.ValidationError(\\n 'Esta sala ja esta reservada para esse horario')\\n\\n return data\",\n \"def validate(self):\\n\\n raise NotImplementedError('Ando validation is not implemented yet.')\",\n \"def validate(self):\",\n \"def validate(self):\",\n \"def validate(cls, data, errors):\",\n \"def clean(self):\\n data = super().clean()\\n if self.pre_user:\\n pre_ci = self.pre_user.perfil.cedula_identidad\\n ci = self.cleaned_data['cedula_identidad']\\n\\n if pre_ci == ci:\\n return data\\n\\n cedula_identidad = self.cleaned_data['cedula_identidad']\\n cedula_existente = Perfil.objects.filter(\\n cedula_identidad=cedula_identidad).exists()\\n if cedula_existente:\\n raise forms.ValidationError(\\n 'Tu cédula se encuentra registrada. Verifica los datos')\\n return data\",\n \"def clean(self):\\n data = super().clean()\\n if self.pre_user:\\n pre_ci = self.pre_user.perfil.cedula_identidad\\n ci = self.cleaned_data['cedula_identidad']\\n\\n if pre_ci == ci:\\n return data\\n\\n cedula_identidad = self.cleaned_data['cedula_identidad']\\n cedula_existente = Perfil.objects.filter(\\n cedula_identidad=cedula_identidad).exists()\\n if cedula_existente:\\n raise forms.ValidationError(\\n 'Tu cédula se encuentra registrada. Verifica los datos')\\n return data\",\n \"def clean(self):\\n data = super().clean()\\n if self.pre_user:\\n pre_ci = self.pre_user.perfil.cedula_identidad\\n ci = self.cleaned_data['cedula_identidad']\\n\\n if pre_ci == ci:\\n return data\\n\\n cedula_identidad = self.cleaned_data['cedula_identidad']\\n cedula_existente = Perfil.objects.filter(\\n cedula_identidad=cedula_identidad).exists()\\n if cedula_existente:\\n raise forms.ValidationError(\\n 'Tu cédula se encuentra registrada. Verifica los datos')\\n return data\",\n \"def clean(self):\\n\\n # Getting cleaned email and username data.\\n email = self.cleaned_data.get('email')\\n username = self.cleaned_data.get('username')\\n\\n # Get possible user objects based on email and username.\\n user_email = User.objects.filter(email=email)\\n user_uname = User.objects.filter(username=username)\\n\\n # If the user has changed his email\\n # and if the email already exists.\\n if email != self.user.email:\\n if user_email:\\n raise forms.ValidationError(\\\"Email address is already taken\\\")\\n\\n # If the user has changed his username\\n # and if the username already exists.\\n if username != self.user.username:\\n if user_uname:\\n raise forms.ValidationError(\\\"Username is already taken\\\")\",\n \"def validate(self, data):\\n if data.has_key('site'):\\n if FieldSightXF.objects.filter(\\n xf__id=data['xf'], is_staged=False, is_scheduled=True, site=data['site']).exists():\\n raise serializers.ValidationError(\\\"Form Already Exists, Duplicate Forms Not Allowded\\\")\\n elif data.has_key('project'):\\n if FieldSightXF.objects.filter(\\n xf__id=data['xf'], is_staged=False, is_scheduled=True, project=data['project']).exists():\\n raise serializers.ValidationError(\\\"Form Already Exists, Duplicate Forms Not Allowded\\\")\\n return data\",\n \"def validate(self, field, row):\\n raise NotImplementedError\",\n \"def _checkstate(self, queryset):\\n for presup in queryset.filter(estado='borrador').exclude(fecha_realizado=None):\\n if ((presup.fecha_realizado + timedelta(days=21) < datetime.now().date() and presup.tipo != 'asistencia') or\\n (presup.fecha_realizado + timedelta(days=60) < datetime.now().date() and presup.tipo == 'asistencia')):\\n presup._toState_cancelado(obs=\\\"Registro automático: Presupuesto vencido\\\")\",\n \"def clean(self):\\n\\n\\t\\tfor form in self.forms:\\n\\t\\t\\tstatus = form.cleaned_data.get('status')\\n\\t\\t\\tif not status:\\n\\t\\t\\t\\traise ValidationError('Keinen Status', 'error')\",\n \"def _validate_select_where(self):\",\n \"def isValid(self):\\n\\t\\treturn ValidadorChaveAcessoNFe.validar(self)\",\n \"def test_validation_function(self):\\n\\n for data in ('tbldata', 'dihedraldata', 'rdcdata', 'danidata'):\\n v = self.web.query_nodes(key=data)\\n\\n if not v.empty():\\n self.assertTrue(validate_tbl(v.value, pcs=False))\",\n \"def validate(ob):\",\n \"def validate(self) -> None:\\n\\n if self.field not in self.model.table_fields:\\n raise ValueError(f\\\"Value field {self.field} not present in {self.model.table}\\\")\\n\\n if self.pivot:\\n if self.pivot not in self.model.table_fields:\\n raise ValueError(\\n f\\\"Pivot field {self.pivot} not present in {self.model.table}\\\"\\n )\\n\\n if self.connector:\\n if self.connector not in self.model.table_fields:\\n raise ValueError(\\n f\\\"Connector field {self.connector} not present in {self.model.table}\\\"\\n )\\n\\n for field in self.selectors:\\n if field not in self.model.table_fields:\\n raise ValueError(f\\\"Selector field {field} not present in {self.model.table}\\\")\",\n \"def is_valid(self, data_model: DataModel) -> bool:\",\n \"def _validate(self, queryset):\\n values_distinct = queryset.values(\\n *self._invoice_report_common_fields\\n ).distinct()\\n if values_distinct.count() != 1:\\n raise ValidationError(self._get_non_unique_error(queryset))\\n if not all(values_distinct[0].values()):\\n raise ValidationError(\\\"None of {} can't be empty\\\".format(', '.join(\\n self._invoice_report_common_fields\\n )))\",\n \"def _check_employee(self):\\n\\n for record in self:\\n\\n if record.nik_number:\\n # find duplicate nik\\n employee_ids = self.search([('id', 'not in', self.ids), ('nik_number', '=', record.nik_number)])\\n if employee_ids:\\n error_msg = _(\\\"There is duplicate of Employee Identity Number.\\\")\\n raise ValidationError(error_msg)\\n\\n # check nik format. it required base_indonesia\\n if not record._check_nik(record):\\n error_msg = _(\\\"NIK did not match with Company Code.\\\")\\n raise ValidationError(error_msg)\\n\\n if record.identification_id:\\n employee_ids = self.search([('id', 'not in', self.ids), ('identification_id', '=', record.identification_id)])\\n if employee_ids:\\n error_msg = _(\\\"There is duplicate of Identification Number.\\\")\\n raise ValidationError(error_msg)\\n\\n return True\",\n \"def validate(self, row):\\n raise NotImplementedError\",\n \"def validate(self):\\n for rule in self.get_rules():\\n rule.validate(self.get_val())\",\n \"def check_validity(self):\",\n \"def test_clean(self):\\n doc = Document.objects.create()\\n\\n form = DocumentMergeForm()\\n form.cleaned_data = {\\n \\\"primary_document\\\": doc.id,\\n \\\"rationale\\\": \\\"other\\\",\\n \\\"rationale_notes\\\": \\\"\\\",\\n }\\n form.clean()\\n assert len(form.errors) == 1\\n\\n # should not produce an error if rationale notes provided\\n form = DocumentSearchForm()\\n form.cleaned_data = {\\n \\\"primary_document\\\": doc.id,\\n \\\"rationale\\\": \\\"other\\\",\\n \\\"rationale_notes\\\": \\\"test\\\",\\n }\\n form.clean()\\n assert len(form.errors) == 0\\n\\n # should not produce an error if rational is \\\"duplicate\\\" or \\\"join\\\"\\n form = DocumentSearchForm()\\n form.cleaned_data = {\\n \\\"primary_document\\\": doc.id,\\n \\\"rationale\\\": \\\"duplicate\\\",\\n \\\"rationale_notes\\\": \\\"\\\",\\n }\\n form.clean()\\n assert len(form.errors) == 0\",\n \"def question_new_validate():\",\n \"def form_valid(self, form):\\n\\t self.object = form.save()\\n\\t registro = self.object\\n\\t cliente = registro.cliente\\n\\t cpf = cliente.cpf\\n\\t self.validar_registros(cpf)\\n\\t premios_cliente = Premios.objects.filter(cliente__cpf = cpf)\\n\\t return render(self.request, 'ctfidelidade/lista_premios.html', {'premios': premios_cliente})\",\n \"def validate(self, registration):\\n self.l.info(\\\"Starting registration validation\\\")\\n\\n validation_errors = []\\n\\n # Check if registrant_id is a valid UUID\\n if not utils.is_valid_uuid(registration.registrant_id):\\n validation_errors += [\\\"Invalid UUID registrant_id\\\"]\\n\\n # Check that required fields are provided and valid\\n data_fields = registration.data.keys()\\n\\n if registration.reg_type == \\\"pmtct_prebirth\\\":\\n validation_errors += self.check_lang(data_fields, registration)\\n validation_errors += self.check_mom_dob(data_fields, registration)\\n validation_errors += self.check_edd(data_fields, registration)\\n validation_errors += self.check_operator_id(\\n data_fields, registration)\\n\\n elif registration.reg_type == \\\"pmtct_postbirth\\\":\\n validation_errors += self.check_lang(data_fields, registration)\\n validation_errors += self.check_mom_dob(data_fields, registration)\\n validation_errors += self.check_baby_dob(data_fields, registration)\\n validation_errors += self.check_operator_id(\\n data_fields, registration)\\n\\n elif registration.reg_type == \\\"nurseconnect\\\":\\n validation_errors += self.check_faccode(\\n data_fields, registration)\\n validation_errors += self.check_operator_id(\\n data_fields, registration)\\n validation_errors += self.check_msisdn_registrant(\\n data_fields, registration)\\n validation_errors += self.check_msisdn_device(\\n data_fields, registration)\\n validation_errors += self.check_lang(\\n data_fields, registration)\\n\\n elif registration.reg_type == \\\"momconnect_prebirth\\\":\\n # Checks that apply to clinic, chw, public\\n validation_errors += self.check_operator_id(\\n data_fields, registration)\\n validation_errors += self.check_msisdn_registrant(\\n data_fields, registration)\\n validation_errors += self.check_msisdn_device(\\n data_fields, registration)\\n validation_errors += self.check_lang(\\n data_fields, registration)\\n validation_errors += self.check_consent(\\n data_fields, registration)\\n\\n # Checks that apply to clinic, chw\\n if registration.source.authority in [\\\"hw_full\\\", \\\"hw_partial\\\"]:\\n validation_errors += self.check_id(\\n data_fields, registration)\\n\\n # Checks that apply to clinic only\\n if registration.source.authority == \\\"hw_full\\\":\\n validation_errors += self.check_edd(\\n data_fields, registration)\\n validation_errors += self.check_faccode(\\n data_fields, registration)\\n\\n elif registration.reg_type == \\\"momconnect_postbirth\\\":\\n validation_errors.append(\\\"Momconnect postbirth not yet supported\\\")\\n\\n elif registration.reg_type == \\\"loss_general\\\":\\n validation_errors.append(\\\"Loss general not yet supported\\\")\\n\\n # Evaluate if there were any problems, save and return\\n if len(validation_errors) == 0:\\n self.l.info(\\\"Registration validated successfully - updating \\\"\\n \\\"registration object\\\")\\n registration.validated = True\\n registration.save()\\n self.l.info(\\\"Registration object updated.\\\")\\n return True\\n else:\\n self.l.info(\\\"Registration validation failed - updating \\\"\\n \\\"registration object\\\")\\n registration.data[\\\"invalid_fields\\\"] = validation_errors\\n registration.save()\\n self.l.info(\\\"Registration object updated.\\\")\\n return False\",\n \"def validare(film):\\n\\n erori = []\\n if not film.get_fid():\\n erori.append(\\\"Trebuie introdus un ID!\\\")\\n if not type(film.get_fid()) is int or film.get_fid() <= 0:\\n erori.append(\\\"ID-ul filmului trebuie sa fie un numar intreg mai mare sau egal decat 1!\\\")\\n if not film.get_titlu():\\n erori.append(\\\"Trebuie introdus un titlu!\\\")\\n if not film.get_desc():\\n erori.append(\\\"Trebuie introdusa o descriere!\\\")\\n if not film.get_gen():\\n erori.append(\\\"Trebuie introdus un gen!\\\")\\n\\n if len(erori) > 0:\\n raise exceptie_film(erori)\",\n \"def __call__(self, data):\\n if not self.instance or self.instance.status not in self.mapping:\\n return\\n\\n combiner = DataCombiner(self.instance, data, model=self.instance.__class__)\\n\\n editable_fields = self.mapping[self.instance.status]\\n for field in combiner.data:\\n if field not in editable_fields and self._has_changed(field, combiner):\\n raise ValidationError({field: self.message})\",\n \"def validate_all_fields(self):\\n\\n if self.validate_byr() and \\\\\\n self.validate_iyr() and \\\\\\n self.validate_eyr() and \\\\\\n self.validate_hgt() and \\\\\\n self.validate_hcl() and \\\\\\n self.validate_ecl() and \\\\\\n self.validate_pid() and \\\\\\n self.validate_cid():\\n return True\\n return False\",\n \"def validate(self):\\r\\n # Check KeyError\\r\\n try:\\r\\n self.fields[\\\"product_name_fr\\\"]\\r\\n self.fields[\\\"generic_name\\\"]\\r\\n self.fields[\\\"url\\\"]\\r\\n self.fields[\\\"nutrition_grade_fr\\\"]\\r\\n self.fields[\\\"categories\\\"]\\r\\n self.fields[\\\"stores\\\"]\\r\\n self.fields[\\\"brands\\\"]\\r\\n except KeyError:\\r\\n return False\\r\\n\\r\\n # Check empty field and lenght of generic_name\\r\\n for key, value in self.fields.items():\\r\\n if value == '':\\r\\n return False\\r\\n break\\r\\n if key == \\\"generic_name\\\":\\r\\n if len(value) > 255:\\r\\n return False\\r\\n\\r\\n try:\\r\\n self.categories = ProductFromApiToDatabase.clean_tag(\\r\\n self.fields[\\\"categories\\\"], 100)\\r\\n self.stores = ProductFromApiToDatabase.clean_tag(\\r\\n self.fields[\\\"stores\\\"], 45)\\r\\n self.brands = ProductFromApiToDatabase.clean_tag(\\r\\n self.fields[\\\"brands\\\"], 45)\\r\\n self.category_index = self.categories.index(self.category)\\r\\n except KeyError:\\r\\n return False\\r\\n except ValueError:\\r\\n return False\\r\\n except AttributeError:\\r\\n self.errors += 1\\r\\n print(self.errors)\\r\\n return False\",\n \"def valida_digital(id, codigo):\\n id = str(id)\\n codigo = str(codigo)\\n sql = \\\"SELECT digital from eleitores WHERE id_eleitor=\\\" + \\\\\\n id + \\\" AND digital=\\\" + codigo\\n estado = consulta.execute(sql)\\n\\n if(estado):\\n return True\\n else:\\n return False\",\n \"def clean(self):\\n\\n self.cleaned_data = super(RoomForm, self).clean()\\n\\n cleaned_data = self.cleaned_data.copy()\\n\\n if cleaned_data.get(\\\"trial\\\") is None:\\n self.cleaned_data[\\\"trial\\\"] = False\\n else:\\n cleaned_data.pop(\\\"trial\\\")\\n\\n if not all(cleaned_data.get(field) for field in cleaned_data.keys()):\\n\\n raise forms.ValidationError(\\n message=\\\"You must fill in all the required fields!\\\",\\n )\",\n \"def validate_batch(self, *arg, **kwargs):\\n pass\",\n \"def test_domain_validator(self):\\n id_stud = 1\\n nume_stud = \\\"Mircea Augustin\\\"\\n stud = Student(id_stud,nume_stud)\\n self.assertEqual(stud.get_id_stud(),id_stud)\\n self.assertEqual(stud.get_nume_stud(),nume_stud)\\n self.assertEqual(str(stud),\\\"1 | Mircea Augustin\\\")\\n stud2 = Student(id_stud,\\\"Horatiu Murdar\\\")\\n self.assertEqual(stud,stud2)\\n \\\"\\\"\\\"\\n test validator student\\n \\\"\\\"\\\"\\n validStud = ValidatorStudent()\\n stud = Student(1,\\\"Caine Rau\\\")\\n validStud.valideaza_stud(stud)\\n stud2 = Student(-1,\\\"\\\")\\n self.assertRaisesRegex(ValidError,\\\"Id student invalid!Nume student invalid!\\\",validStud.valideaza_stud,stud2)\\n \\\"\\\"\\\"\\n test domeniu pt disc\\n \\\"\\\"\\\"\\n id_disc = 1\\n nume_disc = \\\"Analiza\\\"\\n nume_prof = \\\"Berinde Stefan\\\"\\n disc = Disciplina(id_disc,nume_disc,nume_prof)\\n self.assertEqual(disc.get_id_disc(),id_disc)\\n self.assertEqual(disc.get_nume_disc(),nume_disc)\\n self.assertEqual(disc.get_nume_prof(),nume_prof)\\n self.assertEqual(str(disc),\\\"1 | Analiza | Berinde Stefan\\\")\\n disc2 = Disciplina(id_disc,\\\"Logica\\\",\\\"Pop\\\")\\n self.assertEqual(disc,disc2)\\n \\\"\\\"\\\"\\n test validare disc\\n \\\"\\\"\\\"\\n validDisc = ValidatorDisciplina()\\n id_disc = 1\\n nume_disc = \\\"Analiza\\\"\\n nume_prof = \\\"Berinde Stefan\\\"\\n disc = Disciplina(id_disc,nume_disc,nume_prof)\\n validDisc.valideaza_disc(disc)\\n disc_gresit = Disciplina(-1,\\\"\\\",\\\"\\\")\\n self.assertRaisesRegex(ValidError,\\\"Id disciplina invalid!Nume disciplina invalid!Nume profesor invalid!\\\",validDisc.valideaza_disc,disc_gresit)\\n \\\"\\\"\\\"\\n test domeniu pt note\\n \\\"\\\"\\\"\\n id_nota = 0\\n id_stud = 3\\n nume_stud = \\\"Mircea Augustin\\\"\\n stud = Student(id_stud,nume_stud)\\n id_disc = 2\\n nume_disc = \\\"Analiza\\\"\\n nume_prof = \\\"Berinde Stefan\\\"\\n disc = Disciplina(id_disc,nume_disc,nume_prof)\\n val_nota = 9.5\\n nota = Nota(id_nota,stud,disc,val_nota)\\n self.assertEqual(nota.get_id_nota(),id_nota)\\n self.assertEqual(nota.get_stud(),stud)\\n self.assertEqual(nota.get_disc(),disc)\\n self.assertEqual(nota.get_val_nota(),val_nota)\\n nota2 = Nota(id_nota,Student(0,\\\"a\\\"),Disciplina(0,\\\"a\\\",\\\"a\\\"),5.4)\\n self.assertEqual(nota,nota2)\\n \\\"\\\"\\\"\\n test validare pt note - BlackBox\\n \\\"\\\"\\\"\\n validNota = ValidatorNota()\\n stud = Student(0,\\\"a\\\")\\n disc = Disciplina(0,\\\"a\\\",\\\"a\\\")\\n validNota.valideaza_nota(Nota(3,Student(3,\\\"Chef Scarlatescu\\\"),Disciplina(2,\\\"Analiza\\\",\\\"Berinde Stefan\\\"),5.4))\\n validNota.valideaza_nota(Nota(0,Student(-1,\\\"a\\\"),Disciplina(0,\\\"\\\",\\\"a\\\"),1))\\n validNota.valideaza_nota(Nota(0,stud,disc,10))\\n self.assertRaisesRegex(ValidError,\\\"Id nota invalid! Valoare nota invalida!\\\",validNota.valideaza_nota, Nota(-1,stud,disc,0.9) )\\n self.assertRaisesRegex(ValidError,\\\"Id nota invalid! Valoare nota invalida!\\\",validNota.valideaza_nota, Nota(-50,stud,disc,0) )\\n self.assertRaisesRegex(ValidError,\\\"Valoare nota invalida!\\\",validNota.valideaza_nota, Nota(0,stud,disc,-1) )\\n self.assertRaisesRegex(ValidError,\\\"Valoare nota invalida!\\\",validNota.valideaza_nota, Nota(0,stud,disc,10.1) )\\n self.assertRaisesRegex(ValidError,\\\"Valoare nota invalida!\\\",validNota.valideaza_nota, Nota(0,stud,disc,50) )\",\n \"def validate_fields(self, window, values):\\n \\n #Check if record id is new\\n is_valid = True\\n problem_field_name = \\\"\\\"\\n experiment_names = GUI.api.get_experiment_names()\\n if values['record_id'] in experiment_names:\\n is_valid = False\\n problem_field_name = \\\"Record ID\\\"\\n return is_valid, problem_field_name \\n \\n metadata = GUI.api.get_metadata()\\n enbaled_fields = filter(lambda elem: (elem['form_name']=='material_information' or elem['form_name']=='printer_information') \\n and not (isinstance(window[elem['field_name']], sg.Text) or window[elem['field_name']].Disabled), metadata)#only validate enbaled fields\\n for field in enbaled_fields:\\n validation = field['text_validation_type_or_show_slider_number']\\n value = values[field['field_name']]\\n if (validation == \\\"number\\\" and value.isdigit()):\\n #check if correct ranges\\n if field['text_validation_max'] != \\\"\\\":\\n if value > field['text_validation_max']:\\n is_valid = False \\n problem_field_name = field['field_label']\\n return is_valid, problem_field_name \\n if field['text_validation_min'] != \\\"\\\":\\n if value < field['text_validation_min']:\\n is_valid = False \\n problem_field_name = field['field_label']\\n return is_valid, problem_field_name \\n elif (validation == \\\"number\\\" and not value.isdigit()):\\n is_valid = False\\n problem_field_name = field['field_label']\\n return is_valid, problem_field_name\\n return is_valid, problem_field_name\",\n \"def validate(self,data):\\n if self.context['request'].user!=data['offered_by']:\\n raise serializer.ValidationError('Ride offered on behalf of others are not allowed')\\n user = data['offered_by']\\n circle = self.context['circle']\\n \\n try:\\n membership=MemberShip.objects.get(\\n user=user,\\n circle=circle,\\n is_active=True)\\n except MemberShip.DoesNotExist:\\n raise serializers.ValidationError('user is not an active member of circle')\\n\\n # La llegada tiene que ser despues de la salida, si la salida es mayor o igual a la llegada marca error\\n if data['arrival_date']<=data['departure_date']:\\n raise serializers.ValidationError('Departure date must happen offer arrival date')\\n \\n self.context['membership']=membership\\n return data\",\n \"def clean(self):\\n cleaned_data = super(AddShopForm, self).clean()\\n self.validate_unique_domain(cleaned_data)\\n return cleaned_data\",\n \"def __validate():\\n # TODO: implement\",\n \"def validate(self, data):\\n choices = data['answer']\\n question = Question.objects.get(id=data['question'])\\n if question.qtype != 'select-multiple' and len(choices) > 1:\\n raise serializers.ValidationError('This is a question with single choice')\\n queryset = Choice.objects.filter(question_id=data['question'])\\n for choice in choices:\\n get_object_or_404(queryset, id=choice)\\n return data\",\n \"def clean(self):\\n\\n # Fetch cleaned email and username data.\\n email = self.cleaned_data.get('email')\\n username = self.cleaned_data.get('username')\\n\\n # Fetch possible user objects from the database\\n # based on provided and email and password.\\n user_email = User.objects.filter(email=email)\\n user_uname = User.objects.filter(username=username)\\n\\n # If user exists based on the email address or username,\\n # raise validation error.\\n if user_email:\\n self._errors[\\\"email\\\"] = \\\"Email address is already associated with another account\\\"\\n\\n if user_uname:\\n self._errors[\\\"username\\\"] = \\\"Usename is already associated with another account\\\"\",\n \"def clean_form(self):\\n\\n for condition in self.conditional_rules:\\n\\n all_conditions_passed = True\\n\\n # Only check if the field is required\\n field_required = condition['required']\\n if field_required:\\n action = condition['action']\\n\\n for cond in condition['conditions']:\\n condition_rule = cond['rule']\\n condition_value = cond['value']\\n condition_field_value = self.cleaned_data.get(cond['field_name'], None)\\n\\n all_conditions_passed = conditions_passed(condition_rule, condition_value, condition_field_value)\\n if not all_conditions_passed:\\n break\\n\\n if not all_conditions_passed and action == consts.FIELD_ACTION_SHOW:\\n if condition['field_name'] in self._errors:\\n del self._errors[condition['field_name']]\\n elif all_conditions_passed and action == consts.FIELD_ACTION_HIDE:\\n if condition['field_name'] in self._errors:\\n del self._errors[condition['field_name']]\\n\\n return self.cleaned_data\",\n \"def _validate(self):\\n _models = {'hrrr', 'hrrrak', 'rap'}\\n _fields = {'prs', 'sfc', 'nat', 'subh'}\\n \\n self.date = pd.to_datetime(self.date)\\n \\n if self.model == 'alaska':\\n self.model == 'hrrrak'\\n\\n assert self.fxx in range(49), \\\"Forecast lead time `fxx` is too large\\\"\\n assert self.model in _models, f\\\"`model` must be one of {_models}\\\"\\n if self.model in ['hrrr', 'hrrrak']:\\n assert self.field in _fields, f\\\"`field must be one of {_fields}\\\"\\n else:\\n # field is not needed for RAP model.\\n self.field = ''\\n \\n if isinstance(self.priority, str):\\n self.priority = [self.priority]\\n \\n self.priority = [i.lower() for i in self.priority]\\n\\n # Don't look for data from NOMADS if requested date is earlier\\n # than yesterday. NOMADS doesn't keep data that old.\\n if 'nomads' in self.priority:\\n yesterday = datetime.utcnow() - timedelta(hours=24)\\n yesterday = pd.to_datetime(f\\\"{yesterday:%Y-%m-%d}\\\")\\n if self.date < yesterday:\\n self.priority.remove('nomads')\",\n \"def validate(self, data):\\r\\n budgets = Budget.objects.filter(\\r\\n campaign__pk=self.context['view'].kwargs['campaign_pk']\\r\\n )\\r\\n covered = budgets.values_list('start_date', 'end_date')\\r\\n for each in covered:\\r\\n sd_fail = (\\r\\n data['start_date'] >= each[0] and\\r\\n data['start_date'] <= each[1]\\r\\n )\\r\\n ed_fail = (\\r\\n data['end_date'] >= each[0] and\\r\\n data['end_date'] <= each[1]\\r\\n )\\r\\n over_fail = (\\r\\n each[0] >= data['start_date'] and\\r\\n each[1] <= data['end_date']\\r\\n )\\r\\n if sd_fail:\\r\\n raise serializers.ValidationError({\\r\\n 'start_date': \\\"Start date conflicts with existing budget\\\"\\r\\n })\\r\\n if ed_fail:\\r\\n raise serializers.ValidationError({\\r\\n 'end_date': \\\"End date conflicts with existing budget\\\"\\r\\n })\\r\\n if over_fail:\\r\\n raise serializers.ValidationError(\\r\\n \\\"Date conflicts with existing budget\\\"\\r\\n )\\r\\n return data\",\n \"def validate(self):\\n ...\",\n \"def validate(self, value):\\n # Use the parent's handling of required fields, etc.\\n super().validate(value)\\n for email in value:\\n validate_email(email)\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def test_listar_expedientes_grado(self):\\n self.assertNotEqual(self.almacen.listar_expedientes_grado(self.grado_valido), None)\",\n \"def __call__(self, form, field):\\n\\t\\tif not hasattr(self.model, self.field_name):\\n\\t\\t\\tmessage = \\\"Terjadi kesalahan, hubungi administrator!\\\"\\n\\t\\t\\tfield.errors = []\\n\\t\\t\\traise validators.StopValidation(message)\\n\\n\\t\\tif field.data and isinstance(field.data, string_types) and \\\\\\n\\t\\t\\tfield.data.strip():\\n\\t\\t\\t\\tobj = getattr(self.model, self.field_name)\\n\\t\\t\\t\\tuser = self.model.query.filter(obj == field.data).first()\\n\\t\\t\\t\\tif user:\\n\\t\\t\\t\\t\\tfield.errors = []\\n\\t\\t\\t\\t\\tif self.message is None:\\n\\t\\t\\t\\t\\t\\tmessage = \\\"Data {} sudah ada!\\\".format(self.field_name)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tmessage = self.message\\n\\t\\t\\t\\t\\traise validators.StopValidation(message)\",\n \"def clean(self, *args, **kwargs):\\n self.is_cleaned = True\",\n \"def validate(self):\\n if self.id == None:\\n return False\\n return self.validator.validate(self.fields)\",\n \"def _check_date(self, cr, uid,ids, context=None):\\n for act in self.browse(cr, uid, ids, context):\\n line_date = datetime.strptime(str(act.date), \\\"%Y-%m-%d\\\")\\n if int(line_date.month)!=int(act.enrich_id.month) or int(line_date.year)!=int(act.enrich_id.year):\\n raise osv.except_osv(_('ValidateError'), _(\\\"Payment Enrich Date Must Be Within Enrich Month And Year %s - %s\\\")%(act.enrich_id.month,act.enrich_id.year))\\n return True\",\n \"def check(id_establecimiento):\\n establecimientos = Establecimientos().get_establecimientos()\\n datos = Establecimientos().get_establecimiento(id_establecimiento)\\n datos['db_clean'] = False\\n form = Establecimiento_form(**datos)\\n return render_template(\\n 'restaurar/check.html.jinja',\\n form=form,\\n establecimientos=establecimientos,\\n active=int(id_establecimiento)\\n )\",\n \"def validate(self, name, values):\\r\\n \\r\\n pass\",\n \"def aggAtributos(request,idTI):\\n atributos= Atributo.objects.filter(ti_id=idTI)\\n\\n if validar_permiso(request.user,\\\"is_gerente\\\",atributos[0].ti.fase.id_Proyecto) or request.user.has_perm('crear_item',atributos[0].ti.fase.id_Proyecto) and validar_rol_fase('crear_item',atributos[0].ti.fase,request.user):\\n print('tiene el permiso de crear_item')\\n else:\\n messages.error(request,\\\"NO SE POSEE EL PERMISO: crear_item\\\" + \\\" SOLICITE EL PERMISO CORRESPONDINTE PARA REALIZAR LA ACCION\\\")\\n return redirect('gestion:detallesFase',atributos.ti.fase.id_Proyecto.id_proyecto)\\n\\n if(atributos.count() == 0):\\n return HttpResponse(request,\\\"id de TI invalida\\\",status=400)\\n\\n if request.method == 'POST':\\n itemID = Item.objects.last()\\n ti = TipoItem.objects.get(id_ti=idTI)\\n contador=0\\n\\n for c in atributos:\\n print(c.id_atributo)\\n contador=contador+1\\n\\n item=Item.objects.last()#SE OBTIENE EL ITEM CREADO RECIENTEMENTE\\n list=[]\\n for atributos in atributos:#SE RECORRE POR VALOR INGRESADO CONSULTANDO SI ES OBLIGARORIO Y ESTA VACIO-->MUESTRA ERROR\\n ok = False\\n if(atributos.tipo_dato == 'Boolean'):\\n x = request.POST.getlist(atributos.tipo_dato)\\n tiposAtributo = Atributo.objects.filter(ti_id=idTI, tipo_dato=atributos.tipo_dato)\\n print(x)\\n for ini in range(len(x)):\\n if(x[ini] == '' and tiposAtributo[ini].es_obligatorio == True):\\n ok=True\\n nombre=tiposAtributo[ini].nombre\\n if( ok == True ):\\n messages.error(request, \\\"EL ATRIBUTO ES OBLIGATRIO FAVOR INGRESE UN VALOR PARA EL ATRIBUTO: \\\"+nombre)\\n return redirect('gestion:aggAtributos', idTI)\\n\\n list=[\\\"Decimal\\\",\\\"Boolean\\\",\\\"File\\\",\\\"String\\\",\\\"Date\\\"]\\n for ini in range(len(list)): #SI INGRESO VALORES CORRECTAMENTE LOS GUARDA RELACIONANDO CON EL ITEM CORRESPONDIENTE\\n print('este es el valor de ini'+str(ini))\\n print(list[ini])\\n try:\\n tiposAtributo = Atributo.objects.filter(ti_id=idTI, tipo_dato=list[ini])\\n x=request.POST.getlist(list[ini])\\n except:\\n tiposAtributo=None\\n\\n if (tiposAtributo!=None):\\n for valor in range(tiposAtributo.count()):\\n if(list[ini]==\\\"File\\\"):\\n list2 = []\\n for atr in tiposAtributo:\\n DOC = request.FILES.getlist(str(atr.id_atributo))\\n if (DOC != list2):\\n print(\\\"no vacio\\\",DOC[0])\\n ruta = str(ti.fase.id_Proyecto.id_proyecto) + \\\"/\\\" + str(itemID.id_item)\\n PATH = f'/{ruta}/{DOC[0]}'\\n #SubirArchivo(DOC[0], PATH)\\n print(\\\"--\\\",PATH)\\n\\n\\n ##se sube archivo a dropbox en segundo plano\\n t2 = Thread(\\n target=SubirArchivo,\\n args=(DOC[0],PATH),\\n )\\n t2.start()\\n\\n p = Atributo_Item(idAtributoTI=atr, id_item=item, valor=str(PATH))\\n p.save()\\n else:\\n print(\\\"vacio\\\",DOC)\\n p = Atributo_Item(idAtributoTI=atr, id_item=item, valor=\\\"Sin archivos adjuntos\\\")\\n p.save()\\n break\\n else:\\n p = Atributo_Item(idAtributoTI=tiposAtributo[valor],id_item=item,valor=str(x[valor]))\\n p.save()\\n\\n itemID=Item.objects.last()\\n ti=TipoItem.objects.get(id_ti=idTI)\\n return redirect('gestion:relacionarItem',ti.fase.id_Proyecto.id_proyecto,itemID.id_item)\\n item_id = Item.objects.last()\\n contexto={\\n 'atributos':atributos,\\n 'true':True,\\n 'false':False,\\n 'id_item':item_id.id_item\\n }\\n return render (request,'items/aggAtributos.html',contexto)\",\n \"def test_check(self):\\n\\t\\tself.filter.set_operator('.equals')\\n\\t\\tfor lim in [10, '10']:\\n\\t\\t\\tself.filter.set_limit(lim)\\n\\t\\t\\tfor val in [10, '10']:\\n\\t\\t\\t\\tob = Object(field=val)\\n\\t\\t\\t\\tself.assertTrue(self.filter.check(ob), \\\"Failed Filter comparison check - Fields should match!\\\")\",\n \"def validate(self, request):\\n values = {\\n 'robot_match_comments':request.POST['robot_match_comments'],\\n 'did_foul':'did_foul' in request.POST,\\n 'did_technical_foul':'did_technical_foul' in request.POST,\\n 'foul_description':request.POST['foul_description'],\\n 'did_shoot':'did_shoot' in request.POST,\\n 'auto_1':request.POST['auto_1'],\\n 'auto_2':request.POST['auto_2'],\\n 'auto_3':request.POST['auto_3'],\\n 'auto_miss':request.POST['auto_miss'],\\n 'teleop_1':request.POST['teleop_1'],\\n 'teleop_2':request.POST['teleop_2'],\\n 'teleop_3':request.POST['teleop_3'],\\n 'teleop_5':request.POST['teleop_5'],\\n 'teleop_miss':request.POST['teleop_miss'],\\n 'shooting_description':request.POST['shooting_description'],\\n 'did_climb':'did_climb' in request.POST,\\n 'climb_start':request.POST['climb_start'],\\n 'climb_finish':request.POST['climb_finish'],\\n 'level_reached':request.POST.get('level_reached'),\\n 'frisbees_dumped':request.POST['frisbees_dumped'],\\n 'climbing_description':request.POST['climbing_description'],\\n 'did_human_load':'did_human_load' in request.POST,\\n 'did_ground_load':'did_ground_load' in request.POST,\\n 'auto_frisbees_ground_loaded':\\\\\\n request.POST['auto_frisbees_ground_loaded'],\\n 'loading_description':request.POST['loading_description'],\\n }\\n if ((values['did_foul'] or values['did_technical_foul']) and\\n not values['foul_description']):\\n new_values = self.__dict__.copy()\\n new_values.update(values)\\n raise ValidationError(\\n 'Please enter a description of the foul(s) the robot committed',\\n new_values\\n )\\n if values['did_shoot']:\\n try:\\n values['auto_1'] = int(values['auto_1'])\\n values['auto_2'] = int(values['auto_2'])\\n values['auto_3'] = int(values['auto_3'])\\n values['auto_miss'] = int(values['auto_miss'])\\n values['teleop_1'] = int(values['teleop_1'])\\n values['teleop_2'] = int(values['teleop_2'])\\n values['teleop_3'] = int(values['teleop_3'])\\n values['teleop_5'] = int(values['teleop_5'])\\n values['teleop_miss'] = int(values['teleop_miss'])\\n except ValueError:\\n raise ValidationError(\\n 'You must enter a number for all of the shooting numbers',\\n self.__dict__.copy().update(values)\\n )\\n if values['did_climb']:\\n try:\\n values['climb_start'] = int(values['climb_start'])\\n values['climb_finish'] = int(values['climb_finish'])\\n try:\\n values['level_reached'] = int(values['level_reached'])\\n except TypeError:\\n new_values = self.__dict__.copy()\\n new_values.update(values)\\n raise ValidationError(\\n 'You must select a level the robot climbed too',\\n new_values\\n )\\n values['frisbees_dumped'] = int(values['frisbees_dumped'])\\n except ValueError:\\n new_values = self.__dict__.copy()\\n new_values.update(values)\\n raise ValidationError(\\n 'All climbing related numbers must be numbers',\\n new_values\\n )\\n if values['did_ground_load']:\\n try:\\n values['auto_frisbees_ground_loaded'] = int(\\n values['auto_frisbees_ground_loaded'])\\n except ValueError:\\n new_values = self.__dict__.copy()\\n new_values.update(values)\\n raise ValidationError(\\n 'All numbers of frisbees ground loaded must be numbers',\\n new_values\\n )\\n return values\",\n \"def pre_validate(self, form):\\n for item1,item2 in self.choices:\\n if isinstance(item2, (list, tuple)):\\n group_label = item1\\n group_items = item2\\n for val,label in group_items:\\n if val == self.data:\\n return\\n else:\\n val = item1\\n label = item2\\n if val == self.data:\\n return\\n raise ValueError(self.gettext('Not a valid choice!'))\",\n \"def test_clean(self):\\n form = DocumentSearchForm()\\n form.cleaned_data = {\\\"q\\\": \\\"\\\", \\\"sort\\\": \\\"relevance\\\"}\\n form.clean()\\n assert len(form.errors) == 1\\n\\n # Otherwise should not raise an error\\n form = DocumentSearchForm()\\n form.cleaned_data = {\\\"q\\\": \\\"test\\\", \\\"sort\\\": \\\"relevance\\\"}\\n form.clean()\\n assert len(form.errors) == 0\\n form = DocumentSearchForm()\\n form.cleaned_data = {\\\"q\\\": \\\"\\\", \\\"sort\\\": \\\"scholarship_desc\\\"}\\n form.clean()\\n assert len(form.errors) == 0\",\n \"def valid(self):\\n pass\",\n \"def validate(self, instance, value):\",\n \"def validate(self, instance, value):\",\n \"def clean(self):\\n\\n cleaned_data = self.cleaned_data\\n if cleaned_data.get('order') and cleaned_data.get('stock') \\\\\\n and cleaned_data.get('volume') and cleaned_data.get('price'):\\n t = cleaned_data['trader']\\n if cleaned_data['order'] == 'B': # buy order\\n open_orders = Order.objects.filter(trader=t,\\n order='B', completed=False)\\n open_order_value = float(sum([o.volume * o.price for o in open_orders]))\\n open_order_value += int(cleaned_data['volume']) * float(cleaned_data['price'])\\n\\n if open_order_value > t.cash:\\n raise ValidationError(\\\"You don't have enough cash!\\\")\\n\\n elif cleaned_data['order'] == 'S': # sell order!\\n open_orders = sum(Order.objects.filter(trader=t, order='S',\\n stock=cleaned_data['stock'],\\n completed=False).values_list('volume', flat=True))\\n open_orders += cleaned_data['volume']\\n\\n if open_orders > t.holding_set.get(stock=cleaned_data['stock']).shares:\\n raise ValidationError(\\\"You don't have enough shares!\\\")\\n return cleaned_data\",\n \"def clean(self):\\n\\n if self.dateEnd <= self.dateStart:\\n raise ValidationError(\\\"Start date must be before end date!\\\")\\n \\n # If there are bookings in the database with:\\n # - same room__id\\n # - different booking__id\\n # - first date at or after dateStart (but before dateEnd)\\n # - last date at or before dateEnd (but after dateStart)\\n # Raise ValidationError\\n if Booking.objects.filter(room__id=self.room_id).exclude(pk=self.pk).filter(\\n Q(dateEnd__gt=self.dateStart, dateStart__lt=self.dateEnd)\\n ).exists():\\n raise ValidationError(\\\"Overlapping dates, room has been booked.\\\")\",\n \"def _check_active(self):\\n for record in self:\\n count = self.search_count([('active', '=', True), ('id', '!=', record.id)])\\n if count:\\n raise ValidationError(_('Multiple active records. Please Inactive other!'))\",\n \"def validate(data):\\n if 'value' not in data or \\\\\\n 'category' not in data or \\\\\\n 'classification' not in data or \\\\\\n 'account' not in data:\\n raise Exception('Missing required field.')\\n classifications = ['Personal', 'Essential', 'Savings', 'Income']\\n if data['classification'] not in classifications:\\n raise Exception('Invalid classification.')\",\n \"def clean(self):\\n\\n # First, check that something hasn't gone wrong already.\\n if self.errors:\\n return {}\\n\\n # Cleaning IDs\\n category_name = self.cleaned_data['category']\\n if category_name != u'all':\\n matching_category_ids = get_all_matching_category_ids(\\n category_name\\n )\\n self.cleaned_data['category_id'] = matching_category_ids[0]\\n else:\\n self.cleaned_data['category_id'] = 0\\n\\n location_name = self.cleaned_data['location']\\n matching_location_ids = get_all_matching_location_ids(\\n location_name)\\n self.cleaned_data['location_id'] = matching_location_ids[0]\\n location = Location.objects.get(id=matching_location_ids[0])\\n\\n # Cleaning date\\n t = self.cleaned_data['date']\\n now = datetime.now(pytz.timezone(location.timezone)).date()\\n if t < now:\\n raise forms.ValidationError(\\\"It looks like the date you searched for already happened...\\\")\\n\\n return self.cleaned_data\",\n \"def _validate(self):\\n pass\",\n \"def AggComite(request,pk):#esta enlazado con la clase FaseForm del archivo getion/forms\\n ##ESte es lo que yo(presi) le agregue\\n try:\\n proyectos = Proyecto.objects.get(id_proyecto=pk)\\n except Proyecto.DoesNotExist:\\n return HttpResponse(\\\"Proyecto no existe\\\",status=400)\\n\\n try:\\n proyecto = User_Proyecto.objects.filter(proyecto_id=pk)\\n except:\\n return HttpResponse(\\\"Proyecto no existe\\\",status=400)\\n\\n gerente = User.objects.get(id=proyecto[0].user_id)\\n print(gerente.username)\\n\\n proyecto_validar=Proyecto.objects.get(id_proyecto=pk)\\n\\n if validar_permiso(request.user, \\\"is_gerente\\\",proyecto_validar)==False: # primero se valida si es gerente en el proyecto actual)\\n messages.error(request, 'No eres gerente de proyecto, por lo tanto no puedes crear el comite de cambio')\\n return redirect('gestion:comite', pk)\\n\\n proyectos=Proyecto.objects.get(id_proyecto=pk)\\n comite= Comite.objects.all()\\n form = Usuario.objects.all()\\n registrados = User_Proyecto.objects.all()\\n\\n if request.method == 'POST': #preguntamos primero si la petición Http es POST ||| revienta todo con este\\n some_var=request.POST.getlist('checkbox')\\n\\n if ((len(some_var)+1)%2==0 or (len(some_var)+1)==1):# SE VALIDA QUE DEBE DE SER IMPAR Y MAYOR A 1\\n messages.error(request,'EL NUMERO DE USUARIOS EN EL COMITE DEBE DE SER IMPAR Y MAYOR A UNO')\\n return redirect('gestion:AggComite',pk)\\n p=Comite(id_proyecto=pk,id_user=gerente.id)\\n p.save()\\n for id in some_var:###### SE GUARDAN EN USER_PROYECTOS LAS RELACIONES\\n id_user =id\\n usuario=User.objects.get(id=id_user)\\n registrarAuditoriaProyecto(request.user,\\\"Añadio al comite de cambio al usuario: \\\"+str(usuario.username),proyectos.id_proyecto,proyectos.nombre,\\\"\\\")\\n p=Comite(id_proyecto=pk,id_user=id_user)\\n p.save()\\n\\n return redirect('gestion:comite',pk)\\n else:\\n list=[]\\n for i in range(form.count()):\\n ok = False\\n if form[i].esta_aprobado == True:\\n for x in range(registrados.count()):\\n if registrados[x].proyecto_id == pk and registrados[x].user_id == form[i].user.id and registrados[x].activo == True:# esta en el proyecto?\\n ok=True\\n for z in range(comite.count()):#si ya esta en el comite no\\n if form[i].user.id == comite[z].id_user and pk==comite[z].id_proyecto:\\n ok=False\\n if ok:\\n list.append(form[i].user.id)\\n\\n return render(request, 'proyectos/agg_comite.html', {'form': form,'list':list,'proyectos':proyectos,'idGerente':gerente.id})\",\n \"def validate(self):\\n return self.validator.validate(self.fields)\",\n \"def post(self, request, *args, **kwargs):\\n self.object = Usuario.objects.get(pk=kwargs['pk'])\\n form_class = self.get_form_class()\\n form = self.get_form(form_class)\\n contacto_linea_form = Contacto_LineaFormSet(self.request.POST, instance=self.object)\\n direccion_linea_form = Direccion_LineaFormSet(self.request.POST, instance=self.object)\\n if (form.is_valid()\\n and contacto_linea_form.is_valid()\\n and direccion_linea_form.is_valid()):\\n return self.form_valid(form, contacto_linea_form, direccion_linea_form)\\n else:\\n return self.form_invalid(form, contacto_linea_form, direccion_linea_form)\",\n \"def clean(self):\\n return self.cleaned_data\",\n \"def validate_batch(self, *args, **kwargs):\\n raise NotImplementedError()\",\n \"def run_validation(self, data=empty):\\n # ensure Unique and UniqueTogether don't collide with a DB match\\n validators = self.remove_validation_unique()\\n validated_data = super().run_validation(data)\\n # restore Unique or UniqueTogether\\n self.restore_validation_unique(validators)\\n return self.validated_data\",\n \"def validate(self):\\n rv = Form.validate(self)\\n if not rv:\\n return False\\n\\n cur = get_cursor()\\n if email_exists(cur, self.email.data):\\n self.email.errors.append('This email already exists!')\\n return False\\n\\n if username_exists(cur, self.username.data):\\n self.username.errors.append('This username already exists!')\\n return False\\n\\n return True\",\n \"def filter_form_valid(self, filter_form):\\n return True\",\n \"def clean(self):\\n\\n # count subjects with matching subject_code and parent project,\\n # which don't have this pk\\n try:\\n Project.objects.get(id=self.project_id)\\n except ObjectDoesNotExist:\\n return # Project is required and will get caught by Form.is_valid()\\n\\n if self.subject_group is not None:\\n if self.subject_group.project_id != self.project_id:\\n raise ValidationError(\\\"Group chosen is not in this Project\\\")\\n\\n subject_duplicates = Subject.objects.filter(\\n subject_code=self.subject_code,\\n project=self.project).exclude(\\n id=self.id)\\n if subject_duplicates.count() > 0:\\n raise ValidationError(\\n \\\"Subject code already exists in this project.\\\"\\n )\",\n \"def do_validate(self, request, _object):\\n\\n pass\",\n \"def test_validation(self):\\n def test(**invalid_fields):\\n with self.assertRaises(AssertionError):\\n c = models.Citizen(**{'relatives': [], **invalid_fields})\\n\\n # Must throw errors in every case\\n test(citizen_id=-1, )\\n test(apartment='WRONG-STRING')\\n test(town='', )\\n test(street='', )\\n test(building='', )\\n test(name=None, )\\n test(birth_date='WRONG-STRING')\\n test(birth_date='31.02.2019')\\n test(relatives=['a'])\",\n \"def clean(self):\\n cleaned_data = super().clean()\\n if all(val == \\\"\\\" for val in cleaned_data.values()):\\n raise ValidationError(\\\"You must fill at least one field!\\\")\\n\\n return cleaned_data\",\n \"def _validate_data(self, vms, fw_rules):\\n self._validate_vms(vms)\\n self._validate_fw_rules(fw_rules)\\n self._validated = True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6289605","0.62867755","0.6256649","0.6026372","0.60167295","0.6007827","0.5972027","0.59138054","0.59136087","0.5894613","0.5884084","0.5839058","0.58008385","0.5777286","0.5777286","0.5755494","0.57548887","0.57548887","0.57548887","0.5723866","0.5715058","0.57049733","0.5703674","0.5675187","0.56408185","0.56397307","0.56347734","0.5622656","0.5593381","0.55743396","0.55418783","0.553047","0.5508373","0.55072296","0.5489816","0.5474267","0.54665565","0.5455343","0.5451623","0.5446891","0.5443929","0.54353815","0.54228723","0.5420614","0.54162884","0.54154426","0.5411352","0.54092056","0.5374675","0.53708196","0.5356606","0.53542715","0.5354095","0.5336952","0.529989","0.5298322","0.5290135","0.52898294","0.5279788","0.5279788","0.5279788","0.5279788","0.5279788","0.5279788","0.5279788","0.5279788","0.52784836","0.52577925","0.5256219","0.52561414","0.5239237","0.5235704","0.52341205","0.52222663","0.52063084","0.52015066","0.5199681","0.5196473","0.51957047","0.5194278","0.5194278","0.5193682","0.519059","0.5184362","0.5183434","0.51799715","0.51744103","0.5174054","0.5169099","0.51665497","0.51625997","0.51567215","0.51532173","0.5147414","0.5145112","0.5144959","0.5143992","0.5142319","0.5136386","0.5135423"],"string":"[\n \"0.6289605\",\n \"0.62867755\",\n \"0.6256649\",\n \"0.6026372\",\n \"0.60167295\",\n \"0.6007827\",\n \"0.5972027\",\n \"0.59138054\",\n \"0.59136087\",\n \"0.5894613\",\n \"0.5884084\",\n \"0.5839058\",\n \"0.58008385\",\n \"0.5777286\",\n \"0.5777286\",\n \"0.5755494\",\n \"0.57548887\",\n \"0.57548887\",\n \"0.57548887\",\n \"0.5723866\",\n \"0.5715058\",\n \"0.57049733\",\n \"0.5703674\",\n \"0.5675187\",\n \"0.56408185\",\n \"0.56397307\",\n \"0.56347734\",\n \"0.5622656\",\n \"0.5593381\",\n \"0.55743396\",\n \"0.55418783\",\n \"0.553047\",\n \"0.5508373\",\n \"0.55072296\",\n \"0.5489816\",\n \"0.5474267\",\n \"0.54665565\",\n \"0.5455343\",\n \"0.5451623\",\n \"0.5446891\",\n \"0.5443929\",\n \"0.54353815\",\n \"0.54228723\",\n \"0.5420614\",\n \"0.54162884\",\n \"0.54154426\",\n \"0.5411352\",\n \"0.54092056\",\n \"0.5374675\",\n \"0.53708196\",\n \"0.5356606\",\n \"0.53542715\",\n \"0.5354095\",\n \"0.5336952\",\n \"0.529989\",\n \"0.5298322\",\n \"0.5290135\",\n \"0.52898294\",\n \"0.5279788\",\n \"0.5279788\",\n \"0.5279788\",\n \"0.5279788\",\n \"0.5279788\",\n \"0.5279788\",\n \"0.5279788\",\n \"0.5279788\",\n \"0.52784836\",\n \"0.52577925\",\n \"0.5256219\",\n \"0.52561414\",\n \"0.5239237\",\n \"0.5235704\",\n \"0.52341205\",\n \"0.52222663\",\n \"0.52063084\",\n \"0.52015066\",\n \"0.5199681\",\n \"0.5196473\",\n \"0.51957047\",\n \"0.5194278\",\n \"0.5194278\",\n \"0.5193682\",\n \"0.519059\",\n \"0.5184362\",\n \"0.5183434\",\n \"0.51799715\",\n \"0.51744103\",\n \"0.5174054\",\n \"0.5169099\",\n \"0.51665497\",\n \"0.51625997\",\n \"0.51567215\",\n \"0.51532173\",\n \"0.5147414\",\n \"0.5145112\",\n \"0.5144959\",\n \"0.5143992\",\n \"0.5142319\",\n \"0.5136386\",\n \"0.5135423\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5682032"},"document_rank":{"kind":"string","value":"23"}}},{"rowIdx":76675,"cells":{"query":{"kind":"string","value":"Main hook entry point"},"document":{"kind":"string","value":"def execute(self, operation, file_path, context, parent_action, file_version, read_only, **kwargs):\n\n # AARDMAN ADDITION\n # adds short cuts to file browser\n aaNukeUtils.aaSetupShortCuts()\n\n if file_path:\n file_path = file_path.replace(\"/\", os.path.sep)\n\n if operation == \"current_path\":\n # return the current script path\n return nuke.root().name().replace(\"/\", os.path.sep)\n\n elif operation == \"open\":\n # open the specified script\n nuke.scriptOpen(file_path)\n\n # reset any write node render paths:\n if self._reset_write_node_render_paths():\n # something changed so make sure to save the script again:\n nuke.scriptSave()\n\n elif operation == \"save\":\n # save the current script:\n nuke.scriptSave()\n\n elif operation == \"save_as\":\n old_path = nuke.root()[\"name\"].value()\n try:\n # rename script:\n nuke.root()[\"name\"].setValue(file_path)\n\n # reset all write nodes:\n self._reset_write_node_render_paths()\n\n # save script:\n nuke.scriptSaveAs(file_path, -1)\n except Exception, e:\n # something went wrong so reset to old path:\n nuke.root()[\"name\"].setValue(old_path)\n raise TankError(\"Failed to save scene %s\", e)\n\n elif operation == \"reset\":\n \"\"\"\n Reset the scene to an empty state\n \"\"\"\n while nuke.root().modified():\n # changes have been made to the scene\n res = QtGui.QMessageBox.question(None,\n \"Save your script?\",\n \"Your script has unsaved changes. Save before proceeding?\",\n QtGui.QMessageBox.Yes|QtGui.QMessageBox.No|QtGui.QMessageBox.Cancel)\n\n if res == QtGui.QMessageBox.Cancel:\n return False\n elif res == QtGui.QMessageBox.No:\n break\n else:\n nuke.scriptSave()\n\n # now clear the script:\n nuke.scriptClear()\n\n return True"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def entry_point():","def entry_point():","def entry_point():","def _hook(self):","def on_hook(self) -> None:","def setup_hooks(self):\n pass","def main():\n pass","def main(self):\r\n pass","def _post_hooks(self):","def main():\n PLUGIN_ENTRY().run(\"\")","def main():\n\tpass","def main(self) -> None:\n pass","def register_main_hook(callback):\n main_hooks.append(callback)","def main(self):\n \n self.log = sys.argv[1]\n self.listener = pyxhook.HookManager()\n self.listener.KeyDown = self.capture_key_press\n self.listener.HookKeyboard()\n self.listener.start()","def main():\n return","def entry_point():\n pass","def entry_point():\n\n\n plac.call(main)","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n pass","def main():\n\n pass","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main(self):","def start():\n log(\"=========== hook: start ===========\")","def main() -> None:","def main() -> None:","def main() -> None:","def main() -> None:","def main(ctx, verbose):\n return","def main(self):\n pass","def main():\n pass","def main(self, **kwargs) -> None:\n ...","def main():\n for h in ['-?', '-h', '--help']:\n if h in sys.argv:\n print(_('Git Fusion pre-receive hook.'))\n return 2\n p4gf_version_3.print_and_exit_if_argv()\n p4gf_branch.init_case_handling()\n prl = PreReceiveTupleLists.from_stdin(sys.stdin)\n # Preflight rejects the push by raising an exception, which is handled\n # in the logging code by printing the message to stderr.\n with Timer('pre-receive'):\n return PreflightHook('pre-receive preflight', prl).do_it()","def run():\n main()","def main():\n logging.basicConfig(level=logging.INFO)\n # update some config\n update_config()\n\n # The hook will only be used in the Qt GUI right now\n util.setup_thread_excepthook()\n\n # parse command line\n parser = get_parser()\n preprocess_cmdline_args(sys.argv)\n args = parser.parse_args()\n\n config_options = process_config_options(args)\n\n # todo: defer this to gui\n config = SimpleConfig(config_options)\n cmdname = args.cmd if args.cmd is not None else \"gui\"\n\n # run non-RPC commands separately\n if cmdname in [\"create\", \"restore\"]:\n run_non_rpc(config)\n sys.exit(0)\n\n if cmdname == \"gui\":\n result = run_gui(config, config_options)\n elif cmdname == \"daemon\":\n result = run_daemon(config, config_options)\n else:\n result = run_cmdline(config, config_options, cmdname)\n\n print_result(result)\n sys.exit(0)","def main() -> None:\n return","def main():\n\n pass\n\n return None","def main():\n\n pass\n\n return None","def main(args):","def main(args):","def startTestHook(self):","def main(args=None):","def main(args=None):","def main():\n Main()","def main():\n Fire(cli)","def add_run_hook(h):\n add_hook(run, h)","def main():\n print(\"Call your main application code here\")","def main():\n print(\"Call your main application code here\")","def main():\n print(\"Call your main application code here\")","def main():\n args = parse_args()\n process_args(args)","def run ( self ):\n setup_env = self.setup_env\n options = setup_env.options\n command = options ['hook.action']\n hook_name = options ['hook.name']\n hook_events = options ['hook.events']\n\n if command in { 'show', }:\n self.info ( '\\n'.join ( self.gen_hook_info_lines() ) )\n # -- end <show>\n\n elif command in { 'add', }:\n hooks = list ( self.hook_root.find_all_by_name_begin ( hook_name ) )\n if not hooks:\n self.error (\n \"no hooks found matching {!r}\\n\".format ( hook_name )\n )\n # FIXME: exit code?\n\n elif \"all\" in hooks:\n self.error (\n \"cannot add hooks to the (virtual) \\'all\\' event!\\n\"\n )\n\n elif len ( hooks ) == 1:\n # exactly one hook matches\n self.link_hooks_to_events ( hooks, hook_events )\n\n else:\n # > 1 matches, find exact matches\n exact_matches = [ k for k in hooks if k.name == hook_name ]\n\n if not exact_matches or len ( exact_matches ) != 1:\n self.error (\n \"ambiguous hook name: {!r} could match {}\\n\".format (\n hook_name, ', '.join ( hook.name for hook in hooks )\n )\n )\n else:\n self.link_hooks_to_events ( exact_matches, hook_events )\n\n # -- end <add>\n\n elif command in { 'del', }:\n hooks_to_unlink = []\n\n if hook_events and not \"all\" in hook_events:\n for event in hook_events:\n hooks = list (\n self.user_hooks.find_all_by_name_begin (\n hook_name, event=event\n )\n )\n if not hooks:\n self.error (\n \"no hooks found for event {!r} matching {!r}\\n\".format (\n event, hook_name\n )\n )\n\n elif len ( hooks ) == 1:\n hooks_to_unlink.append ( hooks[0] )\n\n else:\n exact_matches = [ k for k in hooks if k.name == hook_name ]\n\n if not exact_matches or len ( exact_matches ) != 1:\n self.error (\n 'ambiguous hook name {!r} for event {!r}: '\n 'could match {}\\n'.format (\n hook_name, event,\n ', '.join ( hook.name for hook in hooks )\n )\n )\n else:\n hooks_to_unlink.append ( exact_matches[0] )\n # -- end for\n else:\n hooks = list (\n self.user_hooks.find_all_by_name_begin (\n hook_name, event=None\n )\n )\n\n if not hooks:\n self.error (\n \"no hooks found matching {!r}\\n\".format ( hook_name )\n )\n elif len ( hooks ) == 1:\n #hooks_to_unlink = hooks\n hooks_to_unlink.append ( hooks[0] )\n else:\n # COULDFIX: it would be better to check if the hooks'\n # realpaths (link dest) are identical\n exact_matches = [ k for k in hooks if k.name == hook_name ]\n num_event_matches = (\n collections.Counter ( k.event for k in exact_matches )\n )\n if exact_matches and all (\n k < 2 for k in num_event_matches.values()\n ):\n #hooks_to_unlink = exact_matches\n hooks_to_unlink.extend ( exact_matches )\n else:\n self.error (\n 'ambiguous hook name {!r}: could match {}\\n'.format (\n hook_name,\n ', '.join ( set ( k.name for k in hooks ) )\n )\n )\n # -- end if\n\n self.unlink_hooks ( hooks_to_unlink )\n # -- end <del>\n\n else:\n raise NotImplementedError ( command )","def preprocess_main():","def _post_run_hook(self, runtime):\n pass","def run_main():\n main(sys.argv)","def console_entry():\n #main()","def entry_point():\n raise SystemExit(main(sys.argv))","def entry_point():\n raise SystemExit(main(sys.argv))","def entry_point():\n raise SystemExit(main(sys.argv))","def run():\n # main(sys.argv[1:])\n main()"],"string":"[\n \"def entry_point():\",\n \"def entry_point():\",\n \"def entry_point():\",\n \"def _hook(self):\",\n \"def on_hook(self) -> None:\",\n \"def setup_hooks(self):\\n pass\",\n \"def main():\\n pass\",\n \"def main(self):\\r\\n pass\",\n \"def _post_hooks(self):\",\n \"def main():\\n PLUGIN_ENTRY().run(\\\"\\\")\",\n \"def main():\\n\\tpass\",\n \"def main(self) -> None:\\n pass\",\n \"def register_main_hook(callback):\\n main_hooks.append(callback)\",\n \"def main(self):\\n \\n self.log = sys.argv[1]\\n self.listener = pyxhook.HookManager()\\n self.listener.KeyDown = self.capture_key_press\\n self.listener.HookKeyboard()\\n self.listener.start()\",\n \"def main():\\n return\",\n \"def entry_point():\\n pass\",\n \"def entry_point():\\n\\n\\n plac.call(main)\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n\\n pass\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main(self):\",\n \"def start():\\n log(\\\"=========== hook: start ===========\\\")\",\n \"def main() -> None:\",\n \"def main() -> None:\",\n \"def main() -> None:\",\n \"def main() -> None:\",\n \"def main(ctx, verbose):\\n return\",\n \"def main(self):\\n pass\",\n \"def main():\\n pass\",\n \"def main(self, **kwargs) -> None:\\n ...\",\n \"def main():\\n for h in ['-?', '-h', '--help']:\\n if h in sys.argv:\\n print(_('Git Fusion pre-receive hook.'))\\n return 2\\n p4gf_version_3.print_and_exit_if_argv()\\n p4gf_branch.init_case_handling()\\n prl = PreReceiveTupleLists.from_stdin(sys.stdin)\\n # Preflight rejects the push by raising an exception, which is handled\\n # in the logging code by printing the message to stderr.\\n with Timer('pre-receive'):\\n return PreflightHook('pre-receive preflight', prl).do_it()\",\n \"def run():\\n main()\",\n \"def main():\\n logging.basicConfig(level=logging.INFO)\\n # update some config\\n update_config()\\n\\n # The hook will only be used in the Qt GUI right now\\n util.setup_thread_excepthook()\\n\\n # parse command line\\n parser = get_parser()\\n preprocess_cmdline_args(sys.argv)\\n args = parser.parse_args()\\n\\n config_options = process_config_options(args)\\n\\n # todo: defer this to gui\\n config = SimpleConfig(config_options)\\n cmdname = args.cmd if args.cmd is not None else \\\"gui\\\"\\n\\n # run non-RPC commands separately\\n if cmdname in [\\\"create\\\", \\\"restore\\\"]:\\n run_non_rpc(config)\\n sys.exit(0)\\n\\n if cmdname == \\\"gui\\\":\\n result = run_gui(config, config_options)\\n elif cmdname == \\\"daemon\\\":\\n result = run_daemon(config, config_options)\\n else:\\n result = run_cmdline(config, config_options, cmdname)\\n\\n print_result(result)\\n sys.exit(0)\",\n \"def main() -> None:\\n return\",\n \"def main():\\n\\n pass\\n\\n return None\",\n \"def main():\\n\\n pass\\n\\n return None\",\n \"def main(args):\",\n \"def main(args):\",\n \"def startTestHook(self):\",\n \"def main(args=None):\",\n \"def main(args=None):\",\n \"def main():\\n Main()\",\n \"def main():\\n Fire(cli)\",\n \"def add_run_hook(h):\\n add_hook(run, h)\",\n \"def main():\\n print(\\\"Call your main application code here\\\")\",\n \"def main():\\n print(\\\"Call your main application code here\\\")\",\n \"def main():\\n print(\\\"Call your main application code here\\\")\",\n \"def main():\\n args = parse_args()\\n process_args(args)\",\n \"def run ( self ):\\n setup_env = self.setup_env\\n options = setup_env.options\\n command = options ['hook.action']\\n hook_name = options ['hook.name']\\n hook_events = options ['hook.events']\\n\\n if command in { 'show', }:\\n self.info ( '\\\\n'.join ( self.gen_hook_info_lines() ) )\\n # -- end <show>\\n\\n elif command in { 'add', }:\\n hooks = list ( self.hook_root.find_all_by_name_begin ( hook_name ) )\\n if not hooks:\\n self.error (\\n \\\"no hooks found matching {!r}\\\\n\\\".format ( hook_name )\\n )\\n # FIXME: exit code?\\n\\n elif \\\"all\\\" in hooks:\\n self.error (\\n \\\"cannot add hooks to the (virtual) \\\\'all\\\\' event!\\\\n\\\"\\n )\\n\\n elif len ( hooks ) == 1:\\n # exactly one hook matches\\n self.link_hooks_to_events ( hooks, hook_events )\\n\\n else:\\n # > 1 matches, find exact matches\\n exact_matches = [ k for k in hooks if k.name == hook_name ]\\n\\n if not exact_matches or len ( exact_matches ) != 1:\\n self.error (\\n \\\"ambiguous hook name: {!r} could match {}\\\\n\\\".format (\\n hook_name, ', '.join ( hook.name for hook in hooks )\\n )\\n )\\n else:\\n self.link_hooks_to_events ( exact_matches, hook_events )\\n\\n # -- end <add>\\n\\n elif command in { 'del', }:\\n hooks_to_unlink = []\\n\\n if hook_events and not \\\"all\\\" in hook_events:\\n for event in hook_events:\\n hooks = list (\\n self.user_hooks.find_all_by_name_begin (\\n hook_name, event=event\\n )\\n )\\n if not hooks:\\n self.error (\\n \\\"no hooks found for event {!r} matching {!r}\\\\n\\\".format (\\n event, hook_name\\n )\\n )\\n\\n elif len ( hooks ) == 1:\\n hooks_to_unlink.append ( hooks[0] )\\n\\n else:\\n exact_matches = [ k for k in hooks if k.name == hook_name ]\\n\\n if not exact_matches or len ( exact_matches ) != 1:\\n self.error (\\n 'ambiguous hook name {!r} for event {!r}: '\\n 'could match {}\\\\n'.format (\\n hook_name, event,\\n ', '.join ( hook.name for hook in hooks )\\n )\\n )\\n else:\\n hooks_to_unlink.append ( exact_matches[0] )\\n # -- end for\\n else:\\n hooks = list (\\n self.user_hooks.find_all_by_name_begin (\\n hook_name, event=None\\n )\\n )\\n\\n if not hooks:\\n self.error (\\n \\\"no hooks found matching {!r}\\\\n\\\".format ( hook_name )\\n )\\n elif len ( hooks ) == 1:\\n #hooks_to_unlink = hooks\\n hooks_to_unlink.append ( hooks[0] )\\n else:\\n # COULDFIX: it would be better to check if the hooks'\\n # realpaths (link dest) are identical\\n exact_matches = [ k for k in hooks if k.name == hook_name ]\\n num_event_matches = (\\n collections.Counter ( k.event for k in exact_matches )\\n )\\n if exact_matches and all (\\n k < 2 for k in num_event_matches.values()\\n ):\\n #hooks_to_unlink = exact_matches\\n hooks_to_unlink.extend ( exact_matches )\\n else:\\n self.error (\\n 'ambiguous hook name {!r}: could match {}\\\\n'.format (\\n hook_name,\\n ', '.join ( set ( k.name for k in hooks ) )\\n )\\n )\\n # -- end if\\n\\n self.unlink_hooks ( hooks_to_unlink )\\n # -- end <del>\\n\\n else:\\n raise NotImplementedError ( command )\",\n \"def preprocess_main():\",\n \"def _post_run_hook(self, runtime):\\n pass\",\n \"def run_main():\\n main(sys.argv)\",\n \"def console_entry():\\n #main()\",\n \"def entry_point():\\n raise SystemExit(main(sys.argv))\",\n \"def entry_point():\\n raise SystemExit(main(sys.argv))\",\n \"def entry_point():\\n raise SystemExit(main(sys.argv))\",\n \"def run():\\n # main(sys.argv[1:])\\n main()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7423908","0.7423908","0.7423908","0.73854935","0.7256732","0.7225447","0.7175162","0.71340764","0.7110802","0.7008554","0.7006415","0.6989436","0.6963864","0.6871478","0.6857729","0.67905015","0.67806673","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6760804","0.6751286","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.67236096","0.6717073","0.6703173","0.6700465","0.6700465","0.6700465","0.6700465","0.6688917","0.66435367","0.6629855","0.6627318","0.6616984","0.661177","0.6593348","0.6593285","0.65510315","0.65510315","0.6528073","0.6528073","0.65019035","0.6489676","0.6489676","0.6487273","0.64607143","0.64355695","0.643003","0.643003","0.643003","0.64218265","0.63731647","0.6357715","0.635761","0.63483554","0.6342078","0.6341851","0.6341851","0.6341851","0.63394547"],"string":"[\n \"0.7423908\",\n \"0.7423908\",\n \"0.7423908\",\n \"0.73854935\",\n \"0.7256732\",\n \"0.7225447\",\n \"0.7175162\",\n \"0.71340764\",\n \"0.7110802\",\n \"0.7008554\",\n \"0.7006415\",\n \"0.6989436\",\n \"0.6963864\",\n \"0.6871478\",\n \"0.6857729\",\n \"0.67905015\",\n \"0.67806673\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6760804\",\n \"0.6751286\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.67236096\",\n \"0.6717073\",\n \"0.6703173\",\n \"0.6700465\",\n \"0.6700465\",\n \"0.6700465\",\n \"0.6700465\",\n \"0.6688917\",\n \"0.66435367\",\n \"0.6629855\",\n \"0.6627318\",\n \"0.6616984\",\n \"0.661177\",\n \"0.6593348\",\n \"0.6593285\",\n \"0.65510315\",\n \"0.65510315\",\n \"0.6528073\",\n \"0.6528073\",\n \"0.65019035\",\n \"0.6489676\",\n \"0.6489676\",\n \"0.6487273\",\n \"0.64607143\",\n \"0.64355695\",\n \"0.643003\",\n \"0.643003\",\n \"0.643003\",\n \"0.64218265\",\n \"0.63731647\",\n \"0.6357715\",\n \"0.635761\",\n \"0.63483554\",\n \"0.6342078\",\n \"0.6341851\",\n \"0.6341851\",\n \"0.6341851\",\n \"0.63394547\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76676,"cells":{"query":{"kind":"string","value":"Use the tknukewritenode app interface to find and reset the render path of any Tank write nodes in the current script"},"document":{"kind":"string","value":"def _reset_write_node_render_paths(self):\n write_node_app = self.parent.engine.apps.get(\"tk-nuke-writenode\")\n if not write_node_app:\n return False\n\n # only need to forceably reset the write node render paths if the app version\n # is less than or equal to v0.1.11\n from distutils.version import LooseVersion\n if (write_node_app.version == \"Undefined\"\n or LooseVersion(write_node_app.version) > LooseVersion(\"v0.1.11\")):\n return False\n\n write_nodes = write_node_app.get_write_nodes()\n for write_node in write_nodes:\n write_node_app.reset_node_render_path(write_node)\n\n return len(write_nodes) > 0"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def updateTree(self):\n self.reset()\n self.resetTree() \n self.read()","def changeNodeLib(ned, createNodeWin):\n pass","def updatetree(self):\n if self.node:\n self.node.update()\n self.draw()","def fRenderTargetBackupTab():\n node = nuke.thisNode()\n # create tab an button\n tab = nuke.Tab_Knob(\"fRenderTargetBackup_tab\",\"Backup Renders\")\n button = nuke.PyScript_Knob('backup')\n button.setCommand('import dmptools.utils.nukeCommands as nc;nc.fRenderTargetBackup()')\n button.setName('backup renders')\n button.setLabel('backup!')\n button.setTooltip('backup renders to a directory in /tmp/fRenderTarget/<current time>')\n \n # create checkbox\n checkBox = nuke.Boolean_Knob(\"userCustomPath\",\"Use custom path\")\n checkBox.setValue(False)\n # add output textfield\n output = nuke.File_Knob('output', 'backup path')\n output.setValue('/tmp/fRenderTarget/')\n\n # add knobs to the node\n node.addKnob(tab)\n node.addKnob(button)\n node.addKnob(checkBox)\n node.addKnob(output)","def _RemountRootAsReadWrite(self):\n self.RunCmdOnDevice(['mount', '-o', 'remount,rw', '/'])","def run():\n\n def assignToon(context):\n def instanciate_group(nodes, group_name):\n group = nodes.new(type='ShaderNodeGroup')\n group.node_tree = bpy.data.node_groups[group_name]\n return group\n\n def assignToonShader(material):\n '''To do Handle if the material output doesnt exist'''\n toonShader = instanciate_group(material.node_tree.nodes, \"ToonShader_2\")\n node2 = material.node_tree.nodes['Material Output']\n material.node_tree.links.new(toonShader.outputs[0], node2.inputs[0])\n\n objects = bpy.context.selected_objects\n for obj in objects:\n if len(obj.material_slots) < 1:\n\n bpy.ops.object.material_slot_add()\n\n if obj.name not in bpy.data.materials:\n\n mat = bpy.data.materials.new(obj.name)\n else:\n mat = bpy.data.materials[obj.name]\n\n obj.data.materials[0] = mat\n mat.use_nodes = True\n\n for mat in obj.data.materials:\n if mat.name == '':\n mat.name = obj.name\n\n matNodes = mat.node_tree.nodes\n\n assignToonShader(mat)\n if 'Principled BSDF' in matNodes:\n matNodes.remove(matNodes['Principled BSDF'])\n # else:\n # for n in matNodes:\n # if n != material.node_tree.nodes['Material Output']:\n # matNodes.remove(n)\n\n\n shaderPath = r'D:/COMPANIES/loneCoconut/render/MILVIO_CGL/assets/lib/TOONSCEENSETUP/shd/publish/001.000/high/lib_TOONSCEENSETUP_shd.blend'\n collection_name = 'ToonSceneSetup'\n # dict_ = {'company': 'loneCoconut',\n # 'context': 'render',\n # 'project': 'MILVIO',\n # 'scope': 'assets',\n # 'seq': 'lib',\n # 'shot': 'TOONSCEENSETUP',\n # 'task': 'shd',\n # 'user': 'publish',\n # 'resolution': 'high'}\n # shaderPath = lm.LumberObject(dict_)\n # print(shaderPath.latest_version().path_root)\n #\n # collection_name = shaderPath.shot\n\n if collection_name not in bpy.data.collections:\n\n # link all collections starting with 'MyCollection'\n with bpy.data.libraries.load(shaderPath, link=False) as (data_from, data_to):\n data_to.collections = [c for c in data_from.collections if c.startswith(collection_name)]\n\n # link collection to scene collection\n for coll in data_to.collections:\n if coll is not None:\n bpy.data.scenes['Scene'].collection.children.link(coll)\n\n else:\n print(\"Toon Shader Exist\")\n\n\n assignToon(bpy.context)","def set_node_positions(self):","def regenerate_tree(self, newpos):\n self.path = self.tree[newpos][2]\n self.tree = self.get_tree()\n self.pos = self.get_curpos()","def save_node(self):\n # save node in path2node\n if self.full_path in self.file.path2node:\n print \"** Error, created node with path twice:\\n%s\" % self.full_path\n traceback.print_stack()\n sys.exit(1)\n self.file.path2node[self.full_path] = self \n # save node in id_lookups\n id = self.sdef['id']\n ns = self.sdef['ns']\n type = self.sdef['type']\n custom = 'custom' in self.sdef and self.sdef['custom']\n if self.parent is None and self.sdef['df'] and not custom:\n # structure (not custom) created at top level, save in id_lookups\n if id not in self.file.id_lookups[ns]:\n print \"** Error: Unable to find id '%s' in id_lookups when saving node\" % id\n traceback.print_stack()\n sys.exit(1)\n if self.path not in self.file.id_lookups[ns][id]:\n print (\"** Error: Unable to find path '%s' in id_lookups when\"\n \" saving node %s\") % (self.path, id)\n print \"self.sdef['df'] is:\"\n pp.pprint (self.sdef['df'])\n traceback.print_stack()\n sys.exit(1)\n self.file.id_lookups[ns][id][self.path]['created'].append(self)\n # save node in all_nodes, either at top level (if no parent) or inside\n # mstats structure of parent node\n if self.parent is None:\n if self.path in self.file.all_nodes:\n self.file.all_nodes[self.path].append(self)\n else:\n self.file.all_nodes[self.path] = [self, ]\n else:\n if id not in self.parent.mstats:\n if custom:\n # custom node created, add id to mstats of parent\n self.parent.mstats[id] = { 'df': {}, 'type':type, 'ns': ns,\n 'created': [ self, ], 'qty':'?' }\n else:\n print \"** Error: Unable to find key '%s' in parent mstats\" % id\n print \"self.parent.mstats is\"\n pp.pprint (self.parent.mstats)\n traceback.print_stack()\n sys.exit(1)\n else: \n # append node to parent created mstats \n self.parent.mstats[id]['created'].append(self)","def setAsmRoot(self, mode='asset') : \n if mode == 'asset' : \n abcHero = str(self.ui.asset_lineEdit.text())\n pathLineEdit = self.ui.asset_lineEdit\n lineEdit = self.ui.assetRoot_lineEdit\n\n if mode == 'shot' : \n abcHero = str(self.ui.shot_lineEdit.text())\n pathLineEdit = self.ui.shot_lineEdit\n lineEdit = self.ui.shotRoot_lineEdit\n\n if os.path.exists(abcHero) : \n abcAssetInfo = sd_utils.readAbc(abcHero)\n # replace root |\n root = str(abcAssetInfo[0]).replace('|', '')\n statusColor = self.darkRed \n\n if mc.objExists(root) : \n statusColor = self.darkGreen\n\n self.setLineEdit(lineEdit, root, statusColor)\n\n else : \n statusColor = self.darkRed\n self.setLineEdit(lineEdit, '', statusColor)","def addOutputsNode():\n return render_template(\"addOutputsNode.html\")","def reset(self):\r\n self.tree = KDTree()\r\n self.paint()","def write_nml(self, nml_write_path):\n\n # If the object does not have any trees, construct an empty tree before writing to enable webKnossos import\n if self.num_trees() == 0:\n self.add_tree()\n\n nml = self._skeleton_to_nml()\n with open(nml_write_path, \"wb\") as f:\n wknml.write_nml(f, nml)","def reboot(self, node):","def saveTeachPath(self,**kwargs):\n try:\n side = kwargs['side']\n except Exception,e:\n rospy.logerr(\"%s\"%str(e))\n self.mm.neglect()\n return\n self.baxter.br.stopRecording(side)\n\n\n # done with learning - go back to previous\n\n self.bl.update_watch_parameters('after')\n self.bl.create_action()\n self.baxter.yes() # head nod confirm\n self.mm.loadPreviousMenu()","def onReset(self):\n #productive\n profprint()\n fileName = pathToScene = slicer.modules.needlefinder.path.replace(\"NeedleFinder.py\",\"Config/default.cfg\")\n self.logic.loadParameters(fileName)","def setup_holdout_nodetree(obj):\n thisfunc = thisfile + '->setup_holdout_nodetree()'\n\n scene = bpy.context.scene\n engine = scene.render.engine\n if engine != 'CYCLES':\n raise NotImplementedError(engine)\n\n node_tree, nodes = _clear_nodetree_for_active_material(obj)\n\n nodes.new('ShaderNodeHoldout')\n nodes.new('ShaderNodeOutputMaterial')\n node_tree.links.new(nodes['Holdout'].outputs[0], nodes['Material Output'].inputs[0])\n\n # Scene update necessary, as matrix_world is updated lazily\n scene.update()\n\n logging.info(\"%s: Holdout node tree set up for '%s'\", thisfunc, obj.name)","def recache(self, phys):\r\n self.myOutputCache.initialize(phys.app)\r\n\r\n for output in self.myOutputs:\r\n output.initialize(phys.app)\r\n output.run(1)","def define_path(self, node): \n if node.childrens!=[]:\n for child in node.childrens:\n node_child = child['node']\n node_child.times_used+=1\n self.define_path(node_child)\n \n \n #take care of not used nodes, set their gradient to 0\n for node in self.input_node:\n if node.times_used==0:\n node.gradient=np.zeros((node.output_dim, self.output_node.output_dim))","def exportToTreepad(self):\n defaultPath = ''\n rootFileName = os.path.splitext(self.interface.getCurrentFileName())[0]\n if os.path.basename(rootFileName):\n defaultPath = '{0}.{1}'.format(rootFileName, 'hjt')\n filters = 'Treepad Files (*.hjt);;All Files (*)'\n filePath = QtGui.QFileDialog.getSaveFileName(self.interface.\n getActiveWindow(),\n 'TreeLine', defaultPath,\n filters)\n if filePath:\n textList = self.exportNode(self.interface.getRootNode())\n textList.insert(0, '<hj-Treepad version 0.9>')\n encoding = locale.getpreferredencoding()\n try:\n with open(filePath, 'w', encoding=encoding) as f:\n f.writelines([(line + '\\n') for line in textList])\n except IOError:\n QtGui.QMessageBox.warning(self.interface.getActiveWindow(),\n 'TreeLine',\n 'Error - could not write to {}'\n .format(filePath))","def _Freeze(self) -> None:\n self._SetNodes(_FROZEN_NODE_COUNT)","def reset(self):\n self.robot_path_ind = 0\n self.goal_path_ind = None\n self.global_plan = Path()","def execute(self, operation, file_path, context, parent_action, file_version, read_only, **kwargs):\n\n # AARDMAN ADDITION\n # adds short cuts to file browser\n aaNukeUtils.aaSetupShortCuts()\n\n if file_path:\n file_path = file_path.replace(\"/\", os.path.sep)\n\n if operation == \"current_path\":\n # return the current script path\n return nuke.root().name().replace(\"/\", os.path.sep)\n\n elif operation == \"open\":\n # open the specified script\n nuke.scriptOpen(file_path)\n\n # reset any write node render paths:\n if self._reset_write_node_render_paths():\n # something changed so make sure to save the script again:\n nuke.scriptSave()\n\n elif operation == \"save\":\n # save the current script:\n nuke.scriptSave()\n\n elif operation == \"save_as\":\n old_path = nuke.root()[\"name\"].value()\n try:\n # rename script:\n nuke.root()[\"name\"].setValue(file_path)\n\n # reset all write nodes:\n self._reset_write_node_render_paths()\n\n # save script:\n nuke.scriptSaveAs(file_path, -1)\n except Exception, e:\n # something went wrong so reset to old path:\n nuke.root()[\"name\"].setValue(old_path)\n raise TankError(\"Failed to save scene %s\", e)\n\n elif operation == \"reset\":\n \"\"\"\n Reset the scene to an empty state\n \"\"\"\n while nuke.root().modified():\n # changes have been made to the scene\n res = QtGui.QMessageBox.question(None,\n \"Save your script?\",\n \"Your script has unsaved changes. Save before proceeding?\",\n QtGui.QMessageBox.Yes|QtGui.QMessageBox.No|QtGui.QMessageBox.Cancel)\n\n if res == QtGui.QMessageBox.Cancel:\n return False\n elif res == QtGui.QMessageBox.No:\n break\n else:\n nuke.scriptSave()\n\n # now clear the script:\n nuke.scriptClear()\n\n return True","def map(self, app, node):","def update_nodes(self):\n raise NotImplementedError('ERROR: sweeper has to implement update_nodes(self)')","def rollout(self, node, scratch_game):\n pass","def write(self, entry):\n if entry is \"all\":\n for k, item in self.nodes.items():\n if type(item) is list:\n for node in item:\n node.write()\n else:\n item.write()\n else:\n # other option is 'rooms'\n for node in self.nodes[entry]:\n node.write()","def on_saveFile(self):\n self.stored = None\n self.rf_widgetVis()\n if str(self.parent.objectName()) == 'grapherUi':\n self.parent.grapher.ud_commentFromUi(self.parent)\n elif str(self.parent.objectName()) == 'nodeEditor':\n self.rf_widgetVis(state=False)","def draw_tree(self) -> None:\n import turtle\n\n def height(head):\n return 1 + max(height(head.left), height(head.right)) if head else -1\n\n def jump_to(x, y):\n t.penup()\n t.goto(x, y)\n t.pendown()\n\n def draw(node, x, y, dx):\n if node:\n t.goto(x, y)\n jump_to(x, y - 20)\n t.write(node.val, align=\"center\", font=(\"Arial\", 15, \"normal\"))\n t.circle(10)\n draw(node.left, x - dx, y - 50, dx / 2)\n jump_to(x, y - 20)\n draw(node.right, x + dx, y - 50, dx / 2)\n\n t = turtle.Turtle()\n t.speed(0)\n turtle.delay(0)\n h = height(self)\n jump_to(0, 100 + 10 * h)\n draw(self, 0, 100 + 10 * h, 20 * h)\n t.hideturtle()\n turtle.mainloop()","def removeAfterRender(call, args=(), kwargs={}, nodeClass='Write'):","def rebalance(self):\r\n points = [p for p in self.tree]\r\n if points:\r\n self.tree = kd_factory.generate(points)\r\n self.paint()","def setTimeDepNode(self, TPName, waveform):\n \tflag = 0\n for node in self.getNodes():\n if (TPName == node.getParam(\"TPName\")):\n flag = 1\n node.setParam(\"waveform\",waveform)\n self.__TDNodeDict[TPName] = node\n if not flag:\n print \"The\",TPName,\"is not found.\"\n sys.exit(1)","def drawtree(self):\r\n\r\n Phylo.draw(self.tree)","def rf_treeMode(self, selTree, treeDict):\n for node in treeDict['tree']['_order']:\n newItem = TreeNode(**treeDict['tree'][node])\n if len(node.split('/')) == 1:\n self.addTopLevelItem(newItem)\n else:\n parent = self._getItemFromTreePath('/'.join(node.split('/')[:-1]))\n parent.addChild(newItem)\n if getattr(newItem, 'nodeType') == 'shotNode':\n newItem._itemPath = node\n newItem._dataPath = os.path.join(self.pm._treePath, selTree)\n for fld in node.split('/'):\n newItem._dataPath = os.path.join(newItem._dataPath, fld)\n newItem._dataPath = pFile.conformPath(newItem._dataPath)\n newItem._dataFile = \"%s.py\" % newItem._dataPath\n for step in treeDict['steps']:\n newStep = TreeNode(nodeType='step', nodeLabel=step, nodeName=step)\n newStep._tree = selTree\n newStep._step = step\n newStep._dataPath = newItem._dataPath\n newStep._ltPath = pFile.conformPath(os.path.join(newStep._dataPath, 'lt', step))\n newStep._dataFile = newItem._dataFile\n newItem.addChild(newStep)","def generate(self):\n self.graph_repl = self.master.graph_repl","def index_siteroot(context):\n portal = getSite()\n portal.reindexObject()","def main():\n rospy.init_node(\"tree\")\n splintered_reality = SplinteredReality()\n rospy.on_shutdown(splintered_reality.shutdown)\n if not splintered_reality.setup():\n console.logerror(\"failed to setup the tree, aborting.\")\n sys.exit(1)\n splintered_reality.tick_tock()","def createStabilizedView():\n global cc\n y = b.createNode('StartStabilized')\n dd = b.createNode('Dot', '', False)\n de = b.createNode('Dot', '', False)\n w = b.createNode('EndStabilized')\n if cc:\n y = gV(y)\n w = gX(w)\n y['tile_color'].setValue(int('%02x%02x%02x%02x' % (232.05, 145.095, 0, 255), 16))\n w['tile_color'].setValue(int('%02x%02x%02x%02x' % (232.05, 145.095, 0, 255), 16))\n w.connectInput(2, y)\n stabilizedPrecompNode___connectPrecompNodes(y, w)\n cd = 250\n df = 250\n ez = 50\n eA = 7\n cP(dd)\n de.setSelected(True)\n eB = b.nodes.BackdropNode(xpos=y.xpos() + cd / 2, bdwidth=cd * 1.5, ypos=y.ypos() - ez, bdheight=df + 2 * ez,\n tile_color=int('%02x%02x%02x%02x' % (232.05, 145.095, 0, 255), 16),\n note_font_color=4294967040, note_font_size=36, name='MochaImport+')\n eB['label'].setValue('Stabilized View+')\n dd.setXpos(y.xpos() + cd)\n dd.setYpos(y.ypos() + eA)\n de.setXpos(y.xpos() + cd)\n de.setYpos(y.ypos() + df + eA)\n w.setXpos(y.xpos())\n w.setYpos(y.ypos() + df)\n eB['help'].setValue(\n \"\\n<h2>Basic Usage of Stabilized View Rig</h2>\\n<ol>\\n<li>From the MochaImport+ menu, create the stabilized view rig consisting of the StartStabilized node, the EndStabilized node, and the Stabilized View Backdrop.</li>\\n<li>Load your mocha tracking data in the StartStabilized node.\\n</li><li>Do arbitrary manipulations in the Stabilized View by inserting new nodes inside the backdrop. All changes you do there in a stabilized setting will also be visible in your original perspective after the EndStabilized node.</li>\\n</ol>\\n\\n<h2>Lens Distortion</h2>\\n<p>If you've used the mocha Lens module to analyze the lens distortion of your clip, you need to do the following things to get an undistorted stabilized view and reapply the lens distortion to the final result:\\n</p><ul>\\n<li>make sure the mocha corner pin data you load into the StartStabilized node is exported from mocha Pro with the option 'Remove lens distortion'</li>\\n<li>as UndistMap input of the StartStabilized node use a Distortion Map Clip (ST Map) exported with the mocha Pro lens module with option 'undistort'.</li>\\n<li>as DistMap input of the EndStabilized node use a Distortion Map Clip (ST Map) exported with the mocha Pro lens module with option 'distort'.</li>\\n</ul>\".replace(\n '\\n', '').replace('\\r', ''))","def create_nodes(self):","def get_batch_custom_ui_actions():\n\n def scope_node(selection):\n \"\"\"Scope the custom action to the nodes residing in a Batch Schematic.\"\"\"\n import flame\n for item in selection:\n if isinstance(item, flame.PyNode):\n \tif item.type == \"Clip\":\n \treturn True\n return False\n\n def reconstructCycles(selection):\n\t\timport string\n\t\timport flame\n\n\t\t# get selected clip in batch\n\t\tclipNode = flame.batch.current_node.get_value()\n\n\t\t# get selected clip name\n\t\tclipName = clipNode.name\n\n\t\tpreviousNode = clipNode\n\n\t\t# node position offsets at creation time\n\t\taddXoffset = 500\n\t\taddYoffset = 700\n\n\t\tmultXoffset = addXoffset + 100\n\t\tmultYoffset = addYoffset - 200\n\n\t\t# create empty PyNode list\n\t\tFULLcompNodes = []\n\n\n\t\t# cycle through all output sockets of selected node\n\t\tfor outSocket in clipNode.output_sockets:\n\n\n\t\t\t##########################################\n\t\t\t# REBUILD DIFFUSE PASS #\n\t\t\t##########################################\n\n\t\t\t# check if 'Diff' is in render pass name\n\t\t\tif 'Diff' in outSocket:\n\n\t\t\t\t# find 'Color' render pass\n\t\t\t\tif 'Col' in outSocket:\n\n\t\t\t\t\tdiffuseColSocket = outSocket\n\n\t\t\t\t\t# create a 'Comp' node for FULL pass reconstruction\n\t\t\t\t\tFULLdiffuseCompNode = flame.batch.create_node(\"Comp\")\n\n\t\t\t\t\t# set RAW 'Comp' node to 'Add'\n\t\t\t\t\tFULLdiffuseCompNode.flame_blend_mode = \"Multiply\"\n\n\t\t\t\t\t# connect 'Front' to 'Col' render pass\n\t\t\t\t\tflame.batch.connect_nodes(clipNode, diffuseColSocket, FULLdiffuseCompNode, \"Front\")\n\n\t\t\t\t\t# set node position\n\t\t\t\t\tFULLdiffuseCompNode.pos_x = previousNode.pos_x + multXoffset\n\t\t\t\t\tFULLdiffuseCompNode.pos_y = previousNode.pos_y - multYoffset\n\n\t\t\t\t# find 'Direct' render pass\n\t\t\t\tif 'Dir' in outSocket:\n\n\t\t\t\t\tdiffuseDirectSocket = outSocket\n\n\t\t\t\t\t# create a 'Comp' node for RAW pass reconstruction\n\t\t\t\t\tRAWdiffuseCompNode = flame.batch.create_node(\"Comp\")\n\n\t\t\t\t\t# set RAW 'Comp' node to 'Add'\n\t\t\t\t\tRAWdiffuseCompNode.flame_blend_mode = \"Add\"\n\n\t\t\t\t\t# connect 'Front' to 'Direct' render pass\n\t\t\t\t\tflame.batch.connect_nodes(clipNode, diffuseDirectSocket, RAWdiffuseCompNode, \"Front\")\n\n\t\t\t\t\t# set node position\n\t\t\t\t\tRAWdiffuseCompNode.pos_x = previousNode.pos_x + addXoffset\n\t\t\t\t\tRAWdiffuseCompNode.pos_y = previousNode.pos_y - addYoffset\n\n\n\t\t\t\t# find 'Indirect' render pass\n\t\t\t\tif 'Ind' in outSocket:\n\n\t\t\t\t\tdiffuseIndirectSocket = outSocket\n\n\t\t\t\t\t# connect 'Indirect' render pass to 'Back'\n\t\t\t\t\tflame.batch.connect_nodes(clipNode, diffuseIndirectSocket, RAWdiffuseCompNode, \"Back\")\n\n\t\t\t\t\t# connect RAW node to FULL nodediffuseIndirectSocket\n\t\t\t\t\tflame.batch.connect_nodes(RAWdiffuseCompNode, 'Result', FULLdiffuseCompNode, \"Back\")\n\n\t\t\t\t\tFULLcompNodes.append(FULLdiffuseCompNode)\n\n\t\t\t\t\tpreviousNode = FULLdiffuseCompNode\n\n\t\t\t\t\t# CREATE COMPASS FOR DIFFUSE\n\t\t\t\t\tdiffuseCompass = flame.batch.create_node(\"Compass\")\n\n\t\t\t\t\tdiffuseCompass.name = 'DIFFUSE_' + clipName\n\n\t\t\t\t\tdiffuseCompass.pos_x = RAWdiffuseCompNode.pos_x -200\n\t\t\t\t\tdiffuseCompass.pos_y = RAWdiffuseCompNode.pos_y -150\n\n\t\t\t\t\tdiffuseCompass.width = 550\n\t\t\t\t\tdiffuseCompass.height = 550\n\n\t\t\t\t\tdiffuseCompass.colour = (0.13,0.2,0.13)\n\n\n\t\t\t########################################\n\t\t\t# REBUILD GLOSS PASS #\n\t\t\t########################################\n\n\t\t\t# check if 'Gloss' is in render pass name\n\t\t\tif 'Gloss' in outSocket:\n\n\t\t\t\t# find 'Color' render pass\n\t\t\t\tif 'Col' in outSocket:\n\n\t\t\t\t\tglossColSocket = outSocket\n\n\t\t\t\t\t# create a 'Comp' node for FULL pass reconstruction\n\t\t\t\t\tFULLglossCompNode = flame.batch.create_node(\"Comp\")\n\n\t\t\t\t\t# set RAW 'Comp' node to 'Add'\n\t\t\t\t\tFULLglossCompNode.flame_blend_mode = \"Multiply\"\n\n\t\t\t\t\t# connect 'Front' to 'Col' render pass\n\t\t\t\t\tflame.batch.connect_nodes(clipNode, glossColSocket, FULLglossCompNode, \"Front\")\n\n\t\t\t\t\t# set node position\n\t\t\t\t\tFULLglossCompNode.pos_x = previousNode.pos_x + multXoffset\n\t\t\t\t\tFULLglossCompNode.pos_y = previousNode.pos_y - multYoffset\n\n\t\t\t\t# find 'Direct' render pass\n\t\t\t\tif 'Dir' in outSocket:\n\n\t\t\t\t\tglossDirectSocket = outSocket\n\n\t\t\t\t\t# create a 'Comp' node for RAW pass reconstruction\n\t\t\t\t\tRAWglossCompNode = flame.batch.create_node(\"Comp\")\n\n\t\t\t\t\t# set RAW 'Comp' node to 'Add'\n\t\t\t\t\tRAWglossCompNode.flame_blend_mode = \"Add\"\n\n\t\t\t\t\t# connect 'Front' to 'Direct' render pass\n\t\t\t\t\tflame.batch.connect_nodes(clipNode, glossDirectSocket, RAWglossCompNode, \"Front\")\n\n\t\t\t\t\t# set node position\n\t\t\t\t\tRAWglossCompNode.pos_x = previousNode.pos_x + addXoffset\n\t\t\t\t\tRAWglossCompNode.pos_y = previousNode.pos_y - addYoffset\n\n\n\t\t\t\t# find 'Indirect' render pass\n\t\t\t\tif 'Ind' in outSocket:\n\n\t\t\t\t\tglossIndirectSocket = outSocket\n\n\t\t\t\t\t# connect 'Indirect' render pass to 'Back'\n\t\t\t\t\tflame.batch.connect_nodes(clipNode, glossIndirectSocket, RAWglossCompNode, \"Back\")\n\n\t\t\t\t\t# connect RAW node to FULL node\n\t\t\t\t\tflame.batch.connect_nodes(RAWglossCompNode, 'Result', FULLglossCompNode, \"Back\")\n\n\t\t\t\t\tFULLcompNodes.append(FULLglossCompNode)\n\n\t\t\t\t\tpreviousNode = FULLglossCompNode\n\n\t\t\t\t\t# CREATE COMPASS FOR DIFFUSE\n\t\t\t\t\tglossCompass = flame.batch.create_node(\"Compass\")\n\n\t\t\t\t\tglossCompass.pos_x = RAWglossCompNode.pos_x -200\n\t\t\t\t\tglossCompass.pos_y = RAWglossCompNode.pos_y -150\n\n\t\t\t\t\tglossCompass.width = 550\n\t\t\t\t\tglossCompass.height = 550\n\n\t\t\t\t\tglossCompass.name = 'GLOSS_' + clipName\n\n\t\t\t\t\tglossCompass.colour = (0.25,0.0,0.25)\n\n\n\n\n\t\t\t#############################################\n\t\t\t# REBUILD SUBSURFACE PASS #\n\t\t\t#############################################\n\n\t\t\t# check if 'Subsurface' is in render pass name\n\t\t\tif 'Subsurface' in outSocket:\n\n\t\t\t\t# find 'Color' render pass\n\t\t\t\tif 'Col' in outSocket:\n\n\t\t\t\t\tsubsurfaceColSocket = outSocket\n\n\t\t\t\t\t# create a 'Comp' node for FULL pass reconstruction\n\t\t\t\t\tFULLSubsurfaceCompNode = flame.batch.create_node(\"Comp\")\n\n\t\t\t\t\t# set RAW 'Comp' node to 'Add'\n\t\t\t\t\tFULLSubsurfaceCompNode.flame_blend_mode = \"Multiply\"\n\n\t\t\t\t\t# connect 'Front' to 'Col' render pass\n\t\t\t\t\tflame.batch.connect_nodes(clipNode, subsurfaceColSocket, FULLSubsurfaceCompNode, \"Front\")\n\n\t\t\t\t\t# set node position\n\t\t\t\t\tFULLSubsurfaceCompNode.pos_x = previousNode.pos_x + multXoffset\n\t\t\t\t\tFULLSubsurfaceCompNode.pos_y = previousNode.pos_y - multYoffset\n\n\t\t\t\t# find 'Direct' render pass\n\t\t\t\tif 'Dir' in outSocket:\n\n\t\t\t\t\tsubsurfaceDirectSocket = outSocket\n\n\t\t\t\t\t# create a 'Comp' node for RAW pass reconstruction\n\t\t\t\t\tRAWsubsurfaceCompNode = flame.batch.create_node(\"Comp\")\n\n\t\t\t\t\t# set RAW 'Comp' node to 'Add'\n\t\t\t\t\tRAWsubsurfaceCompNode.flame_blend_mode = \"Add\"\n\n\t\t\t\t\t# connect 'Front' to 'Direct' render pass\n\t\t\t\t\tflame.batch.connect_nodes(clipNode, subsurfaceDirectSocket, RAWsubsurfaceCompNode, \"Front\")\n\n\t\t\t\t\t# set node position\n\t\t\t\t\tRAWsubsurfaceCompNode.pos_x = previousNode.pos_x + addXoffset\n\t\t\t\t\tRAWsubsurfaceCompNode.pos_y = previousNode.pos_y - addYoffset\n\n\n\t\t\t\t# find 'Indirect' render pass\n\t\t\t\tif 'Ind' in outSocket:\n\n\t\t\t\t\tsubsurfaceIndirectSocket = outSocket\n\n\t\t\t\t\t# connect 'Indirect' render pass to 'Back'\n\t\t\t\t\tflame.batch.connect_nodes(clipNode, subsurfaceIndirectSocket, RAWsubsurfaceCompNode, \"Back\")\n\n\n\t\t\t\t\t# connect RAW node to FULL nodediffuseIndirectSocket\n\t\t\t\t\tflame.batch.connect_nodes(RAWsubsurfaceCompNode, 'Result', FULLSubsurfaceCompNode, \"Back\")\n\n\n\t\t\t\t\tFULLcompNodes.append(FULLSubsurfaceCompNode)\n\n\n\t\t\t\t\tpreviousNode = FULLSubsurfaceCompNode\n\n\t\t\t\t\t# CREATE COMPASS FOR SUBSURFACE\n\t\t\t\t\tsubsurfaceCompass = flame.batch.create_node(\"Compass\")\n\n\t\t\t\t\tsubsurfaceCompass.name = 'SSS_' + clipName\n\n\t\t\t\t\tsubsurfaceCompass.pos_x = RAWsubsurfaceCompNode.pos_x -200\n\t\t\t\t\tsubsurfaceCompass.pos_y = RAWsubsurfaceCompNode.pos_y -150\n\n\t\t\t\t\tsubsurfaceCompass.width = 550\n\t\t\t\t\tsubsurfaceCompass.height = 550\n\n\t\t\t\t\tsubsurfaceCompass.colour = (0.13,0.24,0.24)\n\n\n\n\t\t\t###############################################\n\t\t\t# REBUILD TRANSMISSION PASS #\n\t\t\t###############################################\n\n\t\t\t# check if 'Transmission' is in render pass name\n\t\t\tif 'Trans' in outSocket:\n\n\t\t\t\t# find 'Color' render pass\n\t\t\t\tif 'Col' in outSocket:\n\n\t\t\t\t\ttransmissionColSocket = outSocket\n\n\t\t\t\t\t# create a 'Comp' node for FULL pass reconstruction\n\t\t\t\t\tFULLtransmissionCompNode = flame.batch.create_node(\"Comp\")\n\n\t\t\t\t\t# set RAW 'Comp' node to 'Add'\n\t\t\t\t\tFULLtransmissionCompNode.flame_blend_mode = \"Multiply\"\n\n\t\t\t\t\t# connect 'Front' to 'Col' render pass\n\t\t\t\t\tflame.batch.connect_nodes(clipNode, transmissionColSocket, FULLtransmissionCompNode, \"Front\")\n\n\t\t\t\t\t# set node position\n\t\t\t\t\tFULLtransmissionCompNode.pos_x = previousNode.pos_x + multXoffset\n\t\t\t\t\tFULLtransmissionCompNode.pos_y = previousNode.pos_y - multYoffset\n\n\t\t\t\t# find 'Direct' render pass\n\t\t\t\tif 'Dir' in outSocket:\n\n\t\t\t\t\ttransmissionDirectSocket = outSocket\n\n\t\t\t\t\t# create a 'Comp' node for RAW pass reconstruction\n\t\t\t\t\tRAWtransmissionCompNode = flame.batch.create_node(\"Comp\")\n\n\t\t\t\t\t# set RAW 'Comp' node to 'Add'\n\t\t\t\t\tRAWtransmissionCompNode.flame_blend_mode = \"Add\"\n\n\t\t\t\t\t# connect 'Front' to 'Direct' render pass\n\t\t\t\t\tflame.batch.connect_nodes(clipNode, transmissionDirectSocket, RAWtransmissionCompNode, \"Front\")\n\n\t\t\t\t\t# set node position\n\t\t\t\t\tRAWtransmissionCompNode.pos_x = previousNode.pos_x + addXoffset\n\t\t\t\t\tRAWtransmissionCompNode.pos_y = previousNode.pos_y - addYoffset\n\n\n\t\t\t\t# find 'Indirect' render pass\n\t\t\t\tif 'Ind' in outSocket:\n\n\t\t\t\t\ttransmissionIndirectSocket = outSocket\n\n\t\t\t\t\t# connect 'Indirect' render pass to 'Back'\n\t\t\t\t\tflame.batch.connect_nodes(clipNode, transmissionIndirectSocket, RAWtransmissionCompNode, \"Back\")\n\n\n\t\t\t\t\t# connect RAW node to FULL nodediffuseIndirectSocket\n\t\t\t\t\tflame.batch.connect_nodes(RAWtransmissionCompNode, 'Result', FULLtransmissionCompNode, \"Back\")\n\n\n\t\t\t\t\tFULLcompNodes.append(FULLtransmissionCompNode)\n\n\n\t\t\t\t\tpreviousNode = FULLtransmissionCompNode\n\n\t\t\t\t\t# CREATE COMPASS FOR TRANSMISSION\n\t\t\t\t\ttransmissionCompass = flame.batch.create_node(\"Compass\")\n\n\t\t\t\t\ttransmissionCompass.name = 'TRANSMISSION_' + clipName\n\n\t\t\t\t\ttransmissionCompass.pos_x = RAWtransmissionCompNode.pos_x -200\n\t\t\t\t\ttransmissionCompass.pos_y = RAWtransmissionCompNode.pos_y -150\n\n\t\t\t\t\ttransmissionCompass.width = 550\n\t\t\t\t\ttransmissionCompass.height = 550\n\n\t\t\t\t\ttransmissionCompass.colour = (0.45,0.16,0.16)\n\n\n\n\n\t\t# comp all reconstructed passes\n\t\tif len(FULLcompNodes) >= 2 :\n\n\t\t\tfirstNode = True\n\n\t\t\tcounter = 0\n\n\t\t\twhile counter < (len(FULLcompNodes)-1):\n\n\t\t\t\tprint counter\n\n\t\t\t\taddNode = flame.batch.create_node(\"Comp\")\n\t\t\t\taddNode.flame_blend_mode = \"Add\"\n\n\t\t\t\t\n\n\t\t\t\tif firstNode == True:\n\t\t\t\t\tflame.batch.connect_nodes(FULLcompNodes[counter], \"Result\", addNode, \"Front\")\n\t\t\t\t\tflame.batch.connect_nodes(FULLcompNodes[counter+1], \"Result\", addNode, \"Back\")\n\n\t\t\t\t\taddNode.pos_x = FULLcompNodes[counter].pos_x + multXoffset + 400\n\t\t\t\t\taddNode.pos_y = FULLcompNodes[counter].pos_y\n\n\t\t\t\t\tpreviousNode = addNode\n\n\t\t\t\telse :\n\t\t\t\t\tflame.batch.connect_nodes(previousNode, \"Result\", addNode, \"Front\")\n\t\t\t\t\tflame.batch.connect_nodes(FULLcompNodes[counter+1], \"Result\", addNode, \"Back\")\n\n\t\t\t\t\taddNode.pos_x = FULLcompNodes[counter].pos_x + multXoffset + 400\n\t\t\t\t\taddNode.pos_y = previousNode.pos_y\n\n\t\t\t\t\tpreviousNode = addNode\n\t\t\t\t\t\t\n\n\t\t\t\tcounter += 1\n\t\t\t\tfirstNode = False\n\n\n\t\t# cycle through all output sockets of selected node\n\t\tfor outSocket in clipNode.output_sockets:\n\n\t\t\t###############################################\n\t\t\t# ADD EMISSION PASS #\n\t\t\t###############################################\n\n\t\t\t# check if 'Emission' is in render pass name\n\t\t\tif 'Emit' in outSocket:\n\n\t\t\t\temissionColSocket = outSocket\n\n\t\t\t\t# create a 'Comp' node for Emission pass\n\t\t\t\temissionCompNode = flame.batch.create_node(\"Comp\")\n\n\t\t\t\t# set Emission 'Comp' node to 'Add'\n\t\t\t\temissionCompNode.flame_blend_mode = \"Add\"\n\n\t\t\t\t# connect 'Front' to 'Col' render pass\n\t\t\t\tflame.batch.connect_nodes(clipNode, emissionColSocket, emissionCompNode, \"Front\")\n\t\t\t\tflame.batch.connect_nodes(previousNode, 'Result', emissionCompNode, \"Back\")\n\n\t\t\t\t# set node position\n\t\t\t\temissionCompNode.pos_x = previousNode.pos_x + multXoffset\n\t\t\t\temissionCompNode.pos_y = previousNode.pos_y + multYoffset\n\n\t\t\t\tpreviousNode = emissionCompNode\n\n\n\t\t\t\t# CREATE COMPASS FOR EMISSION\n\t\t\t\temissionCompass = flame.batch.create_node(\"Compass\")\n\n\t\t\t\temissionCompass.name = 'EMISSION_' + clipName\n\n\t\t\t\temissionCompass.width = 400\n\t\t\t\temissionCompass.height = 400\n\n\t\t\t\temissionCompass.pos_x = emissionCompNode.pos_x - (emissionCompass.width/2)\n\t\t\t\temissionCompass.pos_y = emissionCompNode.pos_y - (emissionCompass.height/2)\n\n\t\t\t\temissionCompass.colour = (0.15,0.08,0.43)\n\n\t\t\t###############################################\n\t\t\t# ADD ENVIRONMENT PASS #\n\t\t\t###############################################\n\n\t\t\t# check if 'Transmission' is in render pass name\n\t\t\tif 'Env' in outSocket:\n\n\t\t\t\tenvironmentSocket = outSocket\n\n\t\t\t\t# create a 'Comp' node for Emission pass\n\t\t\t\tenvironmentCompNode = flame.batch.create_node(\"Comp\")\n\n\t\t\t\t# set Emission 'Comp' node to 'Add'\n\t\t\t\tenvironmentCompNode.flame_blend_mode = \"Add\"\n\n\t\t\t\t# connect 'Front' to 'Col' render pass\n\t\t\t\tflame.batch.connect_nodes(clipNode, environmentSocket, environmentCompNode, \"Front\")\n\t\t\t\tflame.batch.connect_nodes(previousNode, 'Result', environmentCompNode, \"Back\")\n\n\n\t\t\t\t# set node position\n\t\t\t\tenvironmentCompNode.pos_x = previousNode.pos_x + multXoffset\n\t\t\t\tenvironmentCompNode.pos_y = previousNode.pos_y + multYoffset\n\n\t\t\t\tpreviousNode = environmentCompNode\n\n\n\t\t\t\t# CREATE COMPASS FOR ENVIRONMENT\n\t\t\t\tenvironmentCompass = flame.batch.create_node(\"Compass\")\n\n\t\t\t\tenvironmentCompass.name = 'ENVIRONMENT_' + clipName\n\n\t\t\t\tenvironmentCompass.width = 400\n\t\t\t\tenvironmentCompass.height = 400\n\n\t\t\t\tenvironmentCompass.pos_x = environmentCompNode.pos_x - (environmentCompass.width/2)\n\t\t\t\tenvironmentCompass.pos_y = environmentCompNode.pos_y - (environmentCompass.height/2)\n\n\t\t\t\tenvironmentCompass.colour = (0.15,0.25,0.43)\n\n\n return [\n {\n \"name\": \"PYTHON: NODES\",\n \"actions\": [\n {\n \"name\": \"Reconstruct Blender Beauty\",\n \"isVisible\": scope_node,\n \"execute\": reconstructCycles\n }\n ]\n }\n ]","def _add_image_switching_node( self,\n node_tree,\n image_path,\n last_element,\n image_ID_node=None,\n node_index=0,\n uv_map=None, \n node_offset=[0,0]):\n\n # define local variables #######################################################################################\n _step_node_width = 200 # x seperation of nodes\n _step_node_height = 200 # y seperation of nodes\n ################################################################################ end of define local variables #\n\n # create image ID handle #######################################################################################\n if image_ID_node is None:\n image_ID_node = node_tree.node_tree.nodes.new(\"ShaderNodeValue\")\n image_ID_node.location = ((node_offset[0]-400,node_offset[1]-100))\n image_ID_node.name = \"image_step_ID\"\n image_ID_node.label = \"image_step_ID\"\n image_ID_node.outputs[0].default_value = 1\n ############################################################################### end of create image ID handle #\n\n # create image nodes ###########################################################################################\n _x_offset = (node_index+1)*_step_node_width + node_offset[0]\n _y_offset = (node_index+1)*_step_node_height + node_offset[1]\n _current_image_node = node_tree.node_tree.nodes.new('ShaderNodeTexEnvironment')\n _img = bpy.data.images.load(image_path)\n _current_image_node.image = _img\n _current_image_node.location = (((node_index+1)*_step_node_width*2 + node_offset[0] - 800,\n (node_index+1)*_step_node_height + node_offset[1]))\n\n # attach uv map if possible\n if uv_map is not None:\n node_tree.node_tree.links.new(_current_image_node.inputs[0], uv_map)\n\n # create new mix node ######################################################################################\n _current_mix_shader_node = node_tree.node_tree.nodes.new(\"ShaderNodeMixRGB\")\n _current_mix_shader_node.location = (((node_index+1)*_step_node_width*2 + node_offset[0],\n (node_index+1)*_step_node_height + node_offset[1]))\n ############################################################################### end of create new mix node #\n\n # create compare node ######################################################################################\n _current_compare_node = node_tree.node_tree.nodes.new(\"ShaderNodeMath\")\n _current_compare_node.location = (((node_index+1)*_step_node_width*2 + node_offset[0],\n node_offset[1]-_step_node_height))\n _current_compare_node.operation = 'COMPARE'\n _current_compare_node.inputs[0].default_value = node_index\n _current_compare_node.inputs[2].default_value = 0 # delta value should be zero for equal comparison\n ############################################################################### end of create compare node #\n\n\n # link nodes togther #######################################################################################\n node_tree.node_tree.links.new(_current_mix_shader_node.inputs[0], _current_compare_node.outputs[0])\n if last_element is not None:\n node_tree.node_tree.links.new(_current_mix_shader_node.inputs[1], last_element.outputs[0])\n node_tree.node_tree.links.new(_current_mix_shader_node.inputs[2], _current_image_node.outputs[0])\n \n node_tree.node_tree.links.new(_current_compare_node.inputs[1], image_ID_node.outputs[0])\n ################################################################################ end of link nodes togther #\n #################################################################################### end of create image nodes #\n\n # return last mix shader node\n return _current_mix_shader_node, image_ID_node","def on_ResetNode_clicked(self):\n # TODO: not implemented yet\n raise NotImplementedError","def switch():\n temp = p['paths']['war3']\n p['paths']['war3'] = p['paths']['war3r']\n p['paths']['war3r'] = temp","def reset_tree(self):\n self.root = None\n self.action = None\n self.dist_probability = None","def rewrite(self):\n return None","def _do_nuke_post_publish(self, work_template, progress_cb): \n import nuke\n \n progress_cb(0, \"Versioning up the script\")\n \n # get the current script path:\n original_path = nuke.root().name()\n script_path = os.path.abspath(original_path.replace(\"/\", os.path.sep))\n \n # increment version and construct new name:\n progress_cb(25, \"Finding next version number\")\n fields = work_template.get_fields(script_path)\n next_version = self._get_next_work_file_version(work_template, fields)\n fields[\"version\"] = next_version \n new_path = work_template.apply_fields(fields)\n \n # log info\n self.parent.log_debug(\"Version up work file %s --> %s...\" % (script_path, new_path))\n\n # rename script:\n nuke.root()[\"name\"].setValue(new_path)\n \n # update write nodes:\n write_node_app = tank.platform.current_engine().apps.get(\"tk-nuke-writenode\")\n if write_node_app:\n progress_cb(50, \"Resetting render paths for write nodes\")\n # reset render paths for all write nodes:\n for wn in write_node_app.get_write_nodes():\n write_node_app.reset_node_render_path(wn)\n \n # save the script:\n progress_cb(75, \"Saving the scene file\")\n nuke.scriptSaveAs(new_path.replace(os.path.sep, \"/\"))\n \n progress_cb(100)","def update_path():\n #TODO update path information\n pass","def playout(self, state):\n node = self.root\n isTerminal = False\n depth = 0\n\n while not isTerminal and depth < self.playout_depth:\n #A = len(node.children) # num_children\n A = len(node.children[0]) + len(node.children[1]) \n if A < self.num_initActions:\n #if len(node.children[0]) < self.num_initActions:\n node, init_action_xy, init_spin = self.initChildren(node, state, depth)\n _, ShotVec = CreateShot(_ShotPos(init_action_xy[0], init_action_xy[1], init_spin))\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\n isTerminal = (state.ShotNum==0)\n \n depth += 1\n break\n \n n_a = [c.n_visits for c in node.children[0].values()] + [c.n_visits for c in node.children[1].values()]\n # progressive widening\n # if chilren node has been visited much times then expand\n #if np.sqrt(sum(n_a)) >= A:\n if sum(n_a) >= 10 * A: \n # expand\n node, expanded_action_xy, expanded_spin = self.expand(node)\n _, ShotVec = CreateShot(_ShotPos(expanded_action_xy[0],expanded_action_xy[1], expanded_spin))\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\n isTerminal = (state.ShotNum==0) # one end game\n \n depth += 1\n break\n\n # select\n node, selected_action_xy, selected_spin = self.ucb_select(node)\n _, ShotVec = CreateShot(_ShotPos(selected_action_xy[0], selected_action_xy[1], selected_spin))\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\n isTerminal = (state.ShotNum==0) # one end game\n \n depth += 1\n\n if isTerminal:\n break\n\n if not isTerminal and depth < self.playout_depth:\n # save the rollout_state for speed.\n #if node.rollout_state is None:\n state = self.rollOut(node, state, depth)\n #node.rollout_state = state\n #else:\n # state = node.rollout_state\n \n self.update(node, state)","def onResetParameters(self):\r\n # productive #button\r\n profprint()\r\n fileName = pathToScene = slicer.modules.needlefinder.path.replace(\"NeedleFinder.py\", \"Config/default.cfg\")\r\n self.logic.loadParameters(fileName)","def showTF(tf,outDir):\n\n nlo2lo,data2lo,data2nlo,data2lo_A,data2nlo_A=tf\n\n c=ROOT.TCanvas('c','c',500,500)\n c.SetBottomMargin(0)\n c.SetTopMargin(0)\n c.SetLeftMargin(0)\n c.SetRightMargin(0)\n c.cd()\n\n p1=ROOT.TPad('p1','p1',0,0.5,1,1.0)\n p1.Draw()\n p1.SetRightMargin(0.03)\n p1.SetLeftMargin(0.12)\n p1.SetTopMargin(0.1)\n p1.SetBottomMargin(0.01)\n p1.SetGridy()\n p1.cd()\n nlo2lo.Draw('e2')\n nlo2lo.GetYaxis().SetTitle('Z ratio')\n nlo2lo.GetYaxis().SetNdivisions(5)\n nlo2lo.GetXaxis().SetTitleSize(0)\n nlo2lo.GetXaxis().SetLabelSize(0)\n nlo2lo.GetYaxis().SetTitleSize(0.08)\n nlo2lo.GetYaxis().SetTitleOffset(0.8)\n nlo2lo.GetYaxis().SetLabelSize(0.08)\n nlo2lo.GetYaxis().SetRangeUser(0.01,1.94)\n data2lo.Draw('e1same')\n data2nlo.Draw('e1same')\n\n leg1=p1.BuildLegend(0.7,0.88,0.95,0.66)\n leg1.SetFillStyle(0)\n leg1.SetBorderSize(0)\n leg1.SetTextFont(42)\n leg1.SetTextSize(0.06)\n\n l1=ROOT.TLine()\n l1.SetLineWidth(2)\n l1.SetLineColor(ROOT.kBlue)\n l1.DrawLine(data2lo.GetXaxis().GetXmin(),1,data2lo.GetXaxis().GetXmax(),1)\n\n txt=ROOT.TLatex()\n txt.SetNDC(True)\n txt.SetTextFont(42)\n txt.SetTextSize(0.08)\n txt.SetTextAlign(12)\n txt.DrawLatex(0.12,0.95,'#bf{CMS} #it{preliminary}')\n p1.RedrawAxis()\n\n c.cd()\n p2=ROOT.TPad('p2','p2',0,0,1,0.5)\n p2.SetRightMargin(0.03)\n p2.SetLeftMargin(0.12)\n p2.SetTopMargin(0.01)\n p2.SetBottomMargin(0.18)\n p2.SetGridy()\n p2.Draw()\n p2.cd()\n data2lo_A.Draw('e1')\n data2lo_A.GetYaxis().SetTitle('#gamma ratio')\n data2lo_A.GetYaxis().SetNdivisions(5)\n data2lo_A.GetYaxis().SetRangeUser(0.01,1.94)\n data2lo_A.GetXaxis().SetTitleSize(0.08)\n data2lo_A.GetXaxis().SetLabelSize(0.08)\n data2lo_A.GetYaxis().SetTitleSize(0.08)\n data2lo_A.GetYaxis().SetLabelSize(0.08)\n data2lo_A.GetYaxis().SetTitleOffset(0.8)\n data2nlo_A.Draw('e1same')\n \n leg2=p2.BuildLegend(0.7,0.94,0.95,0.80)\n leg2.SetFillStyle(0)\n leg2.SetBorderSize(0)\n leg2.SetTextFont(42)\n leg2.SetTextSize(0.06)\n \n l2=ROOT.TLine()\n l2.SetLineColor(ROOT.kBlue)\n l2.SetLineWidth(2)\n l2.DrawLine(data2lo_A.GetXaxis().GetXmin(),1,data2lo_A.GetXaxis().GetXmax(),1)\n\n p2.RedrawAxis()\n\n c.cd()\n c.Modified()\n c.Update()\n for ext in ['png','pdf']:\n c.SaveAs('{0}.{1}'.format(outDir,ext))","def ChooseNodePath(self):\n nodePath = ChooseContainerNode()\n if nodePath:\n self.nodePathEdit.setText(nodePath)","def View_Tree_0(self):\r\n self.system.Set_Tree_View_Mode(0)","def test_ctrl_space_index_preservation(self):\n from omtk.modules import rigFK\n\n def check_targets_index_match(ctrl):\n self.assertEqual(len(ctrl.targets), len(ctrl.targets_indexes))\n attr_space = ctrl.node.space\n for target, target_index in attr_space.getEnums().iteritems():\n if target == 'Local':\n continue\n target = pymel.PyNode(target)\n self.assertIn(target, ctrl.targets) # Ensure the target is stored\n logical_index = ctrl.targets.index(target)\n self.assertEqual(target_index, ctrl.targets_indexes[logical_index])\n\n # Create a simple influence hierarhy\n inf_a = pymel.createNode('joint')\n inf_b = pymel.createNode('joint', parent=inf_a)\n inf_c = pymel.createNode('joint', parent=inf_b)\n inf_d = pymel.createNode('joint', parent=inf_c)\n\n # Create a simple rig\n r = omtk.create()\n mod_a = r.add_module(rigFK.FK([inf_a]))\n mod_b = r.add_module(rigFK.FK([inf_b]))\n mod_c = r.add_module(rigFK.FK([inf_c]))\n mod_d = r.add_module(rigFK.FK([inf_d]))\n\n # Build the last module\n mod_d.build()\n\n # Analyse the indexes\n c = mod_d.ctrls[0]\n old_targets = c.targets\n old_targets_indexes = c.targets_indexes\n check_targets_index_match(c)\n\n # Unbulid the last module, change the hierarchy and rebuilt it\n mod_d.unbuild()\n inf_d.setParent(inf_b)\n mod_d.build()\n\n # Analyse the indexes\n c = mod_d.ctrls[0]\n new_targets = c.targets\n new_targets_indexes = c.targets_indexes\n check_targets_index_match(c)\n\n self.assertListEqual(old_targets, new_targets)\n self.assertListEqual(old_targets_indexes, new_targets_indexes)","def reset(self):\r\n self._root_dir = None","def deploy():\n update_treesheets()\n restart_treesheets()","def save_render(self, savefile=\"tree.gv\"):\n from graphviz import Source\n src = Source(self.get_dot())\n path = 'graphviz/{}'.format(savefile)\n src.render(path)","def __manage_tree(self):\n for pre, fill, node in RenderTree(self.tree):\n if node.name is 'count':\n logger.info(\n \"Tree info %s%s: %s %s p/s attack: %s\",\n pre, node.name, node.value, node.pps, node.attack)\n else:\n logger.info(\"Pre - [%s], Fill - [%s], Node - [%s]\",\n pre, fill, node.name)","def onReadNodeKnobChanged():\n ...","def clear(context):\n\n log.log(level=0, msg=\"clear !\")\n log.log(level=0, msg=\"controller set\")\n log.log(level=0, msg=\"mesh set\")\n log.log(level=0, msg=\"joint set\")\n\n # node = pm.PyNode(node)\n #\n # for script in info[const.POSTSCRIPTS]:\n # mod = importlib.import_module(script)\n # mod.PrePost.run(context)","def __save_node(self):\n print(self._encoder.encode(self._current_node))\n self._count += 1","def patch(lines):\n if not get_root():\n set_root(os.getcwd())","def node():\n return render_template('nodes.html')","def reset(self):\n self.current = self.root\n env = self.env\n obs, infos = env.reset()\n obs = obs[obs.find(\"=\"):] # removes textworld legend\n\n return env, obs, infos","def outputSet_write(path, data):\n nonlocal filesSaved\n nonlocal index\n nonlocal d_tree\n nonlocal fn_analysisCallback\n nonlocal b_persistAnalysisResults\n\n # self.simpleProgress_show(index, total, '%s:%s' %\n # ('%25s' % threading.currentThread().getName(),\n # '%25s' % fn_outputWriteCallback.__name__)\n # )\n\n if len(self.str_outputLeafDir):\n (dirname, basename) = os.path.split(path)\n str_format = '\\'%s\\'' % self.str_outputLeafDir\n new_basename = str_format + ' % basename'\n str_eval = eval(new_basename)\n path = '%s/%s' % (dirname, str_eval)\n\n d_output = fn_outputWriteCallback(\n ( '%s/%s' % (self.str_outputDir, path), data), **kwargs\n )\n\n if 'status' in d_output.keys():\n if not b_persistAnalysisResults:\n d_tree[path] = d_output\n filesSaved += d_output['filesSaved']\n else:\n self.dp.qprint(\n \"The outputWriteCallback callback did not return a 'status' value!\",\n comms = 'error',\n level = 0\n )\n error.fatal(self, 'outputWriteCallback', drawBox = True)\n return d_output","def removenode(self, uid):\n\n raise NotImplementedError","def setupRender():\n prefs = getPreferences()\n\n # Check of the built-in environment maps path can be located.\n # Discontinue if it cannot be found.\n envPath = prefs.path_value\n if not envPath:\n return {'WARNING'}, \"No environment images path defined\"\n\n # Discontinue if there is no output path defined.\n renderPath = outputPath()\n if not renderPath:\n return {'WARNING'}, \"The scene needs to be saved before rendering\"\n\n if prefs.image_value == 'NONE':\n return {'WARNING'}, \"No environment image defined\"\n\n setRenderSettings(os.path.join(renderPath, IMAGE_NAME))\n createCamera()\n createWorld(envPath)\n return renderPath","def run():\n\n if lm.scene_object().type == 'env':\n read_layout(outFile=lm.scene_object().copy(ext='json').path_root)\n\n else:\n read_layout()","def trace_path(node):\r\n if node.parent == None:\r\n print(\"---------\")\r\n print(\"Move \",node.g)\r\n print(node.data[:3])\r\n print(node.data[3:6])\r\n print(node.data[6:9])\r\n return\r\n\r\n trace_path(node.parent)\r\n print(\"---------\")\r\n print(\"Move \",node.g)\r\n print(node.data[:3])\r\n print(node.data[3:6])\r\n print(node.data[6:9])\r\n return","def rf_stepMode(self, selTree, treeDict):\n for step in treeDict['steps']:\n newStep = TreeNode(nodeType='step', nodeLabel=step, nodeName=step)\n self.addTopLevelItem(newStep)\n for node in treeDict['tree']['_order']:\n newItem = TreeNode(**treeDict['tree'][node])\n if len(node.split('/')) == 1:\n newStep.addChild(newItem)\n else:\n rootPath = \"%s/%s\" % (step, '/'.join(node.split('/')[:-1]))\n parent = self._getItemFromTreePath(rootPath)\n parent.addChild(newItem)\n if getattr(newItem, 'nodeType') == 'shotNode':\n newItem._tree = selTree\n newItem._step = step\n newItem._itemPath = node\n newItem._dataPath = os.path.join(self.pm._treePath, selTree)\n for fld in node.split('/'):\n newItem._dataPath = os.path.join(newItem._dataPath, fld)\n newItem._dataPath = pFile.conformPath(newItem._dataPath)\n newItem._ltPath = pFile.conformPath(os.path.join(newItem._dataPath, 'lt', step))\n newItem._dataFile = \"%s.py\" % newItem._dataPath","def addRawDataNode():\n return render_template(\"addRawDataNode.html\")","def save_node(self, node: Node):","def setDataRoot(path):\n global dataRoot\n dataRoot = os.path.realpath(path)","def invalidateOutputs(self):\n all_outputs = self.getFullOutputs()\n for node_id in all_outputs:\n if self.model.existNode(node_id):\n self.model.getNode(node_id).silentInvalidate()","def writePathways( self ):\n\n self.logger.info( 'writePathways: START' )\n\n # Generate inserts for meabolic pathways.\n self.importerPathway.writePathways()\n\n self.logger.info( 'writePathways: DONE' )","def addAfterRender(call, args=(), kwargs={}, nodeClass='Write'):","def run(self):\n print('Running test of the markups in different views')\n\n #\n # first load the data\n #\n import SampleData\n sampleDataLogic = SampleData.SampleDataLogic()\n print(\"Getting MR Head Volume\")\n mrHeadVolume = sampleDataLogic.downloadMRHead()\n\n #\n # link the viewers\n #\n sliceLogic = slicer.app.layoutManager().sliceWidget('Red').sliceLogic()\n compositeNode = sliceLogic.GetSliceCompositeNode()\n compositeNode.SetLinkedControl(1)\n\n #\n # MR Head in the background\n #\n sliceLogic.StartSliceCompositeNodeInteraction(1)\n compositeNode.SetBackgroundVolumeID(mrHeadVolume.GetID())\n sliceLogic.EndSliceCompositeNodeInteraction()\n\n #\n # switch to conventional layout\n #\n lm = slicer.app.layoutManager()\n lm.setLayout(2)\n\n # create a fiducial list\n displayNode = slicer.vtkMRMLMarkupsDisplayNode()\n slicer.mrmlScene.AddNode(displayNode)\n fidNode = slicer.vtkMRMLMarkupsFiducialNode()\n slicer.mrmlScene.AddNode(fidNode)\n fidNode.SetAndObserveDisplayNodeID(displayNode.GetID())\n\n # make it active\n selectionNode = slicer.mrmlScene.GetNodeByID(\"vtkMRMLSelectionNodeSingleton\")\n if (selectionNode is not None):\n selectionNode.SetReferenceActivePlaceNodeID(fidNode.GetID())\n\n # add some known points to it\n eye1 = [33.4975, 79.4042, -10.2143]\n eye2 = [-31.283, 80.9652, -16.2143]\n nose = [4.61944, 114.526, -33.2143]\n index = fidNode.AddFiducialFromArray(eye1)\n fidNode.SetNthFiducialLabel(index, \"eye-1\")\n index = fidNode.AddFiducialFromArray(eye2)\n fidNode.SetNthFiducialLabel(index, \"eye-2\")\n # hide the second eye as a test of visibility flags\n fidNode.SetNthFiducialVisibility(index, 0)\n index = fidNode.AddFiducialFromArray(nose)\n fidNode.SetNthFiducialLabel(index, \"nose\")\n\n self.logicDelayDisplay(\"Placed 3 fiducials\")\n\n # self.printViewAndSliceNodes()\n\n if self.widgetVisible(fidNode, 'vtkMRMLViewNode1') == 0:\n self.logicDelayDisplay(\"Test failed: widget is not visible in view 1\")\n # self.printViewNodeIDs(displayNode)\n return False\n\n #\n # switch to 2 3D views layout\n #\n lm.setLayout(15)\n self.logicDelayDisplay(\"Switched to 2 3D views\")\n # self.printViewAndSliceNodes()\n\n if self.widgetVisible(fidNode, 'vtkMRMLViewNode1') == 0 or self.widgetVisible(fidNode, 'vtkMRMLViewNode2') == 0:\n self.logicDelayDisplay(\"Test failed: widget is not visible in view 1 and 2\")\n # self.printViewNodeIDs(displayNode)\n return False\n\n #\n # show only in view 2\n #\n displayNode.AddViewNodeID(\"vtkMRMLViewNode2\")\n self.logicDelayDisplay(\"Showing only in view 2\")\n if self.widgetVisible(fidNode, 'vtkMRMLViewNode1') == 1:\n self.logicDelayDisplay(\"Test failed: widget is not supposed to be visible in view 1\")\n # self.printViewNodeIDs(displayNode)\n return False\n if self.widgetVisible(fidNode, 'vtkMRMLViewNode2') == 0:\n self.logicDelayDisplay(\"Test failed: widget is not visible in view 2\")\n # self.printViewNodeIDs(displayNode)\n return False\n\n #\n # remove it so show in all\n #\n displayNode.RemoveAllViewNodeIDs()\n self.logicDelayDisplay(\"Showing in both views\")\n if self.widgetVisible(fidNode, 'vtkMRMLViewNode1') == 0 or self.widgetVisible(fidNode, 'vtkMRMLViewNode2') == 0:\n self.logicDelayDisplay(\"Test failed: widget is not visible in view 1 and 2\")\n self.printViewNodeIDs(displayNode)\n return False\n\n #\n # show only in view 1\n #\n displayNode.AddViewNodeID(\"vtkMRMLViewNode1\")\n self.logicDelayDisplay(\"Showing only in view 1\")\n if self.widgetVisible(fidNode, 'vtkMRMLViewNode2') == 1:\n self.logicDelayDisplay(\"Test failed: widget is not supposed to be visible in view 2\")\n # self.printViewNodeIDs(displayNode)\n return False\n if self.widgetVisible(fidNode, 'vtkMRMLViewNode1') == 0:\n self.logicDelayDisplay(\"Test failed: widget is not visible in view 1\")\n # self.printViewNodeIDs(displayNode)\n return False\n\n # switch back to conventional\n lm.setLayout(2)\n self.logicDelayDisplay(\"Switched back to conventional layout\")\n # self.printViewAndSliceNodes()\n\n # test of the visibility in slice views\n displayNode.RemoveAllViewNodeIDs()\n\n # jump to the last fiducial\n slicer.modules.markups.logic().JumpSlicesToNthPointInMarkup(fidNode.GetID(), index, 1)\n # refocus the 3D cameras as well\n slicer.modules.markups.logic().FocusCamerasOnNthPointInMarkup(fidNode.GetID(), index)\n\n # show only in red\n displayNode.AddViewNodeID('vtkMRMLSliceNodeRed')\n self.logicDelayDisplay(\"Show only in red slice\")\n if self.widgetVisibleOnSlice(fidNode,'vtkMRMLSliceNodeRed') != 1:\n self.logicDelayDisplay(\"Test failed: widget not displayed on red slice\")\n # self.printViewNodeIDs(displayNode)\n return False\n\n # remove all, add green\n # print 'before remove all, after added red'\n # self.printViewNodeIDs(displayNode)\n displayNode.RemoveAllViewNodeIDs()\n # print 'after removed all'\n # self.printViewNodeIDs(displayNode)\n displayNode.AddViewNodeID('vtkMRMLSliceNodeGreen')\n self.logicDelayDisplay('Show only in green slice')\n if self.widgetVisibleOnSlice(fidNode,'vtkMRMLSliceNodeRed') != 0 or self.widgetVisibleOnSlice(fidNode,'vtkMRMLSliceNodeGreen') != 1:\n self.logicDelayDisplay(\"Test failed: widget not displayed only on green slice\")\n print '\\tred = ',self.widgetVisibleOnSlice(fidNode,'vtkMRMLSliceNodeRed')\n print '\\tgreen =',self.widgetVisibleOnSlice(fidNode,'vtkMRMLSliceNodeGreen')\n self.printViewNodeIDs(displayNode)\n return False\n\n return True","def dynamic_caller(self):\n if not self.target_changed:\n self.path = self.dynamic_rrt(start = self.curr_pos, end = self.curr_target, path = self.curr_path, obstacle_list = self.obstacle_list)","def run(self, root):\r\n pass","def on_rbTree(self):\n self.twTree.rf_tree()\n if self.mainUi.getSelTab() == 'OverView':\n self.mainUi.wgOverView._refresh()","def setReCreateTreeSignalHandler():\n signal.signal(signal.SIGUSR2,reCreateTreeSignalHandler)","def go_home(node):\n if node.attr('t').isSettable():\n node.setAttr('t', (0, 0, 0))\n if node.attr('r').isSettable():\n node.setAttr('r', (0, 0, 0))\n if node.attr('s').isSettable():\n node.setAttr('s', (1, 1, 1))","def export( self, captionMode, copyFiles, outputDir ):\n scene = slicer.mrmlScene\n nodes = scene.GetNumberOfNodes()\n\n self.__nodes = {}\n\n # 1 for model name, 2 for parent name\n self.__captionMode = captionMode\n # TRUE if we shall copy the files to the outputDir\n self.__copyFiles = copyFiles\n self.__outputDir = outputDir\n\n self.__tree = Tree()\n self.__tree.create_node( \"Scene\", \"scene\" )\n\n for n in xrange( nodes ):\n\n node = scene.GetNthNode( n )\n\n self.parseNode( node )\n\n [header, footer] = self.configureXrenderers()\n output = header\n output += self.createXtree( \"scene\" )\n output += footer\n\n return output","def removeRtree(self):\n try:\n os.remove(str(self.dim)+'d_index.data')\n os.remove(str(self.dim)+'d_index.index')\n print('Files removed')\n except:\n print('No such files')","def write_newtree(node,text):\r\n if node.left!=None: # if the left node is not equal to none\r\n text.write(\"Question:\\n\") # write the question\r\n text.write(node.data) # write the data\r\n write_newtree(node.left,text) # recursivly call to insert question to the left node\r\n write_newtree(node.right,text) # recursivly call to insert question to the right node\r\n return node # updating node\r\n\r\n else: # if the left node is equal to none\r\n text.write(\"Guess:\\n\") # write the guess\r\n text.write(node.data) # write the data\r\n return node # updating node\r","def save_tree(self, name='tree'):\n alsoProvides(self.request, IDisableCSRFProtection)\n if not name:\n name = datetime.utcnow().isoformat()\n handle = self.target_connector.get_handle()\n handle.setContext(self.target_connector)\n fname = fs.path.join('.', name + '.json')\n with handle.open(fname, 'wb') as fp:\n fp.write(self.request.BODY)\n self.request.response.setStatus(200)","def write_syntax_tree(self, compilation_engine):\n output_file = '{}ktCST.xml'.format(compilation_engine.filename[:-5])\n with open(output_file, 'w') as f:\n print 'writing syntax tree to {}'.format(output_file)\n f.write(''.join(compilation_engine.contents))","def setup_scene_for_rgb_render(scene, outdir):\n # Use node rendering for python control\n scene.use_nodes = True\n tree = scene.node_tree\n links = tree.links\n\n # Make sure there are no existing nodes\n for node in tree.nodes:\n tree.nodes.remove(node)\n\n # Set up a renderlayer and plug it into our remapping layer\n inp = tree.nodes.new('CompositorNodeRLayers')\n\n if (bpy.app.version[1] >= 70): # Don't apply color transformation -- changed in Blender 2.70\n scene.view_settings.view_transform = 'Raw'\n scene.sequencer_colorspace_settings.name = 'Non-Color'\n\n # Save it out\n if outdir:\n out = tree.nodes.new('CompositorNodeOutputFile')\n ident = str(uu.uuid4())\n out.file_slots[0].path = ident\n out.base_path = outdir\n # out.format.color_mode = 'BW'\n # out.format.color_depth = settings.DEPTH_BITS_PER_CHANNEL\n out.format.color_mode = 'RGB'\n out.format.color_depth = settings.COLOR_BITS_PER_CHANNEL\n out.format.file_format = settings.PREFERRED_IMG_EXT.upper()\n links.new(inp.outputs[0], out.inputs[0])\n ext = utils.img_format_to_ext[settings.PREFERRED_IMG_EXT.lower()]\n temp_filename = \"{0}0001.{1}\".format(ident, ext)\n return os.path.join(outdir, temp_filename)\n else:\n out = tree.nodes.new('CompositorNodeComposite')\n links.new(inp.outputs[0], out.inputs[0])\n return None","def node_command(ctx, old, new):\n try:\n with ctx.obj[\"reader\"] as reader, ctx.obj[\"writer\"] as writer:\n writer.copy_schema(reader)\n writer.prepare_encode_cache()\n writer.rename_node(old.encode(\"utf-8\"), new.encode(\"utf-8\"))\n writer.write(reader)\n except Exception:\n click.secho(\"Failed!\", fg=\"red\", bold=True, err=True)\n raise\n else:\n click.secho(\"Done!\", fg=\"green\", err=True, bold=True)","def updateIndex(self):\n for root, dirs, files in os.walk(self.serverdir):\n for d in dirs:\n if not d.startswith('.'):\n relpath = os.path.relpath(os.path.join(root, d), self.serverdir)\n self.serverindex[relpath] = (self.getNametype(os.path.join(root,d)), os.path.getmtime(os.path.join(root, d)))\n for f in files:\n if not f.startswith('.'):\n relpath = os.path.relpath(os.path.join(root, f), self.serverdir)\n self.serverindex[relpath] = (self.getNametype(os.path.join(root,f)), os.path.getmtime(os.path.join(root, f)))","def node_mapping(self):\n ...","def reset_context():\n global root_dir\n global wells_list\n global tops_list\n\n del root_dir, wells_list, tops_list\n root_dir = WellsDir(None, 'root')\n wells_list = list()\n tops_list = list()","def paint(self):\r\n self.canvas.delete(tkinter.ALL)\r\n self.visit(self.tree.root)","def on_treeNode(self):\n if self.mainUi.getSelTab() == 'Shots':\n self.mainUi.wgShots._refresh()\n elif self.mainUi.getSelTab() == 'LineTest':\n self.mainUi.wgLineTest._refresh()\n elif self.mainUi.getSelTab() == 'OverView':\n self.mainUi.wgOverView._refresh()","def instrumented_test_render(self, context):\n signals.template_rendered.send(sender=self, template=self, context=context)\n return self.nodelist.render(context)","def index(name=None):\r\n return render_template('tree.html', name=name)","def touch(self, node):\n pass","def get_node_outputs(node_path):\n \n item = ix.get_item(node_path)\n\n obj_array = ix.api.OfItemArray(1)\n obj_array[0] = item\n item_outputs = ix.api.OfItemVector()\n\n ix.application.get_factory().get_items_outputs(obj_array, item_outputs, False)\n\n node_outputs = []\n for item_ in range(item_outputs.get_count()):\n\n for i in range(item_outputs[item_].get_attribute_count()):\n\n attr= item_outputs[item_].get_attribute(i)\n\n if attr.get_texture():\n\n if str(attr.get_texture()) == item.get_full_name():\n\n #attrs[attr] = target_node.get_full_name()\n node_outputs.append(attr)\n return node_outputs","def refresh():\n global tree\n tree = build_tree()\n tree.order_by_create()\n return index()","def OnUpdatePlantCtrl(self, _):\n self.saveTexts()\n self.updateMarkers()","def setup_ant(self):\n self.visited_nodes[1:] = []\n self.actual_node = self.start_pos"],"string":"[\n \"def updateTree(self):\\n self.reset()\\n self.resetTree() \\n self.read()\",\n \"def changeNodeLib(ned, createNodeWin):\\n pass\",\n \"def updatetree(self):\\n if self.node:\\n self.node.update()\\n self.draw()\",\n \"def fRenderTargetBackupTab():\\n node = nuke.thisNode()\\n # create tab an button\\n tab = nuke.Tab_Knob(\\\"fRenderTargetBackup_tab\\\",\\\"Backup Renders\\\")\\n button = nuke.PyScript_Knob('backup')\\n button.setCommand('import dmptools.utils.nukeCommands as nc;nc.fRenderTargetBackup()')\\n button.setName('backup renders')\\n button.setLabel('backup!')\\n button.setTooltip('backup renders to a directory in /tmp/fRenderTarget/<current time>')\\n \\n # create checkbox\\n checkBox = nuke.Boolean_Knob(\\\"userCustomPath\\\",\\\"Use custom path\\\")\\n checkBox.setValue(False)\\n # add output textfield\\n output = nuke.File_Knob('output', 'backup path')\\n output.setValue('/tmp/fRenderTarget/')\\n\\n # add knobs to the node\\n node.addKnob(tab)\\n node.addKnob(button)\\n node.addKnob(checkBox)\\n node.addKnob(output)\",\n \"def _RemountRootAsReadWrite(self):\\n self.RunCmdOnDevice(['mount', '-o', 'remount,rw', '/'])\",\n \"def run():\\n\\n def assignToon(context):\\n def instanciate_group(nodes, group_name):\\n group = nodes.new(type='ShaderNodeGroup')\\n group.node_tree = bpy.data.node_groups[group_name]\\n return group\\n\\n def assignToonShader(material):\\n '''To do Handle if the material output doesnt exist'''\\n toonShader = instanciate_group(material.node_tree.nodes, \\\"ToonShader_2\\\")\\n node2 = material.node_tree.nodes['Material Output']\\n material.node_tree.links.new(toonShader.outputs[0], node2.inputs[0])\\n\\n objects = bpy.context.selected_objects\\n for obj in objects:\\n if len(obj.material_slots) < 1:\\n\\n bpy.ops.object.material_slot_add()\\n\\n if obj.name not in bpy.data.materials:\\n\\n mat = bpy.data.materials.new(obj.name)\\n else:\\n mat = bpy.data.materials[obj.name]\\n\\n obj.data.materials[0] = mat\\n mat.use_nodes = True\\n\\n for mat in obj.data.materials:\\n if mat.name == '':\\n mat.name = obj.name\\n\\n matNodes = mat.node_tree.nodes\\n\\n assignToonShader(mat)\\n if 'Principled BSDF' in matNodes:\\n matNodes.remove(matNodes['Principled BSDF'])\\n # else:\\n # for n in matNodes:\\n # if n != material.node_tree.nodes['Material Output']:\\n # matNodes.remove(n)\\n\\n\\n shaderPath = r'D:/COMPANIES/loneCoconut/render/MILVIO_CGL/assets/lib/TOONSCEENSETUP/shd/publish/001.000/high/lib_TOONSCEENSETUP_shd.blend'\\n collection_name = 'ToonSceneSetup'\\n # dict_ = {'company': 'loneCoconut',\\n # 'context': 'render',\\n # 'project': 'MILVIO',\\n # 'scope': 'assets',\\n # 'seq': 'lib',\\n # 'shot': 'TOONSCEENSETUP',\\n # 'task': 'shd',\\n # 'user': 'publish',\\n # 'resolution': 'high'}\\n # shaderPath = lm.LumberObject(dict_)\\n # print(shaderPath.latest_version().path_root)\\n #\\n # collection_name = shaderPath.shot\\n\\n if collection_name not in bpy.data.collections:\\n\\n # link all collections starting with 'MyCollection'\\n with bpy.data.libraries.load(shaderPath, link=False) as (data_from, data_to):\\n data_to.collections = [c for c in data_from.collections if c.startswith(collection_name)]\\n\\n # link collection to scene collection\\n for coll in data_to.collections:\\n if coll is not None:\\n bpy.data.scenes['Scene'].collection.children.link(coll)\\n\\n else:\\n print(\\\"Toon Shader Exist\\\")\\n\\n\\n assignToon(bpy.context)\",\n \"def set_node_positions(self):\",\n \"def regenerate_tree(self, newpos):\\n self.path = self.tree[newpos][2]\\n self.tree = self.get_tree()\\n self.pos = self.get_curpos()\",\n \"def save_node(self):\\n # save node in path2node\\n if self.full_path in self.file.path2node:\\n print \\\"** Error, created node with path twice:\\\\n%s\\\" % self.full_path\\n traceback.print_stack()\\n sys.exit(1)\\n self.file.path2node[self.full_path] = self \\n # save node in id_lookups\\n id = self.sdef['id']\\n ns = self.sdef['ns']\\n type = self.sdef['type']\\n custom = 'custom' in self.sdef and self.sdef['custom']\\n if self.parent is None and self.sdef['df'] and not custom:\\n # structure (not custom) created at top level, save in id_lookups\\n if id not in self.file.id_lookups[ns]:\\n print \\\"** Error: Unable to find id '%s' in id_lookups when saving node\\\" % id\\n traceback.print_stack()\\n sys.exit(1)\\n if self.path not in self.file.id_lookups[ns][id]:\\n print (\\\"** Error: Unable to find path '%s' in id_lookups when\\\"\\n \\\" saving node %s\\\") % (self.path, id)\\n print \\\"self.sdef['df'] is:\\\"\\n pp.pprint (self.sdef['df'])\\n traceback.print_stack()\\n sys.exit(1)\\n self.file.id_lookups[ns][id][self.path]['created'].append(self)\\n # save node in all_nodes, either at top level (if no parent) or inside\\n # mstats structure of parent node\\n if self.parent is None:\\n if self.path in self.file.all_nodes:\\n self.file.all_nodes[self.path].append(self)\\n else:\\n self.file.all_nodes[self.path] = [self, ]\\n else:\\n if id not in self.parent.mstats:\\n if custom:\\n # custom node created, add id to mstats of parent\\n self.parent.mstats[id] = { 'df': {}, 'type':type, 'ns': ns,\\n 'created': [ self, ], 'qty':'?' }\\n else:\\n print \\\"** Error: Unable to find key '%s' in parent mstats\\\" % id\\n print \\\"self.parent.mstats is\\\"\\n pp.pprint (self.parent.mstats)\\n traceback.print_stack()\\n sys.exit(1)\\n else: \\n # append node to parent created mstats \\n self.parent.mstats[id]['created'].append(self)\",\n \"def setAsmRoot(self, mode='asset') : \\n if mode == 'asset' : \\n abcHero = str(self.ui.asset_lineEdit.text())\\n pathLineEdit = self.ui.asset_lineEdit\\n lineEdit = self.ui.assetRoot_lineEdit\\n\\n if mode == 'shot' : \\n abcHero = str(self.ui.shot_lineEdit.text())\\n pathLineEdit = self.ui.shot_lineEdit\\n lineEdit = self.ui.shotRoot_lineEdit\\n\\n if os.path.exists(abcHero) : \\n abcAssetInfo = sd_utils.readAbc(abcHero)\\n # replace root |\\n root = str(abcAssetInfo[0]).replace('|', '')\\n statusColor = self.darkRed \\n\\n if mc.objExists(root) : \\n statusColor = self.darkGreen\\n\\n self.setLineEdit(lineEdit, root, statusColor)\\n\\n else : \\n statusColor = self.darkRed\\n self.setLineEdit(lineEdit, '', statusColor)\",\n \"def addOutputsNode():\\n return render_template(\\\"addOutputsNode.html\\\")\",\n \"def reset(self):\\r\\n self.tree = KDTree()\\r\\n self.paint()\",\n \"def write_nml(self, nml_write_path):\\n\\n # If the object does not have any trees, construct an empty tree before writing to enable webKnossos import\\n if self.num_trees() == 0:\\n self.add_tree()\\n\\n nml = self._skeleton_to_nml()\\n with open(nml_write_path, \\\"wb\\\") as f:\\n wknml.write_nml(f, nml)\",\n \"def reboot(self, node):\",\n \"def saveTeachPath(self,**kwargs):\\n try:\\n side = kwargs['side']\\n except Exception,e:\\n rospy.logerr(\\\"%s\\\"%str(e))\\n self.mm.neglect()\\n return\\n self.baxter.br.stopRecording(side)\\n\\n\\n # done with learning - go back to previous\\n\\n self.bl.update_watch_parameters('after')\\n self.bl.create_action()\\n self.baxter.yes() # head nod confirm\\n self.mm.loadPreviousMenu()\",\n \"def onReset(self):\\n #productive\\n profprint()\\n fileName = pathToScene = slicer.modules.needlefinder.path.replace(\\\"NeedleFinder.py\\\",\\\"Config/default.cfg\\\")\\n self.logic.loadParameters(fileName)\",\n \"def setup_holdout_nodetree(obj):\\n thisfunc = thisfile + '->setup_holdout_nodetree()'\\n\\n scene = bpy.context.scene\\n engine = scene.render.engine\\n if engine != 'CYCLES':\\n raise NotImplementedError(engine)\\n\\n node_tree, nodes = _clear_nodetree_for_active_material(obj)\\n\\n nodes.new('ShaderNodeHoldout')\\n nodes.new('ShaderNodeOutputMaterial')\\n node_tree.links.new(nodes['Holdout'].outputs[0], nodes['Material Output'].inputs[0])\\n\\n # Scene update necessary, as matrix_world is updated lazily\\n scene.update()\\n\\n logging.info(\\\"%s: Holdout node tree set up for '%s'\\\", thisfunc, obj.name)\",\n \"def recache(self, phys):\\r\\n self.myOutputCache.initialize(phys.app)\\r\\n\\r\\n for output in self.myOutputs:\\r\\n output.initialize(phys.app)\\r\\n output.run(1)\",\n \"def define_path(self, node): \\n if node.childrens!=[]:\\n for child in node.childrens:\\n node_child = child['node']\\n node_child.times_used+=1\\n self.define_path(node_child)\\n \\n \\n #take care of not used nodes, set their gradient to 0\\n for node in self.input_node:\\n if node.times_used==0:\\n node.gradient=np.zeros((node.output_dim, self.output_node.output_dim))\",\n \"def exportToTreepad(self):\\n defaultPath = ''\\n rootFileName = os.path.splitext(self.interface.getCurrentFileName())[0]\\n if os.path.basename(rootFileName):\\n defaultPath = '{0}.{1}'.format(rootFileName, 'hjt')\\n filters = 'Treepad Files (*.hjt);;All Files (*)'\\n filePath = QtGui.QFileDialog.getSaveFileName(self.interface.\\n getActiveWindow(),\\n 'TreeLine', defaultPath,\\n filters)\\n if filePath:\\n textList = self.exportNode(self.interface.getRootNode())\\n textList.insert(0, '<hj-Treepad version 0.9>')\\n encoding = locale.getpreferredencoding()\\n try:\\n with open(filePath, 'w', encoding=encoding) as f:\\n f.writelines([(line + '\\\\n') for line in textList])\\n except IOError:\\n QtGui.QMessageBox.warning(self.interface.getActiveWindow(),\\n 'TreeLine',\\n 'Error - could not write to {}'\\n .format(filePath))\",\n \"def _Freeze(self) -> None:\\n self._SetNodes(_FROZEN_NODE_COUNT)\",\n \"def reset(self):\\n self.robot_path_ind = 0\\n self.goal_path_ind = None\\n self.global_plan = Path()\",\n \"def execute(self, operation, file_path, context, parent_action, file_version, read_only, **kwargs):\\n\\n # AARDMAN ADDITION\\n # adds short cuts to file browser\\n aaNukeUtils.aaSetupShortCuts()\\n\\n if file_path:\\n file_path = file_path.replace(\\\"/\\\", os.path.sep)\\n\\n if operation == \\\"current_path\\\":\\n # return the current script path\\n return nuke.root().name().replace(\\\"/\\\", os.path.sep)\\n\\n elif operation == \\\"open\\\":\\n # open the specified script\\n nuke.scriptOpen(file_path)\\n\\n # reset any write node render paths:\\n if self._reset_write_node_render_paths():\\n # something changed so make sure to save the script again:\\n nuke.scriptSave()\\n\\n elif operation == \\\"save\\\":\\n # save the current script:\\n nuke.scriptSave()\\n\\n elif operation == \\\"save_as\\\":\\n old_path = nuke.root()[\\\"name\\\"].value()\\n try:\\n # rename script:\\n nuke.root()[\\\"name\\\"].setValue(file_path)\\n\\n # reset all write nodes:\\n self._reset_write_node_render_paths()\\n\\n # save script:\\n nuke.scriptSaveAs(file_path, -1)\\n except Exception, e:\\n # something went wrong so reset to old path:\\n nuke.root()[\\\"name\\\"].setValue(old_path)\\n raise TankError(\\\"Failed to save scene %s\\\", e)\\n\\n elif operation == \\\"reset\\\":\\n \\\"\\\"\\\"\\n Reset the scene to an empty state\\n \\\"\\\"\\\"\\n while nuke.root().modified():\\n # changes have been made to the scene\\n res = QtGui.QMessageBox.question(None,\\n \\\"Save your script?\\\",\\n \\\"Your script has unsaved changes. Save before proceeding?\\\",\\n QtGui.QMessageBox.Yes|QtGui.QMessageBox.No|QtGui.QMessageBox.Cancel)\\n\\n if res == QtGui.QMessageBox.Cancel:\\n return False\\n elif res == QtGui.QMessageBox.No:\\n break\\n else:\\n nuke.scriptSave()\\n\\n # now clear the script:\\n nuke.scriptClear()\\n\\n return True\",\n \"def map(self, app, node):\",\n \"def update_nodes(self):\\n raise NotImplementedError('ERROR: sweeper has to implement update_nodes(self)')\",\n \"def rollout(self, node, scratch_game):\\n pass\",\n \"def write(self, entry):\\n if entry is \\\"all\\\":\\n for k, item in self.nodes.items():\\n if type(item) is list:\\n for node in item:\\n node.write()\\n else:\\n item.write()\\n else:\\n # other option is 'rooms'\\n for node in self.nodes[entry]:\\n node.write()\",\n \"def on_saveFile(self):\\n self.stored = None\\n self.rf_widgetVis()\\n if str(self.parent.objectName()) == 'grapherUi':\\n self.parent.grapher.ud_commentFromUi(self.parent)\\n elif str(self.parent.objectName()) == 'nodeEditor':\\n self.rf_widgetVis(state=False)\",\n \"def draw_tree(self) -> None:\\n import turtle\\n\\n def height(head):\\n return 1 + max(height(head.left), height(head.right)) if head else -1\\n\\n def jump_to(x, y):\\n t.penup()\\n t.goto(x, y)\\n t.pendown()\\n\\n def draw(node, x, y, dx):\\n if node:\\n t.goto(x, y)\\n jump_to(x, y - 20)\\n t.write(node.val, align=\\\"center\\\", font=(\\\"Arial\\\", 15, \\\"normal\\\"))\\n t.circle(10)\\n draw(node.left, x - dx, y - 50, dx / 2)\\n jump_to(x, y - 20)\\n draw(node.right, x + dx, y - 50, dx / 2)\\n\\n t = turtle.Turtle()\\n t.speed(0)\\n turtle.delay(0)\\n h = height(self)\\n jump_to(0, 100 + 10 * h)\\n draw(self, 0, 100 + 10 * h, 20 * h)\\n t.hideturtle()\\n turtle.mainloop()\",\n \"def removeAfterRender(call, args=(), kwargs={}, nodeClass='Write'):\",\n \"def rebalance(self):\\r\\n points = [p for p in self.tree]\\r\\n if points:\\r\\n self.tree = kd_factory.generate(points)\\r\\n self.paint()\",\n \"def setTimeDepNode(self, TPName, waveform):\\n \\tflag = 0\\n for node in self.getNodes():\\n if (TPName == node.getParam(\\\"TPName\\\")):\\n flag = 1\\n node.setParam(\\\"waveform\\\",waveform)\\n self.__TDNodeDict[TPName] = node\\n if not flag:\\n print \\\"The\\\",TPName,\\\"is not found.\\\"\\n sys.exit(1)\",\n \"def drawtree(self):\\r\\n\\r\\n Phylo.draw(self.tree)\",\n \"def rf_treeMode(self, selTree, treeDict):\\n for node in treeDict['tree']['_order']:\\n newItem = TreeNode(**treeDict['tree'][node])\\n if len(node.split('/')) == 1:\\n self.addTopLevelItem(newItem)\\n else:\\n parent = self._getItemFromTreePath('/'.join(node.split('/')[:-1]))\\n parent.addChild(newItem)\\n if getattr(newItem, 'nodeType') == 'shotNode':\\n newItem._itemPath = node\\n newItem._dataPath = os.path.join(self.pm._treePath, selTree)\\n for fld in node.split('/'):\\n newItem._dataPath = os.path.join(newItem._dataPath, fld)\\n newItem._dataPath = pFile.conformPath(newItem._dataPath)\\n newItem._dataFile = \\\"%s.py\\\" % newItem._dataPath\\n for step in treeDict['steps']:\\n newStep = TreeNode(nodeType='step', nodeLabel=step, nodeName=step)\\n newStep._tree = selTree\\n newStep._step = step\\n newStep._dataPath = newItem._dataPath\\n newStep._ltPath = pFile.conformPath(os.path.join(newStep._dataPath, 'lt', step))\\n newStep._dataFile = newItem._dataFile\\n newItem.addChild(newStep)\",\n \"def generate(self):\\n self.graph_repl = self.master.graph_repl\",\n \"def index_siteroot(context):\\n portal = getSite()\\n portal.reindexObject()\",\n \"def main():\\n rospy.init_node(\\\"tree\\\")\\n splintered_reality = SplinteredReality()\\n rospy.on_shutdown(splintered_reality.shutdown)\\n if not splintered_reality.setup():\\n console.logerror(\\\"failed to setup the tree, aborting.\\\")\\n sys.exit(1)\\n splintered_reality.tick_tock()\",\n \"def createStabilizedView():\\n global cc\\n y = b.createNode('StartStabilized')\\n dd = b.createNode('Dot', '', False)\\n de = b.createNode('Dot', '', False)\\n w = b.createNode('EndStabilized')\\n if cc:\\n y = gV(y)\\n w = gX(w)\\n y['tile_color'].setValue(int('%02x%02x%02x%02x' % (232.05, 145.095, 0, 255), 16))\\n w['tile_color'].setValue(int('%02x%02x%02x%02x' % (232.05, 145.095, 0, 255), 16))\\n w.connectInput(2, y)\\n stabilizedPrecompNode___connectPrecompNodes(y, w)\\n cd = 250\\n df = 250\\n ez = 50\\n eA = 7\\n cP(dd)\\n de.setSelected(True)\\n eB = b.nodes.BackdropNode(xpos=y.xpos() + cd / 2, bdwidth=cd * 1.5, ypos=y.ypos() - ez, bdheight=df + 2 * ez,\\n tile_color=int('%02x%02x%02x%02x' % (232.05, 145.095, 0, 255), 16),\\n note_font_color=4294967040, note_font_size=36, name='MochaImport+')\\n eB['label'].setValue('Stabilized View+')\\n dd.setXpos(y.xpos() + cd)\\n dd.setYpos(y.ypos() + eA)\\n de.setXpos(y.xpos() + cd)\\n de.setYpos(y.ypos() + df + eA)\\n w.setXpos(y.xpos())\\n w.setYpos(y.ypos() + df)\\n eB['help'].setValue(\\n \\\"\\\\n<h2>Basic Usage of Stabilized View Rig</h2>\\\\n<ol>\\\\n<li>From the MochaImport+ menu, create the stabilized view rig consisting of the StartStabilized node, the EndStabilized node, and the Stabilized View Backdrop.</li>\\\\n<li>Load your mocha tracking data in the StartStabilized node.\\\\n</li><li>Do arbitrary manipulations in the Stabilized View by inserting new nodes inside the backdrop. All changes you do there in a stabilized setting will also be visible in your original perspective after the EndStabilized node.</li>\\\\n</ol>\\\\n\\\\n<h2>Lens Distortion</h2>\\\\n<p>If you've used the mocha Lens module to analyze the lens distortion of your clip, you need to do the following things to get an undistorted stabilized view and reapply the lens distortion to the final result:\\\\n</p><ul>\\\\n<li>make sure the mocha corner pin data you load into the StartStabilized node is exported from mocha Pro with the option 'Remove lens distortion'</li>\\\\n<li>as UndistMap input of the StartStabilized node use a Distortion Map Clip (ST Map) exported with the mocha Pro lens module with option 'undistort'.</li>\\\\n<li>as DistMap input of the EndStabilized node use a Distortion Map Clip (ST Map) exported with the mocha Pro lens module with option 'distort'.</li>\\\\n</ul>\\\".replace(\\n '\\\\n', '').replace('\\\\r', ''))\",\n \"def create_nodes(self):\",\n \"def get_batch_custom_ui_actions():\\n\\n def scope_node(selection):\\n \\\"\\\"\\\"Scope the custom action to the nodes residing in a Batch Schematic.\\\"\\\"\\\"\\n import flame\\n for item in selection:\\n if isinstance(item, flame.PyNode):\\n \\tif item.type == \\\"Clip\\\":\\n \\treturn True\\n return False\\n\\n def reconstructCycles(selection):\\n\\t\\timport string\\n\\t\\timport flame\\n\\n\\t\\t# get selected clip in batch\\n\\t\\tclipNode = flame.batch.current_node.get_value()\\n\\n\\t\\t# get selected clip name\\n\\t\\tclipName = clipNode.name\\n\\n\\t\\tpreviousNode = clipNode\\n\\n\\t\\t# node position offsets at creation time\\n\\t\\taddXoffset = 500\\n\\t\\taddYoffset = 700\\n\\n\\t\\tmultXoffset = addXoffset + 100\\n\\t\\tmultYoffset = addYoffset - 200\\n\\n\\t\\t# create empty PyNode list\\n\\t\\tFULLcompNodes = []\\n\\n\\n\\t\\t# cycle through all output sockets of selected node\\n\\t\\tfor outSocket in clipNode.output_sockets:\\n\\n\\n\\t\\t\\t##########################################\\n\\t\\t\\t# REBUILD DIFFUSE PASS #\\n\\t\\t\\t##########################################\\n\\n\\t\\t\\t# check if 'Diff' is in render pass name\\n\\t\\t\\tif 'Diff' in outSocket:\\n\\n\\t\\t\\t\\t# find 'Color' render pass\\n\\t\\t\\t\\tif 'Col' in outSocket:\\n\\n\\t\\t\\t\\t\\tdiffuseColSocket = outSocket\\n\\n\\t\\t\\t\\t\\t# create a 'Comp' node for FULL pass reconstruction\\n\\t\\t\\t\\t\\tFULLdiffuseCompNode = flame.batch.create_node(\\\"Comp\\\")\\n\\n\\t\\t\\t\\t\\t# set RAW 'Comp' node to 'Add'\\n\\t\\t\\t\\t\\tFULLdiffuseCompNode.flame_blend_mode = \\\"Multiply\\\"\\n\\n\\t\\t\\t\\t\\t# connect 'Front' to 'Col' render pass\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(clipNode, diffuseColSocket, FULLdiffuseCompNode, \\\"Front\\\")\\n\\n\\t\\t\\t\\t\\t# set node position\\n\\t\\t\\t\\t\\tFULLdiffuseCompNode.pos_x = previousNode.pos_x + multXoffset\\n\\t\\t\\t\\t\\tFULLdiffuseCompNode.pos_y = previousNode.pos_y - multYoffset\\n\\n\\t\\t\\t\\t# find 'Direct' render pass\\n\\t\\t\\t\\tif 'Dir' in outSocket:\\n\\n\\t\\t\\t\\t\\tdiffuseDirectSocket = outSocket\\n\\n\\t\\t\\t\\t\\t# create a 'Comp' node for RAW pass reconstruction\\n\\t\\t\\t\\t\\tRAWdiffuseCompNode = flame.batch.create_node(\\\"Comp\\\")\\n\\n\\t\\t\\t\\t\\t# set RAW 'Comp' node to 'Add'\\n\\t\\t\\t\\t\\tRAWdiffuseCompNode.flame_blend_mode = \\\"Add\\\"\\n\\n\\t\\t\\t\\t\\t# connect 'Front' to 'Direct' render pass\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(clipNode, diffuseDirectSocket, RAWdiffuseCompNode, \\\"Front\\\")\\n\\n\\t\\t\\t\\t\\t# set node position\\n\\t\\t\\t\\t\\tRAWdiffuseCompNode.pos_x = previousNode.pos_x + addXoffset\\n\\t\\t\\t\\t\\tRAWdiffuseCompNode.pos_y = previousNode.pos_y - addYoffset\\n\\n\\n\\t\\t\\t\\t# find 'Indirect' render pass\\n\\t\\t\\t\\tif 'Ind' in outSocket:\\n\\n\\t\\t\\t\\t\\tdiffuseIndirectSocket = outSocket\\n\\n\\t\\t\\t\\t\\t# connect 'Indirect' render pass to 'Back'\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(clipNode, diffuseIndirectSocket, RAWdiffuseCompNode, \\\"Back\\\")\\n\\n\\t\\t\\t\\t\\t# connect RAW node to FULL nodediffuseIndirectSocket\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(RAWdiffuseCompNode, 'Result', FULLdiffuseCompNode, \\\"Back\\\")\\n\\n\\t\\t\\t\\t\\tFULLcompNodes.append(FULLdiffuseCompNode)\\n\\n\\t\\t\\t\\t\\tpreviousNode = FULLdiffuseCompNode\\n\\n\\t\\t\\t\\t\\t# CREATE COMPASS FOR DIFFUSE\\n\\t\\t\\t\\t\\tdiffuseCompass = flame.batch.create_node(\\\"Compass\\\")\\n\\n\\t\\t\\t\\t\\tdiffuseCompass.name = 'DIFFUSE_' + clipName\\n\\n\\t\\t\\t\\t\\tdiffuseCompass.pos_x = RAWdiffuseCompNode.pos_x -200\\n\\t\\t\\t\\t\\tdiffuseCompass.pos_y = RAWdiffuseCompNode.pos_y -150\\n\\n\\t\\t\\t\\t\\tdiffuseCompass.width = 550\\n\\t\\t\\t\\t\\tdiffuseCompass.height = 550\\n\\n\\t\\t\\t\\t\\tdiffuseCompass.colour = (0.13,0.2,0.13)\\n\\n\\n\\t\\t\\t########################################\\n\\t\\t\\t# REBUILD GLOSS PASS #\\n\\t\\t\\t########################################\\n\\n\\t\\t\\t# check if 'Gloss' is in render pass name\\n\\t\\t\\tif 'Gloss' in outSocket:\\n\\n\\t\\t\\t\\t# find 'Color' render pass\\n\\t\\t\\t\\tif 'Col' in outSocket:\\n\\n\\t\\t\\t\\t\\tglossColSocket = outSocket\\n\\n\\t\\t\\t\\t\\t# create a 'Comp' node for FULL pass reconstruction\\n\\t\\t\\t\\t\\tFULLglossCompNode = flame.batch.create_node(\\\"Comp\\\")\\n\\n\\t\\t\\t\\t\\t# set RAW 'Comp' node to 'Add'\\n\\t\\t\\t\\t\\tFULLglossCompNode.flame_blend_mode = \\\"Multiply\\\"\\n\\n\\t\\t\\t\\t\\t# connect 'Front' to 'Col' render pass\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(clipNode, glossColSocket, FULLglossCompNode, \\\"Front\\\")\\n\\n\\t\\t\\t\\t\\t# set node position\\n\\t\\t\\t\\t\\tFULLglossCompNode.pos_x = previousNode.pos_x + multXoffset\\n\\t\\t\\t\\t\\tFULLglossCompNode.pos_y = previousNode.pos_y - multYoffset\\n\\n\\t\\t\\t\\t# find 'Direct' render pass\\n\\t\\t\\t\\tif 'Dir' in outSocket:\\n\\n\\t\\t\\t\\t\\tglossDirectSocket = outSocket\\n\\n\\t\\t\\t\\t\\t# create a 'Comp' node for RAW pass reconstruction\\n\\t\\t\\t\\t\\tRAWglossCompNode = flame.batch.create_node(\\\"Comp\\\")\\n\\n\\t\\t\\t\\t\\t# set RAW 'Comp' node to 'Add'\\n\\t\\t\\t\\t\\tRAWglossCompNode.flame_blend_mode = \\\"Add\\\"\\n\\n\\t\\t\\t\\t\\t# connect 'Front' to 'Direct' render pass\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(clipNode, glossDirectSocket, RAWglossCompNode, \\\"Front\\\")\\n\\n\\t\\t\\t\\t\\t# set node position\\n\\t\\t\\t\\t\\tRAWglossCompNode.pos_x = previousNode.pos_x + addXoffset\\n\\t\\t\\t\\t\\tRAWglossCompNode.pos_y = previousNode.pos_y - addYoffset\\n\\n\\n\\t\\t\\t\\t# find 'Indirect' render pass\\n\\t\\t\\t\\tif 'Ind' in outSocket:\\n\\n\\t\\t\\t\\t\\tglossIndirectSocket = outSocket\\n\\n\\t\\t\\t\\t\\t# connect 'Indirect' render pass to 'Back'\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(clipNode, glossIndirectSocket, RAWglossCompNode, \\\"Back\\\")\\n\\n\\t\\t\\t\\t\\t# connect RAW node to FULL node\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(RAWglossCompNode, 'Result', FULLglossCompNode, \\\"Back\\\")\\n\\n\\t\\t\\t\\t\\tFULLcompNodes.append(FULLglossCompNode)\\n\\n\\t\\t\\t\\t\\tpreviousNode = FULLglossCompNode\\n\\n\\t\\t\\t\\t\\t# CREATE COMPASS FOR DIFFUSE\\n\\t\\t\\t\\t\\tglossCompass = flame.batch.create_node(\\\"Compass\\\")\\n\\n\\t\\t\\t\\t\\tglossCompass.pos_x = RAWglossCompNode.pos_x -200\\n\\t\\t\\t\\t\\tglossCompass.pos_y = RAWglossCompNode.pos_y -150\\n\\n\\t\\t\\t\\t\\tglossCompass.width = 550\\n\\t\\t\\t\\t\\tglossCompass.height = 550\\n\\n\\t\\t\\t\\t\\tglossCompass.name = 'GLOSS_' + clipName\\n\\n\\t\\t\\t\\t\\tglossCompass.colour = (0.25,0.0,0.25)\\n\\n\\n\\n\\n\\t\\t\\t#############################################\\n\\t\\t\\t# REBUILD SUBSURFACE PASS #\\n\\t\\t\\t#############################################\\n\\n\\t\\t\\t# check if 'Subsurface' is in render pass name\\n\\t\\t\\tif 'Subsurface' in outSocket:\\n\\n\\t\\t\\t\\t# find 'Color' render pass\\n\\t\\t\\t\\tif 'Col' in outSocket:\\n\\n\\t\\t\\t\\t\\tsubsurfaceColSocket = outSocket\\n\\n\\t\\t\\t\\t\\t# create a 'Comp' node for FULL pass reconstruction\\n\\t\\t\\t\\t\\tFULLSubsurfaceCompNode = flame.batch.create_node(\\\"Comp\\\")\\n\\n\\t\\t\\t\\t\\t# set RAW 'Comp' node to 'Add'\\n\\t\\t\\t\\t\\tFULLSubsurfaceCompNode.flame_blend_mode = \\\"Multiply\\\"\\n\\n\\t\\t\\t\\t\\t# connect 'Front' to 'Col' render pass\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(clipNode, subsurfaceColSocket, FULLSubsurfaceCompNode, \\\"Front\\\")\\n\\n\\t\\t\\t\\t\\t# set node position\\n\\t\\t\\t\\t\\tFULLSubsurfaceCompNode.pos_x = previousNode.pos_x + multXoffset\\n\\t\\t\\t\\t\\tFULLSubsurfaceCompNode.pos_y = previousNode.pos_y - multYoffset\\n\\n\\t\\t\\t\\t# find 'Direct' render pass\\n\\t\\t\\t\\tif 'Dir' in outSocket:\\n\\n\\t\\t\\t\\t\\tsubsurfaceDirectSocket = outSocket\\n\\n\\t\\t\\t\\t\\t# create a 'Comp' node for RAW pass reconstruction\\n\\t\\t\\t\\t\\tRAWsubsurfaceCompNode = flame.batch.create_node(\\\"Comp\\\")\\n\\n\\t\\t\\t\\t\\t# set RAW 'Comp' node to 'Add'\\n\\t\\t\\t\\t\\tRAWsubsurfaceCompNode.flame_blend_mode = \\\"Add\\\"\\n\\n\\t\\t\\t\\t\\t# connect 'Front' to 'Direct' render pass\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(clipNode, subsurfaceDirectSocket, RAWsubsurfaceCompNode, \\\"Front\\\")\\n\\n\\t\\t\\t\\t\\t# set node position\\n\\t\\t\\t\\t\\tRAWsubsurfaceCompNode.pos_x = previousNode.pos_x + addXoffset\\n\\t\\t\\t\\t\\tRAWsubsurfaceCompNode.pos_y = previousNode.pos_y - addYoffset\\n\\n\\n\\t\\t\\t\\t# find 'Indirect' render pass\\n\\t\\t\\t\\tif 'Ind' in outSocket:\\n\\n\\t\\t\\t\\t\\tsubsurfaceIndirectSocket = outSocket\\n\\n\\t\\t\\t\\t\\t# connect 'Indirect' render pass to 'Back'\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(clipNode, subsurfaceIndirectSocket, RAWsubsurfaceCompNode, \\\"Back\\\")\\n\\n\\n\\t\\t\\t\\t\\t# connect RAW node to FULL nodediffuseIndirectSocket\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(RAWsubsurfaceCompNode, 'Result', FULLSubsurfaceCompNode, \\\"Back\\\")\\n\\n\\n\\t\\t\\t\\t\\tFULLcompNodes.append(FULLSubsurfaceCompNode)\\n\\n\\n\\t\\t\\t\\t\\tpreviousNode = FULLSubsurfaceCompNode\\n\\n\\t\\t\\t\\t\\t# CREATE COMPASS FOR SUBSURFACE\\n\\t\\t\\t\\t\\tsubsurfaceCompass = flame.batch.create_node(\\\"Compass\\\")\\n\\n\\t\\t\\t\\t\\tsubsurfaceCompass.name = 'SSS_' + clipName\\n\\n\\t\\t\\t\\t\\tsubsurfaceCompass.pos_x = RAWsubsurfaceCompNode.pos_x -200\\n\\t\\t\\t\\t\\tsubsurfaceCompass.pos_y = RAWsubsurfaceCompNode.pos_y -150\\n\\n\\t\\t\\t\\t\\tsubsurfaceCompass.width = 550\\n\\t\\t\\t\\t\\tsubsurfaceCompass.height = 550\\n\\n\\t\\t\\t\\t\\tsubsurfaceCompass.colour = (0.13,0.24,0.24)\\n\\n\\n\\n\\t\\t\\t###############################################\\n\\t\\t\\t# REBUILD TRANSMISSION PASS #\\n\\t\\t\\t###############################################\\n\\n\\t\\t\\t# check if 'Transmission' is in render pass name\\n\\t\\t\\tif 'Trans' in outSocket:\\n\\n\\t\\t\\t\\t# find 'Color' render pass\\n\\t\\t\\t\\tif 'Col' in outSocket:\\n\\n\\t\\t\\t\\t\\ttransmissionColSocket = outSocket\\n\\n\\t\\t\\t\\t\\t# create a 'Comp' node for FULL pass reconstruction\\n\\t\\t\\t\\t\\tFULLtransmissionCompNode = flame.batch.create_node(\\\"Comp\\\")\\n\\n\\t\\t\\t\\t\\t# set RAW 'Comp' node to 'Add'\\n\\t\\t\\t\\t\\tFULLtransmissionCompNode.flame_blend_mode = \\\"Multiply\\\"\\n\\n\\t\\t\\t\\t\\t# connect 'Front' to 'Col' render pass\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(clipNode, transmissionColSocket, FULLtransmissionCompNode, \\\"Front\\\")\\n\\n\\t\\t\\t\\t\\t# set node position\\n\\t\\t\\t\\t\\tFULLtransmissionCompNode.pos_x = previousNode.pos_x + multXoffset\\n\\t\\t\\t\\t\\tFULLtransmissionCompNode.pos_y = previousNode.pos_y - multYoffset\\n\\n\\t\\t\\t\\t# find 'Direct' render pass\\n\\t\\t\\t\\tif 'Dir' in outSocket:\\n\\n\\t\\t\\t\\t\\ttransmissionDirectSocket = outSocket\\n\\n\\t\\t\\t\\t\\t# create a 'Comp' node for RAW pass reconstruction\\n\\t\\t\\t\\t\\tRAWtransmissionCompNode = flame.batch.create_node(\\\"Comp\\\")\\n\\n\\t\\t\\t\\t\\t# set RAW 'Comp' node to 'Add'\\n\\t\\t\\t\\t\\tRAWtransmissionCompNode.flame_blend_mode = \\\"Add\\\"\\n\\n\\t\\t\\t\\t\\t# connect 'Front' to 'Direct' render pass\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(clipNode, transmissionDirectSocket, RAWtransmissionCompNode, \\\"Front\\\")\\n\\n\\t\\t\\t\\t\\t# set node position\\n\\t\\t\\t\\t\\tRAWtransmissionCompNode.pos_x = previousNode.pos_x + addXoffset\\n\\t\\t\\t\\t\\tRAWtransmissionCompNode.pos_y = previousNode.pos_y - addYoffset\\n\\n\\n\\t\\t\\t\\t# find 'Indirect' render pass\\n\\t\\t\\t\\tif 'Ind' in outSocket:\\n\\n\\t\\t\\t\\t\\ttransmissionIndirectSocket = outSocket\\n\\n\\t\\t\\t\\t\\t# connect 'Indirect' render pass to 'Back'\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(clipNode, transmissionIndirectSocket, RAWtransmissionCompNode, \\\"Back\\\")\\n\\n\\n\\t\\t\\t\\t\\t# connect RAW node to FULL nodediffuseIndirectSocket\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(RAWtransmissionCompNode, 'Result', FULLtransmissionCompNode, \\\"Back\\\")\\n\\n\\n\\t\\t\\t\\t\\tFULLcompNodes.append(FULLtransmissionCompNode)\\n\\n\\n\\t\\t\\t\\t\\tpreviousNode = FULLtransmissionCompNode\\n\\n\\t\\t\\t\\t\\t# CREATE COMPASS FOR TRANSMISSION\\n\\t\\t\\t\\t\\ttransmissionCompass = flame.batch.create_node(\\\"Compass\\\")\\n\\n\\t\\t\\t\\t\\ttransmissionCompass.name = 'TRANSMISSION_' + clipName\\n\\n\\t\\t\\t\\t\\ttransmissionCompass.pos_x = RAWtransmissionCompNode.pos_x -200\\n\\t\\t\\t\\t\\ttransmissionCompass.pos_y = RAWtransmissionCompNode.pos_y -150\\n\\n\\t\\t\\t\\t\\ttransmissionCompass.width = 550\\n\\t\\t\\t\\t\\ttransmissionCompass.height = 550\\n\\n\\t\\t\\t\\t\\ttransmissionCompass.colour = (0.45,0.16,0.16)\\n\\n\\n\\n\\n\\t\\t# comp all reconstructed passes\\n\\t\\tif len(FULLcompNodes) >= 2 :\\n\\n\\t\\t\\tfirstNode = True\\n\\n\\t\\t\\tcounter = 0\\n\\n\\t\\t\\twhile counter < (len(FULLcompNodes)-1):\\n\\n\\t\\t\\t\\tprint counter\\n\\n\\t\\t\\t\\taddNode = flame.batch.create_node(\\\"Comp\\\")\\n\\t\\t\\t\\taddNode.flame_blend_mode = \\\"Add\\\"\\n\\n\\t\\t\\t\\t\\n\\n\\t\\t\\t\\tif firstNode == True:\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(FULLcompNodes[counter], \\\"Result\\\", addNode, \\\"Front\\\")\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(FULLcompNodes[counter+1], \\\"Result\\\", addNode, \\\"Back\\\")\\n\\n\\t\\t\\t\\t\\taddNode.pos_x = FULLcompNodes[counter].pos_x + multXoffset + 400\\n\\t\\t\\t\\t\\taddNode.pos_y = FULLcompNodes[counter].pos_y\\n\\n\\t\\t\\t\\t\\tpreviousNode = addNode\\n\\n\\t\\t\\t\\telse :\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(previousNode, \\\"Result\\\", addNode, \\\"Front\\\")\\n\\t\\t\\t\\t\\tflame.batch.connect_nodes(FULLcompNodes[counter+1], \\\"Result\\\", addNode, \\\"Back\\\")\\n\\n\\t\\t\\t\\t\\taddNode.pos_x = FULLcompNodes[counter].pos_x + multXoffset + 400\\n\\t\\t\\t\\t\\taddNode.pos_y = previousNode.pos_y\\n\\n\\t\\t\\t\\t\\tpreviousNode = addNode\\n\\t\\t\\t\\t\\t\\t\\n\\n\\t\\t\\t\\tcounter += 1\\n\\t\\t\\t\\tfirstNode = False\\n\\n\\n\\t\\t# cycle through all output sockets of selected node\\n\\t\\tfor outSocket in clipNode.output_sockets:\\n\\n\\t\\t\\t###############################################\\n\\t\\t\\t# ADD EMISSION PASS #\\n\\t\\t\\t###############################################\\n\\n\\t\\t\\t# check if 'Emission' is in render pass name\\n\\t\\t\\tif 'Emit' in outSocket:\\n\\n\\t\\t\\t\\temissionColSocket = outSocket\\n\\n\\t\\t\\t\\t# create a 'Comp' node for Emission pass\\n\\t\\t\\t\\temissionCompNode = flame.batch.create_node(\\\"Comp\\\")\\n\\n\\t\\t\\t\\t# set Emission 'Comp' node to 'Add'\\n\\t\\t\\t\\temissionCompNode.flame_blend_mode = \\\"Add\\\"\\n\\n\\t\\t\\t\\t# connect 'Front' to 'Col' render pass\\n\\t\\t\\t\\tflame.batch.connect_nodes(clipNode, emissionColSocket, emissionCompNode, \\\"Front\\\")\\n\\t\\t\\t\\tflame.batch.connect_nodes(previousNode, 'Result', emissionCompNode, \\\"Back\\\")\\n\\n\\t\\t\\t\\t# set node position\\n\\t\\t\\t\\temissionCompNode.pos_x = previousNode.pos_x + multXoffset\\n\\t\\t\\t\\temissionCompNode.pos_y = previousNode.pos_y + multYoffset\\n\\n\\t\\t\\t\\tpreviousNode = emissionCompNode\\n\\n\\n\\t\\t\\t\\t# CREATE COMPASS FOR EMISSION\\n\\t\\t\\t\\temissionCompass = flame.batch.create_node(\\\"Compass\\\")\\n\\n\\t\\t\\t\\temissionCompass.name = 'EMISSION_' + clipName\\n\\n\\t\\t\\t\\temissionCompass.width = 400\\n\\t\\t\\t\\temissionCompass.height = 400\\n\\n\\t\\t\\t\\temissionCompass.pos_x = emissionCompNode.pos_x - (emissionCompass.width/2)\\n\\t\\t\\t\\temissionCompass.pos_y = emissionCompNode.pos_y - (emissionCompass.height/2)\\n\\n\\t\\t\\t\\temissionCompass.colour = (0.15,0.08,0.43)\\n\\n\\t\\t\\t###############################################\\n\\t\\t\\t# ADD ENVIRONMENT PASS #\\n\\t\\t\\t###############################################\\n\\n\\t\\t\\t# check if 'Transmission' is in render pass name\\n\\t\\t\\tif 'Env' in outSocket:\\n\\n\\t\\t\\t\\tenvironmentSocket = outSocket\\n\\n\\t\\t\\t\\t# create a 'Comp' node for Emission pass\\n\\t\\t\\t\\tenvironmentCompNode = flame.batch.create_node(\\\"Comp\\\")\\n\\n\\t\\t\\t\\t# set Emission 'Comp' node to 'Add'\\n\\t\\t\\t\\tenvironmentCompNode.flame_blend_mode = \\\"Add\\\"\\n\\n\\t\\t\\t\\t# connect 'Front' to 'Col' render pass\\n\\t\\t\\t\\tflame.batch.connect_nodes(clipNode, environmentSocket, environmentCompNode, \\\"Front\\\")\\n\\t\\t\\t\\tflame.batch.connect_nodes(previousNode, 'Result', environmentCompNode, \\\"Back\\\")\\n\\n\\n\\t\\t\\t\\t# set node position\\n\\t\\t\\t\\tenvironmentCompNode.pos_x = previousNode.pos_x + multXoffset\\n\\t\\t\\t\\tenvironmentCompNode.pos_y = previousNode.pos_y + multYoffset\\n\\n\\t\\t\\t\\tpreviousNode = environmentCompNode\\n\\n\\n\\t\\t\\t\\t# CREATE COMPASS FOR ENVIRONMENT\\n\\t\\t\\t\\tenvironmentCompass = flame.batch.create_node(\\\"Compass\\\")\\n\\n\\t\\t\\t\\tenvironmentCompass.name = 'ENVIRONMENT_' + clipName\\n\\n\\t\\t\\t\\tenvironmentCompass.width = 400\\n\\t\\t\\t\\tenvironmentCompass.height = 400\\n\\n\\t\\t\\t\\tenvironmentCompass.pos_x = environmentCompNode.pos_x - (environmentCompass.width/2)\\n\\t\\t\\t\\tenvironmentCompass.pos_y = environmentCompNode.pos_y - (environmentCompass.height/2)\\n\\n\\t\\t\\t\\tenvironmentCompass.colour = (0.15,0.25,0.43)\\n\\n\\n return [\\n {\\n \\\"name\\\": \\\"PYTHON: NODES\\\",\\n \\\"actions\\\": [\\n {\\n \\\"name\\\": \\\"Reconstruct Blender Beauty\\\",\\n \\\"isVisible\\\": scope_node,\\n \\\"execute\\\": reconstructCycles\\n }\\n ]\\n }\\n ]\",\n \"def _add_image_switching_node( self,\\n node_tree,\\n image_path,\\n last_element,\\n image_ID_node=None,\\n node_index=0,\\n uv_map=None, \\n node_offset=[0,0]):\\n\\n # define local variables #######################################################################################\\n _step_node_width = 200 # x seperation of nodes\\n _step_node_height = 200 # y seperation of nodes\\n ################################################################################ end of define local variables #\\n\\n # create image ID handle #######################################################################################\\n if image_ID_node is None:\\n image_ID_node = node_tree.node_tree.nodes.new(\\\"ShaderNodeValue\\\")\\n image_ID_node.location = ((node_offset[0]-400,node_offset[1]-100))\\n image_ID_node.name = \\\"image_step_ID\\\"\\n image_ID_node.label = \\\"image_step_ID\\\"\\n image_ID_node.outputs[0].default_value = 1\\n ############################################################################### end of create image ID handle #\\n\\n # create image nodes ###########################################################################################\\n _x_offset = (node_index+1)*_step_node_width + node_offset[0]\\n _y_offset = (node_index+1)*_step_node_height + node_offset[1]\\n _current_image_node = node_tree.node_tree.nodes.new('ShaderNodeTexEnvironment')\\n _img = bpy.data.images.load(image_path)\\n _current_image_node.image = _img\\n _current_image_node.location = (((node_index+1)*_step_node_width*2 + node_offset[0] - 800,\\n (node_index+1)*_step_node_height + node_offset[1]))\\n\\n # attach uv map if possible\\n if uv_map is not None:\\n node_tree.node_tree.links.new(_current_image_node.inputs[0], uv_map)\\n\\n # create new mix node ######################################################################################\\n _current_mix_shader_node = node_tree.node_tree.nodes.new(\\\"ShaderNodeMixRGB\\\")\\n _current_mix_shader_node.location = (((node_index+1)*_step_node_width*2 + node_offset[0],\\n (node_index+1)*_step_node_height + node_offset[1]))\\n ############################################################################### end of create new mix node #\\n\\n # create compare node ######################################################################################\\n _current_compare_node = node_tree.node_tree.nodes.new(\\\"ShaderNodeMath\\\")\\n _current_compare_node.location = (((node_index+1)*_step_node_width*2 + node_offset[0],\\n node_offset[1]-_step_node_height))\\n _current_compare_node.operation = 'COMPARE'\\n _current_compare_node.inputs[0].default_value = node_index\\n _current_compare_node.inputs[2].default_value = 0 # delta value should be zero for equal comparison\\n ############################################################################### end of create compare node #\\n\\n\\n # link nodes togther #######################################################################################\\n node_tree.node_tree.links.new(_current_mix_shader_node.inputs[0], _current_compare_node.outputs[0])\\n if last_element is not None:\\n node_tree.node_tree.links.new(_current_mix_shader_node.inputs[1], last_element.outputs[0])\\n node_tree.node_tree.links.new(_current_mix_shader_node.inputs[2], _current_image_node.outputs[0])\\n \\n node_tree.node_tree.links.new(_current_compare_node.inputs[1], image_ID_node.outputs[0])\\n ################################################################################ end of link nodes togther #\\n #################################################################################### end of create image nodes #\\n\\n # return last mix shader node\\n return _current_mix_shader_node, image_ID_node\",\n \"def on_ResetNode_clicked(self):\\n # TODO: not implemented yet\\n raise NotImplementedError\",\n \"def switch():\\n temp = p['paths']['war3']\\n p['paths']['war3'] = p['paths']['war3r']\\n p['paths']['war3r'] = temp\",\n \"def reset_tree(self):\\n self.root = None\\n self.action = None\\n self.dist_probability = None\",\n \"def rewrite(self):\\n return None\",\n \"def _do_nuke_post_publish(self, work_template, progress_cb): \\n import nuke\\n \\n progress_cb(0, \\\"Versioning up the script\\\")\\n \\n # get the current script path:\\n original_path = nuke.root().name()\\n script_path = os.path.abspath(original_path.replace(\\\"/\\\", os.path.sep))\\n \\n # increment version and construct new name:\\n progress_cb(25, \\\"Finding next version number\\\")\\n fields = work_template.get_fields(script_path)\\n next_version = self._get_next_work_file_version(work_template, fields)\\n fields[\\\"version\\\"] = next_version \\n new_path = work_template.apply_fields(fields)\\n \\n # log info\\n self.parent.log_debug(\\\"Version up work file %s --> %s...\\\" % (script_path, new_path))\\n\\n # rename script:\\n nuke.root()[\\\"name\\\"].setValue(new_path)\\n \\n # update write nodes:\\n write_node_app = tank.platform.current_engine().apps.get(\\\"tk-nuke-writenode\\\")\\n if write_node_app:\\n progress_cb(50, \\\"Resetting render paths for write nodes\\\")\\n # reset render paths for all write nodes:\\n for wn in write_node_app.get_write_nodes():\\n write_node_app.reset_node_render_path(wn)\\n \\n # save the script:\\n progress_cb(75, \\\"Saving the scene file\\\")\\n nuke.scriptSaveAs(new_path.replace(os.path.sep, \\\"/\\\"))\\n \\n progress_cb(100)\",\n \"def update_path():\\n #TODO update path information\\n pass\",\n \"def playout(self, state):\\n node = self.root\\n isTerminal = False\\n depth = 0\\n\\n while not isTerminal and depth < self.playout_depth:\\n #A = len(node.children) # num_children\\n A = len(node.children[0]) + len(node.children[1]) \\n if A < self.num_initActions:\\n #if len(node.children[0]) < self.num_initActions:\\n node, init_action_xy, init_spin = self.initChildren(node, state, depth)\\n _, ShotVec = CreateShot(_ShotPos(init_action_xy[0], init_action_xy[1], init_spin))\\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\\n isTerminal = (state.ShotNum==0)\\n \\n depth += 1\\n break\\n \\n n_a = [c.n_visits for c in node.children[0].values()] + [c.n_visits for c in node.children[1].values()]\\n # progressive widening\\n # if chilren node has been visited much times then expand\\n #if np.sqrt(sum(n_a)) >= A:\\n if sum(n_a) >= 10 * A: \\n # expand\\n node, expanded_action_xy, expanded_spin = self.expand(node)\\n _, ShotVec = CreateShot(_ShotPos(expanded_action_xy[0],expanded_action_xy[1], expanded_spin))\\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\\n isTerminal = (state.ShotNum==0) # one end game\\n \\n depth += 1\\n break\\n\\n # select\\n node, selected_action_xy, selected_spin = self.ucb_select(node)\\n _, ShotVec = CreateShot(_ShotPos(selected_action_xy[0], selected_action_xy[1], selected_spin))\\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\\n isTerminal = (state.ShotNum==0) # one end game\\n \\n depth += 1\\n\\n if isTerminal:\\n break\\n\\n if not isTerminal and depth < self.playout_depth:\\n # save the rollout_state for speed.\\n #if node.rollout_state is None:\\n state = self.rollOut(node, state, depth)\\n #node.rollout_state = state\\n #else:\\n # state = node.rollout_state\\n \\n self.update(node, state)\",\n \"def onResetParameters(self):\\r\\n # productive #button\\r\\n profprint()\\r\\n fileName = pathToScene = slicer.modules.needlefinder.path.replace(\\\"NeedleFinder.py\\\", \\\"Config/default.cfg\\\")\\r\\n self.logic.loadParameters(fileName)\",\n \"def showTF(tf,outDir):\\n\\n nlo2lo,data2lo,data2nlo,data2lo_A,data2nlo_A=tf\\n\\n c=ROOT.TCanvas('c','c',500,500)\\n c.SetBottomMargin(0)\\n c.SetTopMargin(0)\\n c.SetLeftMargin(0)\\n c.SetRightMargin(0)\\n c.cd()\\n\\n p1=ROOT.TPad('p1','p1',0,0.5,1,1.0)\\n p1.Draw()\\n p1.SetRightMargin(0.03)\\n p1.SetLeftMargin(0.12)\\n p1.SetTopMargin(0.1)\\n p1.SetBottomMargin(0.01)\\n p1.SetGridy()\\n p1.cd()\\n nlo2lo.Draw('e2')\\n nlo2lo.GetYaxis().SetTitle('Z ratio')\\n nlo2lo.GetYaxis().SetNdivisions(5)\\n nlo2lo.GetXaxis().SetTitleSize(0)\\n nlo2lo.GetXaxis().SetLabelSize(0)\\n nlo2lo.GetYaxis().SetTitleSize(0.08)\\n nlo2lo.GetYaxis().SetTitleOffset(0.8)\\n nlo2lo.GetYaxis().SetLabelSize(0.08)\\n nlo2lo.GetYaxis().SetRangeUser(0.01,1.94)\\n data2lo.Draw('e1same')\\n data2nlo.Draw('e1same')\\n\\n leg1=p1.BuildLegend(0.7,0.88,0.95,0.66)\\n leg1.SetFillStyle(0)\\n leg1.SetBorderSize(0)\\n leg1.SetTextFont(42)\\n leg1.SetTextSize(0.06)\\n\\n l1=ROOT.TLine()\\n l1.SetLineWidth(2)\\n l1.SetLineColor(ROOT.kBlue)\\n l1.DrawLine(data2lo.GetXaxis().GetXmin(),1,data2lo.GetXaxis().GetXmax(),1)\\n\\n txt=ROOT.TLatex()\\n txt.SetNDC(True)\\n txt.SetTextFont(42)\\n txt.SetTextSize(0.08)\\n txt.SetTextAlign(12)\\n txt.DrawLatex(0.12,0.95,'#bf{CMS} #it{preliminary}')\\n p1.RedrawAxis()\\n\\n c.cd()\\n p2=ROOT.TPad('p2','p2',0,0,1,0.5)\\n p2.SetRightMargin(0.03)\\n p2.SetLeftMargin(0.12)\\n p2.SetTopMargin(0.01)\\n p2.SetBottomMargin(0.18)\\n p2.SetGridy()\\n p2.Draw()\\n p2.cd()\\n data2lo_A.Draw('e1')\\n data2lo_A.GetYaxis().SetTitle('#gamma ratio')\\n data2lo_A.GetYaxis().SetNdivisions(5)\\n data2lo_A.GetYaxis().SetRangeUser(0.01,1.94)\\n data2lo_A.GetXaxis().SetTitleSize(0.08)\\n data2lo_A.GetXaxis().SetLabelSize(0.08)\\n data2lo_A.GetYaxis().SetTitleSize(0.08)\\n data2lo_A.GetYaxis().SetLabelSize(0.08)\\n data2lo_A.GetYaxis().SetTitleOffset(0.8)\\n data2nlo_A.Draw('e1same')\\n \\n leg2=p2.BuildLegend(0.7,0.94,0.95,0.80)\\n leg2.SetFillStyle(0)\\n leg2.SetBorderSize(0)\\n leg2.SetTextFont(42)\\n leg2.SetTextSize(0.06)\\n \\n l2=ROOT.TLine()\\n l2.SetLineColor(ROOT.kBlue)\\n l2.SetLineWidth(2)\\n l2.DrawLine(data2lo_A.GetXaxis().GetXmin(),1,data2lo_A.GetXaxis().GetXmax(),1)\\n\\n p2.RedrawAxis()\\n\\n c.cd()\\n c.Modified()\\n c.Update()\\n for ext in ['png','pdf']:\\n c.SaveAs('{0}.{1}'.format(outDir,ext))\",\n \"def ChooseNodePath(self):\\n nodePath = ChooseContainerNode()\\n if nodePath:\\n self.nodePathEdit.setText(nodePath)\",\n \"def View_Tree_0(self):\\r\\n self.system.Set_Tree_View_Mode(0)\",\n \"def test_ctrl_space_index_preservation(self):\\n from omtk.modules import rigFK\\n\\n def check_targets_index_match(ctrl):\\n self.assertEqual(len(ctrl.targets), len(ctrl.targets_indexes))\\n attr_space = ctrl.node.space\\n for target, target_index in attr_space.getEnums().iteritems():\\n if target == 'Local':\\n continue\\n target = pymel.PyNode(target)\\n self.assertIn(target, ctrl.targets) # Ensure the target is stored\\n logical_index = ctrl.targets.index(target)\\n self.assertEqual(target_index, ctrl.targets_indexes[logical_index])\\n\\n # Create a simple influence hierarhy\\n inf_a = pymel.createNode('joint')\\n inf_b = pymel.createNode('joint', parent=inf_a)\\n inf_c = pymel.createNode('joint', parent=inf_b)\\n inf_d = pymel.createNode('joint', parent=inf_c)\\n\\n # Create a simple rig\\n r = omtk.create()\\n mod_a = r.add_module(rigFK.FK([inf_a]))\\n mod_b = r.add_module(rigFK.FK([inf_b]))\\n mod_c = r.add_module(rigFK.FK([inf_c]))\\n mod_d = r.add_module(rigFK.FK([inf_d]))\\n\\n # Build the last module\\n mod_d.build()\\n\\n # Analyse the indexes\\n c = mod_d.ctrls[0]\\n old_targets = c.targets\\n old_targets_indexes = c.targets_indexes\\n check_targets_index_match(c)\\n\\n # Unbulid the last module, change the hierarchy and rebuilt it\\n mod_d.unbuild()\\n inf_d.setParent(inf_b)\\n mod_d.build()\\n\\n # Analyse the indexes\\n c = mod_d.ctrls[0]\\n new_targets = c.targets\\n new_targets_indexes = c.targets_indexes\\n check_targets_index_match(c)\\n\\n self.assertListEqual(old_targets, new_targets)\\n self.assertListEqual(old_targets_indexes, new_targets_indexes)\",\n \"def reset(self):\\r\\n self._root_dir = None\",\n \"def deploy():\\n update_treesheets()\\n restart_treesheets()\",\n \"def save_render(self, savefile=\\\"tree.gv\\\"):\\n from graphviz import Source\\n src = Source(self.get_dot())\\n path = 'graphviz/{}'.format(savefile)\\n src.render(path)\",\n \"def __manage_tree(self):\\n for pre, fill, node in RenderTree(self.tree):\\n if node.name is 'count':\\n logger.info(\\n \\\"Tree info %s%s: %s %s p/s attack: %s\\\",\\n pre, node.name, node.value, node.pps, node.attack)\\n else:\\n logger.info(\\\"Pre - [%s], Fill - [%s], Node - [%s]\\\",\\n pre, fill, node.name)\",\n \"def onReadNodeKnobChanged():\\n ...\",\n \"def clear(context):\\n\\n log.log(level=0, msg=\\\"clear !\\\")\\n log.log(level=0, msg=\\\"controller set\\\")\\n log.log(level=0, msg=\\\"mesh set\\\")\\n log.log(level=0, msg=\\\"joint set\\\")\\n\\n # node = pm.PyNode(node)\\n #\\n # for script in info[const.POSTSCRIPTS]:\\n # mod = importlib.import_module(script)\\n # mod.PrePost.run(context)\",\n \"def __save_node(self):\\n print(self._encoder.encode(self._current_node))\\n self._count += 1\",\n \"def patch(lines):\\n if not get_root():\\n set_root(os.getcwd())\",\n \"def node():\\n return render_template('nodes.html')\",\n \"def reset(self):\\n self.current = self.root\\n env = self.env\\n obs, infos = env.reset()\\n obs = obs[obs.find(\\\"=\\\"):] # removes textworld legend\\n\\n return env, obs, infos\",\n \"def outputSet_write(path, data):\\n nonlocal filesSaved\\n nonlocal index\\n nonlocal d_tree\\n nonlocal fn_analysisCallback\\n nonlocal b_persistAnalysisResults\\n\\n # self.simpleProgress_show(index, total, '%s:%s' %\\n # ('%25s' % threading.currentThread().getName(),\\n # '%25s' % fn_outputWriteCallback.__name__)\\n # )\\n\\n if len(self.str_outputLeafDir):\\n (dirname, basename) = os.path.split(path)\\n str_format = '\\\\'%s\\\\'' % self.str_outputLeafDir\\n new_basename = str_format + ' % basename'\\n str_eval = eval(new_basename)\\n path = '%s/%s' % (dirname, str_eval)\\n\\n d_output = fn_outputWriteCallback(\\n ( '%s/%s' % (self.str_outputDir, path), data), **kwargs\\n )\\n\\n if 'status' in d_output.keys():\\n if not b_persistAnalysisResults:\\n d_tree[path] = d_output\\n filesSaved += d_output['filesSaved']\\n else:\\n self.dp.qprint(\\n \\\"The outputWriteCallback callback did not return a 'status' value!\\\",\\n comms = 'error',\\n level = 0\\n )\\n error.fatal(self, 'outputWriteCallback', drawBox = True)\\n return d_output\",\n \"def removenode(self, uid):\\n\\n raise NotImplementedError\",\n \"def setupRender():\\n prefs = getPreferences()\\n\\n # Check of the built-in environment maps path can be located.\\n # Discontinue if it cannot be found.\\n envPath = prefs.path_value\\n if not envPath:\\n return {'WARNING'}, \\\"No environment images path defined\\\"\\n\\n # Discontinue if there is no output path defined.\\n renderPath = outputPath()\\n if not renderPath:\\n return {'WARNING'}, \\\"The scene needs to be saved before rendering\\\"\\n\\n if prefs.image_value == 'NONE':\\n return {'WARNING'}, \\\"No environment image defined\\\"\\n\\n setRenderSettings(os.path.join(renderPath, IMAGE_NAME))\\n createCamera()\\n createWorld(envPath)\\n return renderPath\",\n \"def run():\\n\\n if lm.scene_object().type == 'env':\\n read_layout(outFile=lm.scene_object().copy(ext='json').path_root)\\n\\n else:\\n read_layout()\",\n \"def trace_path(node):\\r\\n if node.parent == None:\\r\\n print(\\\"---------\\\")\\r\\n print(\\\"Move \\\",node.g)\\r\\n print(node.data[:3])\\r\\n print(node.data[3:6])\\r\\n print(node.data[6:9])\\r\\n return\\r\\n\\r\\n trace_path(node.parent)\\r\\n print(\\\"---------\\\")\\r\\n print(\\\"Move \\\",node.g)\\r\\n print(node.data[:3])\\r\\n print(node.data[3:6])\\r\\n print(node.data[6:9])\\r\\n return\",\n \"def rf_stepMode(self, selTree, treeDict):\\n for step in treeDict['steps']:\\n newStep = TreeNode(nodeType='step', nodeLabel=step, nodeName=step)\\n self.addTopLevelItem(newStep)\\n for node in treeDict['tree']['_order']:\\n newItem = TreeNode(**treeDict['tree'][node])\\n if len(node.split('/')) == 1:\\n newStep.addChild(newItem)\\n else:\\n rootPath = \\\"%s/%s\\\" % (step, '/'.join(node.split('/')[:-1]))\\n parent = self._getItemFromTreePath(rootPath)\\n parent.addChild(newItem)\\n if getattr(newItem, 'nodeType') == 'shotNode':\\n newItem._tree = selTree\\n newItem._step = step\\n newItem._itemPath = node\\n newItem._dataPath = os.path.join(self.pm._treePath, selTree)\\n for fld in node.split('/'):\\n newItem._dataPath = os.path.join(newItem._dataPath, fld)\\n newItem._dataPath = pFile.conformPath(newItem._dataPath)\\n newItem._ltPath = pFile.conformPath(os.path.join(newItem._dataPath, 'lt', step))\\n newItem._dataFile = \\\"%s.py\\\" % newItem._dataPath\",\n \"def addRawDataNode():\\n return render_template(\\\"addRawDataNode.html\\\")\",\n \"def save_node(self, node: Node):\",\n \"def setDataRoot(path):\\n global dataRoot\\n dataRoot = os.path.realpath(path)\",\n \"def invalidateOutputs(self):\\n all_outputs = self.getFullOutputs()\\n for node_id in all_outputs:\\n if self.model.existNode(node_id):\\n self.model.getNode(node_id).silentInvalidate()\",\n \"def writePathways( self ):\\n\\n self.logger.info( 'writePathways: START' )\\n\\n # Generate inserts for meabolic pathways.\\n self.importerPathway.writePathways()\\n\\n self.logger.info( 'writePathways: DONE' )\",\n \"def addAfterRender(call, args=(), kwargs={}, nodeClass='Write'):\",\n \"def run(self):\\n print('Running test of the markups in different views')\\n\\n #\\n # first load the data\\n #\\n import SampleData\\n sampleDataLogic = SampleData.SampleDataLogic()\\n print(\\\"Getting MR Head Volume\\\")\\n mrHeadVolume = sampleDataLogic.downloadMRHead()\\n\\n #\\n # link the viewers\\n #\\n sliceLogic = slicer.app.layoutManager().sliceWidget('Red').sliceLogic()\\n compositeNode = sliceLogic.GetSliceCompositeNode()\\n compositeNode.SetLinkedControl(1)\\n\\n #\\n # MR Head in the background\\n #\\n sliceLogic.StartSliceCompositeNodeInteraction(1)\\n compositeNode.SetBackgroundVolumeID(mrHeadVolume.GetID())\\n sliceLogic.EndSliceCompositeNodeInteraction()\\n\\n #\\n # switch to conventional layout\\n #\\n lm = slicer.app.layoutManager()\\n lm.setLayout(2)\\n\\n # create a fiducial list\\n displayNode = slicer.vtkMRMLMarkupsDisplayNode()\\n slicer.mrmlScene.AddNode(displayNode)\\n fidNode = slicer.vtkMRMLMarkupsFiducialNode()\\n slicer.mrmlScene.AddNode(fidNode)\\n fidNode.SetAndObserveDisplayNodeID(displayNode.GetID())\\n\\n # make it active\\n selectionNode = slicer.mrmlScene.GetNodeByID(\\\"vtkMRMLSelectionNodeSingleton\\\")\\n if (selectionNode is not None):\\n selectionNode.SetReferenceActivePlaceNodeID(fidNode.GetID())\\n\\n # add some known points to it\\n eye1 = [33.4975, 79.4042, -10.2143]\\n eye2 = [-31.283, 80.9652, -16.2143]\\n nose = [4.61944, 114.526, -33.2143]\\n index = fidNode.AddFiducialFromArray(eye1)\\n fidNode.SetNthFiducialLabel(index, \\\"eye-1\\\")\\n index = fidNode.AddFiducialFromArray(eye2)\\n fidNode.SetNthFiducialLabel(index, \\\"eye-2\\\")\\n # hide the second eye as a test of visibility flags\\n fidNode.SetNthFiducialVisibility(index, 0)\\n index = fidNode.AddFiducialFromArray(nose)\\n fidNode.SetNthFiducialLabel(index, \\\"nose\\\")\\n\\n self.logicDelayDisplay(\\\"Placed 3 fiducials\\\")\\n\\n # self.printViewAndSliceNodes()\\n\\n if self.widgetVisible(fidNode, 'vtkMRMLViewNode1') == 0:\\n self.logicDelayDisplay(\\\"Test failed: widget is not visible in view 1\\\")\\n # self.printViewNodeIDs(displayNode)\\n return False\\n\\n #\\n # switch to 2 3D views layout\\n #\\n lm.setLayout(15)\\n self.logicDelayDisplay(\\\"Switched to 2 3D views\\\")\\n # self.printViewAndSliceNodes()\\n\\n if self.widgetVisible(fidNode, 'vtkMRMLViewNode1') == 0 or self.widgetVisible(fidNode, 'vtkMRMLViewNode2') == 0:\\n self.logicDelayDisplay(\\\"Test failed: widget is not visible in view 1 and 2\\\")\\n # self.printViewNodeIDs(displayNode)\\n return False\\n\\n #\\n # show only in view 2\\n #\\n displayNode.AddViewNodeID(\\\"vtkMRMLViewNode2\\\")\\n self.logicDelayDisplay(\\\"Showing only in view 2\\\")\\n if self.widgetVisible(fidNode, 'vtkMRMLViewNode1') == 1:\\n self.logicDelayDisplay(\\\"Test failed: widget is not supposed to be visible in view 1\\\")\\n # self.printViewNodeIDs(displayNode)\\n return False\\n if self.widgetVisible(fidNode, 'vtkMRMLViewNode2') == 0:\\n self.logicDelayDisplay(\\\"Test failed: widget is not visible in view 2\\\")\\n # self.printViewNodeIDs(displayNode)\\n return False\\n\\n #\\n # remove it so show in all\\n #\\n displayNode.RemoveAllViewNodeIDs()\\n self.logicDelayDisplay(\\\"Showing in both views\\\")\\n if self.widgetVisible(fidNode, 'vtkMRMLViewNode1') == 0 or self.widgetVisible(fidNode, 'vtkMRMLViewNode2') == 0:\\n self.logicDelayDisplay(\\\"Test failed: widget is not visible in view 1 and 2\\\")\\n self.printViewNodeIDs(displayNode)\\n return False\\n\\n #\\n # show only in view 1\\n #\\n displayNode.AddViewNodeID(\\\"vtkMRMLViewNode1\\\")\\n self.logicDelayDisplay(\\\"Showing only in view 1\\\")\\n if self.widgetVisible(fidNode, 'vtkMRMLViewNode2') == 1:\\n self.logicDelayDisplay(\\\"Test failed: widget is not supposed to be visible in view 2\\\")\\n # self.printViewNodeIDs(displayNode)\\n return False\\n if self.widgetVisible(fidNode, 'vtkMRMLViewNode1') == 0:\\n self.logicDelayDisplay(\\\"Test failed: widget is not visible in view 1\\\")\\n # self.printViewNodeIDs(displayNode)\\n return False\\n\\n # switch back to conventional\\n lm.setLayout(2)\\n self.logicDelayDisplay(\\\"Switched back to conventional layout\\\")\\n # self.printViewAndSliceNodes()\\n\\n # test of the visibility in slice views\\n displayNode.RemoveAllViewNodeIDs()\\n\\n # jump to the last fiducial\\n slicer.modules.markups.logic().JumpSlicesToNthPointInMarkup(fidNode.GetID(), index, 1)\\n # refocus the 3D cameras as well\\n slicer.modules.markups.logic().FocusCamerasOnNthPointInMarkup(fidNode.GetID(), index)\\n\\n # show only in red\\n displayNode.AddViewNodeID('vtkMRMLSliceNodeRed')\\n self.logicDelayDisplay(\\\"Show only in red slice\\\")\\n if self.widgetVisibleOnSlice(fidNode,'vtkMRMLSliceNodeRed') != 1:\\n self.logicDelayDisplay(\\\"Test failed: widget not displayed on red slice\\\")\\n # self.printViewNodeIDs(displayNode)\\n return False\\n\\n # remove all, add green\\n # print 'before remove all, after added red'\\n # self.printViewNodeIDs(displayNode)\\n displayNode.RemoveAllViewNodeIDs()\\n # print 'after removed all'\\n # self.printViewNodeIDs(displayNode)\\n displayNode.AddViewNodeID('vtkMRMLSliceNodeGreen')\\n self.logicDelayDisplay('Show only in green slice')\\n if self.widgetVisibleOnSlice(fidNode,'vtkMRMLSliceNodeRed') != 0 or self.widgetVisibleOnSlice(fidNode,'vtkMRMLSliceNodeGreen') != 1:\\n self.logicDelayDisplay(\\\"Test failed: widget not displayed only on green slice\\\")\\n print '\\\\tred = ',self.widgetVisibleOnSlice(fidNode,'vtkMRMLSliceNodeRed')\\n print '\\\\tgreen =',self.widgetVisibleOnSlice(fidNode,'vtkMRMLSliceNodeGreen')\\n self.printViewNodeIDs(displayNode)\\n return False\\n\\n return True\",\n \"def dynamic_caller(self):\\n if not self.target_changed:\\n self.path = self.dynamic_rrt(start = self.curr_pos, end = self.curr_target, path = self.curr_path, obstacle_list = self.obstacle_list)\",\n \"def run(self, root):\\r\\n pass\",\n \"def on_rbTree(self):\\n self.twTree.rf_tree()\\n if self.mainUi.getSelTab() == 'OverView':\\n self.mainUi.wgOverView._refresh()\",\n \"def setReCreateTreeSignalHandler():\\n signal.signal(signal.SIGUSR2,reCreateTreeSignalHandler)\",\n \"def go_home(node):\\n if node.attr('t').isSettable():\\n node.setAttr('t', (0, 0, 0))\\n if node.attr('r').isSettable():\\n node.setAttr('r', (0, 0, 0))\\n if node.attr('s').isSettable():\\n node.setAttr('s', (1, 1, 1))\",\n \"def export( self, captionMode, copyFiles, outputDir ):\\n scene = slicer.mrmlScene\\n nodes = scene.GetNumberOfNodes()\\n\\n self.__nodes = {}\\n\\n # 1 for model name, 2 for parent name\\n self.__captionMode = captionMode\\n # TRUE if we shall copy the files to the outputDir\\n self.__copyFiles = copyFiles\\n self.__outputDir = outputDir\\n\\n self.__tree = Tree()\\n self.__tree.create_node( \\\"Scene\\\", \\\"scene\\\" )\\n\\n for n in xrange( nodes ):\\n\\n node = scene.GetNthNode( n )\\n\\n self.parseNode( node )\\n\\n [header, footer] = self.configureXrenderers()\\n output = header\\n output += self.createXtree( \\\"scene\\\" )\\n output += footer\\n\\n return output\",\n \"def removeRtree(self):\\n try:\\n os.remove(str(self.dim)+'d_index.data')\\n os.remove(str(self.dim)+'d_index.index')\\n print('Files removed')\\n except:\\n print('No such files')\",\n \"def write_newtree(node,text):\\r\\n if node.left!=None: # if the left node is not equal to none\\r\\n text.write(\\\"Question:\\\\n\\\") # write the question\\r\\n text.write(node.data) # write the data\\r\\n write_newtree(node.left,text) # recursivly call to insert question to the left node\\r\\n write_newtree(node.right,text) # recursivly call to insert question to the right node\\r\\n return node # updating node\\r\\n\\r\\n else: # if the left node is equal to none\\r\\n text.write(\\\"Guess:\\\\n\\\") # write the guess\\r\\n text.write(node.data) # write the data\\r\\n return node # updating node\\r\",\n \"def save_tree(self, name='tree'):\\n alsoProvides(self.request, IDisableCSRFProtection)\\n if not name:\\n name = datetime.utcnow().isoformat()\\n handle = self.target_connector.get_handle()\\n handle.setContext(self.target_connector)\\n fname = fs.path.join('.', name + '.json')\\n with handle.open(fname, 'wb') as fp:\\n fp.write(self.request.BODY)\\n self.request.response.setStatus(200)\",\n \"def write_syntax_tree(self, compilation_engine):\\n output_file = '{}ktCST.xml'.format(compilation_engine.filename[:-5])\\n with open(output_file, 'w') as f:\\n print 'writing syntax tree to {}'.format(output_file)\\n f.write(''.join(compilation_engine.contents))\",\n \"def setup_scene_for_rgb_render(scene, outdir):\\n # Use node rendering for python control\\n scene.use_nodes = True\\n tree = scene.node_tree\\n links = tree.links\\n\\n # Make sure there are no existing nodes\\n for node in tree.nodes:\\n tree.nodes.remove(node)\\n\\n # Set up a renderlayer and plug it into our remapping layer\\n inp = tree.nodes.new('CompositorNodeRLayers')\\n\\n if (bpy.app.version[1] >= 70): # Don't apply color transformation -- changed in Blender 2.70\\n scene.view_settings.view_transform = 'Raw'\\n scene.sequencer_colorspace_settings.name = 'Non-Color'\\n\\n # Save it out\\n if outdir:\\n out = tree.nodes.new('CompositorNodeOutputFile')\\n ident = str(uu.uuid4())\\n out.file_slots[0].path = ident\\n out.base_path = outdir\\n # out.format.color_mode = 'BW'\\n # out.format.color_depth = settings.DEPTH_BITS_PER_CHANNEL\\n out.format.color_mode = 'RGB'\\n out.format.color_depth = settings.COLOR_BITS_PER_CHANNEL\\n out.format.file_format = settings.PREFERRED_IMG_EXT.upper()\\n links.new(inp.outputs[0], out.inputs[0])\\n ext = utils.img_format_to_ext[settings.PREFERRED_IMG_EXT.lower()]\\n temp_filename = \\\"{0}0001.{1}\\\".format(ident, ext)\\n return os.path.join(outdir, temp_filename)\\n else:\\n out = tree.nodes.new('CompositorNodeComposite')\\n links.new(inp.outputs[0], out.inputs[0])\\n return None\",\n \"def node_command(ctx, old, new):\\n try:\\n with ctx.obj[\\\"reader\\\"] as reader, ctx.obj[\\\"writer\\\"] as writer:\\n writer.copy_schema(reader)\\n writer.prepare_encode_cache()\\n writer.rename_node(old.encode(\\\"utf-8\\\"), new.encode(\\\"utf-8\\\"))\\n writer.write(reader)\\n except Exception:\\n click.secho(\\\"Failed!\\\", fg=\\\"red\\\", bold=True, err=True)\\n raise\\n else:\\n click.secho(\\\"Done!\\\", fg=\\\"green\\\", err=True, bold=True)\",\n \"def updateIndex(self):\\n for root, dirs, files in os.walk(self.serverdir):\\n for d in dirs:\\n if not d.startswith('.'):\\n relpath = os.path.relpath(os.path.join(root, d), self.serverdir)\\n self.serverindex[relpath] = (self.getNametype(os.path.join(root,d)), os.path.getmtime(os.path.join(root, d)))\\n for f in files:\\n if not f.startswith('.'):\\n relpath = os.path.relpath(os.path.join(root, f), self.serverdir)\\n self.serverindex[relpath] = (self.getNametype(os.path.join(root,f)), os.path.getmtime(os.path.join(root, f)))\",\n \"def node_mapping(self):\\n ...\",\n \"def reset_context():\\n global root_dir\\n global wells_list\\n global tops_list\\n\\n del root_dir, wells_list, tops_list\\n root_dir = WellsDir(None, 'root')\\n wells_list = list()\\n tops_list = list()\",\n \"def paint(self):\\r\\n self.canvas.delete(tkinter.ALL)\\r\\n self.visit(self.tree.root)\",\n \"def on_treeNode(self):\\n if self.mainUi.getSelTab() == 'Shots':\\n self.mainUi.wgShots._refresh()\\n elif self.mainUi.getSelTab() == 'LineTest':\\n self.mainUi.wgLineTest._refresh()\\n elif self.mainUi.getSelTab() == 'OverView':\\n self.mainUi.wgOverView._refresh()\",\n \"def instrumented_test_render(self, context):\\n signals.template_rendered.send(sender=self, template=self, context=context)\\n return self.nodelist.render(context)\",\n \"def index(name=None):\\r\\n return render_template('tree.html', name=name)\",\n \"def touch(self, node):\\n pass\",\n \"def get_node_outputs(node_path):\\n \\n item = ix.get_item(node_path)\\n\\n obj_array = ix.api.OfItemArray(1)\\n obj_array[0] = item\\n item_outputs = ix.api.OfItemVector()\\n\\n ix.application.get_factory().get_items_outputs(obj_array, item_outputs, False)\\n\\n node_outputs = []\\n for item_ in range(item_outputs.get_count()):\\n\\n for i in range(item_outputs[item_].get_attribute_count()):\\n\\n attr= item_outputs[item_].get_attribute(i)\\n\\n if attr.get_texture():\\n\\n if str(attr.get_texture()) == item.get_full_name():\\n\\n #attrs[attr] = target_node.get_full_name()\\n node_outputs.append(attr)\\n return node_outputs\",\n \"def refresh():\\n global tree\\n tree = build_tree()\\n tree.order_by_create()\\n return index()\",\n \"def OnUpdatePlantCtrl(self, _):\\n self.saveTexts()\\n self.updateMarkers()\",\n \"def setup_ant(self):\\n self.visited_nodes[1:] = []\\n self.actual_node = self.start_pos\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5345771","0.5233489","0.5084201","0.49955806","0.49239224","0.4833652","0.48127708","0.4807053","0.48013398","0.4722902","0.47158727","0.47130924","0.47050682","0.4693489","0.46925712","0.4680197","0.46643654","0.46602747","0.46585917","0.46569446","0.46294433","0.46230307","0.46209478","0.46207163","0.45778358","0.4572522","0.45524088","0.45515808","0.45499355","0.4548511","0.45457134","0.45378968","0.45242262","0.45173535","0.45162156","0.4513672","0.44954273","0.44931233","0.44881597","0.44824344","0.4478432","0.44677567","0.4459972","0.4457805","0.44504026","0.44502765","0.44454092","0.44403294","0.44389236","0.44383678","0.44363657","0.4433398","0.44248992","0.44240922","0.44226015","0.44219425","0.44146755","0.44120878","0.4407376","0.44005325","0.43960318","0.43903765","0.43897837","0.43804848","0.43775764","0.43720153","0.43718225","0.43707663","0.43700966","0.43623486","0.43594953","0.43587643","0.4357441","0.43551594","0.43493754","0.43476462","0.43460846","0.43459997","0.4345403","0.43424734","0.43416852","0.43405816","0.4335956","0.43289262","0.43269554","0.4323606","0.43197766","0.43161166","0.4314926","0.431398","0.4313534","0.43131587","0.43115273","0.43110728","0.43109682","0.43082777","0.4302193","0.42985657","0.4297766","0.42948726"],"string":"[\n \"0.5345771\",\n \"0.5233489\",\n \"0.5084201\",\n \"0.49955806\",\n \"0.49239224\",\n \"0.4833652\",\n \"0.48127708\",\n \"0.4807053\",\n \"0.48013398\",\n \"0.4722902\",\n \"0.47158727\",\n \"0.47130924\",\n \"0.47050682\",\n \"0.4693489\",\n \"0.46925712\",\n \"0.4680197\",\n \"0.46643654\",\n \"0.46602747\",\n \"0.46585917\",\n \"0.46569446\",\n \"0.46294433\",\n \"0.46230307\",\n \"0.46209478\",\n \"0.46207163\",\n \"0.45778358\",\n \"0.4572522\",\n \"0.45524088\",\n \"0.45515808\",\n \"0.45499355\",\n \"0.4548511\",\n \"0.45457134\",\n \"0.45378968\",\n \"0.45242262\",\n \"0.45173535\",\n \"0.45162156\",\n \"0.4513672\",\n \"0.44954273\",\n \"0.44931233\",\n \"0.44881597\",\n \"0.44824344\",\n \"0.4478432\",\n \"0.44677567\",\n \"0.4459972\",\n \"0.4457805\",\n \"0.44504026\",\n \"0.44502765\",\n \"0.44454092\",\n \"0.44403294\",\n \"0.44389236\",\n \"0.44383678\",\n \"0.44363657\",\n \"0.4433398\",\n \"0.44248992\",\n \"0.44240922\",\n \"0.44226015\",\n \"0.44219425\",\n \"0.44146755\",\n \"0.44120878\",\n \"0.4407376\",\n \"0.44005325\",\n \"0.43960318\",\n \"0.43903765\",\n \"0.43897837\",\n \"0.43804848\",\n \"0.43775764\",\n \"0.43720153\",\n \"0.43718225\",\n \"0.43707663\",\n \"0.43700966\",\n \"0.43623486\",\n \"0.43594953\",\n \"0.43587643\",\n \"0.4357441\",\n \"0.43551594\",\n \"0.43493754\",\n \"0.43476462\",\n \"0.43460846\",\n \"0.43459997\",\n \"0.4345403\",\n \"0.43424734\",\n \"0.43416852\",\n \"0.43405816\",\n \"0.4335956\",\n \"0.43289262\",\n \"0.43269554\",\n \"0.4323606\",\n \"0.43197766\",\n \"0.43161166\",\n \"0.4314926\",\n \"0.431398\",\n \"0.4313534\",\n \"0.43131587\",\n \"0.43115273\",\n \"0.43110728\",\n \"0.43109682\",\n \"0.43082777\",\n \"0.4302193\",\n \"0.42985657\",\n \"0.4297766\",\n \"0.42948726\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65005773"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":76677,"cells":{"query":{"kind":"string","value":"Initialisation, where df is a pandas DataFrame and var is the name of the column to study and init_pars is a dictionary with initial values"},"document":{"kind":"string","value":"def __init__(self,df, init_pars, var='dep_var', var_name='Volume of Nile'):\n self.df = df\n self.var = var\n self.var_name = var_name\n self.y = np.array(df[var].values.flatten())\n self.times = df.index\n self.pardict = init_pars\n self.options = {'eps':1e-09,\n 'maxiter':2000}"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def init(param):\n MODULE_HELPER.check_parameter(param, key='featureCount_exec', dtype=str)\n MODULE_HELPER.check_parameter(param, key='featureCount_t', dtype=str)\n MODULE_HELPER.check_parameter(param, key='featureCount_id', dtype=str)\n MODULE_HELPER.check_parameter(param, key='featureCount_by_meta', dtype=bool)\n MODULE_HELPER.check_parameter(param, key='Rscript_exec', dtype=str)\n\n #deriving the stranded parameter\n if param['stranded'] == 'reverse':\n param['featureCount_s'] = '2'\n elif param['stranded'] == 'yes':\n param['featureCount_s'] = '1'\n else:\n param['featureCount_s'] = '0'","def setup_initial_values(self, init_params={}):\n for row in self.panel[1:]:\n for widget in row:\n if widget.name in init_params:\n widget.value = init_params[widget.name]","def __init__(self, df):\n self.original_data = df\n self.preprocessed_data = pd.DataFrame()","def reset_parameters(self, param_init):\n logger.info('===== Initialize %s with lecun style =====' % self.__class__.__name__)\n for n, p in self.named_parameters():\n init_with_lecun_normal(n, p, param_init)","def __init__(self, df):\n self.df = df","def init_model_df(self):\n\n self.model_df = pd.DataFrame(columns=self.query_df[self.column_name].unique())\n\n # add _TIMESTAMP column to dataframe\n self.model_df[self.column_index] = self.min_increments\n\n # set row index to _TIMESTAMP\n self.model_df.set_index(self.column_index, inplace=True)","def initialize_variables(self):\n self.sess.run(self.init)","def reset_parameters_lecun(self, param_init=0.1):\n logger.info('===== Initialize %s with lecun style =====' % self.__class__.__name__)\n for n, p in self.named_parameters():\n init_with_lecun_normal(n, p, param_init)","def _parse_var_initsol(self,varname) :\n\t\tinitsol = self.ss.constraint.initsol\n\t\tparams = getattr(initsol,varname)\n\t\tnvars = len(self.ss.variables) # num of variables\n\n\t\tif varname in ('alpha','beta') : \n\t\t\tself.initsol[varname] = np.ones(nvars)\n\t\t\tkeys = params.keys()\n\t\t\tself.initsol[varname][:] = params['defaultInitialValue']\n\t\t\tfor key in keys : \n\t\t\t\tif re.match(varname+'_\\d+',key)\t:\n\t\t\t\t\tidx = int(key.split('_')[1])\n\t\t\t\t\tself.initsol[varname][idx-1] = params[key]\n\t\telif varname in ('g','h') :\n\t\t\tself.initsol[varname] = np.ones([nvars,nvars])\n\t\t\tkeys = params.keys()\n\t\t\tself.initsol[varname][:] = params['defaultInitialValue']\n\t\t\tfor key in keys : \n\t\t\t\tif re.match(varname+'_\\d+_\\d+',key)\t:\n\t\t\t\t\tidr,idc = map(int,(key.split('_')[1:3]))\n\t\t\t\t\tself.initsol[varname][idr-1][idc-1] = params[key]\n\t\t\n\t\telse :\n\t\t\tlogging.error(\"Unrecognized varname %s quitting..\" \\\n\t\t\t%(varname))\n\t\t\tsys.exit(1)","def __init__(self, df):\n self.data = df","def prepare_estimation(model_params_init_file_name, lower, upper):\n\n # Read in data and init file sources\n model_params_df = pd.read_pickle(model_params_init_file_name)\n model_params_df[\"lower\"] = lower\n model_params_df[\"upper\"] = upper\n\n return model_params_df","def _prepare_initial_params_df(self):\r\n self.logger.info(\"Preparing initial params\")\r\n\r\n # First we must normalize the weights.\r\n #\r\n total_weights = self.optimizer_config.initial_points_pareto_weight \\\r\n + self.optimizer_config.initial_points_cached_good_params_weight \\\r\n + self.optimizer_config.initial_points_random_params_weight\r\n assert total_weights > 0\r\n\r\n initial_points_pareto_fraction = self.optimizer_config.initial_points_pareto_weight / total_weights\r\n initial_points_cached_good_fraction = self.optimizer_config.initial_points_cached_good_params_weight / total_weights\r\n\r\n num_initial_points = self.optimizer_config.num_starting_configs\r\n\r\n # Let's start with the pareto points.\r\n #\r\n pareto_params_df = self.pareto_frontier.params_for_pareto_df\r\n if pareto_params_df is None:\r\n pareto_params_df = pd.DataFrame()\r\n\r\n num_desired_pareto_points = math.floor(num_initial_points * initial_points_pareto_fraction)\r\n num_existing_pareto_points = len(pareto_params_df.index)\r\n\r\n if num_existing_pareto_points > 0:\r\n if num_desired_pareto_points < num_existing_pareto_points:\r\n pareto_params_df = pareto_params_df.sample(n=num_desired_pareto_points, replace=False, axis='index')\r\n self.logger.info(f\"Using {len(pareto_params_df.index)} of {num_existing_pareto_points} pareto points as starting configs.\")\r\n else:\r\n self.logger.info(\"There are no existing pareto points.\")\r\n\r\n # Now let's take the cached good points.\r\n #\r\n num_desired_cached_good_points = math.floor(num_initial_points * initial_points_cached_good_fraction)\r\n cached_params_df = pd.DataFrame()\r\n if self._good_configs_from_the_past_invocations_df is not None:\r\n if num_desired_cached_good_points < len(self._good_configs_from_the_past_invocations_df.index):\r\n cached_params_df = self._good_configs_from_the_past_invocations_df.sample(n=num_desired_cached_good_points, replace=False, axis='index')\r\n else:\r\n cached_params_df = self._good_configs_from_the_past_invocations_df.copy(deep=True)\r\n self.logger.info(\r\n f\"Using {len(cached_params_df.index)} out of {len(self._good_configs_from_the_past_invocations_df.index)} \"\r\n f\"cached good configs as starting configs\"\r\n )\r\n else:\r\n self.logger.info(\"No cached params are available.\")\r\n\r\n # Finally, let's generate the random points.\r\n #\r\n num_desired_random_points = num_initial_points - len(pareto_params_df.index) - len(cached_params_df.index)\r\n random_params_df = self.optimization_problem.parameter_space.random_dataframe(num_samples=num_desired_random_points)\r\n self.logger.info(f\"Using {len(random_params_df.index)} random points as starting configs.\")\r\n\r\n initial_params_df = pd.concat([pareto_params_df, cached_params_df, random_params_df])\r\n initial_params_df.reset_index(drop=True, inplace=True)\r\n return initial_params_df","def init(self, rng_key, num_warmup, init_params, model_args, model_kwargs):\n raise NotImplementedError","def __init__(self, df, Z, Q, H, T, R, d, c, var='dep_var', secondvar=None, method='iterate'):\r\n self.method = method\r\n # Initialize all the system matrices as attributes of the class\r\n self.Z = Z\r\n self.Q = Q\r\n self.H = H\r\n self.T = T\r\n self.R = R\r\n self.d = d\r\n self.c = c\r\n # same for parameters and objects\r\n self.df = df\r\n self.var = var\r\n # The data itself\r\n self.y = np.array(df[var].values.flatten())\r\n self.times = df.index\r\n # Options for the minimizer\r\n self.options = {'eps': 1e-09,\r\n 'maxiter': 200}","def init_parameters(\n self, init_fun: Optional[NNInitFunc] = None, *, seed: Optional[PRNGKeyT] = None\n ):\n if init_fun is None:\n init_fun = normal(stddev=0.01)\n\n rng = nkjax.PRNGSeq(nkjax.PRNGKey(seed))\n\n def new_pars(par):\n return jnp.asarray(\n init_fun(rng.take(1)[0], shape=par.shape, dtype=par.dtype),\n dtype=par.dtype,\n )\n\n self.parameters = jax.tree_map(new_pars, self.parameters)","def __init__(self, df):\n self.df = df\n if 'time' in self.df.columns:\n self.time = True\n self.df = self.df.sort('time')\n else:\n self.time = False\n if 'condition' not in self.df.columns:\n self.df['condition'] = 'all'","def initialise(self):\n self.sc.init.exec_action(self.variables)","def initial_parameters(ship_data: dict) -> dict:\n\n mask = df_parameters[\"brix_lambda\"].notnull()\n df_parameters.loc[mask, \"brix_prime\"] = df_parameters.loc[mask].apply(\n calculate_prime, ship_parameters=ship_data, axis=1\n )\n\n df_parameters[\"prime\"] = df_parameters[\"brix_prime\"]\n\n df_parameters.loc[\"Ydelta\", \"prime\"] = 0.003 # Just guessing\n df_parameters.loc[\"Ndelta\", \"prime\"] = (\n -df_parameters.loc[\"Ydelta\", \"prime\"] / 2\n ) # Just guessing\n\n df_parameters.loc[\"Nu\", \"prime\"] = 0\n df_parameters.loc[\"Nur\", \"prime\"] = 0\n # df_parameters.loc[\"Xdelta\", \"prime\"] = -0.001\n df_parameters.loc[\"Xr\", \"prime\"] = 0\n df_parameters.loc[\"Xrr\", \"prime\"] = 0.000\n df_parameters.loc[\"Xu\", \"prime\"] = 0\n df_parameters.loc[\"Xuu\", \"prime\"] = 0\n df_parameters.loc[\"Xv\", \"prime\"] = 0\n df_parameters.loc[\"Xvr\", \"prime\"] = 0\n df_parameters.loc[\"Yu\", \"prime\"] = 0\n df_parameters.loc[\"Yur\", \"prime\"] = 0.00\n\n df_parameters.loc[\"Nuv\", \"prime\"] = 0.0\n df_parameters.loc[\"Xthrust\", \"prime\"] = 1.0\n df_parameters.loc[\"Yrdeltadelta\", \"prime\"] = 0.0\n df_parameters.loc[\"Xvdelta\", \"prime\"] = 0.0\n df_parameters.loc[\"Xdeltadelta\", \"prime\"] = 0.0\n df_parameters.loc[\"Yvdeltadelta\", \"prime\"] = 0.0\n df_parameters.loc[\"Nrdeltadelta\", \"prime\"] = 0.0\n df_parameters.loc[\"Yuv\", \"prime\"] = 0.0\n df_parameters.loc[\"Nvdeltadelta\", \"prime\"] = 0.0\n\n df_parameters.loc[\"Ythrustdelta\", \"prime\"] = 0.0\n df_parameters.loc[\"Nthrustdelta\", \"prime\"] = 0.0\n\n parameters = df_parameters[\"prime\"].dropna().to_dict()\n\n return parameters","def init_columns(cycle_df, datatype):\n (cycle_ind_col, data_point_col, volt_col, curr_col, dis_cap_col, char_cap_col, charge_or_discharge) = col_variables(datatype)\n assert type(cycle_df) == pd.DataFrame\n assert volt_col in cycle_df.columns\n assert dis_cap_col in cycle_df.columns\n assert char_cap_col in cycle_df.columns\n\n cycle_df = cycle_df.reset_index(drop=True)\n cycle_df['dV'] = None\n cycle_df['Discharge_dQ'] = None\n cycle_df['Charge_dQ'] = None\n #cycle_df['Discharge_dQ/dV'] = None\n #cycle_df['Charge_dQ/dV'] = None\n return cycle_df","def sa_pandas_init(self):\n\n lca = self.lca\n\n ind_activity = 0\n ind_product = 1\n ind_biosphere = 2\n\n cols = []\n rows = []\n inputs = []\n\n #All exchanges in inputs\n for input_ in self.inputs:\n\n if input_ == 'biosphere':\n continue\n\n for i in self.inputs_dict[input_]['tech_params']:\n act = lca.reverse_dict() [ind_activity] [i['col']]\n prod = lca.reverse_dict() [ind_product] [i['row']]\n cols += [ bw.get_activity(act) ['name'] ]\n rows += [ bw.get_activity(prod)['name'] ]\n inputs += [input_]\n for j in self.inputs_dict[input_]['bio_params']:\n act = lca.reverse_dict() [ind_activity] [j['col']]\n bio = lca.reverse_dict() [ind_biosphere] [j['row']]\n cols += [ bw.get_activity(act) ['name'] ]\n rows += [ bw.get_activity(prod)['name'] ]\n inputs += [input_]\n\n if self.parameters != None:\n # All parameters\n parameters_names_list = [name for name in self.parameters_array['name']]\n cols += parameters_names_list\n rows += parameters_names_list\n inputs += ['Parameters']*len(parameters_names_list)\n\n df = pd.DataFrame([inputs, rows, cols], index = ['Inputs', 'Products or flows', 'Activities'])\n df = df.transpose()\n\n self.sensitivity_indices_df = df","def init_vars(self):\n # type: () -> None\n raise NotImplementedError","def init_params(self):\n blah","def _autoInitPars(self):\n for p in self._pars:\n setattr(self,p,self.defaultparval)","def __init__(self, df, df_test, n_days, length, style) :\n self.n_days = n_days\n self.length = length\n self.df = df\n self.df_test = df_test\n self.features = len(df.columns) - 1\n self.style = style\n self.df_true = df_test.copy()","def __init__(self, df, df_test, n_days, length, style) :\n self.n_days = n_days\n self.length = length\n self.df = df\n self.df_test = df_test\n self.features = len(df.columns) - 1\n self.style = style\n self.df_true = df_test.copy()","def _data_dep_init(self, inputs):\n from tensorflow.python.ops.nn import moments\n from tensorflow.python.ops.math_ops import sqrt\n\n with variable_scope.variable_scope('data_dep_init'):\n # Generate data dependent init values\n activation = self.layer.activation\n self.layer.activation = None\n x_init = self.layer.call(inputs)\n m_init, v_init = moments(x_init, self.norm_axes)\n scale_init = 1. / sqrt(v_init + 1e-10)\n\n # Assign data dependent init values\n self.layer.g = self.layer.g * scale_init\n self.layer.bias = (-m_init * scale_init)\n self.layer.activation = activation\n self.initialized = True","def init(*args):\n global dataset\n dataset = args[0]","def __init__(self, df):\n self.df = df\n self._set_hash()","def _init_param_source():\n\n amp_sine = 1\n amp_rand = 1\n rollover = 100 # length of data to display\n update_delay = 100 # time between delays of update in ms\n param_source = ColumnDataSource(dict(\n amp_sine=[amp_sine],\n amp_rand=[amp_rand],\n rollover=[rollover],\n update_delay=[update_delay]\n ))\n return param_source","def _optimizer_state_init(opt_states):\n prefix_list = [\"moments\", \"accum\", \"moment1\", \"moment2\", \"lamb_m\", \"lamb_v\", \"mean_grad\",\n \"mean_square\", \"prev\"]\n for opt_param in opt_states:\n prefix = opt_param.name[:opt_param.name.find(\".\")]\n if opt_param.has_init and (prefix in prefix_list or opt_param.name == \"global_step\"):\n opt_param.init_data()","def reset_parameters(self, param_init):\n logger.info('===== Initialize %s with uniform distribution =====' % self.__class__.__name__)\n for n, p in self.named_parameters():\n init_with_uniform(n, p, param_init)","def init_locals(self):\n pass","def parameter_initialization(self):\n dictsize = settings.PARS.get('numBases')\n numClass = self.train_labels.shape[0] # number of objects\n Dinit = np.empty((self.train_feats.shape[0], 0)) # for C-Ksvd and D-Ksvd\n dictLabel = np.empty((numClass, 0), dtype=np.int)\n numPerClass = dictsize//numClass\n param1 = {\n 'mode': 2,\n 'K': settings.PARS.get('numBases'), # size of the dictionary\n 'lambda1': settings.PARS.get('lambda_'),\n 'lambda2': 0,\n 'iter': settings.PARS.get('iterationini')\n }\n param2 = {\n 'lambda1': settings.PARS.get('lambda_'),\n 'lambda2': 0,\n 'mode': 2\n }\n\n for classid in range(numClass):\n col_ids = np.array(np.nonzero(self.train_labels[classid, :] == 1)).ravel()\n # ensure no zero data elements are chosen\n data_ids = np.array(np.nonzero(np.sum(self.train_feats[:, col_ids]**2, axis=0) > 1e-6)).ravel()\n\n # Raising an error if any zero lement is found\n if col_ids.shape[0] != data_ids.shape[0]:\n raise DatasetZeroElementFound\n\n # Initilization for LC-KSVD (perform KSVD in each class)\n Dpart = self.train_feats[:, col_ids[np.random.choice(data_ids, numPerClass, replace=False)]]\n param1['D'] = Dpart # initial dictionary\n Dpart = trainDL(self.train_feats[:, col_ids[data_ids]], **param1)\n Dinit = np.c_[Dinit, Dpart]\n labelvector = np.zeros((numClass, 1), dtype=np.int)\n labelvector[classid] = 1\n dictLabel = np.c_[dictLabel, np.tile(labelvector, (1, numPerClass))]\n\n param1['D'] = np.asfortranarray(Dinit) # initial dictionary\n # RuntimeError: matrix arg 10 must be a 2d double Fortran Array\n self.train_feats = self.train_feats if np.isfortran(self.train_feats) else np.asfortranarray(self.train_feats)\n Dinit = trainDL(self.train_feats, **param1)\n Xinit = lasso(self.train_feats, Dinit, **param2)\n\n # learning linear classifier parameters\n tmp = np.linalg.inv(Xinit@Xinit.T+np.eye(*(Xinit@Xinit.T).shape))@Xinit\n Winit = tmp@self.train_labels.T\n Winit = Winit.T\n\n Q = np.zeros((dictsize, self.train_feats.shape[1])) # energy matrix\n\n for frameid in range(self.train_feats.shape[1]):\n label_training = self.train_labels[:, frameid]\n maxid1 = label_training.argmax(0)\n\n for itemid in range(Dinit.shape[1]):\n label_item = dictLabel[:, itemid]\n maxid2 = label_item.argmax(0)\n\n if maxid1 == maxid2:\n Q[itemid, frameid] = 1\n\n Tinit = tmp@Q.T\n Tinit = Tinit.T\n\n return Dinit, Winit, Tinit, Q","def __init__(self, df):\n # Transpose dataframe data to make it column oriented\n self.data = df.values.T\n\n assert df.index.tzinfo is not None\n self.tz = df.index.tzinfo\n self.index = df.index.tz_localize(None)\n\n\n self.cols = {}\n for i, col_name in enumerate(df.columns):\n self.cols[col_name] = i","def __init__(self):\n self.current_year = datetime.date.today().year\n self.random_column_mappings = collections.defaultdict(dict)","def reset_parameters(self, param_init):\n logger.info('===== Initialize %s =====' % self.__class__.__name__)\n for n, p in self.named_parameters():\n if p.dim() == 1:\n nn.init.constant_(p, 0.0)\n logger.info('Initialize %s with %s / %.3f' % (n, 'constant', 0.0))\n elif p.dim() == 2:\n nn.init.uniform_(p, a=-param_init, b=param_init)\n logger.info('Initialize %s with %s / %.3f' % (n, 'uniform', param_init))\n else:\n raise ValueError(n)","def _initialize_df(self, df):\n df['values'] = (self.tc.instrument_returns['cumulative'] *\n self.tc.starting_cash).mul(self.target_weights, axis=1).values * (1 - self.tc.commission)\n df['allocations'] = self.df['values'].div(df['values'].sum(axis=1), axis=0)\n df['returns'] = (df['values'].sum(axis=1)).pct_change(1).fillna(0)","def __init__(self, df):\n self.original_data = df\n self.cleaned_data = pd.DataFrame()","def __init__(self, df,\n target_cols,\n problem_type,\n num_folds = 3,\n shuffle = False,\n random_state = 0):\n\n self.dataframe = df\n self.target_cols = target_cols\n self.num_targets = len(target_cols)\n self.problem_type = problem_type\n self.shuffle = shuffle\n self.num_folds = num_folds\n self.random_state = random_state\n\n if self.shuffle:\n self.dataframe = self.dataframe.sample(frac = 1,\n random_state = self.random_state).reset_index(drop = True)\n\n self.dataframe[\"kfold\"] = -1","def __init__(\n self,\n x,\n effect_size='EFFECTSIZE',\n p='P',\n snp='SNP',\n gene='GENE',\n annotation=None,\n logp=True\n ):\n\n # checking the validity of the arguments\n\n # Make sure you have effect_size and p columns and that they are of\n # numeric type\n if effect_size not in x.columns.values:\n raise KeyError(\"Column %s not found in 'x' data.frame\"\n % effect_size)\n else:\n if not is_numeric_dtype(x[effect_size].dtype):\n raise TypeError(\"%s column should be numeric. Do you have \"\n \"'X', 'Y', 'MT', etc? If so change to \"\n \"numbers and try again.\" % effect_size)\n\n if p not in x.columns.values:\n raise KeyError(\"Column %s not found in 'x' data.frame\" % p)\n else:\n if not is_numeric_dtype(x[p].dtype):\n raise TypeError(\"%s column should be numeric type\" % p)\n else:\n if (x[p] < 0).any():\n raise ValueError(\"Negative p-values found.\"\n \" These must be removed.\")\n if (x[p] > 1).any():\n raise ValueError(\"P-values greater than 1 found. \"\n \"These must be removed.\")\n if np.isnan(x[p]).any():\n raise ValueError(\"NaN p-values found. These must be \"\n \"removed\")\n\n # Create a new DataFrame with columns named after effect_size and p.\n self.data = pd.DataFrame(data=x[[effect_size, p]])\n\n if snp is not None:\n if snp not in x.columns.values:\n # Warn if you don't have a snp column\n raise KeyError(\n \"snp argument specified as %s but column not found in \"\n \"'x' data.frame\" % snp)\n else:\n # If the input DataFrame has a snp column, add it to the new\n # DataFrame\n self.data[snp] = x[snp]\n\n if gene is not None:\n if gene not in x.columns.values:\n # Warn if you don't have a gene column\n raise KeyError(\n \"gene argument specified as %s but column not found in \"\n \"'x' data.frame\" % gene)\n else:\n # If the input DataFrame has a gene column, add it to the new\n # DataFrame\n self.data[gene] = x[gene]\n\n if annotation is not None:\n if annotation not in x.columns.values:\n # Warn if you don't have an annotation column\n raise KeyError(\n \"annotation argument specified as %s but column not \"\n \"found in 'x' data.frame\" % annotation\n )\n else:\n # If the input DataFrame has a gene column, add it to the new\n # DataFrame\n self.data[annotation] = x[annotation]\n\n self.xlabel = \"Effect Size\"\n self.ticks = []\n self.ticksLabels = []\n self.effectSize = effect_size\n self.pName = p\n self.snpName = snp\n self.geneName = gene\n self.annotationName = annotation\n self.logp = logp","def initialize_(x, spec):\n activate(x, spec, lookup=[nn.init])","def params_init(self) -> None:\n # Initialize weights and biases with uniform distribution.\n nn.init.uniform_(self.emb.weight, self.init_lower, self.init_upper)\n nn.init.uniform_(self.fc_e2h[1].weight, self.init_lower, self.init_upper)\n nn.init.uniform_(self.fc_e2h[1].bias, self.init_lower, self.init_upper)\n for lyr in range(self.n_lyr):\n self.stack_rnn[2 * lyr].params_init()\n nn.init.uniform_(self.fc_h2e[0].weight, self.init_lower, self.init_upper)\n nn.init.uniform_(self.fc_h2e[0].bias, self.init_lower, self.init_upper)","def initialize(self, algo, verbosity=0):\n if isinstance(self.source, pd.DataFrame):\n data = self.source\n else:\n if verbosity > 0:\n print(f\"{self.name}: Reading file {self.source}\")\n data = PandasFileHelper.read_file(self.source, **self._rpars)\n\n if self._trbs is None:\n if self._col_i is not None and self._col_t is None:\n data.reset_index(inplace=True)\n elif self._col_i is None and self._col_t is not None:\n tnames = algo.farm.turbine_names\n inds = [tnames.index(name) for name in data[self._col_t]]\n data[FC.TURBINE] = inds\n self._col_i = FC.TURBINE\n else:\n raise KeyError(\n f\"{self.name}: Please either specify 'col_tinds' or 'col_tnames'\"\n )\n self._trbs = data[self._col_i].to_numpy()\n n_trbs = len(self._trbs)\n\n self._rcols = []\n for v in self._rvars:\n col_vmin = f\"{v}_min\"\n col_vmin = self._colmap.get(col_vmin, col_vmin)\n if col_vmin not in data.columns:\n raise KeyError(\n f\"{self.name}: Missing column '{col_vmin}', maybe add it to 'colmap'?\"\n )\n\n col_vmax = f\"{v}_max\"\n col_vmax = self._colmap.get(col_vmax, col_vmax)\n if col_vmax not in data.columns:\n raise KeyError(\n f\"{self.name}: Missing column '{col_vmax}', maybe add it to 'colmap'?\"\n )\n\n self._rcols += [col_vmin, col_vmax]\n\n self._tcols = []\n for v in self._tvars:\n col = self._colmap.get(v, v)\n if col not in data.columns:\n raise KeyError(\n f\"{self.name}: Missing column '{col}', maybe add it to 'colmap'?\"\n )\n self._tcols.append(col)\n\n n_rvars = len(self._rvars)\n self._rdata = data[self._rcols].to_numpy().reshape(n_trbs, n_rvars, 2)\n self._tdata = data[self._tcols].to_numpy()\n\n for vi, v in enumerate(self._rvars):\n if v in self._perds:\n self._rdata[:, vi] = np.mod(self._rdata[:, vi], self._perds[v])\n\n return super().initialize(algo, verbosity)","def create_initial_parameters(self):\n update_nested_dictionary(\n self.settings,\n {self.highest_lookup: {\n self.highest_sublookup: self.kw\n }})","def __init__(self, connection_loc, tags_tsdata,\n dummy_var_no):\n self.init_system(connection_loc, tags_tsdata)\n self.removedummyvars(dummy_var_no)\n self.addforwardscale()\n self.addbackwardscale()","def initialize_df(scenario_index,scenarios_nums):\n df = pd.DataFrame(index=scenarios_nums)\n df.index.name = scenario_index\n return df","def _initialize_data_filter(self):\n df_params = self._loading_params.copy()\n df_params[\"filter_negate\"] = True\n df_params[\"filter_upper\"] = True\n self._data_filter = LoadProcessedData(**df_params)","def _set_init(self):\n ## Main information\n self.idxs = None\n self.sp_relative_pos = None\n ## Auxiliar information\n self.ks = None\n self.iss = [0]\n ## Class structural information\n self._setted = False\n self._constant_rel_pos = False\n self.staticneighs = None\n self.staticneighs_set = None","def par_fit(init_file):\n check_presence_init(init_file)\n\n dict_ = read(init_file)\n np.random.seed(dict_[\"SIMULATION\"][\"seed\"])\n\n # We perform some basic consistency checks regarding the user's request.\n check_presence_estimation_dataset(dict_)\n # check_initialization_dict2(dict_)\n # check_init_file(dict_)\n\n # Distribute initialization information.\n data = read_data(dict_[\"ESTIMATION\"][\"file\"])\n num_treated = dict_[\"AUX\"][\"num_covars_treated\"]\n num_untreated = num_treated + dict_[\"AUX\"][\"num_covars_untreated\"]\n\n _, X1, X0, Z1, Z0, Y1, Y0 = process_data(data, dict_)\n\n if dict_[\"ESTIMATION\"][\"maxiter\"] == 0:\n option = \"init\"\n else:\n option = dict_[\"ESTIMATION\"][\"start\"]\n\n # Read data frame\n\n # define starting values\n x0 = start_values(dict_, data, option)\n opts, method = optimizer_options(dict_)\n dict_[\"AUX\"][\"criteria\"] = calculate_criteria(dict_, X1, X0, Z1, Z0, Y1, Y0, x0)\n dict_[\"AUX\"][\"starting_values\"] = backward_transformation(x0)\n rslt_dict = bfgs_dict()\n if opts[\"maxiter\"] == 0:\n rslt = adjust_output(None, dict_, x0, X1, X0, Z1, Z0, Y1, Y0, rslt_dict)\n else:\n opt_rslt = minimize(\n minimizing_interface,\n x0,\n args=(dict_, X1, X0, Z1, Z0, Y1, Y0, num_treated, num_untreated, rslt_dict),\n method=method,\n options=opts,\n )\n rslt = adjust_output(\n opt_rslt, dict_, opt_rslt[\"x\"], X1, X0, Z1, Z0, Y1, Y0, rslt_dict\n )\n # Print Output files\n print_logfile(dict_, rslt)\n\n if \"comparison\" in dict_[\"ESTIMATION\"].keys():\n if dict_[\"ESTIMATION\"][\"comparison\"] == 0:\n pass\n else:\n write_comparison(data, rslt)\n else:\n write_comparison(data, rslt)\n\n return rslt","def _init_table(self, table: \"Table\"):\n if not self.columns:\n self.columns = table.columns\n self._data = table.data","def __init__(__self__, *,\n column_name: Optional[str] = None,\n identifier: Optional[str] = None):\n if column_name is not None:\n pulumi.set(__self__, \"column_name\", column_name)\n if identifier is not None:\n pulumi.set(__self__, \"identifier\", identifier)","def __init__(self,columns_to_fix=[],rows_to_scan='all',keep_dummies=False):\n self.columns_to_fix = columns_to_fix\n self.rows_to_scan = rows_to_scan\n self.keep_dummies = keep_dummies","def __init__(self, column_id='', data_type='', data_format='',\n constant_value=''):\n self.column_id = column_id\n self.data_type = data_type\n self.data_format = data_format\n self.constant_value = constant_value","def _setup_dataframe(self, serie, metadata=None):\n header = self.get_data_header(serie, dataset='cnv')\n df = self.get_data_in_frame(serie, header, dataset='cnv')\n df = self.df_handler.map_column_names_of_dataframe(df)\n\n return df","def __init__(self, df):\n self._binarized_df = None\n self.__schema__ = 'viNet'\n self._confidence = viNetDataframeColumn.confidence.value\n self._predicted = viNetDataframeColumn.predicted.value\n self._groundtruth = viNetDataframeColumn.groundtruth.value\n\n self._validate(df)\n self._dataframe = df\n self._customer = None\n self._windfarm = None\n self._tag = 'Unknown Tag'\n self._config = 'Unknown Config'","def _init_node_parm(self, key):\n try:\n wf_data_conf = WorkflowDataConfFrame(key)\n self.data_conf = wf_data_conf.conf\n except Exception as e:\n raise Exception(\"dataconf_node_fame._init_node_parm Initializing Error : \" +str(e))","def _compute_variables(df: EDAFrame, cfg: Config) -> Dict[str, Any]:\n data: Dict[str, Any] = {}\n # variables\n if cfg.variables.enable:\n for col in df.columns:\n try:\n dtype = df.get_eda_dtype(col)\n # Since it will throw error if a numerical column is all-nan,\n # we transform it to categorical column.\n # We also transform to categorical for small cardinality numerical column.\n if df.get_missing_cnt(col) == df.shape[0]:\n srs = df.get_col_as_str(col, na_as_str=True)\n data[col] = nom_comps(srs, cfg)\n elif isinstance(dtype, (Nominal, GeoGraphy, GeoPoint)):\n data[col] = nom_comps(df.frame[col], cfg)\n elif isinstance(dtype, SmallCardNum):\n srs = df.get_col_as_str(col, na_as_str=False)\n data[col] = nom_comps(srs, cfg)\n elif isinstance(dtype, Continuous):\n data[col] = cont_comps(df.frame[col], cfg)\n # elif isinstance(dtype, DateTime):\n # data[col] = {}\n # data[col][\"stats\"] = calc_stats_dt(df.frame[col])\n # data[col][\"line\"] = dask.delayed(_calc_line_dt)(df.frame[[col]], \"auto\")\n else:\n raise ValueError(f\"unprocessed type in column{col}:{dtype}\")\n except:\n print(f\"error happended in column:{col}\", file=sys.stderr)\n raise\n return data","def __init__(self, df, name):\n try:\n self.df = df\n df.any()\n except AttributeError:\n self.df = df.toPandas()\n\n self.name = name\n\n if self.df.empty:\n raise Exception('Empty Dataset')","def _initialize(self, data):\n if not isinstance(data, pd.DataFrame):\n raise ValueError(\"Provided data is not a pandas.DataFrame\")\n\n super()._initialize(data)\n \n #if not self.num_cols:\n # raise ValueError(\"Entropy methods are not usable on continous data\")\n \n #prepare discretizer for num values\n self.pid_cols = self.num_cols\n intervals = 200\n bins = 8\n alphas = 0.25\n strategy = \"frequency\"\n n = len(self.num_cols)\n mins = list(data[self.pid_cols].min())\n maxs = list(data[self.pid_cols].max())\n \n self.pid = PartitionIncrementalDiscretizer(\n intervals=[intervals]*n, \n mins = mins,\n maxs= maxs,\n alphas=[alphas]*n, \n n_bins=[bins]*n,\n strategy=strategy) \n \n self.pid.fit(data[self.pid_cols])\n self.pid.transform(data[self.pid_cols])\n \n #alter num and cat cols to support default smr methods\n self.num_cols = []\n self.cat_cols += self.pid_cols\n for col in self.pid_cols:\n self.categories[col] = list(range(bins))\n \n self.imp = CategoricalRememberingNaNImputer(categories=self.categories)\n self.pipeline = Pipeline(steps=[\n ('discretizer', self.pid),\n ('imputer', self.imp)])\n \n #init feature dicts\n self.cat_counts = {}\n self.lab_counts = {}\n self.cat_probs = {}\n self.lab_probs = {}\n self.cat_entropies = {}\n self.lab_entropy = 0\n \n #set cat_col dicts\n for col in self.cat_cols:\n self.cat_counts[col] = {}\n self.cat_probs[col] = {}\n for label in self.labels:\n self.cat_counts[col][label] = {}","def _populate_df(self, df, objs,):\n for obj in objs:\n for prop in df.columns:\n df.loc[obj.name, prop] = getattr(obj, prop)","def initDataView(self,student_cols):\n self.notebook.initStudentsView(student_cols)\n #self.dataView.initStudentsView(student_cols)","def __init__(self, *args, **kwargs):\n \n self.dense = True\n\n # Create table\n super().__init__(*args, **kwargs)","def init_sources(self):\n\n columns = ['some', 'column', 'headers']\n self.columns = columns\n\n self.data_source = ColumnDataSource(data=dict(zip(columns, []*len(columns))))","def set_data(self, df):\n self.df = df","def __init__(self, **kwargs):\n perform = kwargs.get(\"perform\", True)\n kwargs[\"perform\"] = False\n self._data = dict(self._DATA)\n self._text = self._DUMMY_TEXT\n self.df = pd.DataFrame()\n self.update(**kwargs)\n if perform:\n self.perform()","def from_dict(self, data: dict):\n super().from_dict(data)\n if self.data is not None:\n try:\n self.data[\"columns\"] = np.array(\n [int(col) for col in self.data[\"columns\"]]\n )\n except:\n pass\n self.data = pd.DataFrame(\n data=self.data[\"data\"],\n columns=self.data[\"columns\"],\n index=self.data[\"index\"],\n )","def _init_params_default(self):\n # if there are some nan -> mean impute\n Yimp = self.Y.copy()\n Inan = sp.isnan(Yimp)\n Yimp[Inan] = Yimp[~Inan].mean()\n if self.P == 1:\n C = sp.array([[Yimp.var()]])\n else:\n C = sp.cov(Yimp.T)\n C /= float(self.n_randEffs)\n for ti in range(self.n_randEffs):\n self.getTraitCovarFun(ti).setCovariance(C)","def __init__(self,columns_to_fix=[],convert_dict={'Y':1,'N':0}):\n self.columns_to_fix = columns_to_fix\n self.convert_dict = convert_dict","def __init__(self, col, val):\n\n self.__col = col\n self.__val = val","def reset_parameters(self, param_init):\n logger.info('===== Initialize %s with uniform distribution =====' % self.__class__.__name__)\n for n, p in self.named_parameters():\n if 'conv' in n:\n continue\n init_with_uniform(n, p, param_init)","def initialize(self):\r\n if not self.context:\r\n self.context = SQLContext(self.url, self.connection, self.schema)\r\n if self.table is None:\r\n self.table = self.context.table(self.table_name)\r\n if not self.fields:\r\n self.read_fields()\r\n self.field_names = self.fields.names()","def reset_parameters(self, param_init):\n logger.info('===== Initialize %s with uniform distribution =====' % self.__class__.__name__)\n for n, p in self.named_parameters():\n if 'score.monotonic_energy.v.weight_g' in n or 'score.monotonic_energy.r' in n:\n logger.info('Skip initialization of %s' % n)\n continue\n if 'score.monotonic_energy.conv1d' in n:\n logger.info('Skip initialization of %s' % n)\n continue\n if 'score.chunk_energy.v.weight_g' in n or 'score.chunk_energy.r' in n:\n logger.info('Skip initialization of %s' % n)\n continue\n if 'linear_lm_gate.fc.bias' in n and p.dim() == 1:\n nn.init.constant_(p, -1.0)\n logger.info('Initialize %s with %s / %.3f' % (n, 'constant', -1.0))\n continue\n init_with_uniform(n, p, param_init)","def variable(self, col: str, name: str, init_fn, *init_args) -> Variable:\n if not self._initialization_allowed:\n raise ValueError(\n 'Variables must be initialized in `setup()` or in a method '\n 'wrapped in `@compact`')\n if self._name_taken(name):\n raise errors.NameInUseError('variable', name, self.__class__.__name__)\n v = self.scope.variable(col, name, init_fn, *init_args)\n self._state.children[name] = col\n return v","def _init_slot_variable(\n self, layer_name, embed_var, slot_name, initial_value\n ):\n if (\n layer_name not in self._tls._slot_variables\n or slot_name not in self._tls._slot_variables[layer_name]\n ):\n slot_var_name = \"%s/%s\" % (embed_var._shared_name, slot_name)\n slot_var = self._opt.add_weight(\n name=slot_var_name,\n shape=(None, None),\n dtype=embed_var.dtype,\n initializer=initial_value,\n trainable=False,\n )\n slot_variables_dict = self._tls._slot_variables.setdefault(\n layer_name, {}\n )\n slot_variables_dict[slot_name] = slot_var\n else:\n slot_var = self._tls._slot_variables[layer_name][slot_name]\n slot_var.assign(initial_value)\n return slot_var","def set_meta(self, name, value):\n # note sometimes during .view, we won't have this var available\n check_meta = not hasattr(self, '_init_arg_check') or self._init_arg_check\n if check_meta and name in self._init_args:\n # note this is largely a failsafe, we shouldn't get to this\n # point via setattr since it'll match the hasattr(self.pobj, name)\n raise Exception('Cannot have member variables that clash with pandas constructor args')\n object.__setattr__(self, name, value)","def __init__(self, X_columns, X_dtypes):\n self.X_columns = X_columns\n self.X_dtypes = X_dtypes","def set_initial_value(self, rel_name, m):\n if not self.is_relation(rel_name):\n raise RelationNameError(rel_name, 'Relation is not defined.')\n expected_shape = self.get_shape(rel_name)\n if m.shape[1] != expected_shape[1]:\n raise ValueError(\n 'relation and initial_value have different columns: %d vs %d' %\n (expected_shape[1], m.shape[1]))\n if self.is_dense(rel_name):\n self._np_initval[rel_name] = m.transpose()\n else:\n self._np_initval[rel_name] = scipy.sparse.coo_matrix(m.transpose())","def initialisation(self):\n self.create_variables()\n self.create_placeholders()\n self.build_model()\n self.reset_lr(None, True)\n self.build_loss()\n self.initialised = True","def __init__(self, **kwargs):\n\t\tself.vars = kwargs\n\t\tself.old_vars = None","def compile_dataframe_default(self):\n\t\tdata = [\n\t\t\t['ford','mustang','coupe','A'],\n\t\t\t['chevy','camaro','coupe','B'],\n\t\t\t['ford','fiesta','sedan','C'],\n\t\t\t['ford','focus','sedan','A'],\n\t\t\t['ford','taurus','sedan','B'],\n\t\t\t['toyota','camry','sedan','B']\n\t\t]\n\n\t\tself.data = pd.DataFrame(data, columns = self.data_cols)","def correct_params():\n params = {\n \"probs\": [0.1, 0.9],\n \"treat\": 2,\n \"data\": pd.DataFrame(data={\"id\": np.arange(100), \"block\": np.arange(100)}),\n \"idx_col\": \"id\",\n }\n return params","def init_agg(self, col):\n\t\traise NotImplementedError()","def preinitialize(self):\n for group in self.param_groups:\n for p in group['params']:\n if group['momentum'] != 0:\n self.state[p][\"momentum_buffer\"] = torch.zeros_like(\n p, device=\"cpu\"\n ).to(p.device)","def _parse_initsol(self) :\n\t\tlogging.debug(\"Parsing initsol initial solution\")\n\n\t\t# Init initsol as an empty dict\n\t\tself.initsol = {}\n\n\t\tfor varname in ['alpha','beta','g','h'] : \n\t\t\tself._parse_var_initsol(varname)","def __init__(self, df):\n self.df = df\n self.min_interval_in_seconds = 99999999999","def __init__(self):\n\n self.active_df = pd.DataFrame([], columns=['txt_name', 'missing_data_days'])","def __init__(self):\n self.model = None\n self.joined_datasets = None\n self.id_col = None\n self.val_col = None\n self.pop_col = None\n self.total_population_per_unit = None\n self.centroids_of_areal_data = None\n self.prepared_data = None\n self.unknown_area_id = None\n\n # Parameters\n self.lags = None\n self.step = None\n self.min_no_of_observations = None\n self.max_search_radius = None","def __init__(self, df=None):\n\n if df is not None:\n # set attributes with list of unique stimulus combinations / categories\n self.all_stimulus_pairs = df.combo.unique().tolist()\n\n # set attribute for number of unique dimensionality reductions\n self.unique_subspace_dims = df.n_components.unique().tolist()\n\n # get df columns\n self.numeric_keys = df.select_dtypes(include=['float64']).columns\n self.object_keys = df.select_dtypes(include=['object']).columns\n\n # get number of jacknnifes\n self.n_jacks = df.jack_idx.values.astype(np.float).max() + 1\n \n # Collapse over jackknifes. Save mean / sd of all numeric params.\n log.info(\"Collapsing results over jackknifes\")\n # returns a dataframe for numeric results\n self.numeric_results = self._collapse_numeric(df) \n \n # objects = arrays (e.g. arrays of eigenvectors, evals, decoding vectors).\n # returns a dictionary of data frames\n self.array_results = self._collapse_objects(df)","def __init__(self, columns=()):\n self.columns = list(columns)\n\n # Create internal dictionary for faster access\n self.column_dict = {}\n\n for column in self.columns:\n self.column_dict[column.column_id] = column","def initVariable(self, state):\n self.nb_agent = state.getNumAgents()\n self.first_call = False","def _init_state(self, init_state=None, init_cov=None): \n ## Initialize the BMI state, assuming \n nS = self.n_states \n if init_state == None: \n init_state = np.mat( np.zeros([nS, 1]) ) \n if self.include_offset: init_state[-1,0] = 1 \n if init_cov == None: \n init_cov = np.mat( np.zeros([nS, nS]) )\n self.state = bmi.GaussianState(init_state, init_cov)","def initialize(self, numSamples, sampleMethod):\n initSamples = initial_samples(self.lb, self.ub, sampleMethod, numSamples)\n if sum(self.xID) != 0:\n xUB = [\n self.ub[np.where(self.xID == 1)[0][0]]] * len(self.xID)\n xSamples = initial_samples([0] * len(self.xID), xUB, 'rand-wor', numSamples)\n for var in range(len(self.varType)):\n if 'i' in self.varType[var] or 'd' in self.varType[var]:\n initSamples[:, var] = np.rint(initSamples[:, var])\n\n if sum(self.xID) != 0:\n initSamples = initSamples * (self.cID + self.iID + self.dID) + xSamples * self.xID\n return initSamples","def changeInitValues(self, betas):\n\n if self.name in betas:\n self.initValue = betas[self.name]","def fill_nid(df, nid_dict):\n assert 'year' in df.columns, \"DataFrame doesn't have a 'year' column\"\n df['nid'] = df['year'].map(nid_dict)\n return df","def standard_init(self, data):\n comm = self.comm\n H = self.H\n my_y = data['y']\n my_N, D = my_y.shape\n\n assert D == self.D\n\n # Calculate averarge W\n W_mean = parallel.allmean(my_y, axis=0, comm=comm) # shape: (D, )\n\n # Calculate data variance\n sigma_sq = parallel.allmean((my_y-W_mean)**2, axis=0, comm=comm) # shape: (D, )\n sigma_init = np.sqrt(sigma_sq).sum() / D # scalar\n\n # Initial W\n noise = sigma_init/4.\n W_init = W_mean + np.random.normal(scale=noise, size=[H, D]) # shape: (H, D)\n\n #Create and set Model Parameters, W columns have the same average!\n model_params = {\n 'W' : W_init, \n 'pi' : 1./H,\n 'sigma' : sigma_init\n }\n\n return model_params","def _init_tkvars(self,PO):\n for name,param in PO.params().items():\n self._create_tkvar(PO,name,param)","def _reload(self):\n if os.path.exists(self.filename):\n self.data = pd.read_csv(self.filename)\n else:\n self.data = pd.DataFrame(columns=self.unique_keys)\n\n # Set these default values\n # if 'weight_rescale' not in self.data.columns:\n # self.data['weight_rescale'] = 'none'\n # if 'norm' not in self.data.columns:\n # self.data['norm'] = 'softmax'\n # if 'update' not in self.data.columns:\n # self.data['update'] = 'all'\n # if 'replay' not in self.data.columns:\n # self.data['replay'] = False\n if 'debug' not in self.data.columns:\n self.data['debug'] = False\n\n # if 'tie' not in self.data.columns:\n # self.data['tie'] = False\n\n if 'update_length' not in self.data.columns:\n self.data['update_length'] = 0\n # for key in self.unique_keys:\n # self.data[key] = np.nan\n # Remaining set to None\n # for k in self.check_keys:\n # if k not in self.data.columns:\n # self.data[k] = None","def record_variable_inits(self):\n old_init = getattr(variables.Variable, '__init__')\n\n def record(*args, **kwargs):\n self._in_variable_creation = True\n old_init(*args, **kwargs)\n self._in_variable_creation = False\n\n setattr(variables.Variable, '__init__', record)\n yield\n setattr(variables.Variable, '__init__', old_init)","def init_data(title, **kwds):\n if(title in data_conf.keys()):\n data_conf[title].update(kwds)\n else:\n logging.debug(\"Initializing source '%s.'\" % title)\n data_conf[title] = kwds\n if(ipc.mpi.is_slave()):\n ipc.mpi.send('__data_conf__', data_conf)","def init_individual(index, columns, initializer=None):\n \n ind = pd.DataFrame(0,index=index, columns=columns)\n \n if initializer is not None:\n \n # sets up the DataFrame with the initializer data\n ind.loc[:, 2:] = initializer.loc[:,1:]\n ind.loc[:, 'in'] = initializer.loc[:, 'in']\n \n # sets the age\n for i in index:\n if ind.loc[i,'in'] != 0:\n ind.loc[i,'age'] = 1\n else:\n ind.loc[i,'age'] = 0\n \n # randomly flips a company in or out of the system\n if random.random() < 0.05:\n if ind.loc[i,'in'] == 0:\n ind.loc[i,'in'] = 1\n ind.loc[i,'age'] = 1\n for j in index:\n if i == j:\n ind.loc[i,j] = 0\n else:\n if random.random() < 0.2:\n ind.loc[i,j] = 1\n ind.loc[j,i] = 1\n else:\n ind.loc[i,:] = 0\n ind.loc[:,i] = 0\n \n # randomly flips correlations\n if ind.loc[i,'in'] == 1: \n for j in index:\n if random.random() < 0.05 and i != j:\n ind.loc[i,j] = abs(ind.loc[i,j] - 1)\n ind.loc[j,i] = ind.at[i,j]\n\n else:\n for i in index:\n # randomly places companies in or out of the network\n if random.random() < 0.2:\n ind.loc[i,'in'] = 1\n ind.loc[i,'age'] = 1\n \n # randomly assigns correlations for companies in the network\n if ind.loc[i,'in'] == 1:\n for j in index:\n if i == j:\n ind.loc[i,j] = 0\n else:\n if random.random() < 0.2\n ind.loc[i,j] = 1\n ind.loc[j,i] = ind.at[i,j]\n \n ind.fillna(0)\n\n return ind"],"string":"[\n \"def init(param):\\n MODULE_HELPER.check_parameter(param, key='featureCount_exec', dtype=str)\\n MODULE_HELPER.check_parameter(param, key='featureCount_t', dtype=str)\\n MODULE_HELPER.check_parameter(param, key='featureCount_id', dtype=str)\\n MODULE_HELPER.check_parameter(param, key='featureCount_by_meta', dtype=bool)\\n MODULE_HELPER.check_parameter(param, key='Rscript_exec', dtype=str)\\n\\n #deriving the stranded parameter\\n if param['stranded'] == 'reverse':\\n param['featureCount_s'] = '2'\\n elif param['stranded'] == 'yes':\\n param['featureCount_s'] = '1'\\n else:\\n param['featureCount_s'] = '0'\",\n \"def setup_initial_values(self, init_params={}):\\n for row in self.panel[1:]:\\n for widget in row:\\n if widget.name in init_params:\\n widget.value = init_params[widget.name]\",\n \"def __init__(self, df):\\n self.original_data = df\\n self.preprocessed_data = pd.DataFrame()\",\n \"def reset_parameters(self, param_init):\\n logger.info('===== Initialize %s with lecun style =====' % self.__class__.__name__)\\n for n, p in self.named_parameters():\\n init_with_lecun_normal(n, p, param_init)\",\n \"def __init__(self, df):\\n self.df = df\",\n \"def init_model_df(self):\\n\\n self.model_df = pd.DataFrame(columns=self.query_df[self.column_name].unique())\\n\\n # add _TIMESTAMP column to dataframe\\n self.model_df[self.column_index] = self.min_increments\\n\\n # set row index to _TIMESTAMP\\n self.model_df.set_index(self.column_index, inplace=True)\",\n \"def initialize_variables(self):\\n self.sess.run(self.init)\",\n \"def reset_parameters_lecun(self, param_init=0.1):\\n logger.info('===== Initialize %s with lecun style =====' % self.__class__.__name__)\\n for n, p in self.named_parameters():\\n init_with_lecun_normal(n, p, param_init)\",\n \"def _parse_var_initsol(self,varname) :\\n\\t\\tinitsol = self.ss.constraint.initsol\\n\\t\\tparams = getattr(initsol,varname)\\n\\t\\tnvars = len(self.ss.variables) # num of variables\\n\\n\\t\\tif varname in ('alpha','beta') : \\n\\t\\t\\tself.initsol[varname] = np.ones(nvars)\\n\\t\\t\\tkeys = params.keys()\\n\\t\\t\\tself.initsol[varname][:] = params['defaultInitialValue']\\n\\t\\t\\tfor key in keys : \\n\\t\\t\\t\\tif re.match(varname+'_\\\\d+',key)\\t:\\n\\t\\t\\t\\t\\tidx = int(key.split('_')[1])\\n\\t\\t\\t\\t\\tself.initsol[varname][idx-1] = params[key]\\n\\t\\telif varname in ('g','h') :\\n\\t\\t\\tself.initsol[varname] = np.ones([nvars,nvars])\\n\\t\\t\\tkeys = params.keys()\\n\\t\\t\\tself.initsol[varname][:] = params['defaultInitialValue']\\n\\t\\t\\tfor key in keys : \\n\\t\\t\\t\\tif re.match(varname+'_\\\\d+_\\\\d+',key)\\t:\\n\\t\\t\\t\\t\\tidr,idc = map(int,(key.split('_')[1:3]))\\n\\t\\t\\t\\t\\tself.initsol[varname][idr-1][idc-1] = params[key]\\n\\t\\t\\n\\t\\telse :\\n\\t\\t\\tlogging.error(\\\"Unrecognized varname %s quitting..\\\" \\\\\\n\\t\\t\\t%(varname))\\n\\t\\t\\tsys.exit(1)\",\n \"def __init__(self, df):\\n self.data = df\",\n \"def prepare_estimation(model_params_init_file_name, lower, upper):\\n\\n # Read in data and init file sources\\n model_params_df = pd.read_pickle(model_params_init_file_name)\\n model_params_df[\\\"lower\\\"] = lower\\n model_params_df[\\\"upper\\\"] = upper\\n\\n return model_params_df\",\n \"def _prepare_initial_params_df(self):\\r\\n self.logger.info(\\\"Preparing initial params\\\")\\r\\n\\r\\n # First we must normalize the weights.\\r\\n #\\r\\n total_weights = self.optimizer_config.initial_points_pareto_weight \\\\\\r\\n + self.optimizer_config.initial_points_cached_good_params_weight \\\\\\r\\n + self.optimizer_config.initial_points_random_params_weight\\r\\n assert total_weights > 0\\r\\n\\r\\n initial_points_pareto_fraction = self.optimizer_config.initial_points_pareto_weight / total_weights\\r\\n initial_points_cached_good_fraction = self.optimizer_config.initial_points_cached_good_params_weight / total_weights\\r\\n\\r\\n num_initial_points = self.optimizer_config.num_starting_configs\\r\\n\\r\\n # Let's start with the pareto points.\\r\\n #\\r\\n pareto_params_df = self.pareto_frontier.params_for_pareto_df\\r\\n if pareto_params_df is None:\\r\\n pareto_params_df = pd.DataFrame()\\r\\n\\r\\n num_desired_pareto_points = math.floor(num_initial_points * initial_points_pareto_fraction)\\r\\n num_existing_pareto_points = len(pareto_params_df.index)\\r\\n\\r\\n if num_existing_pareto_points > 0:\\r\\n if num_desired_pareto_points < num_existing_pareto_points:\\r\\n pareto_params_df = pareto_params_df.sample(n=num_desired_pareto_points, replace=False, axis='index')\\r\\n self.logger.info(f\\\"Using {len(pareto_params_df.index)} of {num_existing_pareto_points} pareto points as starting configs.\\\")\\r\\n else:\\r\\n self.logger.info(\\\"There are no existing pareto points.\\\")\\r\\n\\r\\n # Now let's take the cached good points.\\r\\n #\\r\\n num_desired_cached_good_points = math.floor(num_initial_points * initial_points_cached_good_fraction)\\r\\n cached_params_df = pd.DataFrame()\\r\\n if self._good_configs_from_the_past_invocations_df is not None:\\r\\n if num_desired_cached_good_points < len(self._good_configs_from_the_past_invocations_df.index):\\r\\n cached_params_df = self._good_configs_from_the_past_invocations_df.sample(n=num_desired_cached_good_points, replace=False, axis='index')\\r\\n else:\\r\\n cached_params_df = self._good_configs_from_the_past_invocations_df.copy(deep=True)\\r\\n self.logger.info(\\r\\n f\\\"Using {len(cached_params_df.index)} out of {len(self._good_configs_from_the_past_invocations_df.index)} \\\"\\r\\n f\\\"cached good configs as starting configs\\\"\\r\\n )\\r\\n else:\\r\\n self.logger.info(\\\"No cached params are available.\\\")\\r\\n\\r\\n # Finally, let's generate the random points.\\r\\n #\\r\\n num_desired_random_points = num_initial_points - len(pareto_params_df.index) - len(cached_params_df.index)\\r\\n random_params_df = self.optimization_problem.parameter_space.random_dataframe(num_samples=num_desired_random_points)\\r\\n self.logger.info(f\\\"Using {len(random_params_df.index)} random points as starting configs.\\\")\\r\\n\\r\\n initial_params_df = pd.concat([pareto_params_df, cached_params_df, random_params_df])\\r\\n initial_params_df.reset_index(drop=True, inplace=True)\\r\\n return initial_params_df\",\n \"def init(self, rng_key, num_warmup, init_params, model_args, model_kwargs):\\n raise NotImplementedError\",\n \"def __init__(self, df, Z, Q, H, T, R, d, c, var='dep_var', secondvar=None, method='iterate'):\\r\\n self.method = method\\r\\n # Initialize all the system matrices as attributes of the class\\r\\n self.Z = Z\\r\\n self.Q = Q\\r\\n self.H = H\\r\\n self.T = T\\r\\n self.R = R\\r\\n self.d = d\\r\\n self.c = c\\r\\n # same for parameters and objects\\r\\n self.df = df\\r\\n self.var = var\\r\\n # The data itself\\r\\n self.y = np.array(df[var].values.flatten())\\r\\n self.times = df.index\\r\\n # Options for the minimizer\\r\\n self.options = {'eps': 1e-09,\\r\\n 'maxiter': 200}\",\n \"def init_parameters(\\n self, init_fun: Optional[NNInitFunc] = None, *, seed: Optional[PRNGKeyT] = None\\n ):\\n if init_fun is None:\\n init_fun = normal(stddev=0.01)\\n\\n rng = nkjax.PRNGSeq(nkjax.PRNGKey(seed))\\n\\n def new_pars(par):\\n return jnp.asarray(\\n init_fun(rng.take(1)[0], shape=par.shape, dtype=par.dtype),\\n dtype=par.dtype,\\n )\\n\\n self.parameters = jax.tree_map(new_pars, self.parameters)\",\n \"def __init__(self, df):\\n self.df = df\\n if 'time' in self.df.columns:\\n self.time = True\\n self.df = self.df.sort('time')\\n else:\\n self.time = False\\n if 'condition' not in self.df.columns:\\n self.df['condition'] = 'all'\",\n \"def initialise(self):\\n self.sc.init.exec_action(self.variables)\",\n \"def initial_parameters(ship_data: dict) -> dict:\\n\\n mask = df_parameters[\\\"brix_lambda\\\"].notnull()\\n df_parameters.loc[mask, \\\"brix_prime\\\"] = df_parameters.loc[mask].apply(\\n calculate_prime, ship_parameters=ship_data, axis=1\\n )\\n\\n df_parameters[\\\"prime\\\"] = df_parameters[\\\"brix_prime\\\"]\\n\\n df_parameters.loc[\\\"Ydelta\\\", \\\"prime\\\"] = 0.003 # Just guessing\\n df_parameters.loc[\\\"Ndelta\\\", \\\"prime\\\"] = (\\n -df_parameters.loc[\\\"Ydelta\\\", \\\"prime\\\"] / 2\\n ) # Just guessing\\n\\n df_parameters.loc[\\\"Nu\\\", \\\"prime\\\"] = 0\\n df_parameters.loc[\\\"Nur\\\", \\\"prime\\\"] = 0\\n # df_parameters.loc[\\\"Xdelta\\\", \\\"prime\\\"] = -0.001\\n df_parameters.loc[\\\"Xr\\\", \\\"prime\\\"] = 0\\n df_parameters.loc[\\\"Xrr\\\", \\\"prime\\\"] = 0.000\\n df_parameters.loc[\\\"Xu\\\", \\\"prime\\\"] = 0\\n df_parameters.loc[\\\"Xuu\\\", \\\"prime\\\"] = 0\\n df_parameters.loc[\\\"Xv\\\", \\\"prime\\\"] = 0\\n df_parameters.loc[\\\"Xvr\\\", \\\"prime\\\"] = 0\\n df_parameters.loc[\\\"Yu\\\", \\\"prime\\\"] = 0\\n df_parameters.loc[\\\"Yur\\\", \\\"prime\\\"] = 0.00\\n\\n df_parameters.loc[\\\"Nuv\\\", \\\"prime\\\"] = 0.0\\n df_parameters.loc[\\\"Xthrust\\\", \\\"prime\\\"] = 1.0\\n df_parameters.loc[\\\"Yrdeltadelta\\\", \\\"prime\\\"] = 0.0\\n df_parameters.loc[\\\"Xvdelta\\\", \\\"prime\\\"] = 0.0\\n df_parameters.loc[\\\"Xdeltadelta\\\", \\\"prime\\\"] = 0.0\\n df_parameters.loc[\\\"Yvdeltadelta\\\", \\\"prime\\\"] = 0.0\\n df_parameters.loc[\\\"Nrdeltadelta\\\", \\\"prime\\\"] = 0.0\\n df_parameters.loc[\\\"Yuv\\\", \\\"prime\\\"] = 0.0\\n df_parameters.loc[\\\"Nvdeltadelta\\\", \\\"prime\\\"] = 0.0\\n\\n df_parameters.loc[\\\"Ythrustdelta\\\", \\\"prime\\\"] = 0.0\\n df_parameters.loc[\\\"Nthrustdelta\\\", \\\"prime\\\"] = 0.0\\n\\n parameters = df_parameters[\\\"prime\\\"].dropna().to_dict()\\n\\n return parameters\",\n \"def init_columns(cycle_df, datatype):\\n (cycle_ind_col, data_point_col, volt_col, curr_col, dis_cap_col, char_cap_col, charge_or_discharge) = col_variables(datatype)\\n assert type(cycle_df) == pd.DataFrame\\n assert volt_col in cycle_df.columns\\n assert dis_cap_col in cycle_df.columns\\n assert char_cap_col in cycle_df.columns\\n\\n cycle_df = cycle_df.reset_index(drop=True)\\n cycle_df['dV'] = None\\n cycle_df['Discharge_dQ'] = None\\n cycle_df['Charge_dQ'] = None\\n #cycle_df['Discharge_dQ/dV'] = None\\n #cycle_df['Charge_dQ/dV'] = None\\n return cycle_df\",\n \"def sa_pandas_init(self):\\n\\n lca = self.lca\\n\\n ind_activity = 0\\n ind_product = 1\\n ind_biosphere = 2\\n\\n cols = []\\n rows = []\\n inputs = []\\n\\n #All exchanges in inputs\\n for input_ in self.inputs:\\n\\n if input_ == 'biosphere':\\n continue\\n\\n for i in self.inputs_dict[input_]['tech_params']:\\n act = lca.reverse_dict() [ind_activity] [i['col']]\\n prod = lca.reverse_dict() [ind_product] [i['row']]\\n cols += [ bw.get_activity(act) ['name'] ]\\n rows += [ bw.get_activity(prod)['name'] ]\\n inputs += [input_]\\n for j in self.inputs_dict[input_]['bio_params']:\\n act = lca.reverse_dict() [ind_activity] [j['col']]\\n bio = lca.reverse_dict() [ind_biosphere] [j['row']]\\n cols += [ bw.get_activity(act) ['name'] ]\\n rows += [ bw.get_activity(prod)['name'] ]\\n inputs += [input_]\\n\\n if self.parameters != None:\\n # All parameters\\n parameters_names_list = [name for name in self.parameters_array['name']]\\n cols += parameters_names_list\\n rows += parameters_names_list\\n inputs += ['Parameters']*len(parameters_names_list)\\n\\n df = pd.DataFrame([inputs, rows, cols], index = ['Inputs', 'Products or flows', 'Activities'])\\n df = df.transpose()\\n\\n self.sensitivity_indices_df = df\",\n \"def init_vars(self):\\n # type: () -> None\\n raise NotImplementedError\",\n \"def init_params(self):\\n blah\",\n \"def _autoInitPars(self):\\n for p in self._pars:\\n setattr(self,p,self.defaultparval)\",\n \"def __init__(self, df, df_test, n_days, length, style) :\\n self.n_days = n_days\\n self.length = length\\n self.df = df\\n self.df_test = df_test\\n self.features = len(df.columns) - 1\\n self.style = style\\n self.df_true = df_test.copy()\",\n \"def __init__(self, df, df_test, n_days, length, style) :\\n self.n_days = n_days\\n self.length = length\\n self.df = df\\n self.df_test = df_test\\n self.features = len(df.columns) - 1\\n self.style = style\\n self.df_true = df_test.copy()\",\n \"def _data_dep_init(self, inputs):\\n from tensorflow.python.ops.nn import moments\\n from tensorflow.python.ops.math_ops import sqrt\\n\\n with variable_scope.variable_scope('data_dep_init'):\\n # Generate data dependent init values\\n activation = self.layer.activation\\n self.layer.activation = None\\n x_init = self.layer.call(inputs)\\n m_init, v_init = moments(x_init, self.norm_axes)\\n scale_init = 1. / sqrt(v_init + 1e-10)\\n\\n # Assign data dependent init values\\n self.layer.g = self.layer.g * scale_init\\n self.layer.bias = (-m_init * scale_init)\\n self.layer.activation = activation\\n self.initialized = True\",\n \"def init(*args):\\n global dataset\\n dataset = args[0]\",\n \"def __init__(self, df):\\n self.df = df\\n self._set_hash()\",\n \"def _init_param_source():\\n\\n amp_sine = 1\\n amp_rand = 1\\n rollover = 100 # length of data to display\\n update_delay = 100 # time between delays of update in ms\\n param_source = ColumnDataSource(dict(\\n amp_sine=[amp_sine],\\n amp_rand=[amp_rand],\\n rollover=[rollover],\\n update_delay=[update_delay]\\n ))\\n return param_source\",\n \"def _optimizer_state_init(opt_states):\\n prefix_list = [\\\"moments\\\", \\\"accum\\\", \\\"moment1\\\", \\\"moment2\\\", \\\"lamb_m\\\", \\\"lamb_v\\\", \\\"mean_grad\\\",\\n \\\"mean_square\\\", \\\"prev\\\"]\\n for opt_param in opt_states:\\n prefix = opt_param.name[:opt_param.name.find(\\\".\\\")]\\n if opt_param.has_init and (prefix in prefix_list or opt_param.name == \\\"global_step\\\"):\\n opt_param.init_data()\",\n \"def reset_parameters(self, param_init):\\n logger.info('===== Initialize %s with uniform distribution =====' % self.__class__.__name__)\\n for n, p in self.named_parameters():\\n init_with_uniform(n, p, param_init)\",\n \"def init_locals(self):\\n pass\",\n \"def parameter_initialization(self):\\n dictsize = settings.PARS.get('numBases')\\n numClass = self.train_labels.shape[0] # number of objects\\n Dinit = np.empty((self.train_feats.shape[0], 0)) # for C-Ksvd and D-Ksvd\\n dictLabel = np.empty((numClass, 0), dtype=np.int)\\n numPerClass = dictsize//numClass\\n param1 = {\\n 'mode': 2,\\n 'K': settings.PARS.get('numBases'), # size of the dictionary\\n 'lambda1': settings.PARS.get('lambda_'),\\n 'lambda2': 0,\\n 'iter': settings.PARS.get('iterationini')\\n }\\n param2 = {\\n 'lambda1': settings.PARS.get('lambda_'),\\n 'lambda2': 0,\\n 'mode': 2\\n }\\n\\n for classid in range(numClass):\\n col_ids = np.array(np.nonzero(self.train_labels[classid, :] == 1)).ravel()\\n # ensure no zero data elements are chosen\\n data_ids = np.array(np.nonzero(np.sum(self.train_feats[:, col_ids]**2, axis=0) > 1e-6)).ravel()\\n\\n # Raising an error if any zero lement is found\\n if col_ids.shape[0] != data_ids.shape[0]:\\n raise DatasetZeroElementFound\\n\\n # Initilization for LC-KSVD (perform KSVD in each class)\\n Dpart = self.train_feats[:, col_ids[np.random.choice(data_ids, numPerClass, replace=False)]]\\n param1['D'] = Dpart # initial dictionary\\n Dpart = trainDL(self.train_feats[:, col_ids[data_ids]], **param1)\\n Dinit = np.c_[Dinit, Dpart]\\n labelvector = np.zeros((numClass, 1), dtype=np.int)\\n labelvector[classid] = 1\\n dictLabel = np.c_[dictLabel, np.tile(labelvector, (1, numPerClass))]\\n\\n param1['D'] = np.asfortranarray(Dinit) # initial dictionary\\n # RuntimeError: matrix arg 10 must be a 2d double Fortran Array\\n self.train_feats = self.train_feats if np.isfortran(self.train_feats) else np.asfortranarray(self.train_feats)\\n Dinit = trainDL(self.train_feats, **param1)\\n Xinit = lasso(self.train_feats, Dinit, **param2)\\n\\n # learning linear classifier parameters\\n tmp = np.linalg.inv(Xinit@Xinit.T+np.eye(*(Xinit@Xinit.T).shape))@Xinit\\n Winit = tmp@self.train_labels.T\\n Winit = Winit.T\\n\\n Q = np.zeros((dictsize, self.train_feats.shape[1])) # energy matrix\\n\\n for frameid in range(self.train_feats.shape[1]):\\n label_training = self.train_labels[:, frameid]\\n maxid1 = label_training.argmax(0)\\n\\n for itemid in range(Dinit.shape[1]):\\n label_item = dictLabel[:, itemid]\\n maxid2 = label_item.argmax(0)\\n\\n if maxid1 == maxid2:\\n Q[itemid, frameid] = 1\\n\\n Tinit = tmp@Q.T\\n Tinit = Tinit.T\\n\\n return Dinit, Winit, Tinit, Q\",\n \"def __init__(self, df):\\n # Transpose dataframe data to make it column oriented\\n self.data = df.values.T\\n\\n assert df.index.tzinfo is not None\\n self.tz = df.index.tzinfo\\n self.index = df.index.tz_localize(None)\\n\\n\\n self.cols = {}\\n for i, col_name in enumerate(df.columns):\\n self.cols[col_name] = i\",\n \"def __init__(self):\\n self.current_year = datetime.date.today().year\\n self.random_column_mappings = collections.defaultdict(dict)\",\n \"def reset_parameters(self, param_init):\\n logger.info('===== Initialize %s =====' % self.__class__.__name__)\\n for n, p in self.named_parameters():\\n if p.dim() == 1:\\n nn.init.constant_(p, 0.0)\\n logger.info('Initialize %s with %s / %.3f' % (n, 'constant', 0.0))\\n elif p.dim() == 2:\\n nn.init.uniform_(p, a=-param_init, b=param_init)\\n logger.info('Initialize %s with %s / %.3f' % (n, 'uniform', param_init))\\n else:\\n raise ValueError(n)\",\n \"def _initialize_df(self, df):\\n df['values'] = (self.tc.instrument_returns['cumulative'] *\\n self.tc.starting_cash).mul(self.target_weights, axis=1).values * (1 - self.tc.commission)\\n df['allocations'] = self.df['values'].div(df['values'].sum(axis=1), axis=0)\\n df['returns'] = (df['values'].sum(axis=1)).pct_change(1).fillna(0)\",\n \"def __init__(self, df):\\n self.original_data = df\\n self.cleaned_data = pd.DataFrame()\",\n \"def __init__(self, df,\\n target_cols,\\n problem_type,\\n num_folds = 3,\\n shuffle = False,\\n random_state = 0):\\n\\n self.dataframe = df\\n self.target_cols = target_cols\\n self.num_targets = len(target_cols)\\n self.problem_type = problem_type\\n self.shuffle = shuffle\\n self.num_folds = num_folds\\n self.random_state = random_state\\n\\n if self.shuffle:\\n self.dataframe = self.dataframe.sample(frac = 1,\\n random_state = self.random_state).reset_index(drop = True)\\n\\n self.dataframe[\\\"kfold\\\"] = -1\",\n \"def __init__(\\n self,\\n x,\\n effect_size='EFFECTSIZE',\\n p='P',\\n snp='SNP',\\n gene='GENE',\\n annotation=None,\\n logp=True\\n ):\\n\\n # checking the validity of the arguments\\n\\n # Make sure you have effect_size and p columns and that they are of\\n # numeric type\\n if effect_size not in x.columns.values:\\n raise KeyError(\\\"Column %s not found in 'x' data.frame\\\"\\n % effect_size)\\n else:\\n if not is_numeric_dtype(x[effect_size].dtype):\\n raise TypeError(\\\"%s column should be numeric. Do you have \\\"\\n \\\"'X', 'Y', 'MT', etc? If so change to \\\"\\n \\\"numbers and try again.\\\" % effect_size)\\n\\n if p not in x.columns.values:\\n raise KeyError(\\\"Column %s not found in 'x' data.frame\\\" % p)\\n else:\\n if not is_numeric_dtype(x[p].dtype):\\n raise TypeError(\\\"%s column should be numeric type\\\" % p)\\n else:\\n if (x[p] < 0).any():\\n raise ValueError(\\\"Negative p-values found.\\\"\\n \\\" These must be removed.\\\")\\n if (x[p] > 1).any():\\n raise ValueError(\\\"P-values greater than 1 found. \\\"\\n \\\"These must be removed.\\\")\\n if np.isnan(x[p]).any():\\n raise ValueError(\\\"NaN p-values found. These must be \\\"\\n \\\"removed\\\")\\n\\n # Create a new DataFrame with columns named after effect_size and p.\\n self.data = pd.DataFrame(data=x[[effect_size, p]])\\n\\n if snp is not None:\\n if snp not in x.columns.values:\\n # Warn if you don't have a snp column\\n raise KeyError(\\n \\\"snp argument specified as %s but column not found in \\\"\\n \\\"'x' data.frame\\\" % snp)\\n else:\\n # If the input DataFrame has a snp column, add it to the new\\n # DataFrame\\n self.data[snp] = x[snp]\\n\\n if gene is not None:\\n if gene not in x.columns.values:\\n # Warn if you don't have a gene column\\n raise KeyError(\\n \\\"gene argument specified as %s but column not found in \\\"\\n \\\"'x' data.frame\\\" % gene)\\n else:\\n # If the input DataFrame has a gene column, add it to the new\\n # DataFrame\\n self.data[gene] = x[gene]\\n\\n if annotation is not None:\\n if annotation not in x.columns.values:\\n # Warn if you don't have an annotation column\\n raise KeyError(\\n \\\"annotation argument specified as %s but column not \\\"\\n \\\"found in 'x' data.frame\\\" % annotation\\n )\\n else:\\n # If the input DataFrame has a gene column, add it to the new\\n # DataFrame\\n self.data[annotation] = x[annotation]\\n\\n self.xlabel = \\\"Effect Size\\\"\\n self.ticks = []\\n self.ticksLabels = []\\n self.effectSize = effect_size\\n self.pName = p\\n self.snpName = snp\\n self.geneName = gene\\n self.annotationName = annotation\\n self.logp = logp\",\n \"def initialize_(x, spec):\\n activate(x, spec, lookup=[nn.init])\",\n \"def params_init(self) -> None:\\n # Initialize weights and biases with uniform distribution.\\n nn.init.uniform_(self.emb.weight, self.init_lower, self.init_upper)\\n nn.init.uniform_(self.fc_e2h[1].weight, self.init_lower, self.init_upper)\\n nn.init.uniform_(self.fc_e2h[1].bias, self.init_lower, self.init_upper)\\n for lyr in range(self.n_lyr):\\n self.stack_rnn[2 * lyr].params_init()\\n nn.init.uniform_(self.fc_h2e[0].weight, self.init_lower, self.init_upper)\\n nn.init.uniform_(self.fc_h2e[0].bias, self.init_lower, self.init_upper)\",\n \"def initialize(self, algo, verbosity=0):\\n if isinstance(self.source, pd.DataFrame):\\n data = self.source\\n else:\\n if verbosity > 0:\\n print(f\\\"{self.name}: Reading file {self.source}\\\")\\n data = PandasFileHelper.read_file(self.source, **self._rpars)\\n\\n if self._trbs is None:\\n if self._col_i is not None and self._col_t is None:\\n data.reset_index(inplace=True)\\n elif self._col_i is None and self._col_t is not None:\\n tnames = algo.farm.turbine_names\\n inds = [tnames.index(name) for name in data[self._col_t]]\\n data[FC.TURBINE] = inds\\n self._col_i = FC.TURBINE\\n else:\\n raise KeyError(\\n f\\\"{self.name}: Please either specify 'col_tinds' or 'col_tnames'\\\"\\n )\\n self._trbs = data[self._col_i].to_numpy()\\n n_trbs = len(self._trbs)\\n\\n self._rcols = []\\n for v in self._rvars:\\n col_vmin = f\\\"{v}_min\\\"\\n col_vmin = self._colmap.get(col_vmin, col_vmin)\\n if col_vmin not in data.columns:\\n raise KeyError(\\n f\\\"{self.name}: Missing column '{col_vmin}', maybe add it to 'colmap'?\\\"\\n )\\n\\n col_vmax = f\\\"{v}_max\\\"\\n col_vmax = self._colmap.get(col_vmax, col_vmax)\\n if col_vmax not in data.columns:\\n raise KeyError(\\n f\\\"{self.name}: Missing column '{col_vmax}', maybe add it to 'colmap'?\\\"\\n )\\n\\n self._rcols += [col_vmin, col_vmax]\\n\\n self._tcols = []\\n for v in self._tvars:\\n col = self._colmap.get(v, v)\\n if col not in data.columns:\\n raise KeyError(\\n f\\\"{self.name}: Missing column '{col}', maybe add it to 'colmap'?\\\"\\n )\\n self._tcols.append(col)\\n\\n n_rvars = len(self._rvars)\\n self._rdata = data[self._rcols].to_numpy().reshape(n_trbs, n_rvars, 2)\\n self._tdata = data[self._tcols].to_numpy()\\n\\n for vi, v in enumerate(self._rvars):\\n if v in self._perds:\\n self._rdata[:, vi] = np.mod(self._rdata[:, vi], self._perds[v])\\n\\n return super().initialize(algo, verbosity)\",\n \"def create_initial_parameters(self):\\n update_nested_dictionary(\\n self.settings,\\n {self.highest_lookup: {\\n self.highest_sublookup: self.kw\\n }})\",\n \"def __init__(self, connection_loc, tags_tsdata,\\n dummy_var_no):\\n self.init_system(connection_loc, tags_tsdata)\\n self.removedummyvars(dummy_var_no)\\n self.addforwardscale()\\n self.addbackwardscale()\",\n \"def initialize_df(scenario_index,scenarios_nums):\\n df = pd.DataFrame(index=scenarios_nums)\\n df.index.name = scenario_index\\n return df\",\n \"def _initialize_data_filter(self):\\n df_params = self._loading_params.copy()\\n df_params[\\\"filter_negate\\\"] = True\\n df_params[\\\"filter_upper\\\"] = True\\n self._data_filter = LoadProcessedData(**df_params)\",\n \"def _set_init(self):\\n ## Main information\\n self.idxs = None\\n self.sp_relative_pos = None\\n ## Auxiliar information\\n self.ks = None\\n self.iss = [0]\\n ## Class structural information\\n self._setted = False\\n self._constant_rel_pos = False\\n self.staticneighs = None\\n self.staticneighs_set = None\",\n \"def par_fit(init_file):\\n check_presence_init(init_file)\\n\\n dict_ = read(init_file)\\n np.random.seed(dict_[\\\"SIMULATION\\\"][\\\"seed\\\"])\\n\\n # We perform some basic consistency checks regarding the user's request.\\n check_presence_estimation_dataset(dict_)\\n # check_initialization_dict2(dict_)\\n # check_init_file(dict_)\\n\\n # Distribute initialization information.\\n data = read_data(dict_[\\\"ESTIMATION\\\"][\\\"file\\\"])\\n num_treated = dict_[\\\"AUX\\\"][\\\"num_covars_treated\\\"]\\n num_untreated = num_treated + dict_[\\\"AUX\\\"][\\\"num_covars_untreated\\\"]\\n\\n _, X1, X0, Z1, Z0, Y1, Y0 = process_data(data, dict_)\\n\\n if dict_[\\\"ESTIMATION\\\"][\\\"maxiter\\\"] == 0:\\n option = \\\"init\\\"\\n else:\\n option = dict_[\\\"ESTIMATION\\\"][\\\"start\\\"]\\n\\n # Read data frame\\n\\n # define starting values\\n x0 = start_values(dict_, data, option)\\n opts, method = optimizer_options(dict_)\\n dict_[\\\"AUX\\\"][\\\"criteria\\\"] = calculate_criteria(dict_, X1, X0, Z1, Z0, Y1, Y0, x0)\\n dict_[\\\"AUX\\\"][\\\"starting_values\\\"] = backward_transformation(x0)\\n rslt_dict = bfgs_dict()\\n if opts[\\\"maxiter\\\"] == 0:\\n rslt = adjust_output(None, dict_, x0, X1, X0, Z1, Z0, Y1, Y0, rslt_dict)\\n else:\\n opt_rslt = minimize(\\n minimizing_interface,\\n x0,\\n args=(dict_, X1, X0, Z1, Z0, Y1, Y0, num_treated, num_untreated, rslt_dict),\\n method=method,\\n options=opts,\\n )\\n rslt = adjust_output(\\n opt_rslt, dict_, opt_rslt[\\\"x\\\"], X1, X0, Z1, Z0, Y1, Y0, rslt_dict\\n )\\n # Print Output files\\n print_logfile(dict_, rslt)\\n\\n if \\\"comparison\\\" in dict_[\\\"ESTIMATION\\\"].keys():\\n if dict_[\\\"ESTIMATION\\\"][\\\"comparison\\\"] == 0:\\n pass\\n else:\\n write_comparison(data, rslt)\\n else:\\n write_comparison(data, rslt)\\n\\n return rslt\",\n \"def _init_table(self, table: \\\"Table\\\"):\\n if not self.columns:\\n self.columns = table.columns\\n self._data = table.data\",\n \"def __init__(__self__, *,\\n column_name: Optional[str] = None,\\n identifier: Optional[str] = None):\\n if column_name is not None:\\n pulumi.set(__self__, \\\"column_name\\\", column_name)\\n if identifier is not None:\\n pulumi.set(__self__, \\\"identifier\\\", identifier)\",\n \"def __init__(self,columns_to_fix=[],rows_to_scan='all',keep_dummies=False):\\n self.columns_to_fix = columns_to_fix\\n self.rows_to_scan = rows_to_scan\\n self.keep_dummies = keep_dummies\",\n \"def __init__(self, column_id='', data_type='', data_format='',\\n constant_value=''):\\n self.column_id = column_id\\n self.data_type = data_type\\n self.data_format = data_format\\n self.constant_value = constant_value\",\n \"def _setup_dataframe(self, serie, metadata=None):\\n header = self.get_data_header(serie, dataset='cnv')\\n df = self.get_data_in_frame(serie, header, dataset='cnv')\\n df = self.df_handler.map_column_names_of_dataframe(df)\\n\\n return df\",\n \"def __init__(self, df):\\n self._binarized_df = None\\n self.__schema__ = 'viNet'\\n self._confidence = viNetDataframeColumn.confidence.value\\n self._predicted = viNetDataframeColumn.predicted.value\\n self._groundtruth = viNetDataframeColumn.groundtruth.value\\n\\n self._validate(df)\\n self._dataframe = df\\n self._customer = None\\n self._windfarm = None\\n self._tag = 'Unknown Tag'\\n self._config = 'Unknown Config'\",\n \"def _init_node_parm(self, key):\\n try:\\n wf_data_conf = WorkflowDataConfFrame(key)\\n self.data_conf = wf_data_conf.conf\\n except Exception as e:\\n raise Exception(\\\"dataconf_node_fame._init_node_parm Initializing Error : \\\" +str(e))\",\n \"def _compute_variables(df: EDAFrame, cfg: Config) -> Dict[str, Any]:\\n data: Dict[str, Any] = {}\\n # variables\\n if cfg.variables.enable:\\n for col in df.columns:\\n try:\\n dtype = df.get_eda_dtype(col)\\n # Since it will throw error if a numerical column is all-nan,\\n # we transform it to categorical column.\\n # We also transform to categorical for small cardinality numerical column.\\n if df.get_missing_cnt(col) == df.shape[0]:\\n srs = df.get_col_as_str(col, na_as_str=True)\\n data[col] = nom_comps(srs, cfg)\\n elif isinstance(dtype, (Nominal, GeoGraphy, GeoPoint)):\\n data[col] = nom_comps(df.frame[col], cfg)\\n elif isinstance(dtype, SmallCardNum):\\n srs = df.get_col_as_str(col, na_as_str=False)\\n data[col] = nom_comps(srs, cfg)\\n elif isinstance(dtype, Continuous):\\n data[col] = cont_comps(df.frame[col], cfg)\\n # elif isinstance(dtype, DateTime):\\n # data[col] = {}\\n # data[col][\\\"stats\\\"] = calc_stats_dt(df.frame[col])\\n # data[col][\\\"line\\\"] = dask.delayed(_calc_line_dt)(df.frame[[col]], \\\"auto\\\")\\n else:\\n raise ValueError(f\\\"unprocessed type in column{col}:{dtype}\\\")\\n except:\\n print(f\\\"error happended in column:{col}\\\", file=sys.stderr)\\n raise\\n return data\",\n \"def __init__(self, df, name):\\n try:\\n self.df = df\\n df.any()\\n except AttributeError:\\n self.df = df.toPandas()\\n\\n self.name = name\\n\\n if self.df.empty:\\n raise Exception('Empty Dataset')\",\n \"def _initialize(self, data):\\n if not isinstance(data, pd.DataFrame):\\n raise ValueError(\\\"Provided data is not a pandas.DataFrame\\\")\\n\\n super()._initialize(data)\\n \\n #if not self.num_cols:\\n # raise ValueError(\\\"Entropy methods are not usable on continous data\\\")\\n \\n #prepare discretizer for num values\\n self.pid_cols = self.num_cols\\n intervals = 200\\n bins = 8\\n alphas = 0.25\\n strategy = \\\"frequency\\\"\\n n = len(self.num_cols)\\n mins = list(data[self.pid_cols].min())\\n maxs = list(data[self.pid_cols].max())\\n \\n self.pid = PartitionIncrementalDiscretizer(\\n intervals=[intervals]*n, \\n mins = mins,\\n maxs= maxs,\\n alphas=[alphas]*n, \\n n_bins=[bins]*n,\\n strategy=strategy) \\n \\n self.pid.fit(data[self.pid_cols])\\n self.pid.transform(data[self.pid_cols])\\n \\n #alter num and cat cols to support default smr methods\\n self.num_cols = []\\n self.cat_cols += self.pid_cols\\n for col in self.pid_cols:\\n self.categories[col] = list(range(bins))\\n \\n self.imp = CategoricalRememberingNaNImputer(categories=self.categories)\\n self.pipeline = Pipeline(steps=[\\n ('discretizer', self.pid),\\n ('imputer', self.imp)])\\n \\n #init feature dicts\\n self.cat_counts = {}\\n self.lab_counts = {}\\n self.cat_probs = {}\\n self.lab_probs = {}\\n self.cat_entropies = {}\\n self.lab_entropy = 0\\n \\n #set cat_col dicts\\n for col in self.cat_cols:\\n self.cat_counts[col] = {}\\n self.cat_probs[col] = {}\\n for label in self.labels:\\n self.cat_counts[col][label] = {}\",\n \"def _populate_df(self, df, objs,):\\n for obj in objs:\\n for prop in df.columns:\\n df.loc[obj.name, prop] = getattr(obj, prop)\",\n \"def initDataView(self,student_cols):\\n self.notebook.initStudentsView(student_cols)\\n #self.dataView.initStudentsView(student_cols)\",\n \"def __init__(self, *args, **kwargs):\\n \\n self.dense = True\\n\\n # Create table\\n super().__init__(*args, **kwargs)\",\n \"def init_sources(self):\\n\\n columns = ['some', 'column', 'headers']\\n self.columns = columns\\n\\n self.data_source = ColumnDataSource(data=dict(zip(columns, []*len(columns))))\",\n \"def set_data(self, df):\\n self.df = df\",\n \"def __init__(self, **kwargs):\\n perform = kwargs.get(\\\"perform\\\", True)\\n kwargs[\\\"perform\\\"] = False\\n self._data = dict(self._DATA)\\n self._text = self._DUMMY_TEXT\\n self.df = pd.DataFrame()\\n self.update(**kwargs)\\n if perform:\\n self.perform()\",\n \"def from_dict(self, data: dict):\\n super().from_dict(data)\\n if self.data is not None:\\n try:\\n self.data[\\\"columns\\\"] = np.array(\\n [int(col) for col in self.data[\\\"columns\\\"]]\\n )\\n except:\\n pass\\n self.data = pd.DataFrame(\\n data=self.data[\\\"data\\\"],\\n columns=self.data[\\\"columns\\\"],\\n index=self.data[\\\"index\\\"],\\n )\",\n \"def _init_params_default(self):\\n # if there are some nan -> mean impute\\n Yimp = self.Y.copy()\\n Inan = sp.isnan(Yimp)\\n Yimp[Inan] = Yimp[~Inan].mean()\\n if self.P == 1:\\n C = sp.array([[Yimp.var()]])\\n else:\\n C = sp.cov(Yimp.T)\\n C /= float(self.n_randEffs)\\n for ti in range(self.n_randEffs):\\n self.getTraitCovarFun(ti).setCovariance(C)\",\n \"def __init__(self,columns_to_fix=[],convert_dict={'Y':1,'N':0}):\\n self.columns_to_fix = columns_to_fix\\n self.convert_dict = convert_dict\",\n \"def __init__(self, col, val):\\n\\n self.__col = col\\n self.__val = val\",\n \"def reset_parameters(self, param_init):\\n logger.info('===== Initialize %s with uniform distribution =====' % self.__class__.__name__)\\n for n, p in self.named_parameters():\\n if 'conv' in n:\\n continue\\n init_with_uniform(n, p, param_init)\",\n \"def initialize(self):\\r\\n if not self.context:\\r\\n self.context = SQLContext(self.url, self.connection, self.schema)\\r\\n if self.table is None:\\r\\n self.table = self.context.table(self.table_name)\\r\\n if not self.fields:\\r\\n self.read_fields()\\r\\n self.field_names = self.fields.names()\",\n \"def reset_parameters(self, param_init):\\n logger.info('===== Initialize %s with uniform distribution =====' % self.__class__.__name__)\\n for n, p in self.named_parameters():\\n if 'score.monotonic_energy.v.weight_g' in n or 'score.monotonic_energy.r' in n:\\n logger.info('Skip initialization of %s' % n)\\n continue\\n if 'score.monotonic_energy.conv1d' in n:\\n logger.info('Skip initialization of %s' % n)\\n continue\\n if 'score.chunk_energy.v.weight_g' in n or 'score.chunk_energy.r' in n:\\n logger.info('Skip initialization of %s' % n)\\n continue\\n if 'linear_lm_gate.fc.bias' in n and p.dim() == 1:\\n nn.init.constant_(p, -1.0)\\n logger.info('Initialize %s with %s / %.3f' % (n, 'constant', -1.0))\\n continue\\n init_with_uniform(n, p, param_init)\",\n \"def variable(self, col: str, name: str, init_fn, *init_args) -> Variable:\\n if not self._initialization_allowed:\\n raise ValueError(\\n 'Variables must be initialized in `setup()` or in a method '\\n 'wrapped in `@compact`')\\n if self._name_taken(name):\\n raise errors.NameInUseError('variable', name, self.__class__.__name__)\\n v = self.scope.variable(col, name, init_fn, *init_args)\\n self._state.children[name] = col\\n return v\",\n \"def _init_slot_variable(\\n self, layer_name, embed_var, slot_name, initial_value\\n ):\\n if (\\n layer_name not in self._tls._slot_variables\\n or slot_name not in self._tls._slot_variables[layer_name]\\n ):\\n slot_var_name = \\\"%s/%s\\\" % (embed_var._shared_name, slot_name)\\n slot_var = self._opt.add_weight(\\n name=slot_var_name,\\n shape=(None, None),\\n dtype=embed_var.dtype,\\n initializer=initial_value,\\n trainable=False,\\n )\\n slot_variables_dict = self._tls._slot_variables.setdefault(\\n layer_name, {}\\n )\\n slot_variables_dict[slot_name] = slot_var\\n else:\\n slot_var = self._tls._slot_variables[layer_name][slot_name]\\n slot_var.assign(initial_value)\\n return slot_var\",\n \"def set_meta(self, name, value):\\n # note sometimes during .view, we won't have this var available\\n check_meta = not hasattr(self, '_init_arg_check') or self._init_arg_check\\n if check_meta and name in self._init_args:\\n # note this is largely a failsafe, we shouldn't get to this\\n # point via setattr since it'll match the hasattr(self.pobj, name)\\n raise Exception('Cannot have member variables that clash with pandas constructor args')\\n object.__setattr__(self, name, value)\",\n \"def __init__(self, X_columns, X_dtypes):\\n self.X_columns = X_columns\\n self.X_dtypes = X_dtypes\",\n \"def set_initial_value(self, rel_name, m):\\n if not self.is_relation(rel_name):\\n raise RelationNameError(rel_name, 'Relation is not defined.')\\n expected_shape = self.get_shape(rel_name)\\n if m.shape[1] != expected_shape[1]:\\n raise ValueError(\\n 'relation and initial_value have different columns: %d vs %d' %\\n (expected_shape[1], m.shape[1]))\\n if self.is_dense(rel_name):\\n self._np_initval[rel_name] = m.transpose()\\n else:\\n self._np_initval[rel_name] = scipy.sparse.coo_matrix(m.transpose())\",\n \"def initialisation(self):\\n self.create_variables()\\n self.create_placeholders()\\n self.build_model()\\n self.reset_lr(None, True)\\n self.build_loss()\\n self.initialised = True\",\n \"def __init__(self, **kwargs):\\n\\t\\tself.vars = kwargs\\n\\t\\tself.old_vars = None\",\n \"def compile_dataframe_default(self):\\n\\t\\tdata = [\\n\\t\\t\\t['ford','mustang','coupe','A'],\\n\\t\\t\\t['chevy','camaro','coupe','B'],\\n\\t\\t\\t['ford','fiesta','sedan','C'],\\n\\t\\t\\t['ford','focus','sedan','A'],\\n\\t\\t\\t['ford','taurus','sedan','B'],\\n\\t\\t\\t['toyota','camry','sedan','B']\\n\\t\\t]\\n\\n\\t\\tself.data = pd.DataFrame(data, columns = self.data_cols)\",\n \"def correct_params():\\n params = {\\n \\\"probs\\\": [0.1, 0.9],\\n \\\"treat\\\": 2,\\n \\\"data\\\": pd.DataFrame(data={\\\"id\\\": np.arange(100), \\\"block\\\": np.arange(100)}),\\n \\\"idx_col\\\": \\\"id\\\",\\n }\\n return params\",\n \"def init_agg(self, col):\\n\\t\\traise NotImplementedError()\",\n \"def preinitialize(self):\\n for group in self.param_groups:\\n for p in group['params']:\\n if group['momentum'] != 0:\\n self.state[p][\\\"momentum_buffer\\\"] = torch.zeros_like(\\n p, device=\\\"cpu\\\"\\n ).to(p.device)\",\n \"def _parse_initsol(self) :\\n\\t\\tlogging.debug(\\\"Parsing initsol initial solution\\\")\\n\\n\\t\\t# Init initsol as an empty dict\\n\\t\\tself.initsol = {}\\n\\n\\t\\tfor varname in ['alpha','beta','g','h'] : \\n\\t\\t\\tself._parse_var_initsol(varname)\",\n \"def __init__(self, df):\\n self.df = df\\n self.min_interval_in_seconds = 99999999999\",\n \"def __init__(self):\\n\\n self.active_df = pd.DataFrame([], columns=['txt_name', 'missing_data_days'])\",\n \"def __init__(self):\\n self.model = None\\n self.joined_datasets = None\\n self.id_col = None\\n self.val_col = None\\n self.pop_col = None\\n self.total_population_per_unit = None\\n self.centroids_of_areal_data = None\\n self.prepared_data = None\\n self.unknown_area_id = None\\n\\n # Parameters\\n self.lags = None\\n self.step = None\\n self.min_no_of_observations = None\\n self.max_search_radius = None\",\n \"def __init__(self, df=None):\\n\\n if df is not None:\\n # set attributes with list of unique stimulus combinations / categories\\n self.all_stimulus_pairs = df.combo.unique().tolist()\\n\\n # set attribute for number of unique dimensionality reductions\\n self.unique_subspace_dims = df.n_components.unique().tolist()\\n\\n # get df columns\\n self.numeric_keys = df.select_dtypes(include=['float64']).columns\\n self.object_keys = df.select_dtypes(include=['object']).columns\\n\\n # get number of jacknnifes\\n self.n_jacks = df.jack_idx.values.astype(np.float).max() + 1\\n \\n # Collapse over jackknifes. Save mean / sd of all numeric params.\\n log.info(\\\"Collapsing results over jackknifes\\\")\\n # returns a dataframe for numeric results\\n self.numeric_results = self._collapse_numeric(df) \\n \\n # objects = arrays (e.g. arrays of eigenvectors, evals, decoding vectors).\\n # returns a dictionary of data frames\\n self.array_results = self._collapse_objects(df)\",\n \"def __init__(self, columns=()):\\n self.columns = list(columns)\\n\\n # Create internal dictionary for faster access\\n self.column_dict = {}\\n\\n for column in self.columns:\\n self.column_dict[column.column_id] = column\",\n \"def initVariable(self, state):\\n self.nb_agent = state.getNumAgents()\\n self.first_call = False\",\n \"def _init_state(self, init_state=None, init_cov=None): \\n ## Initialize the BMI state, assuming \\n nS = self.n_states \\n if init_state == None: \\n init_state = np.mat( np.zeros([nS, 1]) ) \\n if self.include_offset: init_state[-1,0] = 1 \\n if init_cov == None: \\n init_cov = np.mat( np.zeros([nS, nS]) )\\n self.state = bmi.GaussianState(init_state, init_cov)\",\n \"def initialize(self, numSamples, sampleMethod):\\n initSamples = initial_samples(self.lb, self.ub, sampleMethod, numSamples)\\n if sum(self.xID) != 0:\\n xUB = [\\n self.ub[np.where(self.xID == 1)[0][0]]] * len(self.xID)\\n xSamples = initial_samples([0] * len(self.xID), xUB, 'rand-wor', numSamples)\\n for var in range(len(self.varType)):\\n if 'i' in self.varType[var] or 'd' in self.varType[var]:\\n initSamples[:, var] = np.rint(initSamples[:, var])\\n\\n if sum(self.xID) != 0:\\n initSamples = initSamples * (self.cID + self.iID + self.dID) + xSamples * self.xID\\n return initSamples\",\n \"def changeInitValues(self, betas):\\n\\n if self.name in betas:\\n self.initValue = betas[self.name]\",\n \"def fill_nid(df, nid_dict):\\n assert 'year' in df.columns, \\\"DataFrame doesn't have a 'year' column\\\"\\n df['nid'] = df['year'].map(nid_dict)\\n return df\",\n \"def standard_init(self, data):\\n comm = self.comm\\n H = self.H\\n my_y = data['y']\\n my_N, D = my_y.shape\\n\\n assert D == self.D\\n\\n # Calculate averarge W\\n W_mean = parallel.allmean(my_y, axis=0, comm=comm) # shape: (D, )\\n\\n # Calculate data variance\\n sigma_sq = parallel.allmean((my_y-W_mean)**2, axis=0, comm=comm) # shape: (D, )\\n sigma_init = np.sqrt(sigma_sq).sum() / D # scalar\\n\\n # Initial W\\n noise = sigma_init/4.\\n W_init = W_mean + np.random.normal(scale=noise, size=[H, D]) # shape: (H, D)\\n\\n #Create and set Model Parameters, W columns have the same average!\\n model_params = {\\n 'W' : W_init, \\n 'pi' : 1./H,\\n 'sigma' : sigma_init\\n }\\n\\n return model_params\",\n \"def _init_tkvars(self,PO):\\n for name,param in PO.params().items():\\n self._create_tkvar(PO,name,param)\",\n \"def _reload(self):\\n if os.path.exists(self.filename):\\n self.data = pd.read_csv(self.filename)\\n else:\\n self.data = pd.DataFrame(columns=self.unique_keys)\\n\\n # Set these default values\\n # if 'weight_rescale' not in self.data.columns:\\n # self.data['weight_rescale'] = 'none'\\n # if 'norm' not in self.data.columns:\\n # self.data['norm'] = 'softmax'\\n # if 'update' not in self.data.columns:\\n # self.data['update'] = 'all'\\n # if 'replay' not in self.data.columns:\\n # self.data['replay'] = False\\n if 'debug' not in self.data.columns:\\n self.data['debug'] = False\\n\\n # if 'tie' not in self.data.columns:\\n # self.data['tie'] = False\\n\\n if 'update_length' not in self.data.columns:\\n self.data['update_length'] = 0\\n # for key in self.unique_keys:\\n # self.data[key] = np.nan\\n # Remaining set to None\\n # for k in self.check_keys:\\n # if k not in self.data.columns:\\n # self.data[k] = None\",\n \"def record_variable_inits(self):\\n old_init = getattr(variables.Variable, '__init__')\\n\\n def record(*args, **kwargs):\\n self._in_variable_creation = True\\n old_init(*args, **kwargs)\\n self._in_variable_creation = False\\n\\n setattr(variables.Variable, '__init__', record)\\n yield\\n setattr(variables.Variable, '__init__', old_init)\",\n \"def init_data(title, **kwds):\\n if(title in data_conf.keys()):\\n data_conf[title].update(kwds)\\n else:\\n logging.debug(\\\"Initializing source '%s.'\\\" % title)\\n data_conf[title] = kwds\\n if(ipc.mpi.is_slave()):\\n ipc.mpi.send('__data_conf__', data_conf)\",\n \"def init_individual(index, columns, initializer=None):\\n \\n ind = pd.DataFrame(0,index=index, columns=columns)\\n \\n if initializer is not None:\\n \\n # sets up the DataFrame with the initializer data\\n ind.loc[:, 2:] = initializer.loc[:,1:]\\n ind.loc[:, 'in'] = initializer.loc[:, 'in']\\n \\n # sets the age\\n for i in index:\\n if ind.loc[i,'in'] != 0:\\n ind.loc[i,'age'] = 1\\n else:\\n ind.loc[i,'age'] = 0\\n \\n # randomly flips a company in or out of the system\\n if random.random() < 0.05:\\n if ind.loc[i,'in'] == 0:\\n ind.loc[i,'in'] = 1\\n ind.loc[i,'age'] = 1\\n for j in index:\\n if i == j:\\n ind.loc[i,j] = 0\\n else:\\n if random.random() < 0.2:\\n ind.loc[i,j] = 1\\n ind.loc[j,i] = 1\\n else:\\n ind.loc[i,:] = 0\\n ind.loc[:,i] = 0\\n \\n # randomly flips correlations\\n if ind.loc[i,'in'] == 1: \\n for j in index:\\n if random.random() < 0.05 and i != j:\\n ind.loc[i,j] = abs(ind.loc[i,j] - 1)\\n ind.loc[j,i] = ind.at[i,j]\\n\\n else:\\n for i in index:\\n # randomly places companies in or out of the network\\n if random.random() < 0.2:\\n ind.loc[i,'in'] = 1\\n ind.loc[i,'age'] = 1\\n \\n # randomly assigns correlations for companies in the network\\n if ind.loc[i,'in'] == 1:\\n for j in index:\\n if i == j:\\n ind.loc[i,j] = 0\\n else:\\n if random.random() < 0.2\\n ind.loc[i,j] = 1\\n ind.loc[j,i] = ind.at[i,j]\\n \\n ind.fillna(0)\\n\\n return ind\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5726554","0.55129045","0.5489595","0.5355379","0.52944136","0.5286923","0.5284127","0.5245464","0.52217746","0.52201474","0.52157134","0.52084327","0.5173756","0.5162606","0.5160928","0.51536846","0.513594","0.51033354","0.5099015","0.5089083","0.50838184","0.5064277","0.5058843","0.50436324","0.50436324","0.50386655","0.5026358","0.49980214","0.49954018","0.4991121","0.4986702","0.4969653","0.49689212","0.49633482","0.49547347","0.49503878","0.49502832","0.49479136","0.49380696","0.49218935","0.49160045","0.49025482","0.48770142","0.48730806","0.4867955","0.486778","0.48668194","0.48665065","0.48648605","0.48574737","0.4851426","0.4847563","0.48388112","0.48361132","0.4828717","0.48100844","0.4806772","0.48065096","0.47967845","0.47951788","0.47939917","0.47851723","0.47789526","0.47750175","0.47700956","0.47674575","0.4764858","0.47634524","0.4747753","0.47452793","0.47431982","0.47377327","0.47313178","0.4730556","0.4727506","0.4719545","0.47163394","0.4707111","0.46978638","0.4696937","0.46920955","0.4682586","0.46822205","0.4680554","0.46775997","0.46735772","0.4671755","0.46715963","0.4670745","0.4670594","0.46693718","0.4669004","0.46662745","0.46635196","0.46608377","0.46595174","0.4656692","0.4653819","0.4649765","0.4648578"],"string":"[\n \"0.5726554\",\n \"0.55129045\",\n \"0.5489595\",\n \"0.5355379\",\n \"0.52944136\",\n \"0.5286923\",\n \"0.5284127\",\n \"0.5245464\",\n \"0.52217746\",\n \"0.52201474\",\n \"0.52157134\",\n \"0.52084327\",\n \"0.5173756\",\n \"0.5162606\",\n \"0.5160928\",\n \"0.51536846\",\n \"0.513594\",\n \"0.51033354\",\n \"0.5099015\",\n \"0.5089083\",\n \"0.50838184\",\n \"0.5064277\",\n \"0.5058843\",\n \"0.50436324\",\n \"0.50436324\",\n \"0.50386655\",\n \"0.5026358\",\n \"0.49980214\",\n \"0.49954018\",\n \"0.4991121\",\n \"0.4986702\",\n \"0.4969653\",\n \"0.49689212\",\n \"0.49633482\",\n \"0.49547347\",\n \"0.49503878\",\n \"0.49502832\",\n \"0.49479136\",\n \"0.49380696\",\n \"0.49218935\",\n \"0.49160045\",\n \"0.49025482\",\n \"0.48770142\",\n \"0.48730806\",\n \"0.4867955\",\n \"0.486778\",\n \"0.48668194\",\n \"0.48665065\",\n \"0.48648605\",\n \"0.48574737\",\n \"0.4851426\",\n \"0.4847563\",\n \"0.48388112\",\n \"0.48361132\",\n \"0.4828717\",\n \"0.48100844\",\n \"0.4806772\",\n \"0.48065096\",\n \"0.47967845\",\n \"0.47951788\",\n \"0.47939917\",\n \"0.47851723\",\n \"0.47789526\",\n \"0.47750175\",\n \"0.47700956\",\n \"0.47674575\",\n \"0.4764858\",\n \"0.47634524\",\n \"0.4747753\",\n \"0.47452793\",\n \"0.47431982\",\n \"0.47377327\",\n \"0.47313178\",\n \"0.4730556\",\n \"0.4727506\",\n \"0.4719545\",\n \"0.47163394\",\n \"0.4707111\",\n \"0.46978638\",\n \"0.4696937\",\n \"0.46920955\",\n \"0.4682586\",\n \"0.46822205\",\n \"0.4680554\",\n \"0.46775997\",\n \"0.46735772\",\n \"0.4671755\",\n \"0.46715963\",\n \"0.4670745\",\n \"0.4670594\",\n \"0.46693718\",\n \"0.4669004\",\n \"0.46662745\",\n \"0.46635196\",\n \"0.46608377\",\n \"0.46595174\",\n \"0.4656692\",\n \"0.4653819\",\n \"0.4649765\",\n \"0.4648578\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7033673"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":76678,"cells":{"query":{"kind":"string","value":"Iterate over the observations and update the filtered values after each iteration"},"document":{"kind":"string","value":"def iterate(self, plot=True, estimate=False, init_params=None):\n # Create empty arrays to store values\n F = np.zeros(len(self.y))\n a = np.zeros(len(self.y))\n v = np.zeros(len(self.y))\n P = np.zeros(len(self.y))\n # Initialize at the initial values parsed to the class\n P[0] = self.pardict['P1']\n a[0] = self.y[0]\n if estimate == True:\n sigma_eps2 = init_params[0]\n sigma_eta2 = init_params[1]\n else:\n sigma_eps2 = self.pardict['sigma_eps2']\n sigma_eta2 = self.pardict['sigma_eta2']\n # Iterate\n for t in range(0, len(self.y) - 1):\n F[t] = P[t] + sigma_eps2\n # K is defined as ratio of P and F\n Kt = P[t] / F[t]\n v[t] = self.y[t] - a[t]\n a[t + 1] = a[t] + Kt * v[t]\n P[t + 1] = P[t] * (1 - Kt) + sigma_eta2\n F[-1] = P[-1] + sigma_eps2\n v[-1] = self.y[-1] - a[-1]\n\n # Obtain std error of prediction form variance\n std = np.sqrt((P * sigma_eps2) / (P + sigma_eps2))\n\n if plot:\n fig_name = self.var_name + 'Fig21.pdf'\n plot_fig2_1(self.times, self.y, a, std, P, v, F, fname=fig_name, var_name=self.var_name)\n return a, std, P, v, F"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def update(self, other):\n for filter, value in other.items():\n self.__setitem__(filter, value)","def update(self):\n for filter in self.filters:\n filter.update(self.learning_rate)","def filter(self):\n self.filter_means = [self.m_0]\n self.filter_covs = [self.P_0]\n self.marginal_covs = []\n for t in range(self.data.shape[0]):\n m_bar, P_bar = self.one_step_prediction(self.filter_means[-1], self.filter_covs[-1])\n\n # Update step\n y = self.data[t]\n if not np.isnan(y).any():\n v = y[:, None] - self.observation_matrix @ m_bar\n S = self.observation_matrix @ P_bar @ self.observation_matrix.T + self.observation_cov\n K = P_bar @ self.observation_matrix.T @ np.linalg.inv(S)\n\n m_bar = m_bar + K @ v\n P_bar = P_bar - K @ S @ K.T\n\n self.marginal_covs.append(S)\n\n self.filter_means.append(m_bar)\n self.filter_covs.append(P_bar)\n self.filter_means = self.filter_means[1:]\n self.filter_covs = self.filter_covs[1:]","def update(self, updates, predicate):\n for row in self.rows:\n if predicate(row):\n for column, new_value in updates.items():\n row[column] = new_value","def filter_values(self):\n dfilter = self.args.datafilter\n self.logger.info(u'Filtering values with:{f}'.format(f=dfilter))\n data = self.outputdata\n newdata = {}\n for key, value in data.items():\n self.logger.info(u'\\nProcessing Key:{k}, value:{v}'.format(k=key,\n v=value))\n returned_data = dict_value_filter(key, value, dfilter, self.logger)\n if bool(returned_data):\n newdata[key] = returned_data\n self.logger.info(u'Data after filter:{d}'.format(d=newdata))\n\n self.outputdata = newdata","def UpdateSet(self, dataset):\r\n for data in dataset:\r\n self.UpdateOddsRatioVsNoNorm(data)","def filter_update(observations, observation_matrix, observation_variance,\n observation_indices, observation_count,\n state_mean, state_covariance):\n\n sigma = 0.\n detf = 0.\n n_observation = np.int(observation_count)\n for i in range(n_observation):\n observation_index = int(observation_indices[i])\n obsmat = observation_matrix[observation_index, :]\n innovation = (observations[observation_index]\n - np.dot(obsmat, state_mean))\n dot_statecov_obsmat = np.dot(state_covariance, obsmat)\n innovation_covariance = (np.dot(obsmat, dot_statecov_obsmat)\n + observation_variance[observation_index])\n kgain = dot_statecov_obsmat / innovation_covariance\n state_covariance = (state_covariance\n - (np.outer(kgain, kgain)\n * innovation_covariance))\n\n state_mean = state_mean + kgain * innovation\n\n sigma = sigma + (innovation ** 2 / innovation_covariance)\n detf = detf + np.log(innovation_covariance)\n\n return (state_mean, state_covariance, sigma, detf)","def update_filter(self, measurement, robot_pos):\n new_weights = []\n for p in self.particles:\n p = self.__move(p, self.MOTION)\n angle = self.__measurement(p, robot_pos)\n prob = self.__measurement_prob(angle, measurement,\n self.sense_noise)\n new_weights.append(prob)\n new_weights = np.array(new_weights)\n new_weights /= np.sum(new_weights) # normalized weights\n self.weights = new_weights\n\n # if self.__neff() > self.n / 2:\n self.particles = self.__resample()","def set_observations(self, factor):\r\n relevant_obs = set(self.obs).intersection(set(factor.get_variables()))\r\n if relevant_obs:\r\n factor.set_observations({x:self.obs[x] for x in relevant_obs})","def filter(self, update):\n\n raise NotImplementedError","def ObserveEvidence(self,E):\r\n for v,e in E.iteritems():\r\n indx, = (self.var==v).nonzero() # find i where f.var[i]=v\r\n if indx.size != 0:\r\n A = I2A(range(len(self.val)), self.card) # all assignments\r\n A = A[:,indx].flatten() # only interested in 'indx' element of each row\r\n self.val[A != e] = 0","def update(self, predictions, real_vals):\n for pred, actual in zip(predictions, real_vals):\n self.cm.loc[actual, pred] += 1","def filter(self, observable):","def filter_data(self):\n self.data = filter_pandas(self.data, self.filters)","def update_values(self):\n # have to reset params to 0 when recalculating\n self.total_weight = self.total_price = self.total_value = self.total_fitness = 0\n for index, value in enumerate(self.item_stats):\n if value == 1:\n self.total_weight += self.items[index].weight\n self.total_price += self.items[index].price\n self.total_value += self.items[index].value\n self.total_fitness += self.items[index].fitness","def update(self, z):\n raise NotImplementedError('Must implement an update step for the filter.')","def run_batch_filter(self):\n if self.speed_data is None and self.flow_data is None and self.traveltime_data is None:\n print(\n 'Warning: The measurement data must be set before running the batch filter: use function self.set_meas_data()')\n\n # =======================================================================\n # the initial ensembles, which should have been set externally\n X_init = np.matrix(np.zeros((self.dim_state, self.num_ensembles)))\n print(\n 'Setting initial ensembles: rho {0}; qin {1}; qout {2}'.format(self.init_rho, self.init_qin, self.init_qout))\n for ens in range(0, self.num_ensembles):\n X_init[self.x_index['density'][0]:\n self.x_index['density'][self.num_cells - 1], ens] = self.init_rho\n X_init[self.x_index['qin'], ens] = self.init_qin\n X_init[self.x_index['qout'], ens] = self.init_qout\n\n # print('setted qin {0}; qout {1}'.format(X_init[self.x_index['qin'], ens], X_init[self.x_index['qout'], ens] ))\n # add noise to each ensemble\n X_init[:, ens] += np.matrix(np.random.multivariate_normal(\n np.zeros(self.dim_state), self.Q)).reshape((self.dim_state, 1))\n\n self.set_initial_ensembles(X_init)\n\n # =======================================================================\n # DEBUG\n # save the qin and qout in the corresponding probe data\n # save the initial state\n if self.__debug:\n self.qin_f.append(np.squeeze(np.array(self.X_f[self.x_index['qin'], :])).tolist())\n self.qin_a.append(np.squeeze(np.array(self.X_a[self.x_index['qin'], :])).tolist())\n self.qin_obs.append(np.nan)\n\n self.qout_f.append(np.squeeze(np.array(self.X_f[self.x_index['qout'], :])).tolist())\n self.qout_a.append(np.squeeze(np.array(self.X_a[self.x_index['qout'], :])).tolist())\n self.qout_obs.append(np.nan)\n\n # The enKF runs at the finest time grid\n # for each step, update the system\n for step in range(0, self.num_steps):\n\n # update status\n sys.stdout.write('\\r')\n sys.stdout.write('Status: filtering step {0}/{1}'.format(step, self.num_steps))\n sys.stdout.flush()\n # print('Status: filtering step {0}'.format(step))\n\n cur_time = (step + 1) * self.dur_steps\n\n # get the effective measurement\n eff_flow, eff_speed, eff_traveltime = self.__get_eff_meas(cur_time)\n\n # build the observation index\n self.y_index, self.dim_obs, y_obs, cov_noise = self.__build_obs_index(eff_flow, eff_speed, eff_traveltime)\n\n # update the estimate for this step\n est_state = self.update_estimate(y_obs, cov_noise, cur_time)\n\n # =======================================================================\n # DEBUG\n # save the qin and qout in the corresponding probe data\n # save the updated state\n if self.__debug:\n self.qin_f.append(np.squeeze(np.array(self.X_f[self.x_index['qin'], :])).tolist())\n self.qin_a.append(np.squeeze(np.array(self.X_a[self.x_index['qin'], :])).tolist())\n if 'flow' in self.y_index.keys() and self.__debug_entrance_sensor in self.y_index['flow'].keys():\n self.qin_obs.append(y_obs[self.y_index['flow'][self.__debug_entrance_sensor]])\n # print('y_index[flow]:{0}'.format(self.y_index['flow'].keys()))\n # print('y_obs[ y_index[flow][entrance] ]:{0}'.format(\n # y_obs[ self.y_index['flow'][self.__debug_entrance_sensor]],\n # self.__debug_entrance_sensor))\n else:\n self.qin_obs.append(np.nan)\n\n self.qout_f.append(np.squeeze(np.array(self.X_f[self.x_index['qout'], :])).tolist())\n self.qout_a.append(np.squeeze(np.array(self.X_a[self.x_index['qout'], :])).tolist())\n if 'flow' in self.y_index.keys() and self.__debug_exit_sensor in self.y_index['flow'].keys():\n self.qout_obs.append(y_obs[self.y_index['flow'][self.__debug_exit_sensor]])\n else:\n self.qout_obs.append(np.nan)\n # =======================================================================\n # save the estimated state\n self.est_state_all[:, step] = est_state\n\n # decouple and save into self.est_density, self.est_speed, self.est_queue, self.est_traveltime\n self.est_density[:, step] = est_state[0:self.num_cells, 0]\n\n # the speed is computed using the fundamental diagram\n for cell_id in range(0, self.num_cells):\n # use the static FD at this step\n self.est_speed[cell_id, step] = self.__rho2v(self.vm_cells[cell_id, 0], self.beta_cells[cell_id, 0],\n self.rhoc_cells[cell_id, 0], self.wc_cells[cell_id, 0],\n self.est_density[cell_id, step])\n\n # REMARK: the queue and travel time a post-processed from the speed field.\n # They are computed in cross_evaluation class for all algorithms\n # the queue length starts from the first cell with speed below queue_threshold to the end of road\n # index = (self.est_speed[:, step] <= self.queue_threshold)\n #\n # # filter out the outliers\n # index_smoothed = deepcopy(index)\n # outlier_max = 3\n # counter = 0\n # for i in range(0, len(index)):\n #\n # if index[i] == True:\n # # trigger the coutner\n # counter += 1\n # elif index[i] == False and counter != 0:\n # if counter <= outlier_max:\n # # found outliers\n # index_smoothed[ i-counter : i ] = False\n # # reset counter\n # counter = 0\n #\n # # if i != 0 and i != len(index)-1:\n # # if sum( index[i-1:i+3] ) >=2:\n # # index_smoothed[i] = True\n # # else:\n # # index_smoothed[i] = False\n # # elif i == 0:\n # # if sum(index[0: 5] ) >= 3:\n # # index_smoothed[i] = True\n # # else:\n # # index_smoothed[i] = False\n # # elif i == len(index)-1:\n # # if sum(index[ i-4 :len(index)]) >= 3:\n # # index_smoothed[i] = True\n # # else:\n # # index_smoothed[i] = False\n #\n # if sum(index_smoothed) <= 3: # use 4 to suppress false alarms\n # # if less or equal then 2 cells are in congestion, it may be caused by noise.\n # self.est_queue[step] = 0\n # else:\n # # if step > 105 and step < 115:\n # # print(sum(index_smoothed))\n # # print(index_smoothed)\n # # print(index)\n #\n # self.est_queue[step] = \\\n # self.len_cells*( self.num_cells - np.argmax(index_smoothed) )\n # # try:\n # # first_cong_cell_id = [x[0] for x in enumerate( self.est_speed[:,step] ) if x[1] < self.queue_threshold][0]\n # # except IndexError:\n # # # no congested cell\n # # first_cong_cell_id = self.num_cells\n # # # the estimated queue length\n # # self.est_queue[step] = self.len_cells*( self.num_cells - first_cong_cell_id )\n #\n # # the travel time estimate is computed by summing up the travel time in each cell\n # self.est_traveltime[step] = np.sum(self.len_cells/self.est_speed[:,step])\n\n\n # =======================================================================\n # DEBUG\n # plot the update\n if self.__debug:\n plot_len = 19\n # qin\n if False:\n if not np.isnan(self.qin_obs[-1]):\n fig1 = plt.figure(figsize=(10, 5), dpi=100)\n ax1 = fig1.add_subplot(111)\n positions_f = np.arange(0, len(self.qin_f)) - 0.1\n positions_a = np.arange(0, len(self.qin_a)) + 0.1\n positions_obs = np.arange(0, len(self.qin_obs))\n # predicted as red\n bp = ax1.boxplot(self.qin_f[-plot_len:],\n positions=positions_f[-plot_len:], widths=0.15,\n patch_artist=False)\n for box in bp['boxes']:\n # change outline color\n box.set(color='#FF4633', linewidth=1)\n # change fill color\n # box.set( facecolor = '#FF4633' )\n # corrected as green\n bp = ax1.boxplot(self.qin_a[-plot_len:],\n positions=positions_a[-plot_len:], widths=0.15, patch_artist=False)\n for box in bp['boxes']:\n # change outline color\n box.set(color='#07891B', linewidth=1)\n # change fill color\n # box.set( facecolor = '#07891B' )\n # measurement as blue\n ax1.scatter(positions_obs[-plot_len:], self.qin_obs[-plot_len:], color='b', marker='o', s=40,\n label='Observation')\n ax1.set_title('qin')\n # x_ticks = np.arange(0, len(self.qin_f))\n # ax1.set_xticks(x_ticks[-plot_len:])\n plt.show()\n\n # qout\n if False:\n if not np.isnan(self.qout_obs[-1]):\n fig2 = plt.figure(figsize=(10, 5), dpi=100)\n ax2 = fig2.add_subplot(111)\n positions_f = np.arange(0, len(self.qout_f)) - 0.1\n positions_a = np.arange(0, len(self.qout_a)) + 0.1\n positions_obs = np.arange(0, len(self.qout_obs))\n # predicted as red\n bp = ax2.boxplot(self.qout_f[-plot_len:], positions=positions_f[-plot_len:], widths=0.18,\n patch_artist=True)\n for box in bp['boxes']:\n # change outline color\n box.set(color='#7570b3', linewidth=1)\n # change fill color\n box.set(facecolor='#FF4633')\n # corrected as green\n bp = ax2.boxplot(self.qout_a[-plot_len:], positions=positions_a[-plot_len:], widths=0.18,\n patch_artist=True)\n for box in bp['boxes']:\n # change outline color\n box.set(color='#7570b3', linewidth=1)\n # change fill color\n box.set(facecolor='#07891B')\n # measurement as blue\n ax2.scatter(positions_obs[-plot_len:], self.qout_obs[-plot_len:], color='b', marker='o', s=30,\n label='Observation')\n ax2.set_title('qout')\n # x_ticks = np.arange(0, len(self.qout_f))\n # ax2.set_xticks(x_ticks[-plot_len:])\n\n plt.show()\n\n # plot the estimated qin and qout\n if self.__debug:\n if True:\n qin = np.squeeze(np.array(self.est_state_all[self.x_index['qin'], :]))\n qin_meas = np.array(self.qin_obs)[1:]\n print(len(qin), len(qin_meas))\n fig1 = plt.figure(figsize=(10, 5), dpi=100)\n ax1 = fig1.add_subplot(111)\n t = np.arange(len(qin))\n ax1.plot(t, qin, 'r-', label='Estimated')\n not_nan = ~np.isnan(qin_meas)\n ax1.plot(t[not_nan], qin_meas[not_nan], 'b', label='Measured')\n ax1.legend()\n ax1.grid(True)\n ax1.set_title('qin')\n\n plt.draw()\n\n if True:\n qout = np.squeeze(np.array(self.est_state_all[self.x_index['qout'], :]))\n qout_meas = np.array(self.qout_obs)[1:]\n fig2 = plt.figure(figsize=(10, 5), dpi=100)\n ax2 = fig2.add_subplot(111)\n t = np.arange(len(qout))\n ax2.plot(t, qout, 'r-', label='Estimated')\n not_nan = ~np.isnan(qout_meas)\n ax2.plot(t[not_nan], qout_meas[not_nan], 'b', label='Measured')\n ax2.set_title('qout')\n ax2.legend()\n ax2.grid(True)\n plt.draw()","def _update_edges_filtered(self, change=None):\n self._edges_filter.val_range = self.edges_range\n edges_ids = self._edges_filter.val_ids\n self.edges_ids = self._filter_edges(edges_ids)","def update(self, obs, q):\n for o in obs:\n prob = np.exp(-70)\n if self.landmarks:\n # find the data association with ML\n prob, landmark_idx, ass_obs, ass_jacobian, ass_adjcov = self.find_data_association(o)\n if prob < self.TOL:\n # create new landmark\n self.create_landmark(o)\n else:\n # update corresponding EKF\n self.update_landmark(np.transpose(np.array([o])), landmark_idx, ass_obs, ass_jacobian, ass_adjcov)\n else:\n # no initial landmarks\n self.create_landmark(o)\n self.weight *= prob\n \n q.put([self]) ###","def update_filter_params(self, fh):\n (self.data_timestamp, self.framerate,\n self.l, self.d, self.gamma,\n self.eps, self.alex, self.traceswitch) = (fh.attrs['data_timestamp'], fh.attrs['framerate'],\n fh.attrs['l'], fh.attrs['d'], fh.attrs['gamma'],\n fh.attrs['eps'], fh.attrs['alex'], fh.attrs['traceswitch'])","def after_each(self, dataset: pydicom.dataset.Dataset) -> None:\r\n for a_filter in self.filters[::-1]:\r\n a_filter.after_each(dataset)","def update_filters(self):\n\n # Update household filter\n household_filter = [True if agent == 'household' else False for agent \\\n in self.source.data['agent_type']]\n self.household_view.filters[0] = BooleanFilter(household_filter)\n\n # Update neighbourhood filter\n neighbourhood_filter = [True if agent == 'neighbourhood' else False for\\\n agent in self.source.data['agent_type']]\n self.neighbourhood_view.filters[0] = BooleanFilter(\n neighbourhood_filter)\n\n # Update school filter\n school_filter = [True if agent == 'school' else False for agent in \\\n self.source.data['agent_type']]\n self.school_view.filters[0] = BooleanFilter(school_filter)","def __setitem__(self, query_filter, value):\n saved_items = []\n for index, query in enumerate(self.__bound_queries):\n saved_items.append(query.get(query_filter, None))\n try:\n query[query_filter] = value\n except:\n for q, old_value in itertools.izip(self.__bound_queries[:index],\n saved_items):\n if old_value is not None:\n q[query_filter] = old_value\n else:\n del q[query_filter]\n raise","def apply_fn(self,fn):\r\n \r\n self.check_Data()\r\n for split,data_ in self.processed_data.items():\r\n x = data_['x']\r\n x = np.array([fn(xi) for xi in x])\r\n data_['x'] = x","def before_each(self, dataset: pydicom.dataset.Dataset) -> None:\r\n for a_filter in self.filters:\r\n a_filter.before_each(dataset)","def update(self, iteration):\n pass","def update_cards(self, filterdict, values, multiple=True):\n\n self._collection.update(self._constrain_keys(filterdict),\n {'$set': self._constrain_keys(values)},\n multi=multiple)","def fill_obs(self, observation_table, data_store):\n for obs in observation_table:\n events = data_store.obs(obs_id=obs['OBS_ID']).events\n\n # TODO: filter out (mask) possible sources in the data\n # for now, the observation table should not contain any\n # run at or near an existing source\n\n self.counts_cube.fill_events([events])\n self.livetime_cube.data += events.observation_live_time_duration","def reset_filter(self):\n arlen = len(self.variant_list)\n self.filter = np.zeros((arlen, arlen)) == 0","def filter(self, *args):\n from .elements import EqualClauseElement\n for a in args:\n for c in self._criterion:\n if isinstance(c, EqualClauseElement) and isinstance(a, EqualClauseElement) and \\\n c.attribute.node == a.attribute.node and c.attribute.label == a.attribute.label:\n c.value = a.value\n break\n else:\n self._criterion.append(a)\n return self","def apply_filters(self, filters):\n self._data = self.model.objects.filter(**filters)","def process_dataset(dataset, func):\n new_dataset = copy.copy(dataset)\n del new_dataset[\"val\"]\n new_dataset.update(func(dataset))\n return new_dataset","def update(self, observed, axis):\n\n # -------------------------------------------------------------------------\n # YOUR CODE GOES HERE\n #\n total = 0\n \n # zero out irrelevant data and calculate total for observed sample\n for each in self._table:\n if (axis == 1 and each[1] != observed) or (axis == 0 and each[0] != observed):\n self._table[each] = 0\n else:\n total += self._table[each]\n \n # normalize the revelant data\n for each in self._table:\n if (axis == 1 and each[1] == observed) or (axis == 0 and each[0] == observed):\n self._table[each] = self._table[each] / total\n\n #\n # END OF YOUR CODE\n # ------------------------------------------------------------------------- ","def apply_filters(self):\n hurst_cut = 0\n coint_cut = 0\n half_life_cut = 0\n mean_cross_cut = 0\n\n # Create an empty list for pairs that pass the filter tests\n validated_pairs = []\n\n # Create all the pairs combination\n self.create_pair_differences()\n\n # Print the number of potential pairs\n print(f\"Number of potential pairs in before filter: {len(self.__pairs_data)}\")\n\n for pair in self.__pairs_data:\n # Select the stocks from the pair\n stock1 = pair[0]\n stock2 = pair[1]\n\n # Test the hurst filter\n if self.hurst_filter(self, stock1=stock1, stock2=stock2):\n hurst_cut += 1\n if self.engel_filter(self, stock1=stock1, stock2=stock2):\n coint_cut += 1\n if self.half_life_filter(self, stock1=stock1, stock2=stock2):\n half_life_cut += 1\n if self.mean_cross_filter(self, stock1=stock1, stock2=stock2):\n mean_cross_cut += 1\n validated_pairs.append([stock1, stock2])\n\n print(f\"Hurst filter pass: {hurst_cut}\")\n print(f\"Co-integration filter pass: {coint_cut}\")\n print(f\"Half-life filter pass: {half_life_cut}\")\n print(f\"Mean-cross filter pass: {mean_cross_cut}\")\n print(f\"Final Number of validated pairs: {len(validated_pairs)}\")\n print(\"The final validated pairs are: \")\n print(validated_pairs)\n\n # Save it to the attribute\n self.__validated_pairs = validated_pairs\n self.__validated_pairs_diff = self.__pair_diff[self.symbolize_pairs(self.__validated_pairs)]","def _modify_updates(self, updates):\n wxf = self.wxf\n wyf = self.wyf\n wxf_updated = updates[wxf]\n wyf_updated = updates[wyf]\n nwxf = (wxf_updated.std(0) + SMALL)[numpy.newaxis, :]\n nwyf = (wyf_updated.std(0) + SMALL)[numpy.newaxis, :]\n meannxf = nwxf.mean()\n meannyf = nwyf.mean()\n # Center filters\n centered_wxf = wxf_updated - wxf_updated.mean(0)\n centered_wyf = wyf_updated - wyf_updated.mean(0)\n # Fix standard deviation\n wxf_updated = centered_wxf * (meannxf / nwxf)\n wyf_updated = centered_wyf * (meannyf / nwyf)\n updates[wxf] = wxf_updated\n updates[wyf] = wyf_updated","def test_updating_multiple_records_through_filter_with_arg_value(self, test_domain):\n identifier1 = uuid4()\n identifier2 = uuid4()\n identifier3 = uuid4()\n identifier4 = uuid4()\n test_domain.repository_for(Person)._dao.create(\n id=identifier1, first_name=\"Athos\", last_name=\"Musketeer\", age=2\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier2, first_name=\"Porthos\", last_name=\"Musketeer\", age=3\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier3, first_name=\"Aramis\", last_name=\"Musketeer\", age=4\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier4, first_name=\"dArtagnan\", last_name=\"Musketeer\", age=5\n )\n\n # Perform update\n updated_count = (\n test_domain.repository_for(Person)\n ._dao.query.filter(age__gt=3)\n .update_all({\"last_name\": \"Fraud\"})\n )\n\n # Query and check if only the relevant records have been updated\n assert updated_count == 2\n\n u_person1 = test_domain.repository_for(Person)._dao.get(identifier1)\n u_person2 = test_domain.repository_for(Person)._dao.get(identifier2)\n u_person3 = test_domain.repository_for(Person)._dao.get(identifier3)\n u_person4 = test_domain.repository_for(Person)._dao.get(identifier4)\n assert u_person1.last_name == \"Musketeer\"\n assert u_person2.last_name == \"Musketeer\"\n assert u_person3.last_name == \"Fraud\"\n assert u_person4.last_name == \"Fraud\"","def _mutate(self, individuals):\n for cur in individuals:\n if random.random() < self.mutation_probability:\n self.op.mutate(cur['individual'])\n cur['fitness'] = None","def _update_all(self, criteria: Q, *args, **kwargs):\n raise NotImplementedError","def updateIntensities (self,listAtoms):\r\n \r\n for i in range(len(listAtoms)):\r\n for j in range(len(listAtoms[i].spikeArray)):\r\n self.mol[i].spikeArray[j].intensity = listAtoms[i].spikeArray[j].intensity","def updateFitness(self):\r\n for candidate in self.candidates:\r\n candidate.updateFitness()\r\n return","def resample(self):\n self.particles = ParticleFilter.weighted_values(self.particles,\n [p.weight for p in self.particles],\n len(self.particles))\n for p in self.particles:\n p.weight = 1./len(self.particles)","def update_all(self, stream: Iterator[Mapping[str, np.ndarray]]) -> Self:\n\n for element in stream:\n self.update(element)\n\n return self","def update_all(self, stream: Iterator[Mapping[str, np.ndarray]]) -> Self:\n\n for element in stream:\n self.update(element)\n\n return self","def filter(self, observations):\n\n (_, _, _, x_filtered, P_filtered) = filter(self.F, self.Q, self.H, self.R, self.x_0, self.P_0, observations)\n return x_filtered, P_filtered","def filter_(self,fltr:torch.tensor):\n self.container = self.container[:,fltr]\n self.count_hist = self.count_hist[fltr]","def update_individuals(individuals, eval_results):\n for ind, res in zip(individuals, eval_results):\n ind.fitness.values = res[0]\n ind.matching_node_pairs = res[1]\n ind.gtp_precisions = res[2]","def _update_step(self, *, observations: types.ObservationsTorch) -> None:","def filter(self, filters):","def _applyFilters(self) -> None:\n self._dataframe = self._source.loc[:, self._visable_columns]\n for column, value in self._filters.items():\n if value is not None:\n self._dataframe = self._dataframe[self._source[column] == value]\n else:\n self._dataframe = self._dataframe[self._source[column].isnull()]\n\n self.layoutChanged.emit()","def filter(self, filter_dict):\n pass","def update_values(self):\n for key in self.inputs.keys():\n value = self.inputs[key]['entry'].get()\n self.inputs[key]['value'] = value","def prefilter(self, filt=None, verbose=False):\n erased = []\n if verbose:\n msg = 'Prior to filter, we have {} cells.'.format(len(self.cells))\n print(msg)\n # check for supplementary observables to be computed\n raw_obs, func_obs = set_observable_list(filters=[filt, ])\n\n # compute suppl obs for all cells\n if raw_obs:\n for cell in self.cells:\n for obs in raw_obs:\n cell.build(obs)\n for cell in self.cells:\n if filt is not None:\n if not filt(cell):\n erased.append(cell)\n # make Colony.add_cell_recursive non functional\n cell.bpointer = None\n if cell.parent:\n cell.parent.childs.remove(cell)\n # make daughter cells new roots\n for ch in cell.childs:\n ch.bpointer = None\n if verbose:\n msg = '{} cells do not pass filter.'.format(len(erased))\n print(msg)\n for cell in erased:\n self.cells.remove(cell) # otherwise would be considered root\n # clean-up actions for computing extra obs\n # extra obs computation depends on tree decomposition\n # this will be done in lineage.get_timeseries()\n for cell in self.cells:\n for obs in raw_obs:\n del cell._sdata[obs.label]\n if verbose:\n msg = 'After filtering, we get {} cells.'.format(len(self.cells))\n print(msg)\n# self.metadata.filters.append(repr(boofunc))\n return","def run(self, threshold=1e-3, max_iters=100):\n for msr in self.msrs:\n # find state transition matrix and propagate state\n phi_p, state_prop = self._compute_stm(msr.time, self.phis[-1])\n\n # compute observation deviation, obs_state matrix\n y_i, h_tilde = self._msr_resid(msr, state_prop)\n\n # update information and N\n self._update_info_and_n(y_i, h_tilde, phi_p, msr.cov)\n\n # add everything to the appropriate lists\n #new_cov = np.matmul(phi_p, np.matmul(self.cov_list[-1], phi_p))\n #self.cov_list.append(new_cov)\n self.prop_state_list.append(state_prop)\n self.estimates.append(state_prop)\n self.phis.append(phi_p)\n self.times.append(msr.time)\n\n # compute correction\n self.iters += 1\n x_hat_0 = np.linalg.solve(self.fisher_info[-1], self.N[-1])[0]\n\n # check for convergence\n if np.linalg.norm(x_hat_0) <= threshold:\n print(\"Batch filter converged in {} Iterations\".format(self.iters))\n self.cov_batch = np.linalg.inv(self.fisher_info[-1])\n\n elif self.iters >= max_iters:\n raise StopIteration(\"max_iters: {} reached without convergence\".format(max_iters))\n\n else:\n # reset everything and try again\n updated_istate = np.add(self.prop_state_list[0], np.transpose(x_hat_0))\n # fixes a strange bug wher the size of updated istate was changing\n updated_istate = np.resize(updated_istate, (1, len(self.istate)))[0]\n\n self.prop_state_list = [updated_istate]\n #self.cov_list = [self.apriori]\n self.fisher_info = [self.fisher_info[0]]\n self.pert_vec = np.subtract(self.pert_vec, x_hat_0)\n self.N = [np.matmul(self.apriori, self.pert_vec)]\n self.phis = [self.phis[0]]\n self.estimates = [updated_istate]\n self.times = [0]\n self.run()","def _UpdateDataSetValues( self ):\n pass","def set_filter_for_eval(self, x_filter):\n self.x_filter = x_filter\n\n entity_size = len(self.ent_to_idx)\n reln_size = len(self.rel_to_idx)\n\n first_million_primes_list = []\n curr_dir, _ = os.path.split(__file__)\n with open(os.path.join(curr_dir, \"prime_number_list.txt\"), \"r\") as f:\n logger.debug('Reading from prime_number_list.txt.')\n line = f.readline()\n i = 0\n for line in f:\n p_nums_line = line.split(' ')\n first_million_primes_list.extend([np.int64(x) for x in p_nums_line if x != '' and x != '\\n'])\n if len(first_million_primes_list) > (2 * entity_size + reln_size):\n break\n\n # subject\n self.entity_primes_left = first_million_primes_list[:entity_size]\n # obj\n self.entity_primes_right = first_million_primes_list[entity_size:2 * entity_size]\n # reln\n self.relation_primes = first_million_primes_list[2 * entity_size:(2 * entity_size + reln_size)]\n\n self.filter_keys = []\n # subject\n self.filter_keys = [self.entity_primes_left[self.x_filter[i, 0]] for i in range(self.x_filter.shape[0])]\n # obj\n self.filter_keys = [self.filter_keys[i] * self.entity_primes_right[self.x_filter[i, 2]]\n for i in range(self.x_filter.shape[0])]\n # reln\n self.filter_keys = [self.filter_keys[i] * self.relation_primes[self.x_filter[i, 1]]\n for i in range(self.x_filter.shape[0])]\n\n self.is_filtered = True","def test_feature_is_filtered(self):\n\n # Duplicate 1st row in var and assigned to 2nd\n self.validator.adata.var[\"feature_is_filtered\"][0] = True\n for i in range(self.validator.adata.X.shape[0]):\n self.validator.adata.X[i, 0] = 0\n self.validator.adata.X[0, 0] = 1\n\n self.validator.validate_adata()\n self.assertEqual(\n self.validator.errors,\n [\n \"ERROR: Some features are 'True' in 'feature_is_filtered' of dataframe 'var', \"\n \"but there are 1 non-zero values in the corresponding columns of the matrix 'X'. \"\n \"All values for these features must be 0.\"\n ],\n )","def filter(self,state0):\n ok,tchi2 = True,0.\n state = state0.copy()\n ii = 0\n for node in self.nodes:\n zrun = node.zrun\n ok,state,F,Q = self.model.propagate(state,zrun)\n if (not ok):\n warning(\"kfilter.filter not possible to filter at \",(ii,zrun))\n debug(\"kfilter.filter i,ok,chi2 \",(ii,ok,tchi2))\n return ok,tchi2\n node.F = F\n node.Q = Q\n node.setstate('pred',state)\n fstate,fchi2 = node.predict(state)\n node.setstate('filter',fstate)\n node.setchi2('filter',fchi2)\n tchi2+=fchi2\n self.model.user_filter(node)\n state = node.getstate('filter').copy()\n ii+=1\n self.status='filter'\n debug(\"kfilter.filter ok,chi2 \",(ok,tchi2))\n return ok,tchi2","def particle_filter(particle_set_t, measurement_t):\n global count\n n_samples, dim = particle_set_t.shape # no of particles and dimension of each particle\n\n pred_state = np.zeros((n_samples, dim), dtype=\"float64\") # store the predicted state \n weights = np.zeros(n_samples, dtype=\"float64\") # corresponding weights for resampling\n\n particle_set_t1 = np.zeros((n_samples, dim), dtype=\"float64\") # next iteration of particles\n\n\n # this loop calculates \\bar{X_t}, i.e. the predicted belief.\n for n in range(n_samples):\n # predicted motion step:\n xn_t1 = sample_motion_model(particle_set_t[n]) # 3x1 vector: hypothetical state\n\n # measurement correction step:\n weight_xn_t1 = state_likelihood(measurement_t, xn_t1) # scalar value\n\n pred_state[n] = xn_t1\n weights[n] = weight_xn_t1\n\n \n # It was observed that if all weights are 0, the resampling step breaks. \n # Thus, adding a uniform distribution. This is obviously a very bad idea \\ \n # as the true state can easily be discarded in the resampling step: TODO!\n if np.sum(weights) > 0.0:\n weights = weights/np.sum(weights) # normalize array only when sum in not 0\n else:\n print(\"possile divergence!\")\n weights[:] = 1 / n_samples # if sum is 0 then assign uniform distribution throughout\n\n\n # the resampling step:\n # indices = monte_carlo.residual_resample(weights)\n indices = monte_carlo.stratified_resample(weights)\n count += 1\n print(count)\n\n # new particle set is particles at index locations\n for i, index in enumerate(indices):\n particle_set_t1[i] = pred_state[index]\n\n return particle_set_t1","def Filter(self,val):\n \n #set th elength of the lis to 0\n List = [self.InitialList[i] for i in range(0,len(self.InitialList))]\n FilterValues = [None]\n Grab = [None]\n Headers = []\n \n #create the quick index\n for i in range(len(self.Condensensed)):\n \n Headers.append([self.Condensensed[i][l][0] for l in range(len(self.Condensensed[i]))])\n \n #grab the values...\n for j in range(len(self.Variables)):\n \n FilterValues.append(self.Variables[j].get())\n\n if self.Variables[j].get().split(' ')[0] == 'All':\n \n Grab.append(False)\n \n else:\n \n Grab.append(True)\n \n #intermediate list to compare\n ToCompare = []\n \n for i in range(1,len(Grab)):\n \n if Grab[i]:\n \n #find the index\n l = Headers[i].index(FilterValues[i])\n \n #grab it\n ToCompare.append([self.Condensensed[i][l][m] for m in range(len(self.Condensensed[i][l]))])\n\n\n for i in range(0, len(ToCompare)):\n \n List = list(set(List).intersection(ToCompare[i]))\n\n #update the interface\n self.Gatherer(List,list(self.Input))\n self.BuildTree()","def filter_data(self):\n if(self.filter_classes == []):\n return\n \n filtered_idx = []\n for id in range(len(self.image_ids)):\n anns = self.load_annotations(id)\n found = False\n for ann in anns:\n if ann['label'] in self.filter_classes:\n found = True\n break\n if found:\n filtered_idx.append(id)\n \n self.filtered_ids = [self.image_ids[id] for id in filtered_idx]\n # self.image_ids = self.filtered_ids\n print(\"Number of filtered instances:\", len(self.filtered_ids))","def train_filter(filt, U, Y):\n n = len(U)\n X = []\n R = []\n for i in range(n):\n x_hat_bar = filt.time_update(U[i])\n x_hat, P = filt.measurement_update(Y[i], x_hat_bar)\n print(i, x_hat, P)\n X.append(x_hat)\n R.append(P)\n return np.array(X), R","def do_filter(self):\n return np.sum(np.multiply(self._past_values, self._impulse_response))","def update(self, **kwargs):\n for name, item in itertools.chain(\n self._cal_objs.items(),\n self._noise_objs.items()):\n logger.debug(\"update {}\".format(item))\n item.update(**kwargs)","def test_updating_record_through_filter(self, test_domain):\n identifier1 = uuid4()\n identifier2 = uuid4()\n identifier3 = uuid4()\n identifier4 = uuid4()\n test_domain.repository_for(Person)._dao.create(\n id=identifier1, first_name=\"Athos\", last_name=\"Musketeer\", age=2\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier2, first_name=\"Porthos\", last_name=\"Musketeer\", age=3\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier3, first_name=\"Aramis\", last_name=\"Musketeer\", age=4\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier4, first_name=\"dArtagnan\", last_name=\"Musketeer\", age=5\n )\n\n # Perform update\n updated_count = (\n test_domain.repository_for(Person)\n ._dao.query.filter(age__gt=3)\n .update(last_name=\"Fraud\")\n )\n\n # Query and check if only the relevant records have been updated\n assert updated_count == 2\n\n u_person1 = test_domain.repository_for(Person)._dao.get(identifier1)\n u_person2 = test_domain.repository_for(Person)._dao.get(identifier2)\n u_person3 = test_domain.repository_for(Person)._dao.get(identifier3)\n u_person4 = test_domain.repository_for(Person)._dao.get(identifier4)\n assert u_person1.last_name == \"Musketeer\"\n assert u_person2.last_name == \"Musketeer\"\n assert u_person3.last_name == \"Fraud\"\n assert u_person4.last_name == \"Fraud\"","def filter(self):\n self.data = self.data.loc[~self.data.isnull().any(1),:]","def filter_data(self):\n self.df = self.df[HeatStrokeDataFiller.important_features]","def updateValues(self, otherDialog):\n for index in range(len(self._values)):\n for oval in otherDialog._values:\n if self._values[index].getID() == oval.getID():\n self._values[index].combine( oval, intelligent=False )\n break","def observe(self, observation):\n # shallow copy observation (deep copy can be expensive)\n obs = observation.copy()\n batch_idx = self.opt.get('batchindex', 0)\n self.observation = obs\n #self.answers[batch_idx] = None\n return obs","def _update_raw_data(params):\n from scipy.signal import filtfilt\n start = params['t_start']\n stop = params['raw'].time_as_index(start + params['duration'])[0]\n start = params['raw'].time_as_index(start)[0]\n data_picks = _pick_data_channels(params['raw'].info)\n data, times = params['raw'][:, start:stop]\n if params['projector'] is not None:\n data = np.dot(params['projector'], data)\n # remove DC\n if params['remove_dc'] is True:\n data -= np.mean(data, axis=1)[:, np.newaxis]\n if params['ba'] is not None:\n data[data_picks] = filtfilt(params['ba'][0], params['ba'][1],\n data[data_picks], axis=1, padlen=0)\n # scale\n for di in range(data.shape[0]):\n data[di] /= params['scalings'][params['types'][di]]\n # stim channels should be hard limited\n if params['types'][di] == 'stim':\n norm = float(max(data[di]))\n data[di] /= norm if norm > 0 else 1.\n # clip\n if params['clipping'] == 'transparent':\n data[np.logical_or(data > 1, data < -1)] = np.nan\n elif params['clipping'] == 'clamp':\n data = np.clip(data, -1, 1, data)\n params['data'] = data\n params['times'] = times","def _filter_satisfied(self, update_setd=False):\n\n model = self.oracle.get_model()\n setd = set()\n\n for i, cl in enumerate(self.soft):\n if not self.satc[i]:\n if self._satisfied(cl, model):\n self.satc[i] = True\n self.ss_assumps.append(self.sels[i])\n else:\n setd = setd.union(set(cl))\n\n if update_setd:\n self.setd = list(setd)","def _put_resolved_booleans_into_filter(\n filter_operator: Operator, model_filters_to_resolved_values: Dict[ModelFilter, BooleanValues]\n) -> None:\n for operator in _model_filter_in_operator_generator(filter_operator):\n model_filter = operator.unresolved_value\n operator.resolved_value = model_filters_to_resolved_values.get(\n model_filter, BooleanValues.UNKNOWN\n )","def apply_filters(self, new_filters):\n\t\tself.filters = new_filters","def set_observations(self, oseries):\n self.oseries_index = oseries.index\n observations_masked = np.ma.array(oseries,\n mask=(~np.isfinite(oseries)))\n (n_timesteps, dimobs) = observations_masked.shape\n self.observation_indices = np.zeros((n_timesteps, dimobs),\n dtype=np.float64)\n self.observation_count = np.zeros(n_timesteps, dtype=np.int64)\n self.observations = np.zeros((n_timesteps, dimobs), dtype=np.float64)\n\n for t in range(n_timesteps):\n observation = observations_masked[t]\n # add large value to find all finite non-masked values\n obstmp = observation + 1e10\n obsindices = obstmp.nonzero()[0]\n self.observation_count[t] = len(obsindices)\n\n if (len(obsindices) > 0):\n for i, _ in enumerate(obsindices):\n obsid = int(obsindices[i])\n self.observations[t, obsid] = observation[obsid]\n self.observation_indices[t, i] = obsid","def update(self):\n\n terms_toRemove = []\n\n for termIndex, [term_constantFactor, term_unknowns_attributeAddresses] in enumerate(self.LHS):\n\n # Check if coefficient is 0 - then no need to process any of the unknowns since term will be 0 anyways\n if term_constantFactor == 0:\n terms_toRemove.append(termIndex)\n continue # continue to next term, no need to resolve the unknowns of this term since the product will be 0 anyways\n\n # Check if any unknowns became known\n unknowns_toRemove = []\n for unknown_attributeAddress in term_unknowns_attributeAddresses:\n attribute = getattr_fromAddress(*unknown_attributeAddress)\n if isNumeric(attribute):\n # object.attribute which had previously been identified as unknown now has a value, add it to the constant factor product and remove from the unknowns\n self.LHS[termIndex][0] *= attribute # multiply it with the constant factor product\n unknowns_toRemove.append([termIndex, unknown_attributeAddress])\n for termIndex, unknown_attributeAddress in unknowns_toRemove: # remove unknowns which have become known in the end\n # removing in the end not to tamper with the iteration of the above loop\n self.LHS[termIndex][1].remove(unknown_attributeAddress)\n\n # Move constants to RHS\n if self.LHS[termIndex][1] == []:\n # if term has no unknowns, it is a constant, move to RHS\n self.RHS -= self.LHS[termIndex][0]\n self.LHS.pop(termIndex)\n\n for termIndex in reversed(terms_toRemove): # reversed - otherwise would tamper with indices of items identified for removal\n self.LHS.pop(termIndex)\n\n self._gatherUnknowns()","def updateObservation(self, obs):\n self.settingsDb.updateObservation(self.observationsTableName(), obs)","def filter(self):\n M, p, q = self.M, self.p, self.q\n x = self.x\n idx = len(self.x) - (p + 1)\n x_ = self.x_prev + (x[idx + p] - x[idx - q]) / M\n self.t_.append(self.t[idx])\n self.t_filtered.append(self.t[idx])\n self.x_.append(x_)\n self.x_filtered.append(x_)\n self.x_prev = x_","def update_filters(self, **kwargs):\n self._FILTERS = kwargs","def update(self, values, train, eta=.1):\n\t\tfor X, y_true in zip(values, train):\n\t\t\tprediction = self.activate(X)\n\t\t\terror = y_true - prediction\n\t\t\tweight_update = error * eta * X\n\t\t\tself.weights += weight_update","def finalize(self):\n # this forces only some filters will be used for feature computation\n # this is not ideal, but a necessary stop-gap while we revise\n # the PropertyTable SQL\n self._good_filters = ['u', 'g', 'r', 'i', 'z', 'Y']\n self._good_filter_wave = np.array([3569.5, 4766.5, 6214.5, 7544.5, 8707.5, 10039.5])\n\n use_filters = set(self._good_filters) & self.filters\n if not self.filters.issubset(use_filters):\n message = 'Number of useful filters ({}) does not equal number available filters ({}) - some filters will not be used'.format(\n ''.join(use_filters), ''.join(self.filters))\n warnings.warn(message, RuntimeWarning)\n self.filters = set(use_filters)\n mask = np.array([True if x in self.filters else False for x in self.passband])\n\n if mask.size: # Not empty arrays\n self.time = self.time[mask]\n self.flux = self.flux[mask]\n self.fluxErr = self.fluxErr[mask]\n self.obsId = self.obsId[mask]\n self.passband = self.passband[mask]\n self.zeropoint = self.zeropoint[mask]\n for key in self._extra_cols:\n val = getattr(self, key)\n setattr(self, key, val[mask])\n\n self.nobs = len(self.time)\n if self.nobs == 0:\n message = 'Object {} with locus ID {} has no good observations.'.format(self.objectId, self.locusId)\n raise ValueError(message)\n\n return self._remove_flux_extinction()","def step_filter(self, v, imu_meas, meas):\n\n if (imu_meas is not None) and (meas is None):\n xp, Pp = self.prediction(v, imu_meas)\n self.x = xp\n self.P_t = Pp\n return xp\n\n elif (imu_meas is None) and (meas is not None):\n z_t = self.transformMeasurement(meas)\n self.x = z_t\n return z_t\n\n else: # both measurements contain values\n xp, Pp = self.prediction(v, imu_meas)\n z_t = self.transformMeasurement(meas)\n x, P = self.update(z_t, xp, Pp)\n\n self.x = x\n self.P_t = P\n return x","def filter(self, data):\n self.data = pysap.Image(data=self.flt.filter(data))","def _apply_filter(self, fn=lambda ngram, freq: False):\n tmp_ngram = FreqDist()\n for ngram, freq in self.ngram_fd.items():\n if not fn(ngram, freq):\n tmp_ngram[ngram] = freq\n self.ngram_fd = tmp_ngram","def step_filter(self, v, imu_meas, z_t):\n # YOUR CODE HERE\n pass","def filter_data(data,filters):\n final_filter = pd.Series(np.array([True] * data.shape[0]))\n for attribute, value in filters:\n final_filter &= data[attribute] == value\n return data[final_filter]","def parameter_update(self, X, X_mask):\n data_with_mask = np.hstack([X, X_mask])\n X_pred, X_MC_preds = self.predict(data_with_mask)\n X[X_mask] *= self.keep_coeff\n X[X_mask] += self.weight_update_coeff * X_pred[X_mask]\n return X, X_MC_preds","def _set_values(loop_data):\n \n # These are the indexes of all the data that are unassigned\n value_indexes = loop_data.ix[(loop_data.loop==0)&(loop_data.tag==0)&(loop_data.stop==0)].index\n loop_data[u'value'] = 0\n loop_data.ix[value_indexes,u'value'] = 1\n \n # These are the indexes of data who follow either a loop, label, or stop tag\n value_indexes_begin = loop_data.ix[(value_indexes-1)].ix[loop_data.value==0].index + 1\n \n # The first rows of each data correspond to data for their respective loops\n loop_max = loop_data.loop.max()\n loop_range = np.arange(loop_max-1, -1, -1)\n \n for idx in value_indexes_begin:\n loop_data.ix[idx:idx+len(loop_range)-1,u'value'] += loop_range\n \n return loop_data","def update_with_observation(particles, landmark_list):\r\n for p in particles:\r\n for i in range(np.shape(landmark_list)[1]):\r\n p.update_particle(landmark_list[:, i])\r\n\r\n return particles","def process_observation(self, observation):\n return observation","def process_observation(self, observation):\n return observation","def iterate(self, data):\n \n # Append data to self.data\n self.data = np.append(self.data, data)\n \n for i, d in enumerate(data):\n update = self.current*self.likelihood(d)\n self.current = self._normalize(update)\n self.posterior = np.concatenate((self.posterior,[self.current]))\n \n print(str(len(data)) + \" iterations completed!\")\n \n return None","async def _update_values(self, model: Model):\n\n raise NotImplementedError","def Iterate(self):\n\t\tfor atom in self.atoms:\n\t\t\tself.UpdateAtom(atom)","def update_state(self):\n # return initial state if no observation was yet\n if len(self.obs_history) == 0:\n return self.kf.initial_state_mean, self.kf.initial_state_covariance\n\n hist = np.ma.masked_array(self.obs_history, mask=np.zeros((1,)))\n for i in range(len(hist)):\n if hist[i] == -1e8:\n hist[i] = np.ma.masked\n\n # print(hist, hist.shape)\n return self.kf.filter(hist)","def _update_data(self):\n for attribute in [\"flow_rate\"]:\n self._data[attribute] = self._connection.measure","def apply_filters(\n isovar_result,\n filter_thresholds={},\n filter_flags=[]):\n filter_values = OrderedDict(isovar_result.filter_values.items())\n new_filter_values = evaluate_filters(\n isovar_result,\n filter_thresholds=filter_thresholds,\n filter_flags=filter_flags)\n filter_values.update(new_filter_values)\n return isovar_result.clone_with_updates(filter_values=filter_values)","def healthcare_filter(df_all): \n #get requested assets under healthcare tag \n df_filtered = pandas.DataFrame(columns=['osm_id','asset','geometry']) #create df for saving data\n for row in range(len(df_all.index)): \n if 'healthcare' in df_all[\"asset\"][row]: #check if healthcare key is present\n df_filtered = df_filtered.append(df_all.loc[row]) #if so, save in df \n if '\"healthcare\"=>\"doctor\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'doctors' #to be consistent with asset list \n elif '\"healthcare\"=>\"pharmacy\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'pharmacy'\n elif '\"healthcare\"=>\"hospital\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'hospital'\n elif '\"healthcare\"=>\"clinic\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'clinic'\n elif '\"healthcare\"=>\"dentist\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'dentist'\n elif '\"healthcare\"=>\"physiotherapist\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'physiotherapist'\n elif '\"healthcare\"=>\"alternative\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'alternative'\n elif '\"healthcare\"=>\"laboratory\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'laboratory'\n elif '\"healthcare\"=>\"optometrist\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'optometrist'\n elif '\"healthcare\"=>\"rehabilitation\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'rehabilitation'\n elif '\"healthcare\"=>\"blood_donation\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'blood_donation'\n elif '\"healthcare\"=>\"birthing_center\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'birthing_center'\n else:\n df_filtered = df_filtered.drop(index=row)\n \n return df_filtered","def filter_patients(self):\n\n if self.dataset is None:\n self.dataset = h5py.File(self.filename, 'r')['dataset']\n \n # Find feature indices belonging to specific criteria\n inclusion_info = self.filter_params['inclusion']\n # exclusion_info = self.filter_params['exclusion']\n case_control_info = self.filter_params['case_control']\n\n inclusion_inds = self.check_criteria(inclusion_info, case_control=False)\n # exclusion_inds = self.check_criteria(exclusion_info, case_control=False)\n case_inds, control_inds = self.check_criteria(case_control_info, case_control=True)\n\n filtered_inds = {}\n # inclusion_exclusion_inds = np.setdiff1d(inclusion_inds, exclusion_inds)\n filtered_inds['case'] = np.intersect1d(inclusion_inds, case_inds)\n filtered_inds['control'] = np.intersect1d(inclusion_inds, control_inds)\n\n return filtered_inds","def train_filter2(filt, U, Y):\n n = len(U)\n X, R = [], []\n for i in range(n):\n print(i)\n filt.predict(fx_args=(U[i],))\n filt.update(Y[i])\n X.append(filt.x.copy())\n R.append(filt.P.copy())\n X = np.array(X)\n X, R, K = filt.rts_smoother(X, R, fx_args=(U[i],))\n return X, R","def test_updating_multiple_records_through_filter_with_kwarg_value(\n self, test_domain\n ):\n identifier1 = uuid4()\n identifier2 = uuid4()\n identifier3 = uuid4()\n identifier4 = uuid4()\n test_domain.repository_for(Person)._dao.create(\n id=identifier1, first_name=\"Athos\", last_name=\"Musketeer\", age=2\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier2, first_name=\"Porthos\", last_name=\"Musketeer\", age=3\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier3, first_name=\"Aramis\", last_name=\"Musketeer\", age=4\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier4, first_name=\"dArtagnan\", last_name=\"Musketeer\", age=5\n )\n\n # Perform update\n updated_count = (\n test_domain.repository_for(Person)\n ._dao.query.filter(age__gt=3)\n .update_all(last_name=\"Fraud\")\n )\n\n # Query and check if only the relevant records have been updated\n assert updated_count == 2\n\n u_person1 = test_domain.repository_for(Person)._dao.get(identifier1)\n u_person2 = test_domain.repository_for(Person)._dao.get(identifier2)\n u_person3 = test_domain.repository_for(Person)._dao.get(identifier3)\n u_person4 = test_domain.repository_for(Person)._dao.get(identifier4)\n assert u_person1.last_name == \"Musketeer\"\n assert u_person2.last_name == \"Musketeer\"\n assert u_person3.last_name == \"Fraud\"\n assert u_person4.last_name == \"Fraud\"","def apply_filter(self, filter_arg):\n filtered_entries = self.visual.apply_filter(filter_arg, self.get_current_entries())\n # idxs = self.selector.select_by_objects(filtered_entries, yield_ones_index=True)\n self.visual.print_entries_enum(filtered_entries, None)\n # self.list(idxs)","def forc_model(self):\n lag1_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag1_by_store')\n lag2_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag2_by_store')\n for add in self.X.address1.unique():\n add_mask = self.X.address1 == add\n foo = self.X[ add_mask ].sort_values('visit_date', ascending=False)\n top_index = foo.index[0]\n clust = int(foo.cluster.values[0])\n # get values from last visit for store\n base_input = foo[self.model_mask_cols].values[0]\n base_actual = self.y[top_index]\n lag2_val = base_input[lag1_loc]\n lag1_val = base_actual\n\n for i in range(1, self.num_periods + 1):\n model = self.model_list[clust]\n inputs = base_input\n inputs[lag1_loc] = lag1_val\n inputs[lag2_loc] = lag2_val\n \n pred = model.predict(inputs.reshape(1, -1))\n self._update_cust_table(add, i, pred)\n \n lag2_val = lag1_val\n lag1_val = pred"],"string":"[\n \"def update(self, other):\\n for filter, value in other.items():\\n self.__setitem__(filter, value)\",\n \"def update(self):\\n for filter in self.filters:\\n filter.update(self.learning_rate)\",\n \"def filter(self):\\n self.filter_means = [self.m_0]\\n self.filter_covs = [self.P_0]\\n self.marginal_covs = []\\n for t in range(self.data.shape[0]):\\n m_bar, P_bar = self.one_step_prediction(self.filter_means[-1], self.filter_covs[-1])\\n\\n # Update step\\n y = self.data[t]\\n if not np.isnan(y).any():\\n v = y[:, None] - self.observation_matrix @ m_bar\\n S = self.observation_matrix @ P_bar @ self.observation_matrix.T + self.observation_cov\\n K = P_bar @ self.observation_matrix.T @ np.linalg.inv(S)\\n\\n m_bar = m_bar + K @ v\\n P_bar = P_bar - K @ S @ K.T\\n\\n self.marginal_covs.append(S)\\n\\n self.filter_means.append(m_bar)\\n self.filter_covs.append(P_bar)\\n self.filter_means = self.filter_means[1:]\\n self.filter_covs = self.filter_covs[1:]\",\n \"def update(self, updates, predicate):\\n for row in self.rows:\\n if predicate(row):\\n for column, new_value in updates.items():\\n row[column] = new_value\",\n \"def filter_values(self):\\n dfilter = self.args.datafilter\\n self.logger.info(u'Filtering values with:{f}'.format(f=dfilter))\\n data = self.outputdata\\n newdata = {}\\n for key, value in data.items():\\n self.logger.info(u'\\\\nProcessing Key:{k}, value:{v}'.format(k=key,\\n v=value))\\n returned_data = dict_value_filter(key, value, dfilter, self.logger)\\n if bool(returned_data):\\n newdata[key] = returned_data\\n self.logger.info(u'Data after filter:{d}'.format(d=newdata))\\n\\n self.outputdata = newdata\",\n \"def UpdateSet(self, dataset):\\r\\n for data in dataset:\\r\\n self.UpdateOddsRatioVsNoNorm(data)\",\n \"def filter_update(observations, observation_matrix, observation_variance,\\n observation_indices, observation_count,\\n state_mean, state_covariance):\\n\\n sigma = 0.\\n detf = 0.\\n n_observation = np.int(observation_count)\\n for i in range(n_observation):\\n observation_index = int(observation_indices[i])\\n obsmat = observation_matrix[observation_index, :]\\n innovation = (observations[observation_index]\\n - np.dot(obsmat, state_mean))\\n dot_statecov_obsmat = np.dot(state_covariance, obsmat)\\n innovation_covariance = (np.dot(obsmat, dot_statecov_obsmat)\\n + observation_variance[observation_index])\\n kgain = dot_statecov_obsmat / innovation_covariance\\n state_covariance = (state_covariance\\n - (np.outer(kgain, kgain)\\n * innovation_covariance))\\n\\n state_mean = state_mean + kgain * innovation\\n\\n sigma = sigma + (innovation ** 2 / innovation_covariance)\\n detf = detf + np.log(innovation_covariance)\\n\\n return (state_mean, state_covariance, sigma, detf)\",\n \"def update_filter(self, measurement, robot_pos):\\n new_weights = []\\n for p in self.particles:\\n p = self.__move(p, self.MOTION)\\n angle = self.__measurement(p, robot_pos)\\n prob = self.__measurement_prob(angle, measurement,\\n self.sense_noise)\\n new_weights.append(prob)\\n new_weights = np.array(new_weights)\\n new_weights /= np.sum(new_weights) # normalized weights\\n self.weights = new_weights\\n\\n # if self.__neff() > self.n / 2:\\n self.particles = self.__resample()\",\n \"def set_observations(self, factor):\\r\\n relevant_obs = set(self.obs).intersection(set(factor.get_variables()))\\r\\n if relevant_obs:\\r\\n factor.set_observations({x:self.obs[x] for x in relevant_obs})\",\n \"def filter(self, update):\\n\\n raise NotImplementedError\",\n \"def ObserveEvidence(self,E):\\r\\n for v,e in E.iteritems():\\r\\n indx, = (self.var==v).nonzero() # find i where f.var[i]=v\\r\\n if indx.size != 0:\\r\\n A = I2A(range(len(self.val)), self.card) # all assignments\\r\\n A = A[:,indx].flatten() # only interested in 'indx' element of each row\\r\\n self.val[A != e] = 0\",\n \"def update(self, predictions, real_vals):\\n for pred, actual in zip(predictions, real_vals):\\n self.cm.loc[actual, pred] += 1\",\n \"def filter(self, observable):\",\n \"def filter_data(self):\\n self.data = filter_pandas(self.data, self.filters)\",\n \"def update_values(self):\\n # have to reset params to 0 when recalculating\\n self.total_weight = self.total_price = self.total_value = self.total_fitness = 0\\n for index, value in enumerate(self.item_stats):\\n if value == 1:\\n self.total_weight += self.items[index].weight\\n self.total_price += self.items[index].price\\n self.total_value += self.items[index].value\\n self.total_fitness += self.items[index].fitness\",\n \"def update(self, z):\\n raise NotImplementedError('Must implement an update step for the filter.')\",\n \"def run_batch_filter(self):\\n if self.speed_data is None and self.flow_data is None and self.traveltime_data is None:\\n print(\\n 'Warning: The measurement data must be set before running the batch filter: use function self.set_meas_data()')\\n\\n # =======================================================================\\n # the initial ensembles, which should have been set externally\\n X_init = np.matrix(np.zeros((self.dim_state, self.num_ensembles)))\\n print(\\n 'Setting initial ensembles: rho {0}; qin {1}; qout {2}'.format(self.init_rho, self.init_qin, self.init_qout))\\n for ens in range(0, self.num_ensembles):\\n X_init[self.x_index['density'][0]:\\n self.x_index['density'][self.num_cells - 1], ens] = self.init_rho\\n X_init[self.x_index['qin'], ens] = self.init_qin\\n X_init[self.x_index['qout'], ens] = self.init_qout\\n\\n # print('setted qin {0}; qout {1}'.format(X_init[self.x_index['qin'], ens], X_init[self.x_index['qout'], ens] ))\\n # add noise to each ensemble\\n X_init[:, ens] += np.matrix(np.random.multivariate_normal(\\n np.zeros(self.dim_state), self.Q)).reshape((self.dim_state, 1))\\n\\n self.set_initial_ensembles(X_init)\\n\\n # =======================================================================\\n # DEBUG\\n # save the qin and qout in the corresponding probe data\\n # save the initial state\\n if self.__debug:\\n self.qin_f.append(np.squeeze(np.array(self.X_f[self.x_index['qin'], :])).tolist())\\n self.qin_a.append(np.squeeze(np.array(self.X_a[self.x_index['qin'], :])).tolist())\\n self.qin_obs.append(np.nan)\\n\\n self.qout_f.append(np.squeeze(np.array(self.X_f[self.x_index['qout'], :])).tolist())\\n self.qout_a.append(np.squeeze(np.array(self.X_a[self.x_index['qout'], :])).tolist())\\n self.qout_obs.append(np.nan)\\n\\n # The enKF runs at the finest time grid\\n # for each step, update the system\\n for step in range(0, self.num_steps):\\n\\n # update status\\n sys.stdout.write('\\\\r')\\n sys.stdout.write('Status: filtering step {0}/{1}'.format(step, self.num_steps))\\n sys.stdout.flush()\\n # print('Status: filtering step {0}'.format(step))\\n\\n cur_time = (step + 1) * self.dur_steps\\n\\n # get the effective measurement\\n eff_flow, eff_speed, eff_traveltime = self.__get_eff_meas(cur_time)\\n\\n # build the observation index\\n self.y_index, self.dim_obs, y_obs, cov_noise = self.__build_obs_index(eff_flow, eff_speed, eff_traveltime)\\n\\n # update the estimate for this step\\n est_state = self.update_estimate(y_obs, cov_noise, cur_time)\\n\\n # =======================================================================\\n # DEBUG\\n # save the qin and qout in the corresponding probe data\\n # save the updated state\\n if self.__debug:\\n self.qin_f.append(np.squeeze(np.array(self.X_f[self.x_index['qin'], :])).tolist())\\n self.qin_a.append(np.squeeze(np.array(self.X_a[self.x_index['qin'], :])).tolist())\\n if 'flow' in self.y_index.keys() and self.__debug_entrance_sensor in self.y_index['flow'].keys():\\n self.qin_obs.append(y_obs[self.y_index['flow'][self.__debug_entrance_sensor]])\\n # print('y_index[flow]:{0}'.format(self.y_index['flow'].keys()))\\n # print('y_obs[ y_index[flow][entrance] ]:{0}'.format(\\n # y_obs[ self.y_index['flow'][self.__debug_entrance_sensor]],\\n # self.__debug_entrance_sensor))\\n else:\\n self.qin_obs.append(np.nan)\\n\\n self.qout_f.append(np.squeeze(np.array(self.X_f[self.x_index['qout'], :])).tolist())\\n self.qout_a.append(np.squeeze(np.array(self.X_a[self.x_index['qout'], :])).tolist())\\n if 'flow' in self.y_index.keys() and self.__debug_exit_sensor in self.y_index['flow'].keys():\\n self.qout_obs.append(y_obs[self.y_index['flow'][self.__debug_exit_sensor]])\\n else:\\n self.qout_obs.append(np.nan)\\n # =======================================================================\\n # save the estimated state\\n self.est_state_all[:, step] = est_state\\n\\n # decouple and save into self.est_density, self.est_speed, self.est_queue, self.est_traveltime\\n self.est_density[:, step] = est_state[0:self.num_cells, 0]\\n\\n # the speed is computed using the fundamental diagram\\n for cell_id in range(0, self.num_cells):\\n # use the static FD at this step\\n self.est_speed[cell_id, step] = self.__rho2v(self.vm_cells[cell_id, 0], self.beta_cells[cell_id, 0],\\n self.rhoc_cells[cell_id, 0], self.wc_cells[cell_id, 0],\\n self.est_density[cell_id, step])\\n\\n # REMARK: the queue and travel time a post-processed from the speed field.\\n # They are computed in cross_evaluation class for all algorithms\\n # the queue length starts from the first cell with speed below queue_threshold to the end of road\\n # index = (self.est_speed[:, step] <= self.queue_threshold)\\n #\\n # # filter out the outliers\\n # index_smoothed = deepcopy(index)\\n # outlier_max = 3\\n # counter = 0\\n # for i in range(0, len(index)):\\n #\\n # if index[i] == True:\\n # # trigger the coutner\\n # counter += 1\\n # elif index[i] == False and counter != 0:\\n # if counter <= outlier_max:\\n # # found outliers\\n # index_smoothed[ i-counter : i ] = False\\n # # reset counter\\n # counter = 0\\n #\\n # # if i != 0 and i != len(index)-1:\\n # # if sum( index[i-1:i+3] ) >=2:\\n # # index_smoothed[i] = True\\n # # else:\\n # # index_smoothed[i] = False\\n # # elif i == 0:\\n # # if sum(index[0: 5] ) >= 3:\\n # # index_smoothed[i] = True\\n # # else:\\n # # index_smoothed[i] = False\\n # # elif i == len(index)-1:\\n # # if sum(index[ i-4 :len(index)]) >= 3:\\n # # index_smoothed[i] = True\\n # # else:\\n # # index_smoothed[i] = False\\n #\\n # if sum(index_smoothed) <= 3: # use 4 to suppress false alarms\\n # # if less or equal then 2 cells are in congestion, it may be caused by noise.\\n # self.est_queue[step] = 0\\n # else:\\n # # if step > 105 and step < 115:\\n # # print(sum(index_smoothed))\\n # # print(index_smoothed)\\n # # print(index)\\n #\\n # self.est_queue[step] = \\\\\\n # self.len_cells*( self.num_cells - np.argmax(index_smoothed) )\\n # # try:\\n # # first_cong_cell_id = [x[0] for x in enumerate( self.est_speed[:,step] ) if x[1] < self.queue_threshold][0]\\n # # except IndexError:\\n # # # no congested cell\\n # # first_cong_cell_id = self.num_cells\\n # # # the estimated queue length\\n # # self.est_queue[step] = self.len_cells*( self.num_cells - first_cong_cell_id )\\n #\\n # # the travel time estimate is computed by summing up the travel time in each cell\\n # self.est_traveltime[step] = np.sum(self.len_cells/self.est_speed[:,step])\\n\\n\\n # =======================================================================\\n # DEBUG\\n # plot the update\\n if self.__debug:\\n plot_len = 19\\n # qin\\n if False:\\n if not np.isnan(self.qin_obs[-1]):\\n fig1 = plt.figure(figsize=(10, 5), dpi=100)\\n ax1 = fig1.add_subplot(111)\\n positions_f = np.arange(0, len(self.qin_f)) - 0.1\\n positions_a = np.arange(0, len(self.qin_a)) + 0.1\\n positions_obs = np.arange(0, len(self.qin_obs))\\n # predicted as red\\n bp = ax1.boxplot(self.qin_f[-plot_len:],\\n positions=positions_f[-plot_len:], widths=0.15,\\n patch_artist=False)\\n for box in bp['boxes']:\\n # change outline color\\n box.set(color='#FF4633', linewidth=1)\\n # change fill color\\n # box.set( facecolor = '#FF4633' )\\n # corrected as green\\n bp = ax1.boxplot(self.qin_a[-plot_len:],\\n positions=positions_a[-plot_len:], widths=0.15, patch_artist=False)\\n for box in bp['boxes']:\\n # change outline color\\n box.set(color='#07891B', linewidth=1)\\n # change fill color\\n # box.set( facecolor = '#07891B' )\\n # measurement as blue\\n ax1.scatter(positions_obs[-plot_len:], self.qin_obs[-plot_len:], color='b', marker='o', s=40,\\n label='Observation')\\n ax1.set_title('qin')\\n # x_ticks = np.arange(0, len(self.qin_f))\\n # ax1.set_xticks(x_ticks[-plot_len:])\\n plt.show()\\n\\n # qout\\n if False:\\n if not np.isnan(self.qout_obs[-1]):\\n fig2 = plt.figure(figsize=(10, 5), dpi=100)\\n ax2 = fig2.add_subplot(111)\\n positions_f = np.arange(0, len(self.qout_f)) - 0.1\\n positions_a = np.arange(0, len(self.qout_a)) + 0.1\\n positions_obs = np.arange(0, len(self.qout_obs))\\n # predicted as red\\n bp = ax2.boxplot(self.qout_f[-plot_len:], positions=positions_f[-plot_len:], widths=0.18,\\n patch_artist=True)\\n for box in bp['boxes']:\\n # change outline color\\n box.set(color='#7570b3', linewidth=1)\\n # change fill color\\n box.set(facecolor='#FF4633')\\n # corrected as green\\n bp = ax2.boxplot(self.qout_a[-plot_len:], positions=positions_a[-plot_len:], widths=0.18,\\n patch_artist=True)\\n for box in bp['boxes']:\\n # change outline color\\n box.set(color='#7570b3', linewidth=1)\\n # change fill color\\n box.set(facecolor='#07891B')\\n # measurement as blue\\n ax2.scatter(positions_obs[-plot_len:], self.qout_obs[-plot_len:], color='b', marker='o', s=30,\\n label='Observation')\\n ax2.set_title('qout')\\n # x_ticks = np.arange(0, len(self.qout_f))\\n # ax2.set_xticks(x_ticks[-plot_len:])\\n\\n plt.show()\\n\\n # plot the estimated qin and qout\\n if self.__debug:\\n if True:\\n qin = np.squeeze(np.array(self.est_state_all[self.x_index['qin'], :]))\\n qin_meas = np.array(self.qin_obs)[1:]\\n print(len(qin), len(qin_meas))\\n fig1 = plt.figure(figsize=(10, 5), dpi=100)\\n ax1 = fig1.add_subplot(111)\\n t = np.arange(len(qin))\\n ax1.plot(t, qin, 'r-', label='Estimated')\\n not_nan = ~np.isnan(qin_meas)\\n ax1.plot(t[not_nan], qin_meas[not_nan], 'b', label='Measured')\\n ax1.legend()\\n ax1.grid(True)\\n ax1.set_title('qin')\\n\\n plt.draw()\\n\\n if True:\\n qout = np.squeeze(np.array(self.est_state_all[self.x_index['qout'], :]))\\n qout_meas = np.array(self.qout_obs)[1:]\\n fig2 = plt.figure(figsize=(10, 5), dpi=100)\\n ax2 = fig2.add_subplot(111)\\n t = np.arange(len(qout))\\n ax2.plot(t, qout, 'r-', label='Estimated')\\n not_nan = ~np.isnan(qout_meas)\\n ax2.plot(t[not_nan], qout_meas[not_nan], 'b', label='Measured')\\n ax2.set_title('qout')\\n ax2.legend()\\n ax2.grid(True)\\n plt.draw()\",\n \"def _update_edges_filtered(self, change=None):\\n self._edges_filter.val_range = self.edges_range\\n edges_ids = self._edges_filter.val_ids\\n self.edges_ids = self._filter_edges(edges_ids)\",\n \"def update(self, obs, q):\\n for o in obs:\\n prob = np.exp(-70)\\n if self.landmarks:\\n # find the data association with ML\\n prob, landmark_idx, ass_obs, ass_jacobian, ass_adjcov = self.find_data_association(o)\\n if prob < self.TOL:\\n # create new landmark\\n self.create_landmark(o)\\n else:\\n # update corresponding EKF\\n self.update_landmark(np.transpose(np.array([o])), landmark_idx, ass_obs, ass_jacobian, ass_adjcov)\\n else:\\n # no initial landmarks\\n self.create_landmark(o)\\n self.weight *= prob\\n \\n q.put([self]) ###\",\n \"def update_filter_params(self, fh):\\n (self.data_timestamp, self.framerate,\\n self.l, self.d, self.gamma,\\n self.eps, self.alex, self.traceswitch) = (fh.attrs['data_timestamp'], fh.attrs['framerate'],\\n fh.attrs['l'], fh.attrs['d'], fh.attrs['gamma'],\\n fh.attrs['eps'], fh.attrs['alex'], fh.attrs['traceswitch'])\",\n \"def after_each(self, dataset: pydicom.dataset.Dataset) -> None:\\r\\n for a_filter in self.filters[::-1]:\\r\\n a_filter.after_each(dataset)\",\n \"def update_filters(self):\\n\\n # Update household filter\\n household_filter = [True if agent == 'household' else False for agent \\\\\\n in self.source.data['agent_type']]\\n self.household_view.filters[0] = BooleanFilter(household_filter)\\n\\n # Update neighbourhood filter\\n neighbourhood_filter = [True if agent == 'neighbourhood' else False for\\\\\\n agent in self.source.data['agent_type']]\\n self.neighbourhood_view.filters[0] = BooleanFilter(\\n neighbourhood_filter)\\n\\n # Update school filter\\n school_filter = [True if agent == 'school' else False for agent in \\\\\\n self.source.data['agent_type']]\\n self.school_view.filters[0] = BooleanFilter(school_filter)\",\n \"def __setitem__(self, query_filter, value):\\n saved_items = []\\n for index, query in enumerate(self.__bound_queries):\\n saved_items.append(query.get(query_filter, None))\\n try:\\n query[query_filter] = value\\n except:\\n for q, old_value in itertools.izip(self.__bound_queries[:index],\\n saved_items):\\n if old_value is not None:\\n q[query_filter] = old_value\\n else:\\n del q[query_filter]\\n raise\",\n \"def apply_fn(self,fn):\\r\\n \\r\\n self.check_Data()\\r\\n for split,data_ in self.processed_data.items():\\r\\n x = data_['x']\\r\\n x = np.array([fn(xi) for xi in x])\\r\\n data_['x'] = x\",\n \"def before_each(self, dataset: pydicom.dataset.Dataset) -> None:\\r\\n for a_filter in self.filters:\\r\\n a_filter.before_each(dataset)\",\n \"def update(self, iteration):\\n pass\",\n \"def update_cards(self, filterdict, values, multiple=True):\\n\\n self._collection.update(self._constrain_keys(filterdict),\\n {'$set': self._constrain_keys(values)},\\n multi=multiple)\",\n \"def fill_obs(self, observation_table, data_store):\\n for obs in observation_table:\\n events = data_store.obs(obs_id=obs['OBS_ID']).events\\n\\n # TODO: filter out (mask) possible sources in the data\\n # for now, the observation table should not contain any\\n # run at or near an existing source\\n\\n self.counts_cube.fill_events([events])\\n self.livetime_cube.data += events.observation_live_time_duration\",\n \"def reset_filter(self):\\n arlen = len(self.variant_list)\\n self.filter = np.zeros((arlen, arlen)) == 0\",\n \"def filter(self, *args):\\n from .elements import EqualClauseElement\\n for a in args:\\n for c in self._criterion:\\n if isinstance(c, EqualClauseElement) and isinstance(a, EqualClauseElement) and \\\\\\n c.attribute.node == a.attribute.node and c.attribute.label == a.attribute.label:\\n c.value = a.value\\n break\\n else:\\n self._criterion.append(a)\\n return self\",\n \"def apply_filters(self, filters):\\n self._data = self.model.objects.filter(**filters)\",\n \"def process_dataset(dataset, func):\\n new_dataset = copy.copy(dataset)\\n del new_dataset[\\\"val\\\"]\\n new_dataset.update(func(dataset))\\n return new_dataset\",\n \"def update(self, observed, axis):\\n\\n # -------------------------------------------------------------------------\\n # YOUR CODE GOES HERE\\n #\\n total = 0\\n \\n # zero out irrelevant data and calculate total for observed sample\\n for each in self._table:\\n if (axis == 1 and each[1] != observed) or (axis == 0 and each[0] != observed):\\n self._table[each] = 0\\n else:\\n total += self._table[each]\\n \\n # normalize the revelant data\\n for each in self._table:\\n if (axis == 1 and each[1] == observed) or (axis == 0 and each[0] == observed):\\n self._table[each] = self._table[each] / total\\n\\n #\\n # END OF YOUR CODE\\n # ------------------------------------------------------------------------- \",\n \"def apply_filters(self):\\n hurst_cut = 0\\n coint_cut = 0\\n half_life_cut = 0\\n mean_cross_cut = 0\\n\\n # Create an empty list for pairs that pass the filter tests\\n validated_pairs = []\\n\\n # Create all the pairs combination\\n self.create_pair_differences()\\n\\n # Print the number of potential pairs\\n print(f\\\"Number of potential pairs in before filter: {len(self.__pairs_data)}\\\")\\n\\n for pair in self.__pairs_data:\\n # Select the stocks from the pair\\n stock1 = pair[0]\\n stock2 = pair[1]\\n\\n # Test the hurst filter\\n if self.hurst_filter(self, stock1=stock1, stock2=stock2):\\n hurst_cut += 1\\n if self.engel_filter(self, stock1=stock1, stock2=stock2):\\n coint_cut += 1\\n if self.half_life_filter(self, stock1=stock1, stock2=stock2):\\n half_life_cut += 1\\n if self.mean_cross_filter(self, stock1=stock1, stock2=stock2):\\n mean_cross_cut += 1\\n validated_pairs.append([stock1, stock2])\\n\\n print(f\\\"Hurst filter pass: {hurst_cut}\\\")\\n print(f\\\"Co-integration filter pass: {coint_cut}\\\")\\n print(f\\\"Half-life filter pass: {half_life_cut}\\\")\\n print(f\\\"Mean-cross filter pass: {mean_cross_cut}\\\")\\n print(f\\\"Final Number of validated pairs: {len(validated_pairs)}\\\")\\n print(\\\"The final validated pairs are: \\\")\\n print(validated_pairs)\\n\\n # Save it to the attribute\\n self.__validated_pairs = validated_pairs\\n self.__validated_pairs_diff = self.__pair_diff[self.symbolize_pairs(self.__validated_pairs)]\",\n \"def _modify_updates(self, updates):\\n wxf = self.wxf\\n wyf = self.wyf\\n wxf_updated = updates[wxf]\\n wyf_updated = updates[wyf]\\n nwxf = (wxf_updated.std(0) + SMALL)[numpy.newaxis, :]\\n nwyf = (wyf_updated.std(0) + SMALL)[numpy.newaxis, :]\\n meannxf = nwxf.mean()\\n meannyf = nwyf.mean()\\n # Center filters\\n centered_wxf = wxf_updated - wxf_updated.mean(0)\\n centered_wyf = wyf_updated - wyf_updated.mean(0)\\n # Fix standard deviation\\n wxf_updated = centered_wxf * (meannxf / nwxf)\\n wyf_updated = centered_wyf * (meannyf / nwyf)\\n updates[wxf] = wxf_updated\\n updates[wyf] = wyf_updated\",\n \"def test_updating_multiple_records_through_filter_with_arg_value(self, test_domain):\\n identifier1 = uuid4()\\n identifier2 = uuid4()\\n identifier3 = uuid4()\\n identifier4 = uuid4()\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier1, first_name=\\\"Athos\\\", last_name=\\\"Musketeer\\\", age=2\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier2, first_name=\\\"Porthos\\\", last_name=\\\"Musketeer\\\", age=3\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier3, first_name=\\\"Aramis\\\", last_name=\\\"Musketeer\\\", age=4\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier4, first_name=\\\"dArtagnan\\\", last_name=\\\"Musketeer\\\", age=5\\n )\\n\\n # Perform update\\n updated_count = (\\n test_domain.repository_for(Person)\\n ._dao.query.filter(age__gt=3)\\n .update_all({\\\"last_name\\\": \\\"Fraud\\\"})\\n )\\n\\n # Query and check if only the relevant records have been updated\\n assert updated_count == 2\\n\\n u_person1 = test_domain.repository_for(Person)._dao.get(identifier1)\\n u_person2 = test_domain.repository_for(Person)._dao.get(identifier2)\\n u_person3 = test_domain.repository_for(Person)._dao.get(identifier3)\\n u_person4 = test_domain.repository_for(Person)._dao.get(identifier4)\\n assert u_person1.last_name == \\\"Musketeer\\\"\\n assert u_person2.last_name == \\\"Musketeer\\\"\\n assert u_person3.last_name == \\\"Fraud\\\"\\n assert u_person4.last_name == \\\"Fraud\\\"\",\n \"def _mutate(self, individuals):\\n for cur in individuals:\\n if random.random() < self.mutation_probability:\\n self.op.mutate(cur['individual'])\\n cur['fitness'] = None\",\n \"def _update_all(self, criteria: Q, *args, **kwargs):\\n raise NotImplementedError\",\n \"def updateIntensities (self,listAtoms):\\r\\n \\r\\n for i in range(len(listAtoms)):\\r\\n for j in range(len(listAtoms[i].spikeArray)):\\r\\n self.mol[i].spikeArray[j].intensity = listAtoms[i].spikeArray[j].intensity\",\n \"def updateFitness(self):\\r\\n for candidate in self.candidates:\\r\\n candidate.updateFitness()\\r\\n return\",\n \"def resample(self):\\n self.particles = ParticleFilter.weighted_values(self.particles,\\n [p.weight for p in self.particles],\\n len(self.particles))\\n for p in self.particles:\\n p.weight = 1./len(self.particles)\",\n \"def update_all(self, stream: Iterator[Mapping[str, np.ndarray]]) -> Self:\\n\\n for element in stream:\\n self.update(element)\\n\\n return self\",\n \"def update_all(self, stream: Iterator[Mapping[str, np.ndarray]]) -> Self:\\n\\n for element in stream:\\n self.update(element)\\n\\n return self\",\n \"def filter(self, observations):\\n\\n (_, _, _, x_filtered, P_filtered) = filter(self.F, self.Q, self.H, self.R, self.x_0, self.P_0, observations)\\n return x_filtered, P_filtered\",\n \"def filter_(self,fltr:torch.tensor):\\n self.container = self.container[:,fltr]\\n self.count_hist = self.count_hist[fltr]\",\n \"def update_individuals(individuals, eval_results):\\n for ind, res in zip(individuals, eval_results):\\n ind.fitness.values = res[0]\\n ind.matching_node_pairs = res[1]\\n ind.gtp_precisions = res[2]\",\n \"def _update_step(self, *, observations: types.ObservationsTorch) -> None:\",\n \"def filter(self, filters):\",\n \"def _applyFilters(self) -> None:\\n self._dataframe = self._source.loc[:, self._visable_columns]\\n for column, value in self._filters.items():\\n if value is not None:\\n self._dataframe = self._dataframe[self._source[column] == value]\\n else:\\n self._dataframe = self._dataframe[self._source[column].isnull()]\\n\\n self.layoutChanged.emit()\",\n \"def filter(self, filter_dict):\\n pass\",\n \"def update_values(self):\\n for key in self.inputs.keys():\\n value = self.inputs[key]['entry'].get()\\n self.inputs[key]['value'] = value\",\n \"def prefilter(self, filt=None, verbose=False):\\n erased = []\\n if verbose:\\n msg = 'Prior to filter, we have {} cells.'.format(len(self.cells))\\n print(msg)\\n # check for supplementary observables to be computed\\n raw_obs, func_obs = set_observable_list(filters=[filt, ])\\n\\n # compute suppl obs for all cells\\n if raw_obs:\\n for cell in self.cells:\\n for obs in raw_obs:\\n cell.build(obs)\\n for cell in self.cells:\\n if filt is not None:\\n if not filt(cell):\\n erased.append(cell)\\n # make Colony.add_cell_recursive non functional\\n cell.bpointer = None\\n if cell.parent:\\n cell.parent.childs.remove(cell)\\n # make daughter cells new roots\\n for ch in cell.childs:\\n ch.bpointer = None\\n if verbose:\\n msg = '{} cells do not pass filter.'.format(len(erased))\\n print(msg)\\n for cell in erased:\\n self.cells.remove(cell) # otherwise would be considered root\\n # clean-up actions for computing extra obs\\n # extra obs computation depends on tree decomposition\\n # this will be done in lineage.get_timeseries()\\n for cell in self.cells:\\n for obs in raw_obs:\\n del cell._sdata[obs.label]\\n if verbose:\\n msg = 'After filtering, we get {} cells.'.format(len(self.cells))\\n print(msg)\\n# self.metadata.filters.append(repr(boofunc))\\n return\",\n \"def run(self, threshold=1e-3, max_iters=100):\\n for msr in self.msrs:\\n # find state transition matrix and propagate state\\n phi_p, state_prop = self._compute_stm(msr.time, self.phis[-1])\\n\\n # compute observation deviation, obs_state matrix\\n y_i, h_tilde = self._msr_resid(msr, state_prop)\\n\\n # update information and N\\n self._update_info_and_n(y_i, h_tilde, phi_p, msr.cov)\\n\\n # add everything to the appropriate lists\\n #new_cov = np.matmul(phi_p, np.matmul(self.cov_list[-1], phi_p))\\n #self.cov_list.append(new_cov)\\n self.prop_state_list.append(state_prop)\\n self.estimates.append(state_prop)\\n self.phis.append(phi_p)\\n self.times.append(msr.time)\\n\\n # compute correction\\n self.iters += 1\\n x_hat_0 = np.linalg.solve(self.fisher_info[-1], self.N[-1])[0]\\n\\n # check for convergence\\n if np.linalg.norm(x_hat_0) <= threshold:\\n print(\\\"Batch filter converged in {} Iterations\\\".format(self.iters))\\n self.cov_batch = np.linalg.inv(self.fisher_info[-1])\\n\\n elif self.iters >= max_iters:\\n raise StopIteration(\\\"max_iters: {} reached without convergence\\\".format(max_iters))\\n\\n else:\\n # reset everything and try again\\n updated_istate = np.add(self.prop_state_list[0], np.transpose(x_hat_0))\\n # fixes a strange bug wher the size of updated istate was changing\\n updated_istate = np.resize(updated_istate, (1, len(self.istate)))[0]\\n\\n self.prop_state_list = [updated_istate]\\n #self.cov_list = [self.apriori]\\n self.fisher_info = [self.fisher_info[0]]\\n self.pert_vec = np.subtract(self.pert_vec, x_hat_0)\\n self.N = [np.matmul(self.apriori, self.pert_vec)]\\n self.phis = [self.phis[0]]\\n self.estimates = [updated_istate]\\n self.times = [0]\\n self.run()\",\n \"def _UpdateDataSetValues( self ):\\n pass\",\n \"def set_filter_for_eval(self, x_filter):\\n self.x_filter = x_filter\\n\\n entity_size = len(self.ent_to_idx)\\n reln_size = len(self.rel_to_idx)\\n\\n first_million_primes_list = []\\n curr_dir, _ = os.path.split(__file__)\\n with open(os.path.join(curr_dir, \\\"prime_number_list.txt\\\"), \\\"r\\\") as f:\\n logger.debug('Reading from prime_number_list.txt.')\\n line = f.readline()\\n i = 0\\n for line in f:\\n p_nums_line = line.split(' ')\\n first_million_primes_list.extend([np.int64(x) for x in p_nums_line if x != '' and x != '\\\\n'])\\n if len(first_million_primes_list) > (2 * entity_size + reln_size):\\n break\\n\\n # subject\\n self.entity_primes_left = first_million_primes_list[:entity_size]\\n # obj\\n self.entity_primes_right = first_million_primes_list[entity_size:2 * entity_size]\\n # reln\\n self.relation_primes = first_million_primes_list[2 * entity_size:(2 * entity_size + reln_size)]\\n\\n self.filter_keys = []\\n # subject\\n self.filter_keys = [self.entity_primes_left[self.x_filter[i, 0]] for i in range(self.x_filter.shape[0])]\\n # obj\\n self.filter_keys = [self.filter_keys[i] * self.entity_primes_right[self.x_filter[i, 2]]\\n for i in range(self.x_filter.shape[0])]\\n # reln\\n self.filter_keys = [self.filter_keys[i] * self.relation_primes[self.x_filter[i, 1]]\\n for i in range(self.x_filter.shape[0])]\\n\\n self.is_filtered = True\",\n \"def test_feature_is_filtered(self):\\n\\n # Duplicate 1st row in var and assigned to 2nd\\n self.validator.adata.var[\\\"feature_is_filtered\\\"][0] = True\\n for i in range(self.validator.adata.X.shape[0]):\\n self.validator.adata.X[i, 0] = 0\\n self.validator.adata.X[0, 0] = 1\\n\\n self.validator.validate_adata()\\n self.assertEqual(\\n self.validator.errors,\\n [\\n \\\"ERROR: Some features are 'True' in 'feature_is_filtered' of dataframe 'var', \\\"\\n \\\"but there are 1 non-zero values in the corresponding columns of the matrix 'X'. \\\"\\n \\\"All values for these features must be 0.\\\"\\n ],\\n )\",\n \"def filter(self,state0):\\n ok,tchi2 = True,0.\\n state = state0.copy()\\n ii = 0\\n for node in self.nodes:\\n zrun = node.zrun\\n ok,state,F,Q = self.model.propagate(state,zrun)\\n if (not ok):\\n warning(\\\"kfilter.filter not possible to filter at \\\",(ii,zrun))\\n debug(\\\"kfilter.filter i,ok,chi2 \\\",(ii,ok,tchi2))\\n return ok,tchi2\\n node.F = F\\n node.Q = Q\\n node.setstate('pred',state)\\n fstate,fchi2 = node.predict(state)\\n node.setstate('filter',fstate)\\n node.setchi2('filter',fchi2)\\n tchi2+=fchi2\\n self.model.user_filter(node)\\n state = node.getstate('filter').copy()\\n ii+=1\\n self.status='filter'\\n debug(\\\"kfilter.filter ok,chi2 \\\",(ok,tchi2))\\n return ok,tchi2\",\n \"def particle_filter(particle_set_t, measurement_t):\\n global count\\n n_samples, dim = particle_set_t.shape # no of particles and dimension of each particle\\n\\n pred_state = np.zeros((n_samples, dim), dtype=\\\"float64\\\") # store the predicted state \\n weights = np.zeros(n_samples, dtype=\\\"float64\\\") # corresponding weights for resampling\\n\\n particle_set_t1 = np.zeros((n_samples, dim), dtype=\\\"float64\\\") # next iteration of particles\\n\\n\\n # this loop calculates \\\\bar{X_t}, i.e. the predicted belief.\\n for n in range(n_samples):\\n # predicted motion step:\\n xn_t1 = sample_motion_model(particle_set_t[n]) # 3x1 vector: hypothetical state\\n\\n # measurement correction step:\\n weight_xn_t1 = state_likelihood(measurement_t, xn_t1) # scalar value\\n\\n pred_state[n] = xn_t1\\n weights[n] = weight_xn_t1\\n\\n \\n # It was observed that if all weights are 0, the resampling step breaks. \\n # Thus, adding a uniform distribution. This is obviously a very bad idea \\\\ \\n # as the true state can easily be discarded in the resampling step: TODO!\\n if np.sum(weights) > 0.0:\\n weights = weights/np.sum(weights) # normalize array only when sum in not 0\\n else:\\n print(\\\"possile divergence!\\\")\\n weights[:] = 1 / n_samples # if sum is 0 then assign uniform distribution throughout\\n\\n\\n # the resampling step:\\n # indices = monte_carlo.residual_resample(weights)\\n indices = monte_carlo.stratified_resample(weights)\\n count += 1\\n print(count)\\n\\n # new particle set is particles at index locations\\n for i, index in enumerate(indices):\\n particle_set_t1[i] = pred_state[index]\\n\\n return particle_set_t1\",\n \"def Filter(self,val):\\n \\n #set th elength of the lis to 0\\n List = [self.InitialList[i] for i in range(0,len(self.InitialList))]\\n FilterValues = [None]\\n Grab = [None]\\n Headers = []\\n \\n #create the quick index\\n for i in range(len(self.Condensensed)):\\n \\n Headers.append([self.Condensensed[i][l][0] for l in range(len(self.Condensensed[i]))])\\n \\n #grab the values...\\n for j in range(len(self.Variables)):\\n \\n FilterValues.append(self.Variables[j].get())\\n\\n if self.Variables[j].get().split(' ')[0] == 'All':\\n \\n Grab.append(False)\\n \\n else:\\n \\n Grab.append(True)\\n \\n #intermediate list to compare\\n ToCompare = []\\n \\n for i in range(1,len(Grab)):\\n \\n if Grab[i]:\\n \\n #find the index\\n l = Headers[i].index(FilterValues[i])\\n \\n #grab it\\n ToCompare.append([self.Condensensed[i][l][m] for m in range(len(self.Condensensed[i][l]))])\\n\\n\\n for i in range(0, len(ToCompare)):\\n \\n List = list(set(List).intersection(ToCompare[i]))\\n\\n #update the interface\\n self.Gatherer(List,list(self.Input))\\n self.BuildTree()\",\n \"def filter_data(self):\\n if(self.filter_classes == []):\\n return\\n \\n filtered_idx = []\\n for id in range(len(self.image_ids)):\\n anns = self.load_annotations(id)\\n found = False\\n for ann in anns:\\n if ann['label'] in self.filter_classes:\\n found = True\\n break\\n if found:\\n filtered_idx.append(id)\\n \\n self.filtered_ids = [self.image_ids[id] for id in filtered_idx]\\n # self.image_ids = self.filtered_ids\\n print(\\\"Number of filtered instances:\\\", len(self.filtered_ids))\",\n \"def train_filter(filt, U, Y):\\n n = len(U)\\n X = []\\n R = []\\n for i in range(n):\\n x_hat_bar = filt.time_update(U[i])\\n x_hat, P = filt.measurement_update(Y[i], x_hat_bar)\\n print(i, x_hat, P)\\n X.append(x_hat)\\n R.append(P)\\n return np.array(X), R\",\n \"def do_filter(self):\\n return np.sum(np.multiply(self._past_values, self._impulse_response))\",\n \"def update(self, **kwargs):\\n for name, item in itertools.chain(\\n self._cal_objs.items(),\\n self._noise_objs.items()):\\n logger.debug(\\\"update {}\\\".format(item))\\n item.update(**kwargs)\",\n \"def test_updating_record_through_filter(self, test_domain):\\n identifier1 = uuid4()\\n identifier2 = uuid4()\\n identifier3 = uuid4()\\n identifier4 = uuid4()\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier1, first_name=\\\"Athos\\\", last_name=\\\"Musketeer\\\", age=2\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier2, first_name=\\\"Porthos\\\", last_name=\\\"Musketeer\\\", age=3\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier3, first_name=\\\"Aramis\\\", last_name=\\\"Musketeer\\\", age=4\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier4, first_name=\\\"dArtagnan\\\", last_name=\\\"Musketeer\\\", age=5\\n )\\n\\n # Perform update\\n updated_count = (\\n test_domain.repository_for(Person)\\n ._dao.query.filter(age__gt=3)\\n .update(last_name=\\\"Fraud\\\")\\n )\\n\\n # Query and check if only the relevant records have been updated\\n assert updated_count == 2\\n\\n u_person1 = test_domain.repository_for(Person)._dao.get(identifier1)\\n u_person2 = test_domain.repository_for(Person)._dao.get(identifier2)\\n u_person3 = test_domain.repository_for(Person)._dao.get(identifier3)\\n u_person4 = test_domain.repository_for(Person)._dao.get(identifier4)\\n assert u_person1.last_name == \\\"Musketeer\\\"\\n assert u_person2.last_name == \\\"Musketeer\\\"\\n assert u_person3.last_name == \\\"Fraud\\\"\\n assert u_person4.last_name == \\\"Fraud\\\"\",\n \"def filter(self):\\n self.data = self.data.loc[~self.data.isnull().any(1),:]\",\n \"def filter_data(self):\\n self.df = self.df[HeatStrokeDataFiller.important_features]\",\n \"def updateValues(self, otherDialog):\\n for index in range(len(self._values)):\\n for oval in otherDialog._values:\\n if self._values[index].getID() == oval.getID():\\n self._values[index].combine( oval, intelligent=False )\\n break\",\n \"def observe(self, observation):\\n # shallow copy observation (deep copy can be expensive)\\n obs = observation.copy()\\n batch_idx = self.opt.get('batchindex', 0)\\n self.observation = obs\\n #self.answers[batch_idx] = None\\n return obs\",\n \"def _update_raw_data(params):\\n from scipy.signal import filtfilt\\n start = params['t_start']\\n stop = params['raw'].time_as_index(start + params['duration'])[0]\\n start = params['raw'].time_as_index(start)[0]\\n data_picks = _pick_data_channels(params['raw'].info)\\n data, times = params['raw'][:, start:stop]\\n if params['projector'] is not None:\\n data = np.dot(params['projector'], data)\\n # remove DC\\n if params['remove_dc'] is True:\\n data -= np.mean(data, axis=1)[:, np.newaxis]\\n if params['ba'] is not None:\\n data[data_picks] = filtfilt(params['ba'][0], params['ba'][1],\\n data[data_picks], axis=1, padlen=0)\\n # scale\\n for di in range(data.shape[0]):\\n data[di] /= params['scalings'][params['types'][di]]\\n # stim channels should be hard limited\\n if params['types'][di] == 'stim':\\n norm = float(max(data[di]))\\n data[di] /= norm if norm > 0 else 1.\\n # clip\\n if params['clipping'] == 'transparent':\\n data[np.logical_or(data > 1, data < -1)] = np.nan\\n elif params['clipping'] == 'clamp':\\n data = np.clip(data, -1, 1, data)\\n params['data'] = data\\n params['times'] = times\",\n \"def _filter_satisfied(self, update_setd=False):\\n\\n model = self.oracle.get_model()\\n setd = set()\\n\\n for i, cl in enumerate(self.soft):\\n if not self.satc[i]:\\n if self._satisfied(cl, model):\\n self.satc[i] = True\\n self.ss_assumps.append(self.sels[i])\\n else:\\n setd = setd.union(set(cl))\\n\\n if update_setd:\\n self.setd = list(setd)\",\n \"def _put_resolved_booleans_into_filter(\\n filter_operator: Operator, model_filters_to_resolved_values: Dict[ModelFilter, BooleanValues]\\n) -> None:\\n for operator in _model_filter_in_operator_generator(filter_operator):\\n model_filter = operator.unresolved_value\\n operator.resolved_value = model_filters_to_resolved_values.get(\\n model_filter, BooleanValues.UNKNOWN\\n )\",\n \"def apply_filters(self, new_filters):\\n\\t\\tself.filters = new_filters\",\n \"def set_observations(self, oseries):\\n self.oseries_index = oseries.index\\n observations_masked = np.ma.array(oseries,\\n mask=(~np.isfinite(oseries)))\\n (n_timesteps, dimobs) = observations_masked.shape\\n self.observation_indices = np.zeros((n_timesteps, dimobs),\\n dtype=np.float64)\\n self.observation_count = np.zeros(n_timesteps, dtype=np.int64)\\n self.observations = np.zeros((n_timesteps, dimobs), dtype=np.float64)\\n\\n for t in range(n_timesteps):\\n observation = observations_masked[t]\\n # add large value to find all finite non-masked values\\n obstmp = observation + 1e10\\n obsindices = obstmp.nonzero()[0]\\n self.observation_count[t] = len(obsindices)\\n\\n if (len(obsindices) > 0):\\n for i, _ in enumerate(obsindices):\\n obsid = int(obsindices[i])\\n self.observations[t, obsid] = observation[obsid]\\n self.observation_indices[t, i] = obsid\",\n \"def update(self):\\n\\n terms_toRemove = []\\n\\n for termIndex, [term_constantFactor, term_unknowns_attributeAddresses] in enumerate(self.LHS):\\n\\n # Check if coefficient is 0 - then no need to process any of the unknowns since term will be 0 anyways\\n if term_constantFactor == 0:\\n terms_toRemove.append(termIndex)\\n continue # continue to next term, no need to resolve the unknowns of this term since the product will be 0 anyways\\n\\n # Check if any unknowns became known\\n unknowns_toRemove = []\\n for unknown_attributeAddress in term_unknowns_attributeAddresses:\\n attribute = getattr_fromAddress(*unknown_attributeAddress)\\n if isNumeric(attribute):\\n # object.attribute which had previously been identified as unknown now has a value, add it to the constant factor product and remove from the unknowns\\n self.LHS[termIndex][0] *= attribute # multiply it with the constant factor product\\n unknowns_toRemove.append([termIndex, unknown_attributeAddress])\\n for termIndex, unknown_attributeAddress in unknowns_toRemove: # remove unknowns which have become known in the end\\n # removing in the end not to tamper with the iteration of the above loop\\n self.LHS[termIndex][1].remove(unknown_attributeAddress)\\n\\n # Move constants to RHS\\n if self.LHS[termIndex][1] == []:\\n # if term has no unknowns, it is a constant, move to RHS\\n self.RHS -= self.LHS[termIndex][0]\\n self.LHS.pop(termIndex)\\n\\n for termIndex in reversed(terms_toRemove): # reversed - otherwise would tamper with indices of items identified for removal\\n self.LHS.pop(termIndex)\\n\\n self._gatherUnknowns()\",\n \"def updateObservation(self, obs):\\n self.settingsDb.updateObservation(self.observationsTableName(), obs)\",\n \"def filter(self):\\n M, p, q = self.M, self.p, self.q\\n x = self.x\\n idx = len(self.x) - (p + 1)\\n x_ = self.x_prev + (x[idx + p] - x[idx - q]) / M\\n self.t_.append(self.t[idx])\\n self.t_filtered.append(self.t[idx])\\n self.x_.append(x_)\\n self.x_filtered.append(x_)\\n self.x_prev = x_\",\n \"def update_filters(self, **kwargs):\\n self._FILTERS = kwargs\",\n \"def update(self, values, train, eta=.1):\\n\\t\\tfor X, y_true in zip(values, train):\\n\\t\\t\\tprediction = self.activate(X)\\n\\t\\t\\terror = y_true - prediction\\n\\t\\t\\tweight_update = error * eta * X\\n\\t\\t\\tself.weights += weight_update\",\n \"def finalize(self):\\n # this forces only some filters will be used for feature computation\\n # this is not ideal, but a necessary stop-gap while we revise\\n # the PropertyTable SQL\\n self._good_filters = ['u', 'g', 'r', 'i', 'z', 'Y']\\n self._good_filter_wave = np.array([3569.5, 4766.5, 6214.5, 7544.5, 8707.5, 10039.5])\\n\\n use_filters = set(self._good_filters) & self.filters\\n if not self.filters.issubset(use_filters):\\n message = 'Number of useful filters ({}) does not equal number available filters ({}) - some filters will not be used'.format(\\n ''.join(use_filters), ''.join(self.filters))\\n warnings.warn(message, RuntimeWarning)\\n self.filters = set(use_filters)\\n mask = np.array([True if x in self.filters else False for x in self.passband])\\n\\n if mask.size: # Not empty arrays\\n self.time = self.time[mask]\\n self.flux = self.flux[mask]\\n self.fluxErr = self.fluxErr[mask]\\n self.obsId = self.obsId[mask]\\n self.passband = self.passband[mask]\\n self.zeropoint = self.zeropoint[mask]\\n for key in self._extra_cols:\\n val = getattr(self, key)\\n setattr(self, key, val[mask])\\n\\n self.nobs = len(self.time)\\n if self.nobs == 0:\\n message = 'Object {} with locus ID {} has no good observations.'.format(self.objectId, self.locusId)\\n raise ValueError(message)\\n\\n return self._remove_flux_extinction()\",\n \"def step_filter(self, v, imu_meas, meas):\\n\\n if (imu_meas is not None) and (meas is None):\\n xp, Pp = self.prediction(v, imu_meas)\\n self.x = xp\\n self.P_t = Pp\\n return xp\\n\\n elif (imu_meas is None) and (meas is not None):\\n z_t = self.transformMeasurement(meas)\\n self.x = z_t\\n return z_t\\n\\n else: # both measurements contain values\\n xp, Pp = self.prediction(v, imu_meas)\\n z_t = self.transformMeasurement(meas)\\n x, P = self.update(z_t, xp, Pp)\\n\\n self.x = x\\n self.P_t = P\\n return x\",\n \"def filter(self, data):\\n self.data = pysap.Image(data=self.flt.filter(data))\",\n \"def _apply_filter(self, fn=lambda ngram, freq: False):\\n tmp_ngram = FreqDist()\\n for ngram, freq in self.ngram_fd.items():\\n if not fn(ngram, freq):\\n tmp_ngram[ngram] = freq\\n self.ngram_fd = tmp_ngram\",\n \"def step_filter(self, v, imu_meas, z_t):\\n # YOUR CODE HERE\\n pass\",\n \"def filter_data(data,filters):\\n final_filter = pd.Series(np.array([True] * data.shape[0]))\\n for attribute, value in filters:\\n final_filter &= data[attribute] == value\\n return data[final_filter]\",\n \"def parameter_update(self, X, X_mask):\\n data_with_mask = np.hstack([X, X_mask])\\n X_pred, X_MC_preds = self.predict(data_with_mask)\\n X[X_mask] *= self.keep_coeff\\n X[X_mask] += self.weight_update_coeff * X_pred[X_mask]\\n return X, X_MC_preds\",\n \"def _set_values(loop_data):\\n \\n # These are the indexes of all the data that are unassigned\\n value_indexes = loop_data.ix[(loop_data.loop==0)&(loop_data.tag==0)&(loop_data.stop==0)].index\\n loop_data[u'value'] = 0\\n loop_data.ix[value_indexes,u'value'] = 1\\n \\n # These are the indexes of data who follow either a loop, label, or stop tag\\n value_indexes_begin = loop_data.ix[(value_indexes-1)].ix[loop_data.value==0].index + 1\\n \\n # The first rows of each data correspond to data for their respective loops\\n loop_max = loop_data.loop.max()\\n loop_range = np.arange(loop_max-1, -1, -1)\\n \\n for idx in value_indexes_begin:\\n loop_data.ix[idx:idx+len(loop_range)-1,u'value'] += loop_range\\n \\n return loop_data\",\n \"def update_with_observation(particles, landmark_list):\\r\\n for p in particles:\\r\\n for i in range(np.shape(landmark_list)[1]):\\r\\n p.update_particle(landmark_list[:, i])\\r\\n\\r\\n return particles\",\n \"def process_observation(self, observation):\\n return observation\",\n \"def process_observation(self, observation):\\n return observation\",\n \"def iterate(self, data):\\n \\n # Append data to self.data\\n self.data = np.append(self.data, data)\\n \\n for i, d in enumerate(data):\\n update = self.current*self.likelihood(d)\\n self.current = self._normalize(update)\\n self.posterior = np.concatenate((self.posterior,[self.current]))\\n \\n print(str(len(data)) + \\\" iterations completed!\\\")\\n \\n return None\",\n \"async def _update_values(self, model: Model):\\n\\n raise NotImplementedError\",\n \"def Iterate(self):\\n\\t\\tfor atom in self.atoms:\\n\\t\\t\\tself.UpdateAtom(atom)\",\n \"def update_state(self):\\n # return initial state if no observation was yet\\n if len(self.obs_history) == 0:\\n return self.kf.initial_state_mean, self.kf.initial_state_covariance\\n\\n hist = np.ma.masked_array(self.obs_history, mask=np.zeros((1,)))\\n for i in range(len(hist)):\\n if hist[i] == -1e8:\\n hist[i] = np.ma.masked\\n\\n # print(hist, hist.shape)\\n return self.kf.filter(hist)\",\n \"def _update_data(self):\\n for attribute in [\\\"flow_rate\\\"]:\\n self._data[attribute] = self._connection.measure\",\n \"def apply_filters(\\n isovar_result,\\n filter_thresholds={},\\n filter_flags=[]):\\n filter_values = OrderedDict(isovar_result.filter_values.items())\\n new_filter_values = evaluate_filters(\\n isovar_result,\\n filter_thresholds=filter_thresholds,\\n filter_flags=filter_flags)\\n filter_values.update(new_filter_values)\\n return isovar_result.clone_with_updates(filter_values=filter_values)\",\n \"def healthcare_filter(df_all): \\n #get requested assets under healthcare tag \\n df_filtered = pandas.DataFrame(columns=['osm_id','asset','geometry']) #create df for saving data\\n for row in range(len(df_all.index)): \\n if 'healthcare' in df_all[\\\"asset\\\"][row]: #check if healthcare key is present\\n df_filtered = df_filtered.append(df_all.loc[row]) #if so, save in df \\n if '\\\"healthcare\\\"=>\\\"doctor\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'doctors' #to be consistent with asset list \\n elif '\\\"healthcare\\\"=>\\\"pharmacy\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'pharmacy'\\n elif '\\\"healthcare\\\"=>\\\"hospital\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'hospital'\\n elif '\\\"healthcare\\\"=>\\\"clinic\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'clinic'\\n elif '\\\"healthcare\\\"=>\\\"dentist\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'dentist'\\n elif '\\\"healthcare\\\"=>\\\"physiotherapist\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'physiotherapist'\\n elif '\\\"healthcare\\\"=>\\\"alternative\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'alternative'\\n elif '\\\"healthcare\\\"=>\\\"laboratory\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'laboratory'\\n elif '\\\"healthcare\\\"=>\\\"optometrist\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'optometrist'\\n elif '\\\"healthcare\\\"=>\\\"rehabilitation\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'rehabilitation'\\n elif '\\\"healthcare\\\"=>\\\"blood_donation\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'blood_donation'\\n elif '\\\"healthcare\\\"=>\\\"birthing_center\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'birthing_center'\\n else:\\n df_filtered = df_filtered.drop(index=row)\\n \\n return df_filtered\",\n \"def filter_patients(self):\\n\\n if self.dataset is None:\\n self.dataset = h5py.File(self.filename, 'r')['dataset']\\n \\n # Find feature indices belonging to specific criteria\\n inclusion_info = self.filter_params['inclusion']\\n # exclusion_info = self.filter_params['exclusion']\\n case_control_info = self.filter_params['case_control']\\n\\n inclusion_inds = self.check_criteria(inclusion_info, case_control=False)\\n # exclusion_inds = self.check_criteria(exclusion_info, case_control=False)\\n case_inds, control_inds = self.check_criteria(case_control_info, case_control=True)\\n\\n filtered_inds = {}\\n # inclusion_exclusion_inds = np.setdiff1d(inclusion_inds, exclusion_inds)\\n filtered_inds['case'] = np.intersect1d(inclusion_inds, case_inds)\\n filtered_inds['control'] = np.intersect1d(inclusion_inds, control_inds)\\n\\n return filtered_inds\",\n \"def train_filter2(filt, U, Y):\\n n = len(U)\\n X, R = [], []\\n for i in range(n):\\n print(i)\\n filt.predict(fx_args=(U[i],))\\n filt.update(Y[i])\\n X.append(filt.x.copy())\\n R.append(filt.P.copy())\\n X = np.array(X)\\n X, R, K = filt.rts_smoother(X, R, fx_args=(U[i],))\\n return X, R\",\n \"def test_updating_multiple_records_through_filter_with_kwarg_value(\\n self, test_domain\\n ):\\n identifier1 = uuid4()\\n identifier2 = uuid4()\\n identifier3 = uuid4()\\n identifier4 = uuid4()\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier1, first_name=\\\"Athos\\\", last_name=\\\"Musketeer\\\", age=2\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier2, first_name=\\\"Porthos\\\", last_name=\\\"Musketeer\\\", age=3\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier3, first_name=\\\"Aramis\\\", last_name=\\\"Musketeer\\\", age=4\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier4, first_name=\\\"dArtagnan\\\", last_name=\\\"Musketeer\\\", age=5\\n )\\n\\n # Perform update\\n updated_count = (\\n test_domain.repository_for(Person)\\n ._dao.query.filter(age__gt=3)\\n .update_all(last_name=\\\"Fraud\\\")\\n )\\n\\n # Query and check if only the relevant records have been updated\\n assert updated_count == 2\\n\\n u_person1 = test_domain.repository_for(Person)._dao.get(identifier1)\\n u_person2 = test_domain.repository_for(Person)._dao.get(identifier2)\\n u_person3 = test_domain.repository_for(Person)._dao.get(identifier3)\\n u_person4 = test_domain.repository_for(Person)._dao.get(identifier4)\\n assert u_person1.last_name == \\\"Musketeer\\\"\\n assert u_person2.last_name == \\\"Musketeer\\\"\\n assert u_person3.last_name == \\\"Fraud\\\"\\n assert u_person4.last_name == \\\"Fraud\\\"\",\n \"def apply_filter(self, filter_arg):\\n filtered_entries = self.visual.apply_filter(filter_arg, self.get_current_entries())\\n # idxs = self.selector.select_by_objects(filtered_entries, yield_ones_index=True)\\n self.visual.print_entries_enum(filtered_entries, None)\\n # self.list(idxs)\",\n \"def forc_model(self):\\n lag1_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag1_by_store')\\n lag2_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag2_by_store')\\n for add in self.X.address1.unique():\\n add_mask = self.X.address1 == add\\n foo = self.X[ add_mask ].sort_values('visit_date', ascending=False)\\n top_index = foo.index[0]\\n clust = int(foo.cluster.values[0])\\n # get values from last visit for store\\n base_input = foo[self.model_mask_cols].values[0]\\n base_actual = self.y[top_index]\\n lag2_val = base_input[lag1_loc]\\n lag1_val = base_actual\\n\\n for i in range(1, self.num_periods + 1):\\n model = self.model_list[clust]\\n inputs = base_input\\n inputs[lag1_loc] = lag1_val\\n inputs[lag2_loc] = lag2_val\\n \\n pred = model.predict(inputs.reshape(1, -1))\\n self._update_cust_table(add, i, pred)\\n \\n lag2_val = lag1_val\\n lag1_val = pred\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6360926","0.6218109","0.6081106","0.6078264","0.6032189","0.5884993","0.5883774","0.58139133","0.5798917","0.57884675","0.5541844","0.5534231","0.55329645","0.55150574","0.5511411","0.55061525","0.54159504","0.54150975","0.54017776","0.539216","0.53898185","0.53883004","0.5363354","0.53554285","0.53506494","0.53493106","0.534836","0.5346551","0.52854764","0.526658","0.5262552","0.5215013","0.52138335","0.52022606","0.5200379","0.51996994","0.51892465","0.5187068","0.5186022","0.51799494","0.51785356","0.51753813","0.51753813","0.51739955","0.5162357","0.5150138","0.5145529","0.51449263","0.51447374","0.5141466","0.51363844","0.51317626","0.51274484","0.5125815","0.5119857","0.5114205","0.51128614","0.5112476","0.5111384","0.510209","0.50913996","0.5089529","0.50893366","0.50867504","0.5063906","0.5050995","0.504886","0.50437844","0.5040245","0.5035688","0.50351155","0.50338745","0.5018519","0.5010838","0.5010573","0.5007881","0.50043535","0.5002216","0.4999497","0.49961653","0.49921614","0.4980961","0.4977172","0.49659085","0.49655056","0.49590003","0.49559477","0.4955314","0.4955314","0.4944155","0.49418777","0.4934054","0.49311492","0.49248096","0.49187684","0.49079832","0.49010077","0.48982152","0.48964646","0.48964188","0.48927665"],"string":"[\n \"0.6360926\",\n \"0.6218109\",\n \"0.6081106\",\n \"0.6078264\",\n \"0.6032189\",\n \"0.5884993\",\n \"0.5883774\",\n \"0.58139133\",\n \"0.5798917\",\n \"0.57884675\",\n \"0.5541844\",\n \"0.5534231\",\n \"0.55329645\",\n \"0.55150574\",\n \"0.5511411\",\n \"0.55061525\",\n \"0.54159504\",\n \"0.54150975\",\n \"0.54017776\",\n \"0.539216\",\n \"0.53898185\",\n \"0.53883004\",\n \"0.5363354\",\n \"0.53554285\",\n \"0.53506494\",\n \"0.53493106\",\n \"0.534836\",\n \"0.5346551\",\n \"0.52854764\",\n \"0.526658\",\n \"0.5262552\",\n \"0.5215013\",\n \"0.52138335\",\n \"0.52022606\",\n \"0.5200379\",\n \"0.51996994\",\n \"0.51892465\",\n \"0.5187068\",\n \"0.5186022\",\n \"0.51799494\",\n \"0.51785356\",\n \"0.51753813\",\n \"0.51753813\",\n \"0.51739955\",\n \"0.5162357\",\n \"0.5150138\",\n \"0.5145529\",\n \"0.51449263\",\n \"0.51447374\",\n \"0.5141466\",\n \"0.51363844\",\n \"0.51317626\",\n \"0.51274484\",\n \"0.5125815\",\n \"0.5119857\",\n \"0.5114205\",\n \"0.51128614\",\n \"0.5112476\",\n \"0.5111384\",\n \"0.510209\",\n \"0.50913996\",\n \"0.5089529\",\n \"0.50893366\",\n \"0.50867504\",\n \"0.5063906\",\n \"0.5050995\",\n \"0.504886\",\n \"0.50437844\",\n \"0.5040245\",\n \"0.5035688\",\n \"0.50351155\",\n \"0.50338745\",\n \"0.5018519\",\n \"0.5010838\",\n \"0.5010573\",\n \"0.5007881\",\n \"0.50043535\",\n \"0.5002216\",\n \"0.4999497\",\n \"0.49961653\",\n \"0.49921614\",\n \"0.4980961\",\n \"0.4977172\",\n \"0.49659085\",\n \"0.49655056\",\n \"0.49590003\",\n \"0.49559477\",\n \"0.4955314\",\n \"0.4955314\",\n \"0.4944155\",\n \"0.49418777\",\n \"0.4934054\",\n \"0.49311492\",\n \"0.49248096\",\n \"0.49187684\",\n \"0.49079832\",\n \"0.49010077\",\n \"0.48982152\",\n \"0.48964646\",\n \"0.48964188\",\n \"0.48927665\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76679,"cells":{"query":{"kind":"string","value":"Iterate over the observations and update the filtered values after each iteration"},"document":{"kind":"string","value":"def missing_data(self, plot=True):\n # Set some of the observations to missing\n self.remove_data()\n # Create empty arrays to store values\n F = np.zeros(len(self.y))\n a = np.zeros(len(self.y))\n v = np.zeros(len(self.y))\n P = np.zeros(len(self.y))\n # Initialize at the initial values parsed to the class\n P[0] = self.pardict['P1']\n sigma_eps2 = self.pardict['sigma_eps2']\n sigma_eta2 = self.pardict['sigma_eta2']\n # initialise a for estimated model\n if self.var_name != 'Volume of Nile':\n a[0] = self.y[0]\n # Iterate\n for t in range(0, len(self.y) - 1):\n v[t] = np.nan_to_num(self.y[t] - a[t])\n F[t] = P[t] + sigma_eps2 if np.isfinite(self.y[t]) else np.inf\n # K is defined as ratio of P and F\n Kt = P[t] / F[t] if np.isfinite(self.y[t]) else 0\n a[t + 1] = a[t] + Kt * v[t]\n P[t + 1] = P[t] * (1 - Kt) + sigma_eta2\n v[-1] = self.y[-1] - a[-1]\n F[-1] = P[-1] + sigma_eps2\n # Obtain smoothed state\n # Obtain all time values for L\n L = self.pardict['sigma_eps2'] / F\n index_missing = np.argwhere(np.isnan(self.y))\n L[index_missing] = 1\n\n # Do the recursion for r\n r = np.zeros(len(self.y))\n N = np.zeros(len(self.y))\n V = np.zeros(len(self.y))\n\n for t in np.arange(len(self.y) - 1, 0, -1):\n r[t - 1] = v[t] / F[t] + L[t] * r[t]\n for t in np.arange(len(self.y) - 1, 0, -1):\n N[t - 1] = 1 / F[t] + L[t] ** 2 * N[t]\n for t in np.arange(len(self.y) - 1, 0, -1):\n V[t] = P[t] - P[t] ** 2 * N[t - 1]\n\n # Do the recursion for alpha\n alphas = np.zeros(len(self.y))\n alphas[0] = a[t]\n for t in range(1, len(self.y) - 1):\n alphas[t] = a[t] + P[t] * r[t - 1]\n alphas[-1] = np.nan\n\n if plot:\n fig_name = self.var_name + 'Fig25.pdf'\n plot_fig2_5(self.times, self.y, a, P, alphas, V, fname=fig_name, var_name=self.var_name)\n # Restore data\n self.reset_data()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def update(self, other):\n for filter, value in other.items():\n self.__setitem__(filter, value)","def update(self):\n for filter in self.filters:\n filter.update(self.learning_rate)","def update(self, updates, predicate):\n for row in self.rows:\n if predicate(row):\n for column, new_value in updates.items():\n row[column] = new_value","def filter(self):\n self.filter_means = [self.m_0]\n self.filter_covs = [self.P_0]\n self.marginal_covs = []\n for t in range(self.data.shape[0]):\n m_bar, P_bar = self.one_step_prediction(self.filter_means[-1], self.filter_covs[-1])\n\n # Update step\n y = self.data[t]\n if not np.isnan(y).any():\n v = y[:, None] - self.observation_matrix @ m_bar\n S = self.observation_matrix @ P_bar @ self.observation_matrix.T + self.observation_cov\n K = P_bar @ self.observation_matrix.T @ np.linalg.inv(S)\n\n m_bar = m_bar + K @ v\n P_bar = P_bar - K @ S @ K.T\n\n self.marginal_covs.append(S)\n\n self.filter_means.append(m_bar)\n self.filter_covs.append(P_bar)\n self.filter_means = self.filter_means[1:]\n self.filter_covs = self.filter_covs[1:]","def filter_values(self):\n dfilter = self.args.datafilter\n self.logger.info(u'Filtering values with:{f}'.format(f=dfilter))\n data = self.outputdata\n newdata = {}\n for key, value in data.items():\n self.logger.info(u'\\nProcessing Key:{k}, value:{v}'.format(k=key,\n v=value))\n returned_data = dict_value_filter(key, value, dfilter, self.logger)\n if bool(returned_data):\n newdata[key] = returned_data\n self.logger.info(u'Data after filter:{d}'.format(d=newdata))\n\n self.outputdata = newdata","def UpdateSet(self, dataset):\r\n for data in dataset:\r\n self.UpdateOddsRatioVsNoNorm(data)","def filter_update(observations, observation_matrix, observation_variance,\n observation_indices, observation_count,\n state_mean, state_covariance):\n\n sigma = 0.\n detf = 0.\n n_observation = np.int(observation_count)\n for i in range(n_observation):\n observation_index = int(observation_indices[i])\n obsmat = observation_matrix[observation_index, :]\n innovation = (observations[observation_index]\n - np.dot(obsmat, state_mean))\n dot_statecov_obsmat = np.dot(state_covariance, obsmat)\n innovation_covariance = (np.dot(obsmat, dot_statecov_obsmat)\n + observation_variance[observation_index])\n kgain = dot_statecov_obsmat / innovation_covariance\n state_covariance = (state_covariance\n - (np.outer(kgain, kgain)\n * innovation_covariance))\n\n state_mean = state_mean + kgain * innovation\n\n sigma = sigma + (innovation ** 2 / innovation_covariance)\n detf = detf + np.log(innovation_covariance)\n\n return (state_mean, state_covariance, sigma, detf)","def update_filter(self, measurement, robot_pos):\n new_weights = []\n for p in self.particles:\n p = self.__move(p, self.MOTION)\n angle = self.__measurement(p, robot_pos)\n prob = self.__measurement_prob(angle, measurement,\n self.sense_noise)\n new_weights.append(prob)\n new_weights = np.array(new_weights)\n new_weights /= np.sum(new_weights) # normalized weights\n self.weights = new_weights\n\n # if self.__neff() > self.n / 2:\n self.particles = self.__resample()","def set_observations(self, factor):\r\n relevant_obs = set(self.obs).intersection(set(factor.get_variables()))\r\n if relevant_obs:\r\n factor.set_observations({x:self.obs[x] for x in relevant_obs})","def filter(self, update):\n\n raise NotImplementedError","def ObserveEvidence(self,E):\r\n for v,e in E.iteritems():\r\n indx, = (self.var==v).nonzero() # find i where f.var[i]=v\r\n if indx.size != 0:\r\n A = I2A(range(len(self.val)), self.card) # all assignments\r\n A = A[:,indx].flatten() # only interested in 'indx' element of each row\r\n self.val[A != e] = 0","def update(self, predictions, real_vals):\n for pred, actual in zip(predictions, real_vals):\n self.cm.loc[actual, pred] += 1","def filter(self, observable):","def filter_data(self):\n self.data = filter_pandas(self.data, self.filters)","def update_values(self):\n # have to reset params to 0 when recalculating\n self.total_weight = self.total_price = self.total_value = self.total_fitness = 0\n for index, value in enumerate(self.item_stats):\n if value == 1:\n self.total_weight += self.items[index].weight\n self.total_price += self.items[index].price\n self.total_value += self.items[index].value\n self.total_fitness += self.items[index].fitness","def update(self, z):\n raise NotImplementedError('Must implement an update step for the filter.')","def run_batch_filter(self):\n if self.speed_data is None and self.flow_data is None and self.traveltime_data is None:\n print(\n 'Warning: The measurement data must be set before running the batch filter: use function self.set_meas_data()')\n\n # =======================================================================\n # the initial ensembles, which should have been set externally\n X_init = np.matrix(np.zeros((self.dim_state, self.num_ensembles)))\n print(\n 'Setting initial ensembles: rho {0}; qin {1}; qout {2}'.format(self.init_rho, self.init_qin, self.init_qout))\n for ens in range(0, self.num_ensembles):\n X_init[self.x_index['density'][0]:\n self.x_index['density'][self.num_cells - 1], ens] = self.init_rho\n X_init[self.x_index['qin'], ens] = self.init_qin\n X_init[self.x_index['qout'], ens] = self.init_qout\n\n # print('setted qin {0}; qout {1}'.format(X_init[self.x_index['qin'], ens], X_init[self.x_index['qout'], ens] ))\n # add noise to each ensemble\n X_init[:, ens] += np.matrix(np.random.multivariate_normal(\n np.zeros(self.dim_state), self.Q)).reshape((self.dim_state, 1))\n\n self.set_initial_ensembles(X_init)\n\n # =======================================================================\n # DEBUG\n # save the qin and qout in the corresponding probe data\n # save the initial state\n if self.__debug:\n self.qin_f.append(np.squeeze(np.array(self.X_f[self.x_index['qin'], :])).tolist())\n self.qin_a.append(np.squeeze(np.array(self.X_a[self.x_index['qin'], :])).tolist())\n self.qin_obs.append(np.nan)\n\n self.qout_f.append(np.squeeze(np.array(self.X_f[self.x_index['qout'], :])).tolist())\n self.qout_a.append(np.squeeze(np.array(self.X_a[self.x_index['qout'], :])).tolist())\n self.qout_obs.append(np.nan)\n\n # The enKF runs at the finest time grid\n # for each step, update the system\n for step in range(0, self.num_steps):\n\n # update status\n sys.stdout.write('\\r')\n sys.stdout.write('Status: filtering step {0}/{1}'.format(step, self.num_steps))\n sys.stdout.flush()\n # print('Status: filtering step {0}'.format(step))\n\n cur_time = (step + 1) * self.dur_steps\n\n # get the effective measurement\n eff_flow, eff_speed, eff_traveltime = self.__get_eff_meas(cur_time)\n\n # build the observation index\n self.y_index, self.dim_obs, y_obs, cov_noise = self.__build_obs_index(eff_flow, eff_speed, eff_traveltime)\n\n # update the estimate for this step\n est_state = self.update_estimate(y_obs, cov_noise, cur_time)\n\n # =======================================================================\n # DEBUG\n # save the qin and qout in the corresponding probe data\n # save the updated state\n if self.__debug:\n self.qin_f.append(np.squeeze(np.array(self.X_f[self.x_index['qin'], :])).tolist())\n self.qin_a.append(np.squeeze(np.array(self.X_a[self.x_index['qin'], :])).tolist())\n if 'flow' in self.y_index.keys() and self.__debug_entrance_sensor in self.y_index['flow'].keys():\n self.qin_obs.append(y_obs[self.y_index['flow'][self.__debug_entrance_sensor]])\n # print('y_index[flow]:{0}'.format(self.y_index['flow'].keys()))\n # print('y_obs[ y_index[flow][entrance] ]:{0}'.format(\n # y_obs[ self.y_index['flow'][self.__debug_entrance_sensor]],\n # self.__debug_entrance_sensor))\n else:\n self.qin_obs.append(np.nan)\n\n self.qout_f.append(np.squeeze(np.array(self.X_f[self.x_index['qout'], :])).tolist())\n self.qout_a.append(np.squeeze(np.array(self.X_a[self.x_index['qout'], :])).tolist())\n if 'flow' in self.y_index.keys() and self.__debug_exit_sensor in self.y_index['flow'].keys():\n self.qout_obs.append(y_obs[self.y_index['flow'][self.__debug_exit_sensor]])\n else:\n self.qout_obs.append(np.nan)\n # =======================================================================\n # save the estimated state\n self.est_state_all[:, step] = est_state\n\n # decouple and save into self.est_density, self.est_speed, self.est_queue, self.est_traveltime\n self.est_density[:, step] = est_state[0:self.num_cells, 0]\n\n # the speed is computed using the fundamental diagram\n for cell_id in range(0, self.num_cells):\n # use the static FD at this step\n self.est_speed[cell_id, step] = self.__rho2v(self.vm_cells[cell_id, 0], self.beta_cells[cell_id, 0],\n self.rhoc_cells[cell_id, 0], self.wc_cells[cell_id, 0],\n self.est_density[cell_id, step])\n\n # REMARK: the queue and travel time a post-processed from the speed field.\n # They are computed in cross_evaluation class for all algorithms\n # the queue length starts from the first cell with speed below queue_threshold to the end of road\n # index = (self.est_speed[:, step] <= self.queue_threshold)\n #\n # # filter out the outliers\n # index_smoothed = deepcopy(index)\n # outlier_max = 3\n # counter = 0\n # for i in range(0, len(index)):\n #\n # if index[i] == True:\n # # trigger the coutner\n # counter += 1\n # elif index[i] == False and counter != 0:\n # if counter <= outlier_max:\n # # found outliers\n # index_smoothed[ i-counter : i ] = False\n # # reset counter\n # counter = 0\n #\n # # if i != 0 and i != len(index)-1:\n # # if sum( index[i-1:i+3] ) >=2:\n # # index_smoothed[i] = True\n # # else:\n # # index_smoothed[i] = False\n # # elif i == 0:\n # # if sum(index[0: 5] ) >= 3:\n # # index_smoothed[i] = True\n # # else:\n # # index_smoothed[i] = False\n # # elif i == len(index)-1:\n # # if sum(index[ i-4 :len(index)]) >= 3:\n # # index_smoothed[i] = True\n # # else:\n # # index_smoothed[i] = False\n #\n # if sum(index_smoothed) <= 3: # use 4 to suppress false alarms\n # # if less or equal then 2 cells are in congestion, it may be caused by noise.\n # self.est_queue[step] = 0\n # else:\n # # if step > 105 and step < 115:\n # # print(sum(index_smoothed))\n # # print(index_smoothed)\n # # print(index)\n #\n # self.est_queue[step] = \\\n # self.len_cells*( self.num_cells - np.argmax(index_smoothed) )\n # # try:\n # # first_cong_cell_id = [x[0] for x in enumerate( self.est_speed[:,step] ) if x[1] < self.queue_threshold][0]\n # # except IndexError:\n # # # no congested cell\n # # first_cong_cell_id = self.num_cells\n # # # the estimated queue length\n # # self.est_queue[step] = self.len_cells*( self.num_cells - first_cong_cell_id )\n #\n # # the travel time estimate is computed by summing up the travel time in each cell\n # self.est_traveltime[step] = np.sum(self.len_cells/self.est_speed[:,step])\n\n\n # =======================================================================\n # DEBUG\n # plot the update\n if self.__debug:\n plot_len = 19\n # qin\n if False:\n if not np.isnan(self.qin_obs[-1]):\n fig1 = plt.figure(figsize=(10, 5), dpi=100)\n ax1 = fig1.add_subplot(111)\n positions_f = np.arange(0, len(self.qin_f)) - 0.1\n positions_a = np.arange(0, len(self.qin_a)) + 0.1\n positions_obs = np.arange(0, len(self.qin_obs))\n # predicted as red\n bp = ax1.boxplot(self.qin_f[-plot_len:],\n positions=positions_f[-plot_len:], widths=0.15,\n patch_artist=False)\n for box in bp['boxes']:\n # change outline color\n box.set(color='#FF4633', linewidth=1)\n # change fill color\n # box.set( facecolor = '#FF4633' )\n # corrected as green\n bp = ax1.boxplot(self.qin_a[-plot_len:],\n positions=positions_a[-plot_len:], widths=0.15, patch_artist=False)\n for box in bp['boxes']:\n # change outline color\n box.set(color='#07891B', linewidth=1)\n # change fill color\n # box.set( facecolor = '#07891B' )\n # measurement as blue\n ax1.scatter(positions_obs[-plot_len:], self.qin_obs[-plot_len:], color='b', marker='o', s=40,\n label='Observation')\n ax1.set_title('qin')\n # x_ticks = np.arange(0, len(self.qin_f))\n # ax1.set_xticks(x_ticks[-plot_len:])\n plt.show()\n\n # qout\n if False:\n if not np.isnan(self.qout_obs[-1]):\n fig2 = plt.figure(figsize=(10, 5), dpi=100)\n ax2 = fig2.add_subplot(111)\n positions_f = np.arange(0, len(self.qout_f)) - 0.1\n positions_a = np.arange(0, len(self.qout_a)) + 0.1\n positions_obs = np.arange(0, len(self.qout_obs))\n # predicted as red\n bp = ax2.boxplot(self.qout_f[-plot_len:], positions=positions_f[-plot_len:], widths=0.18,\n patch_artist=True)\n for box in bp['boxes']:\n # change outline color\n box.set(color='#7570b3', linewidth=1)\n # change fill color\n box.set(facecolor='#FF4633')\n # corrected as green\n bp = ax2.boxplot(self.qout_a[-plot_len:], positions=positions_a[-plot_len:], widths=0.18,\n patch_artist=True)\n for box in bp['boxes']:\n # change outline color\n box.set(color='#7570b3', linewidth=1)\n # change fill color\n box.set(facecolor='#07891B')\n # measurement as blue\n ax2.scatter(positions_obs[-plot_len:], self.qout_obs[-plot_len:], color='b', marker='o', s=30,\n label='Observation')\n ax2.set_title('qout')\n # x_ticks = np.arange(0, len(self.qout_f))\n # ax2.set_xticks(x_ticks[-plot_len:])\n\n plt.show()\n\n # plot the estimated qin and qout\n if self.__debug:\n if True:\n qin = np.squeeze(np.array(self.est_state_all[self.x_index['qin'], :]))\n qin_meas = np.array(self.qin_obs)[1:]\n print(len(qin), len(qin_meas))\n fig1 = plt.figure(figsize=(10, 5), dpi=100)\n ax1 = fig1.add_subplot(111)\n t = np.arange(len(qin))\n ax1.plot(t, qin, 'r-', label='Estimated')\n not_nan = ~np.isnan(qin_meas)\n ax1.plot(t[not_nan], qin_meas[not_nan], 'b', label='Measured')\n ax1.legend()\n ax1.grid(True)\n ax1.set_title('qin')\n\n plt.draw()\n\n if True:\n qout = np.squeeze(np.array(self.est_state_all[self.x_index['qout'], :]))\n qout_meas = np.array(self.qout_obs)[1:]\n fig2 = plt.figure(figsize=(10, 5), dpi=100)\n ax2 = fig2.add_subplot(111)\n t = np.arange(len(qout))\n ax2.plot(t, qout, 'r-', label='Estimated')\n not_nan = ~np.isnan(qout_meas)\n ax2.plot(t[not_nan], qout_meas[not_nan], 'b', label='Measured')\n ax2.set_title('qout')\n ax2.legend()\n ax2.grid(True)\n plt.draw()","def _update_edges_filtered(self, change=None):\n self._edges_filter.val_range = self.edges_range\n edges_ids = self._edges_filter.val_ids\n self.edges_ids = self._filter_edges(edges_ids)","def update(self, obs, q):\n for o in obs:\n prob = np.exp(-70)\n if self.landmarks:\n # find the data association with ML\n prob, landmark_idx, ass_obs, ass_jacobian, ass_adjcov = self.find_data_association(o)\n if prob < self.TOL:\n # create new landmark\n self.create_landmark(o)\n else:\n # update corresponding EKF\n self.update_landmark(np.transpose(np.array([o])), landmark_idx, ass_obs, ass_jacobian, ass_adjcov)\n else:\n # no initial landmarks\n self.create_landmark(o)\n self.weight *= prob\n \n q.put([self]) ###","def update_filter_params(self, fh):\n (self.data_timestamp, self.framerate,\n self.l, self.d, self.gamma,\n self.eps, self.alex, self.traceswitch) = (fh.attrs['data_timestamp'], fh.attrs['framerate'],\n fh.attrs['l'], fh.attrs['d'], fh.attrs['gamma'],\n fh.attrs['eps'], fh.attrs['alex'], fh.attrs['traceswitch'])","def after_each(self, dataset: pydicom.dataset.Dataset) -> None:\r\n for a_filter in self.filters[::-1]:\r\n a_filter.after_each(dataset)","def update_filters(self):\n\n # Update household filter\n household_filter = [True if agent == 'household' else False for agent \\\n in self.source.data['agent_type']]\n self.household_view.filters[0] = BooleanFilter(household_filter)\n\n # Update neighbourhood filter\n neighbourhood_filter = [True if agent == 'neighbourhood' else False for\\\n agent in self.source.data['agent_type']]\n self.neighbourhood_view.filters[0] = BooleanFilter(\n neighbourhood_filter)\n\n # Update school filter\n school_filter = [True if agent == 'school' else False for agent in \\\n self.source.data['agent_type']]\n self.school_view.filters[0] = BooleanFilter(school_filter)","def __setitem__(self, query_filter, value):\n saved_items = []\n for index, query in enumerate(self.__bound_queries):\n saved_items.append(query.get(query_filter, None))\n try:\n query[query_filter] = value\n except:\n for q, old_value in itertools.izip(self.__bound_queries[:index],\n saved_items):\n if old_value is not None:\n q[query_filter] = old_value\n else:\n del q[query_filter]\n raise","def apply_fn(self,fn):\r\n \r\n self.check_Data()\r\n for split,data_ in self.processed_data.items():\r\n x = data_['x']\r\n x = np.array([fn(xi) for xi in x])\r\n data_['x'] = x","def update(self, iteration):\n pass","def before_each(self, dataset: pydicom.dataset.Dataset) -> None:\r\n for a_filter in self.filters:\r\n a_filter.before_each(dataset)","def update_cards(self, filterdict, values, multiple=True):\n\n self._collection.update(self._constrain_keys(filterdict),\n {'$set': self._constrain_keys(values)},\n multi=multiple)","def fill_obs(self, observation_table, data_store):\n for obs in observation_table:\n events = data_store.obs(obs_id=obs['OBS_ID']).events\n\n # TODO: filter out (mask) possible sources in the data\n # for now, the observation table should not contain any\n # run at or near an existing source\n\n self.counts_cube.fill_events([events])\n self.livetime_cube.data += events.observation_live_time_duration","def reset_filter(self):\n arlen = len(self.variant_list)\n self.filter = np.zeros((arlen, arlen)) == 0","def filter(self, *args):\n from .elements import EqualClauseElement\n for a in args:\n for c in self._criterion:\n if isinstance(c, EqualClauseElement) and isinstance(a, EqualClauseElement) and \\\n c.attribute.node == a.attribute.node and c.attribute.label == a.attribute.label:\n c.value = a.value\n break\n else:\n self._criterion.append(a)\n return self","def apply_filters(self, filters):\n self._data = self.model.objects.filter(**filters)","def process_dataset(dataset, func):\n new_dataset = copy.copy(dataset)\n del new_dataset[\"val\"]\n new_dataset.update(func(dataset))\n return new_dataset","def update(self, observed, axis):\n\n # -------------------------------------------------------------------------\n # YOUR CODE GOES HERE\n #\n total = 0\n \n # zero out irrelevant data and calculate total for observed sample\n for each in self._table:\n if (axis == 1 and each[1] != observed) or (axis == 0 and each[0] != observed):\n self._table[each] = 0\n else:\n total += self._table[each]\n \n # normalize the revelant data\n for each in self._table:\n if (axis == 1 and each[1] == observed) or (axis == 0 and each[0] == observed):\n self._table[each] = self._table[each] / total\n\n #\n # END OF YOUR CODE\n # ------------------------------------------------------------------------- ","def _modify_updates(self, updates):\n wxf = self.wxf\n wyf = self.wyf\n wxf_updated = updates[wxf]\n wyf_updated = updates[wyf]\n nwxf = (wxf_updated.std(0) + SMALL)[numpy.newaxis, :]\n nwyf = (wyf_updated.std(0) + SMALL)[numpy.newaxis, :]\n meannxf = nwxf.mean()\n meannyf = nwyf.mean()\n # Center filters\n centered_wxf = wxf_updated - wxf_updated.mean(0)\n centered_wyf = wyf_updated - wyf_updated.mean(0)\n # Fix standard deviation\n wxf_updated = centered_wxf * (meannxf / nwxf)\n wyf_updated = centered_wyf * (meannyf / nwyf)\n updates[wxf] = wxf_updated\n updates[wyf] = wyf_updated","def apply_filters(self):\n hurst_cut = 0\n coint_cut = 0\n half_life_cut = 0\n mean_cross_cut = 0\n\n # Create an empty list for pairs that pass the filter tests\n validated_pairs = []\n\n # Create all the pairs combination\n self.create_pair_differences()\n\n # Print the number of potential pairs\n print(f\"Number of potential pairs in before filter: {len(self.__pairs_data)}\")\n\n for pair in self.__pairs_data:\n # Select the stocks from the pair\n stock1 = pair[0]\n stock2 = pair[1]\n\n # Test the hurst filter\n if self.hurst_filter(self, stock1=stock1, stock2=stock2):\n hurst_cut += 1\n if self.engel_filter(self, stock1=stock1, stock2=stock2):\n coint_cut += 1\n if self.half_life_filter(self, stock1=stock1, stock2=stock2):\n half_life_cut += 1\n if self.mean_cross_filter(self, stock1=stock1, stock2=stock2):\n mean_cross_cut += 1\n validated_pairs.append([stock1, stock2])\n\n print(f\"Hurst filter pass: {hurst_cut}\")\n print(f\"Co-integration filter pass: {coint_cut}\")\n print(f\"Half-life filter pass: {half_life_cut}\")\n print(f\"Mean-cross filter pass: {mean_cross_cut}\")\n print(f\"Final Number of validated pairs: {len(validated_pairs)}\")\n print(\"The final validated pairs are: \")\n print(validated_pairs)\n\n # Save it to the attribute\n self.__validated_pairs = validated_pairs\n self.__validated_pairs_diff = self.__pair_diff[self.symbolize_pairs(self.__validated_pairs)]","def test_updating_multiple_records_through_filter_with_arg_value(self, test_domain):\n identifier1 = uuid4()\n identifier2 = uuid4()\n identifier3 = uuid4()\n identifier4 = uuid4()\n test_domain.repository_for(Person)._dao.create(\n id=identifier1, first_name=\"Athos\", last_name=\"Musketeer\", age=2\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier2, first_name=\"Porthos\", last_name=\"Musketeer\", age=3\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier3, first_name=\"Aramis\", last_name=\"Musketeer\", age=4\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier4, first_name=\"dArtagnan\", last_name=\"Musketeer\", age=5\n )\n\n # Perform update\n updated_count = (\n test_domain.repository_for(Person)\n ._dao.query.filter(age__gt=3)\n .update_all({\"last_name\": \"Fraud\"})\n )\n\n # Query and check if only the relevant records have been updated\n assert updated_count == 2\n\n u_person1 = test_domain.repository_for(Person)._dao.get(identifier1)\n u_person2 = test_domain.repository_for(Person)._dao.get(identifier2)\n u_person3 = test_domain.repository_for(Person)._dao.get(identifier3)\n u_person4 = test_domain.repository_for(Person)._dao.get(identifier4)\n assert u_person1.last_name == \"Musketeer\"\n assert u_person2.last_name == \"Musketeer\"\n assert u_person3.last_name == \"Fraud\"\n assert u_person4.last_name == \"Fraud\"","def _mutate(self, individuals):\n for cur in individuals:\n if random.random() < self.mutation_probability:\n self.op.mutate(cur['individual'])\n cur['fitness'] = None","def _update_all(self, criteria: Q, *args, **kwargs):\n raise NotImplementedError","def updateIntensities (self,listAtoms):\r\n \r\n for i in range(len(listAtoms)):\r\n for j in range(len(listAtoms[i].spikeArray)):\r\n self.mol[i].spikeArray[j].intensity = listAtoms[i].spikeArray[j].intensity","def updateFitness(self):\r\n for candidate in self.candidates:\r\n candidate.updateFitness()\r\n return","def resample(self):\n self.particles = ParticleFilter.weighted_values(self.particles,\n [p.weight for p in self.particles],\n len(self.particles))\n for p in self.particles:\n p.weight = 1./len(self.particles)","def update_all(self, stream: Iterator[Mapping[str, np.ndarray]]) -> Self:\n\n for element in stream:\n self.update(element)\n\n return self","def update_all(self, stream: Iterator[Mapping[str, np.ndarray]]) -> Self:\n\n for element in stream:\n self.update(element)\n\n return self","def filter(self, observations):\n\n (_, _, _, x_filtered, P_filtered) = filter(self.F, self.Q, self.H, self.R, self.x_0, self.P_0, observations)\n return x_filtered, P_filtered","def filter_(self,fltr:torch.tensor):\n self.container = self.container[:,fltr]\n self.count_hist = self.count_hist[fltr]","def update_individuals(individuals, eval_results):\n for ind, res in zip(individuals, eval_results):\n ind.fitness.values = res[0]\n ind.matching_node_pairs = res[1]\n ind.gtp_precisions = res[2]","def _update_step(self, *, observations: types.ObservationsTorch) -> None:","def _applyFilters(self) -> None:\n self._dataframe = self._source.loc[:, self._visable_columns]\n for column, value in self._filters.items():\n if value is not None:\n self._dataframe = self._dataframe[self._source[column] == value]\n else:\n self._dataframe = self._dataframe[self._source[column].isnull()]\n\n self.layoutChanged.emit()","def filter(self, filters):","def filter(self, filter_dict):\n pass","def update_values(self):\n for key in self.inputs.keys():\n value = self.inputs[key]['entry'].get()\n self.inputs[key]['value'] = value","def prefilter(self, filt=None, verbose=False):\n erased = []\n if verbose:\n msg = 'Prior to filter, we have {} cells.'.format(len(self.cells))\n print(msg)\n # check for supplementary observables to be computed\n raw_obs, func_obs = set_observable_list(filters=[filt, ])\n\n # compute suppl obs for all cells\n if raw_obs:\n for cell in self.cells:\n for obs in raw_obs:\n cell.build(obs)\n for cell in self.cells:\n if filt is not None:\n if not filt(cell):\n erased.append(cell)\n # make Colony.add_cell_recursive non functional\n cell.bpointer = None\n if cell.parent:\n cell.parent.childs.remove(cell)\n # make daughter cells new roots\n for ch in cell.childs:\n ch.bpointer = None\n if verbose:\n msg = '{} cells do not pass filter.'.format(len(erased))\n print(msg)\n for cell in erased:\n self.cells.remove(cell) # otherwise would be considered root\n # clean-up actions for computing extra obs\n # extra obs computation depends on tree decomposition\n # this will be done in lineage.get_timeseries()\n for cell in self.cells:\n for obs in raw_obs:\n del cell._sdata[obs.label]\n if verbose:\n msg = 'After filtering, we get {} cells.'.format(len(self.cells))\n print(msg)\n# self.metadata.filters.append(repr(boofunc))\n return","def run(self, threshold=1e-3, max_iters=100):\n for msr in self.msrs:\n # find state transition matrix and propagate state\n phi_p, state_prop = self._compute_stm(msr.time, self.phis[-1])\n\n # compute observation deviation, obs_state matrix\n y_i, h_tilde = self._msr_resid(msr, state_prop)\n\n # update information and N\n self._update_info_and_n(y_i, h_tilde, phi_p, msr.cov)\n\n # add everything to the appropriate lists\n #new_cov = np.matmul(phi_p, np.matmul(self.cov_list[-1], phi_p))\n #self.cov_list.append(new_cov)\n self.prop_state_list.append(state_prop)\n self.estimates.append(state_prop)\n self.phis.append(phi_p)\n self.times.append(msr.time)\n\n # compute correction\n self.iters += 1\n x_hat_0 = np.linalg.solve(self.fisher_info[-1], self.N[-1])[0]\n\n # check for convergence\n if np.linalg.norm(x_hat_0) <= threshold:\n print(\"Batch filter converged in {} Iterations\".format(self.iters))\n self.cov_batch = np.linalg.inv(self.fisher_info[-1])\n\n elif self.iters >= max_iters:\n raise StopIteration(\"max_iters: {} reached without convergence\".format(max_iters))\n\n else:\n # reset everything and try again\n updated_istate = np.add(self.prop_state_list[0], np.transpose(x_hat_0))\n # fixes a strange bug wher the size of updated istate was changing\n updated_istate = np.resize(updated_istate, (1, len(self.istate)))[0]\n\n self.prop_state_list = [updated_istate]\n #self.cov_list = [self.apriori]\n self.fisher_info = [self.fisher_info[0]]\n self.pert_vec = np.subtract(self.pert_vec, x_hat_0)\n self.N = [np.matmul(self.apriori, self.pert_vec)]\n self.phis = [self.phis[0]]\n self.estimates = [updated_istate]\n self.times = [0]\n self.run()","def _UpdateDataSetValues( self ):\n pass","def set_filter_for_eval(self, x_filter):\n self.x_filter = x_filter\n\n entity_size = len(self.ent_to_idx)\n reln_size = len(self.rel_to_idx)\n\n first_million_primes_list = []\n curr_dir, _ = os.path.split(__file__)\n with open(os.path.join(curr_dir, \"prime_number_list.txt\"), \"r\") as f:\n logger.debug('Reading from prime_number_list.txt.')\n line = f.readline()\n i = 0\n for line in f:\n p_nums_line = line.split(' ')\n first_million_primes_list.extend([np.int64(x) for x in p_nums_line if x != '' and x != '\\n'])\n if len(first_million_primes_list) > (2 * entity_size + reln_size):\n break\n\n # subject\n self.entity_primes_left = first_million_primes_list[:entity_size]\n # obj\n self.entity_primes_right = first_million_primes_list[entity_size:2 * entity_size]\n # reln\n self.relation_primes = first_million_primes_list[2 * entity_size:(2 * entity_size + reln_size)]\n\n self.filter_keys = []\n # subject\n self.filter_keys = [self.entity_primes_left[self.x_filter[i, 0]] for i in range(self.x_filter.shape[0])]\n # obj\n self.filter_keys = [self.filter_keys[i] * self.entity_primes_right[self.x_filter[i, 2]]\n for i in range(self.x_filter.shape[0])]\n # reln\n self.filter_keys = [self.filter_keys[i] * self.relation_primes[self.x_filter[i, 1]]\n for i in range(self.x_filter.shape[0])]\n\n self.is_filtered = True","def test_feature_is_filtered(self):\n\n # Duplicate 1st row in var and assigned to 2nd\n self.validator.adata.var[\"feature_is_filtered\"][0] = True\n for i in range(self.validator.adata.X.shape[0]):\n self.validator.adata.X[i, 0] = 0\n self.validator.adata.X[0, 0] = 1\n\n self.validator.validate_adata()\n self.assertEqual(\n self.validator.errors,\n [\n \"ERROR: Some features are 'True' in 'feature_is_filtered' of dataframe 'var', \"\n \"but there are 1 non-zero values in the corresponding columns of the matrix 'X'. \"\n \"All values for these features must be 0.\"\n ],\n )","def particle_filter(particle_set_t, measurement_t):\n global count\n n_samples, dim = particle_set_t.shape # no of particles and dimension of each particle\n\n pred_state = np.zeros((n_samples, dim), dtype=\"float64\") # store the predicted state \n weights = np.zeros(n_samples, dtype=\"float64\") # corresponding weights for resampling\n\n particle_set_t1 = np.zeros((n_samples, dim), dtype=\"float64\") # next iteration of particles\n\n\n # this loop calculates \\bar{X_t}, i.e. the predicted belief.\n for n in range(n_samples):\n # predicted motion step:\n xn_t1 = sample_motion_model(particle_set_t[n]) # 3x1 vector: hypothetical state\n\n # measurement correction step:\n weight_xn_t1 = state_likelihood(measurement_t, xn_t1) # scalar value\n\n pred_state[n] = xn_t1\n weights[n] = weight_xn_t1\n\n \n # It was observed that if all weights are 0, the resampling step breaks. \n # Thus, adding a uniform distribution. This is obviously a very bad idea \\ \n # as the true state can easily be discarded in the resampling step: TODO!\n if np.sum(weights) > 0.0:\n weights = weights/np.sum(weights) # normalize array only when sum in not 0\n else:\n print(\"possile divergence!\")\n weights[:] = 1 / n_samples # if sum is 0 then assign uniform distribution throughout\n\n\n # the resampling step:\n # indices = monte_carlo.residual_resample(weights)\n indices = monte_carlo.stratified_resample(weights)\n count += 1\n print(count)\n\n # new particle set is particles at index locations\n for i, index in enumerate(indices):\n particle_set_t1[i] = pred_state[index]\n\n return particle_set_t1","def filter(self,state0):\n ok,tchi2 = True,0.\n state = state0.copy()\n ii = 0\n for node in self.nodes:\n zrun = node.zrun\n ok,state,F,Q = self.model.propagate(state,zrun)\n if (not ok):\n warning(\"kfilter.filter not possible to filter at \",(ii,zrun))\n debug(\"kfilter.filter i,ok,chi2 \",(ii,ok,tchi2))\n return ok,tchi2\n node.F = F\n node.Q = Q\n node.setstate('pred',state)\n fstate,fchi2 = node.predict(state)\n node.setstate('filter',fstate)\n node.setchi2('filter',fchi2)\n tchi2+=fchi2\n self.model.user_filter(node)\n state = node.getstate('filter').copy()\n ii+=1\n self.status='filter'\n debug(\"kfilter.filter ok,chi2 \",(ok,tchi2))\n return ok,tchi2","def Filter(self,val):\n \n #set th elength of the lis to 0\n List = [self.InitialList[i] for i in range(0,len(self.InitialList))]\n FilterValues = [None]\n Grab = [None]\n Headers = []\n \n #create the quick index\n for i in range(len(self.Condensensed)):\n \n Headers.append([self.Condensensed[i][l][0] for l in range(len(self.Condensensed[i]))])\n \n #grab the values...\n for j in range(len(self.Variables)):\n \n FilterValues.append(self.Variables[j].get())\n\n if self.Variables[j].get().split(' ')[0] == 'All':\n \n Grab.append(False)\n \n else:\n \n Grab.append(True)\n \n #intermediate list to compare\n ToCompare = []\n \n for i in range(1,len(Grab)):\n \n if Grab[i]:\n \n #find the index\n l = Headers[i].index(FilterValues[i])\n \n #grab it\n ToCompare.append([self.Condensensed[i][l][m] for m in range(len(self.Condensensed[i][l]))])\n\n\n for i in range(0, len(ToCompare)):\n \n List = list(set(List).intersection(ToCompare[i]))\n\n #update the interface\n self.Gatherer(List,list(self.Input))\n self.BuildTree()","def filter_data(self):\n if(self.filter_classes == []):\n return\n \n filtered_idx = []\n for id in range(len(self.image_ids)):\n anns = self.load_annotations(id)\n found = False\n for ann in anns:\n if ann['label'] in self.filter_classes:\n found = True\n break\n if found:\n filtered_idx.append(id)\n \n self.filtered_ids = [self.image_ids[id] for id in filtered_idx]\n # self.image_ids = self.filtered_ids\n print(\"Number of filtered instances:\", len(self.filtered_ids))","def update(self, **kwargs):\n for name, item in itertools.chain(\n self._cal_objs.items(),\n self._noise_objs.items()):\n logger.debug(\"update {}\".format(item))\n item.update(**kwargs)","def train_filter(filt, U, Y):\n n = len(U)\n X = []\n R = []\n for i in range(n):\n x_hat_bar = filt.time_update(U[i])\n x_hat, P = filt.measurement_update(Y[i], x_hat_bar)\n print(i, x_hat, P)\n X.append(x_hat)\n R.append(P)\n return np.array(X), R","def test_updating_record_through_filter(self, test_domain):\n identifier1 = uuid4()\n identifier2 = uuid4()\n identifier3 = uuid4()\n identifier4 = uuid4()\n test_domain.repository_for(Person)._dao.create(\n id=identifier1, first_name=\"Athos\", last_name=\"Musketeer\", age=2\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier2, first_name=\"Porthos\", last_name=\"Musketeer\", age=3\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier3, first_name=\"Aramis\", last_name=\"Musketeer\", age=4\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier4, first_name=\"dArtagnan\", last_name=\"Musketeer\", age=5\n )\n\n # Perform update\n updated_count = (\n test_domain.repository_for(Person)\n ._dao.query.filter(age__gt=3)\n .update(last_name=\"Fraud\")\n )\n\n # Query and check if only the relevant records have been updated\n assert updated_count == 2\n\n u_person1 = test_domain.repository_for(Person)._dao.get(identifier1)\n u_person2 = test_domain.repository_for(Person)._dao.get(identifier2)\n u_person3 = test_domain.repository_for(Person)._dao.get(identifier3)\n u_person4 = test_domain.repository_for(Person)._dao.get(identifier4)\n assert u_person1.last_name == \"Musketeer\"\n assert u_person2.last_name == \"Musketeer\"\n assert u_person3.last_name == \"Fraud\"\n assert u_person4.last_name == \"Fraud\"","def do_filter(self):\n return np.sum(np.multiply(self._past_values, self._impulse_response))","def filter(self):\n self.data = self.data.loc[~self.data.isnull().any(1),:]","def updateValues(self, otherDialog):\n for index in range(len(self._values)):\n for oval in otherDialog._values:\n if self._values[index].getID() == oval.getID():\n self._values[index].combine( oval, intelligent=False )\n break","def filter_data(self):\n self.df = self.df[HeatStrokeDataFiller.important_features]","def observe(self, observation):\n # shallow copy observation (deep copy can be expensive)\n obs = observation.copy()\n batch_idx = self.opt.get('batchindex', 0)\n self.observation = obs\n #self.answers[batch_idx] = None\n return obs","def _update_raw_data(params):\n from scipy.signal import filtfilt\n start = params['t_start']\n stop = params['raw'].time_as_index(start + params['duration'])[0]\n start = params['raw'].time_as_index(start)[0]\n data_picks = _pick_data_channels(params['raw'].info)\n data, times = params['raw'][:, start:stop]\n if params['projector'] is not None:\n data = np.dot(params['projector'], data)\n # remove DC\n if params['remove_dc'] is True:\n data -= np.mean(data, axis=1)[:, np.newaxis]\n if params['ba'] is not None:\n data[data_picks] = filtfilt(params['ba'][0], params['ba'][1],\n data[data_picks], axis=1, padlen=0)\n # scale\n for di in range(data.shape[0]):\n data[di] /= params['scalings'][params['types'][di]]\n # stim channels should be hard limited\n if params['types'][di] == 'stim':\n norm = float(max(data[di]))\n data[di] /= norm if norm > 0 else 1.\n # clip\n if params['clipping'] == 'transparent':\n data[np.logical_or(data > 1, data < -1)] = np.nan\n elif params['clipping'] == 'clamp':\n data = np.clip(data, -1, 1, data)\n params['data'] = data\n params['times'] = times","def _filter_satisfied(self, update_setd=False):\n\n model = self.oracle.get_model()\n setd = set()\n\n for i, cl in enumerate(self.soft):\n if not self.satc[i]:\n if self._satisfied(cl, model):\n self.satc[i] = True\n self.ss_assumps.append(self.sels[i])\n else:\n setd = setd.union(set(cl))\n\n if update_setd:\n self.setd = list(setd)","def _put_resolved_booleans_into_filter(\n filter_operator: Operator, model_filters_to_resolved_values: Dict[ModelFilter, BooleanValues]\n) -> None:\n for operator in _model_filter_in_operator_generator(filter_operator):\n model_filter = operator.unresolved_value\n operator.resolved_value = model_filters_to_resolved_values.get(\n model_filter, BooleanValues.UNKNOWN\n )","def apply_filters(self, new_filters):\n\t\tself.filters = new_filters","def set_observations(self, oseries):\n self.oseries_index = oseries.index\n observations_masked = np.ma.array(oseries,\n mask=(~np.isfinite(oseries)))\n (n_timesteps, dimobs) = observations_masked.shape\n self.observation_indices = np.zeros((n_timesteps, dimobs),\n dtype=np.float64)\n self.observation_count = np.zeros(n_timesteps, dtype=np.int64)\n self.observations = np.zeros((n_timesteps, dimobs), dtype=np.float64)\n\n for t in range(n_timesteps):\n observation = observations_masked[t]\n # add large value to find all finite non-masked values\n obstmp = observation + 1e10\n obsindices = obstmp.nonzero()[0]\n self.observation_count[t] = len(obsindices)\n\n if (len(obsindices) > 0):\n for i, _ in enumerate(obsindices):\n obsid = int(obsindices[i])\n self.observations[t, obsid] = observation[obsid]\n self.observation_indices[t, i] = obsid","def updateObservation(self, obs):\n self.settingsDb.updateObservation(self.observationsTableName(), obs)","def update(self):\n\n terms_toRemove = []\n\n for termIndex, [term_constantFactor, term_unknowns_attributeAddresses] in enumerate(self.LHS):\n\n # Check if coefficient is 0 - then no need to process any of the unknowns since term will be 0 anyways\n if term_constantFactor == 0:\n terms_toRemove.append(termIndex)\n continue # continue to next term, no need to resolve the unknowns of this term since the product will be 0 anyways\n\n # Check if any unknowns became known\n unknowns_toRemove = []\n for unknown_attributeAddress in term_unknowns_attributeAddresses:\n attribute = getattr_fromAddress(*unknown_attributeAddress)\n if isNumeric(attribute):\n # object.attribute which had previously been identified as unknown now has a value, add it to the constant factor product and remove from the unknowns\n self.LHS[termIndex][0] *= attribute # multiply it with the constant factor product\n unknowns_toRemove.append([termIndex, unknown_attributeAddress])\n for termIndex, unknown_attributeAddress in unknowns_toRemove: # remove unknowns which have become known in the end\n # removing in the end not to tamper with the iteration of the above loop\n self.LHS[termIndex][1].remove(unknown_attributeAddress)\n\n # Move constants to RHS\n if self.LHS[termIndex][1] == []:\n # if term has no unknowns, it is a constant, move to RHS\n self.RHS -= self.LHS[termIndex][0]\n self.LHS.pop(termIndex)\n\n for termIndex in reversed(terms_toRemove): # reversed - otherwise would tamper with indices of items identified for removal\n self.LHS.pop(termIndex)\n\n self._gatherUnknowns()","def filter(self):\n M, p, q = self.M, self.p, self.q\n x = self.x\n idx = len(self.x) - (p + 1)\n x_ = self.x_prev + (x[idx + p] - x[idx - q]) / M\n self.t_.append(self.t[idx])\n self.t_filtered.append(self.t[idx])\n self.x_.append(x_)\n self.x_filtered.append(x_)\n self.x_prev = x_","def update_filters(self, **kwargs):\n self._FILTERS = kwargs","def update(self, values, train, eta=.1):\n\t\tfor X, y_true in zip(values, train):\n\t\t\tprediction = self.activate(X)\n\t\t\terror = y_true - prediction\n\t\t\tweight_update = error * eta * X\n\t\t\tself.weights += weight_update","def finalize(self):\n # this forces only some filters will be used for feature computation\n # this is not ideal, but a necessary stop-gap while we revise\n # the PropertyTable SQL\n self._good_filters = ['u', 'g', 'r', 'i', 'z', 'Y']\n self._good_filter_wave = np.array([3569.5, 4766.5, 6214.5, 7544.5, 8707.5, 10039.5])\n\n use_filters = set(self._good_filters) & self.filters\n if not self.filters.issubset(use_filters):\n message = 'Number of useful filters ({}) does not equal number available filters ({}) - some filters will not be used'.format(\n ''.join(use_filters), ''.join(self.filters))\n warnings.warn(message, RuntimeWarning)\n self.filters = set(use_filters)\n mask = np.array([True if x in self.filters else False for x in self.passband])\n\n if mask.size: # Not empty arrays\n self.time = self.time[mask]\n self.flux = self.flux[mask]\n self.fluxErr = self.fluxErr[mask]\n self.obsId = self.obsId[mask]\n self.passband = self.passband[mask]\n self.zeropoint = self.zeropoint[mask]\n for key in self._extra_cols:\n val = getattr(self, key)\n setattr(self, key, val[mask])\n\n self.nobs = len(self.time)\n if self.nobs == 0:\n message = 'Object {} with locus ID {} has no good observations.'.format(self.objectId, self.locusId)\n raise ValueError(message)\n\n return self._remove_flux_extinction()","def step_filter(self, v, imu_meas, meas):\n\n if (imu_meas is not None) and (meas is None):\n xp, Pp = self.prediction(v, imu_meas)\n self.x = xp\n self.P_t = Pp\n return xp\n\n elif (imu_meas is None) and (meas is not None):\n z_t = self.transformMeasurement(meas)\n self.x = z_t\n return z_t\n\n else: # both measurements contain values\n xp, Pp = self.prediction(v, imu_meas)\n z_t = self.transformMeasurement(meas)\n x, P = self.update(z_t, xp, Pp)\n\n self.x = x\n self.P_t = P\n return x","def filter(self, data):\n self.data = pysap.Image(data=self.flt.filter(data))","def _apply_filter(self, fn=lambda ngram, freq: False):\n tmp_ngram = FreqDist()\n for ngram, freq in self.ngram_fd.items():\n if not fn(ngram, freq):\n tmp_ngram[ngram] = freq\n self.ngram_fd = tmp_ngram","def step_filter(self, v, imu_meas, z_t):\n # YOUR CODE HERE\n pass","def parameter_update(self, X, X_mask):\n data_with_mask = np.hstack([X, X_mask])\n X_pred, X_MC_preds = self.predict(data_with_mask)\n X[X_mask] *= self.keep_coeff\n X[X_mask] += self.weight_update_coeff * X_pred[X_mask]\n return X, X_MC_preds","def filter_data(data,filters):\n final_filter = pd.Series(np.array([True] * data.shape[0]))\n for attribute, value in filters:\n final_filter &= data[attribute] == value\n return data[final_filter]","def _set_values(loop_data):\n \n # These are the indexes of all the data that are unassigned\n value_indexes = loop_data.ix[(loop_data.loop==0)&(loop_data.tag==0)&(loop_data.stop==0)].index\n loop_data[u'value'] = 0\n loop_data.ix[value_indexes,u'value'] = 1\n \n # These are the indexes of data who follow either a loop, label, or stop tag\n value_indexes_begin = loop_data.ix[(value_indexes-1)].ix[loop_data.value==0].index + 1\n \n # The first rows of each data correspond to data for their respective loops\n loop_max = loop_data.loop.max()\n loop_range = np.arange(loop_max-1, -1, -1)\n \n for idx in value_indexes_begin:\n loop_data.ix[idx:idx+len(loop_range)-1,u'value'] += loop_range\n \n return loop_data","def update_with_observation(particles, landmark_list):\r\n for p in particles:\r\n for i in range(np.shape(landmark_list)[1]):\r\n p.update_particle(landmark_list[:, i])\r\n\r\n return particles","def process_observation(self, observation):\n return observation","def process_observation(self, observation):\n return observation","def iterate(self, data):\n \n # Append data to self.data\n self.data = np.append(self.data, data)\n \n for i, d in enumerate(data):\n update = self.current*self.likelihood(d)\n self.current = self._normalize(update)\n self.posterior = np.concatenate((self.posterior,[self.current]))\n \n print(str(len(data)) + \" iterations completed!\")\n \n return None","async def _update_values(self, model: Model):\n\n raise NotImplementedError","def Iterate(self):\n\t\tfor atom in self.atoms:\n\t\t\tself.UpdateAtom(atom)","def update_state(self):\n # return initial state if no observation was yet\n if len(self.obs_history) == 0:\n return self.kf.initial_state_mean, self.kf.initial_state_covariance\n\n hist = np.ma.masked_array(self.obs_history, mask=np.zeros((1,)))\n for i in range(len(hist)):\n if hist[i] == -1e8:\n hist[i] = np.ma.masked\n\n # print(hist, hist.shape)\n return self.kf.filter(hist)","def _update_data(self):\n for attribute in [\"flow_rate\"]:\n self._data[attribute] = self._connection.measure","def apply_filters(\n isovar_result,\n filter_thresholds={},\n filter_flags=[]):\n filter_values = OrderedDict(isovar_result.filter_values.items())\n new_filter_values = evaluate_filters(\n isovar_result,\n filter_thresholds=filter_thresholds,\n filter_flags=filter_flags)\n filter_values.update(new_filter_values)\n return isovar_result.clone_with_updates(filter_values=filter_values)","def healthcare_filter(df_all): \n #get requested assets under healthcare tag \n df_filtered = pandas.DataFrame(columns=['osm_id','asset','geometry']) #create df for saving data\n for row in range(len(df_all.index)): \n if 'healthcare' in df_all[\"asset\"][row]: #check if healthcare key is present\n df_filtered = df_filtered.append(df_all.loc[row]) #if so, save in df \n if '\"healthcare\"=>\"doctor\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'doctors' #to be consistent with asset list \n elif '\"healthcare\"=>\"pharmacy\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'pharmacy'\n elif '\"healthcare\"=>\"hospital\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'hospital'\n elif '\"healthcare\"=>\"clinic\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'clinic'\n elif '\"healthcare\"=>\"dentist\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'dentist'\n elif '\"healthcare\"=>\"physiotherapist\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'physiotherapist'\n elif '\"healthcare\"=>\"alternative\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'alternative'\n elif '\"healthcare\"=>\"laboratory\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'laboratory'\n elif '\"healthcare\"=>\"optometrist\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'optometrist'\n elif '\"healthcare\"=>\"rehabilitation\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'rehabilitation'\n elif '\"healthcare\"=>\"blood_donation\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'blood_donation'\n elif '\"healthcare\"=>\"birthing_center\"' in df_filtered[\"asset\"][row]:\n df_filtered[\"asset\"][row] = 'birthing_center'\n else:\n df_filtered = df_filtered.drop(index=row)\n \n return df_filtered","def filter_patients(self):\n\n if self.dataset is None:\n self.dataset = h5py.File(self.filename, 'r')['dataset']\n \n # Find feature indices belonging to specific criteria\n inclusion_info = self.filter_params['inclusion']\n # exclusion_info = self.filter_params['exclusion']\n case_control_info = self.filter_params['case_control']\n\n inclusion_inds = self.check_criteria(inclusion_info, case_control=False)\n # exclusion_inds = self.check_criteria(exclusion_info, case_control=False)\n case_inds, control_inds = self.check_criteria(case_control_info, case_control=True)\n\n filtered_inds = {}\n # inclusion_exclusion_inds = np.setdiff1d(inclusion_inds, exclusion_inds)\n filtered_inds['case'] = np.intersect1d(inclusion_inds, case_inds)\n filtered_inds['control'] = np.intersect1d(inclusion_inds, control_inds)\n\n return filtered_inds","def test_updating_multiple_records_through_filter_with_kwarg_value(\n self, test_domain\n ):\n identifier1 = uuid4()\n identifier2 = uuid4()\n identifier3 = uuid4()\n identifier4 = uuid4()\n test_domain.repository_for(Person)._dao.create(\n id=identifier1, first_name=\"Athos\", last_name=\"Musketeer\", age=2\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier2, first_name=\"Porthos\", last_name=\"Musketeer\", age=3\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier3, first_name=\"Aramis\", last_name=\"Musketeer\", age=4\n )\n test_domain.repository_for(Person)._dao.create(\n id=identifier4, first_name=\"dArtagnan\", last_name=\"Musketeer\", age=5\n )\n\n # Perform update\n updated_count = (\n test_domain.repository_for(Person)\n ._dao.query.filter(age__gt=3)\n .update_all(last_name=\"Fraud\")\n )\n\n # Query and check if only the relevant records have been updated\n assert updated_count == 2\n\n u_person1 = test_domain.repository_for(Person)._dao.get(identifier1)\n u_person2 = test_domain.repository_for(Person)._dao.get(identifier2)\n u_person3 = test_domain.repository_for(Person)._dao.get(identifier3)\n u_person4 = test_domain.repository_for(Person)._dao.get(identifier4)\n assert u_person1.last_name == \"Musketeer\"\n assert u_person2.last_name == \"Musketeer\"\n assert u_person3.last_name == \"Fraud\"\n assert u_person4.last_name == \"Fraud\"","def train_filter2(filt, U, Y):\n n = len(U)\n X, R = [], []\n for i in range(n):\n print(i)\n filt.predict(fx_args=(U[i],))\n filt.update(Y[i])\n X.append(filt.x.copy())\n R.append(filt.P.copy())\n X = np.array(X)\n X, R, K = filt.rts_smoother(X, R, fx_args=(U[i],))\n return X, R","def apply_filter(self, filter_arg):\n filtered_entries = self.visual.apply_filter(filter_arg, self.get_current_entries())\n # idxs = self.selector.select_by_objects(filtered_entries, yield_ones_index=True)\n self.visual.print_entries_enum(filtered_entries, None)\n # self.list(idxs)","def forc_model(self):\n lag1_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag1_by_store')\n lag2_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag2_by_store')\n for add in self.X.address1.unique():\n add_mask = self.X.address1 == add\n foo = self.X[ add_mask ].sort_values('visit_date', ascending=False)\n top_index = foo.index[0]\n clust = int(foo.cluster.values[0])\n # get values from last visit for store\n base_input = foo[self.model_mask_cols].values[0]\n base_actual = self.y[top_index]\n lag2_val = base_input[lag1_loc]\n lag1_val = base_actual\n\n for i in range(1, self.num_periods + 1):\n model = self.model_list[clust]\n inputs = base_input\n inputs[lag1_loc] = lag1_val\n inputs[lag2_loc] = lag2_val\n \n pred = model.predict(inputs.reshape(1, -1))\n self._update_cust_table(add, i, pred)\n \n lag2_val = lag1_val\n lag1_val = pred"],"string":"[\n \"def update(self, other):\\n for filter, value in other.items():\\n self.__setitem__(filter, value)\",\n \"def update(self):\\n for filter in self.filters:\\n filter.update(self.learning_rate)\",\n \"def update(self, updates, predicate):\\n for row in self.rows:\\n if predicate(row):\\n for column, new_value in updates.items():\\n row[column] = new_value\",\n \"def filter(self):\\n self.filter_means = [self.m_0]\\n self.filter_covs = [self.P_0]\\n self.marginal_covs = []\\n for t in range(self.data.shape[0]):\\n m_bar, P_bar = self.one_step_prediction(self.filter_means[-1], self.filter_covs[-1])\\n\\n # Update step\\n y = self.data[t]\\n if not np.isnan(y).any():\\n v = y[:, None] - self.observation_matrix @ m_bar\\n S = self.observation_matrix @ P_bar @ self.observation_matrix.T + self.observation_cov\\n K = P_bar @ self.observation_matrix.T @ np.linalg.inv(S)\\n\\n m_bar = m_bar + K @ v\\n P_bar = P_bar - K @ S @ K.T\\n\\n self.marginal_covs.append(S)\\n\\n self.filter_means.append(m_bar)\\n self.filter_covs.append(P_bar)\\n self.filter_means = self.filter_means[1:]\\n self.filter_covs = self.filter_covs[1:]\",\n \"def filter_values(self):\\n dfilter = self.args.datafilter\\n self.logger.info(u'Filtering values with:{f}'.format(f=dfilter))\\n data = self.outputdata\\n newdata = {}\\n for key, value in data.items():\\n self.logger.info(u'\\\\nProcessing Key:{k}, value:{v}'.format(k=key,\\n v=value))\\n returned_data = dict_value_filter(key, value, dfilter, self.logger)\\n if bool(returned_data):\\n newdata[key] = returned_data\\n self.logger.info(u'Data after filter:{d}'.format(d=newdata))\\n\\n self.outputdata = newdata\",\n \"def UpdateSet(self, dataset):\\r\\n for data in dataset:\\r\\n self.UpdateOddsRatioVsNoNorm(data)\",\n \"def filter_update(observations, observation_matrix, observation_variance,\\n observation_indices, observation_count,\\n state_mean, state_covariance):\\n\\n sigma = 0.\\n detf = 0.\\n n_observation = np.int(observation_count)\\n for i in range(n_observation):\\n observation_index = int(observation_indices[i])\\n obsmat = observation_matrix[observation_index, :]\\n innovation = (observations[observation_index]\\n - np.dot(obsmat, state_mean))\\n dot_statecov_obsmat = np.dot(state_covariance, obsmat)\\n innovation_covariance = (np.dot(obsmat, dot_statecov_obsmat)\\n + observation_variance[observation_index])\\n kgain = dot_statecov_obsmat / innovation_covariance\\n state_covariance = (state_covariance\\n - (np.outer(kgain, kgain)\\n * innovation_covariance))\\n\\n state_mean = state_mean + kgain * innovation\\n\\n sigma = sigma + (innovation ** 2 / innovation_covariance)\\n detf = detf + np.log(innovation_covariance)\\n\\n return (state_mean, state_covariance, sigma, detf)\",\n \"def update_filter(self, measurement, robot_pos):\\n new_weights = []\\n for p in self.particles:\\n p = self.__move(p, self.MOTION)\\n angle = self.__measurement(p, robot_pos)\\n prob = self.__measurement_prob(angle, measurement,\\n self.sense_noise)\\n new_weights.append(prob)\\n new_weights = np.array(new_weights)\\n new_weights /= np.sum(new_weights) # normalized weights\\n self.weights = new_weights\\n\\n # if self.__neff() > self.n / 2:\\n self.particles = self.__resample()\",\n \"def set_observations(self, factor):\\r\\n relevant_obs = set(self.obs).intersection(set(factor.get_variables()))\\r\\n if relevant_obs:\\r\\n factor.set_observations({x:self.obs[x] for x in relevant_obs})\",\n \"def filter(self, update):\\n\\n raise NotImplementedError\",\n \"def ObserveEvidence(self,E):\\r\\n for v,e in E.iteritems():\\r\\n indx, = (self.var==v).nonzero() # find i where f.var[i]=v\\r\\n if indx.size != 0:\\r\\n A = I2A(range(len(self.val)), self.card) # all assignments\\r\\n A = A[:,indx].flatten() # only interested in 'indx' element of each row\\r\\n self.val[A != e] = 0\",\n \"def update(self, predictions, real_vals):\\n for pred, actual in zip(predictions, real_vals):\\n self.cm.loc[actual, pred] += 1\",\n \"def filter(self, observable):\",\n \"def filter_data(self):\\n self.data = filter_pandas(self.data, self.filters)\",\n \"def update_values(self):\\n # have to reset params to 0 when recalculating\\n self.total_weight = self.total_price = self.total_value = self.total_fitness = 0\\n for index, value in enumerate(self.item_stats):\\n if value == 1:\\n self.total_weight += self.items[index].weight\\n self.total_price += self.items[index].price\\n self.total_value += self.items[index].value\\n self.total_fitness += self.items[index].fitness\",\n \"def update(self, z):\\n raise NotImplementedError('Must implement an update step for the filter.')\",\n \"def run_batch_filter(self):\\n if self.speed_data is None and self.flow_data is None and self.traveltime_data is None:\\n print(\\n 'Warning: The measurement data must be set before running the batch filter: use function self.set_meas_data()')\\n\\n # =======================================================================\\n # the initial ensembles, which should have been set externally\\n X_init = np.matrix(np.zeros((self.dim_state, self.num_ensembles)))\\n print(\\n 'Setting initial ensembles: rho {0}; qin {1}; qout {2}'.format(self.init_rho, self.init_qin, self.init_qout))\\n for ens in range(0, self.num_ensembles):\\n X_init[self.x_index['density'][0]:\\n self.x_index['density'][self.num_cells - 1], ens] = self.init_rho\\n X_init[self.x_index['qin'], ens] = self.init_qin\\n X_init[self.x_index['qout'], ens] = self.init_qout\\n\\n # print('setted qin {0}; qout {1}'.format(X_init[self.x_index['qin'], ens], X_init[self.x_index['qout'], ens] ))\\n # add noise to each ensemble\\n X_init[:, ens] += np.matrix(np.random.multivariate_normal(\\n np.zeros(self.dim_state), self.Q)).reshape((self.dim_state, 1))\\n\\n self.set_initial_ensembles(X_init)\\n\\n # =======================================================================\\n # DEBUG\\n # save the qin and qout in the corresponding probe data\\n # save the initial state\\n if self.__debug:\\n self.qin_f.append(np.squeeze(np.array(self.X_f[self.x_index['qin'], :])).tolist())\\n self.qin_a.append(np.squeeze(np.array(self.X_a[self.x_index['qin'], :])).tolist())\\n self.qin_obs.append(np.nan)\\n\\n self.qout_f.append(np.squeeze(np.array(self.X_f[self.x_index['qout'], :])).tolist())\\n self.qout_a.append(np.squeeze(np.array(self.X_a[self.x_index['qout'], :])).tolist())\\n self.qout_obs.append(np.nan)\\n\\n # The enKF runs at the finest time grid\\n # for each step, update the system\\n for step in range(0, self.num_steps):\\n\\n # update status\\n sys.stdout.write('\\\\r')\\n sys.stdout.write('Status: filtering step {0}/{1}'.format(step, self.num_steps))\\n sys.stdout.flush()\\n # print('Status: filtering step {0}'.format(step))\\n\\n cur_time = (step + 1) * self.dur_steps\\n\\n # get the effective measurement\\n eff_flow, eff_speed, eff_traveltime = self.__get_eff_meas(cur_time)\\n\\n # build the observation index\\n self.y_index, self.dim_obs, y_obs, cov_noise = self.__build_obs_index(eff_flow, eff_speed, eff_traveltime)\\n\\n # update the estimate for this step\\n est_state = self.update_estimate(y_obs, cov_noise, cur_time)\\n\\n # =======================================================================\\n # DEBUG\\n # save the qin and qout in the corresponding probe data\\n # save the updated state\\n if self.__debug:\\n self.qin_f.append(np.squeeze(np.array(self.X_f[self.x_index['qin'], :])).tolist())\\n self.qin_a.append(np.squeeze(np.array(self.X_a[self.x_index['qin'], :])).tolist())\\n if 'flow' in self.y_index.keys() and self.__debug_entrance_sensor in self.y_index['flow'].keys():\\n self.qin_obs.append(y_obs[self.y_index['flow'][self.__debug_entrance_sensor]])\\n # print('y_index[flow]:{0}'.format(self.y_index['flow'].keys()))\\n # print('y_obs[ y_index[flow][entrance] ]:{0}'.format(\\n # y_obs[ self.y_index['flow'][self.__debug_entrance_sensor]],\\n # self.__debug_entrance_sensor))\\n else:\\n self.qin_obs.append(np.nan)\\n\\n self.qout_f.append(np.squeeze(np.array(self.X_f[self.x_index['qout'], :])).tolist())\\n self.qout_a.append(np.squeeze(np.array(self.X_a[self.x_index['qout'], :])).tolist())\\n if 'flow' in self.y_index.keys() and self.__debug_exit_sensor in self.y_index['flow'].keys():\\n self.qout_obs.append(y_obs[self.y_index['flow'][self.__debug_exit_sensor]])\\n else:\\n self.qout_obs.append(np.nan)\\n # =======================================================================\\n # save the estimated state\\n self.est_state_all[:, step] = est_state\\n\\n # decouple and save into self.est_density, self.est_speed, self.est_queue, self.est_traveltime\\n self.est_density[:, step] = est_state[0:self.num_cells, 0]\\n\\n # the speed is computed using the fundamental diagram\\n for cell_id in range(0, self.num_cells):\\n # use the static FD at this step\\n self.est_speed[cell_id, step] = self.__rho2v(self.vm_cells[cell_id, 0], self.beta_cells[cell_id, 0],\\n self.rhoc_cells[cell_id, 0], self.wc_cells[cell_id, 0],\\n self.est_density[cell_id, step])\\n\\n # REMARK: the queue and travel time a post-processed from the speed field.\\n # They are computed in cross_evaluation class for all algorithms\\n # the queue length starts from the first cell with speed below queue_threshold to the end of road\\n # index = (self.est_speed[:, step] <= self.queue_threshold)\\n #\\n # # filter out the outliers\\n # index_smoothed = deepcopy(index)\\n # outlier_max = 3\\n # counter = 0\\n # for i in range(0, len(index)):\\n #\\n # if index[i] == True:\\n # # trigger the coutner\\n # counter += 1\\n # elif index[i] == False and counter != 0:\\n # if counter <= outlier_max:\\n # # found outliers\\n # index_smoothed[ i-counter : i ] = False\\n # # reset counter\\n # counter = 0\\n #\\n # # if i != 0 and i != len(index)-1:\\n # # if sum( index[i-1:i+3] ) >=2:\\n # # index_smoothed[i] = True\\n # # else:\\n # # index_smoothed[i] = False\\n # # elif i == 0:\\n # # if sum(index[0: 5] ) >= 3:\\n # # index_smoothed[i] = True\\n # # else:\\n # # index_smoothed[i] = False\\n # # elif i == len(index)-1:\\n # # if sum(index[ i-4 :len(index)]) >= 3:\\n # # index_smoothed[i] = True\\n # # else:\\n # # index_smoothed[i] = False\\n #\\n # if sum(index_smoothed) <= 3: # use 4 to suppress false alarms\\n # # if less or equal then 2 cells are in congestion, it may be caused by noise.\\n # self.est_queue[step] = 0\\n # else:\\n # # if step > 105 and step < 115:\\n # # print(sum(index_smoothed))\\n # # print(index_smoothed)\\n # # print(index)\\n #\\n # self.est_queue[step] = \\\\\\n # self.len_cells*( self.num_cells - np.argmax(index_smoothed) )\\n # # try:\\n # # first_cong_cell_id = [x[0] for x in enumerate( self.est_speed[:,step] ) if x[1] < self.queue_threshold][0]\\n # # except IndexError:\\n # # # no congested cell\\n # # first_cong_cell_id = self.num_cells\\n # # # the estimated queue length\\n # # self.est_queue[step] = self.len_cells*( self.num_cells - first_cong_cell_id )\\n #\\n # # the travel time estimate is computed by summing up the travel time in each cell\\n # self.est_traveltime[step] = np.sum(self.len_cells/self.est_speed[:,step])\\n\\n\\n # =======================================================================\\n # DEBUG\\n # plot the update\\n if self.__debug:\\n plot_len = 19\\n # qin\\n if False:\\n if not np.isnan(self.qin_obs[-1]):\\n fig1 = plt.figure(figsize=(10, 5), dpi=100)\\n ax1 = fig1.add_subplot(111)\\n positions_f = np.arange(0, len(self.qin_f)) - 0.1\\n positions_a = np.arange(0, len(self.qin_a)) + 0.1\\n positions_obs = np.arange(0, len(self.qin_obs))\\n # predicted as red\\n bp = ax1.boxplot(self.qin_f[-plot_len:],\\n positions=positions_f[-plot_len:], widths=0.15,\\n patch_artist=False)\\n for box in bp['boxes']:\\n # change outline color\\n box.set(color='#FF4633', linewidth=1)\\n # change fill color\\n # box.set( facecolor = '#FF4633' )\\n # corrected as green\\n bp = ax1.boxplot(self.qin_a[-plot_len:],\\n positions=positions_a[-plot_len:], widths=0.15, patch_artist=False)\\n for box in bp['boxes']:\\n # change outline color\\n box.set(color='#07891B', linewidth=1)\\n # change fill color\\n # box.set( facecolor = '#07891B' )\\n # measurement as blue\\n ax1.scatter(positions_obs[-plot_len:], self.qin_obs[-plot_len:], color='b', marker='o', s=40,\\n label='Observation')\\n ax1.set_title('qin')\\n # x_ticks = np.arange(0, len(self.qin_f))\\n # ax1.set_xticks(x_ticks[-plot_len:])\\n plt.show()\\n\\n # qout\\n if False:\\n if not np.isnan(self.qout_obs[-1]):\\n fig2 = plt.figure(figsize=(10, 5), dpi=100)\\n ax2 = fig2.add_subplot(111)\\n positions_f = np.arange(0, len(self.qout_f)) - 0.1\\n positions_a = np.arange(0, len(self.qout_a)) + 0.1\\n positions_obs = np.arange(0, len(self.qout_obs))\\n # predicted as red\\n bp = ax2.boxplot(self.qout_f[-plot_len:], positions=positions_f[-plot_len:], widths=0.18,\\n patch_artist=True)\\n for box in bp['boxes']:\\n # change outline color\\n box.set(color='#7570b3', linewidth=1)\\n # change fill color\\n box.set(facecolor='#FF4633')\\n # corrected as green\\n bp = ax2.boxplot(self.qout_a[-plot_len:], positions=positions_a[-plot_len:], widths=0.18,\\n patch_artist=True)\\n for box in bp['boxes']:\\n # change outline color\\n box.set(color='#7570b3', linewidth=1)\\n # change fill color\\n box.set(facecolor='#07891B')\\n # measurement as blue\\n ax2.scatter(positions_obs[-plot_len:], self.qout_obs[-plot_len:], color='b', marker='o', s=30,\\n label='Observation')\\n ax2.set_title('qout')\\n # x_ticks = np.arange(0, len(self.qout_f))\\n # ax2.set_xticks(x_ticks[-plot_len:])\\n\\n plt.show()\\n\\n # plot the estimated qin and qout\\n if self.__debug:\\n if True:\\n qin = np.squeeze(np.array(self.est_state_all[self.x_index['qin'], :]))\\n qin_meas = np.array(self.qin_obs)[1:]\\n print(len(qin), len(qin_meas))\\n fig1 = plt.figure(figsize=(10, 5), dpi=100)\\n ax1 = fig1.add_subplot(111)\\n t = np.arange(len(qin))\\n ax1.plot(t, qin, 'r-', label='Estimated')\\n not_nan = ~np.isnan(qin_meas)\\n ax1.plot(t[not_nan], qin_meas[not_nan], 'b', label='Measured')\\n ax1.legend()\\n ax1.grid(True)\\n ax1.set_title('qin')\\n\\n plt.draw()\\n\\n if True:\\n qout = np.squeeze(np.array(self.est_state_all[self.x_index['qout'], :]))\\n qout_meas = np.array(self.qout_obs)[1:]\\n fig2 = plt.figure(figsize=(10, 5), dpi=100)\\n ax2 = fig2.add_subplot(111)\\n t = np.arange(len(qout))\\n ax2.plot(t, qout, 'r-', label='Estimated')\\n not_nan = ~np.isnan(qout_meas)\\n ax2.plot(t[not_nan], qout_meas[not_nan], 'b', label='Measured')\\n ax2.set_title('qout')\\n ax2.legend()\\n ax2.grid(True)\\n plt.draw()\",\n \"def _update_edges_filtered(self, change=None):\\n self._edges_filter.val_range = self.edges_range\\n edges_ids = self._edges_filter.val_ids\\n self.edges_ids = self._filter_edges(edges_ids)\",\n \"def update(self, obs, q):\\n for o in obs:\\n prob = np.exp(-70)\\n if self.landmarks:\\n # find the data association with ML\\n prob, landmark_idx, ass_obs, ass_jacobian, ass_adjcov = self.find_data_association(o)\\n if prob < self.TOL:\\n # create new landmark\\n self.create_landmark(o)\\n else:\\n # update corresponding EKF\\n self.update_landmark(np.transpose(np.array([o])), landmark_idx, ass_obs, ass_jacobian, ass_adjcov)\\n else:\\n # no initial landmarks\\n self.create_landmark(o)\\n self.weight *= prob\\n \\n q.put([self]) ###\",\n \"def update_filter_params(self, fh):\\n (self.data_timestamp, self.framerate,\\n self.l, self.d, self.gamma,\\n self.eps, self.alex, self.traceswitch) = (fh.attrs['data_timestamp'], fh.attrs['framerate'],\\n fh.attrs['l'], fh.attrs['d'], fh.attrs['gamma'],\\n fh.attrs['eps'], fh.attrs['alex'], fh.attrs['traceswitch'])\",\n \"def after_each(self, dataset: pydicom.dataset.Dataset) -> None:\\r\\n for a_filter in self.filters[::-1]:\\r\\n a_filter.after_each(dataset)\",\n \"def update_filters(self):\\n\\n # Update household filter\\n household_filter = [True if agent == 'household' else False for agent \\\\\\n in self.source.data['agent_type']]\\n self.household_view.filters[0] = BooleanFilter(household_filter)\\n\\n # Update neighbourhood filter\\n neighbourhood_filter = [True if agent == 'neighbourhood' else False for\\\\\\n agent in self.source.data['agent_type']]\\n self.neighbourhood_view.filters[0] = BooleanFilter(\\n neighbourhood_filter)\\n\\n # Update school filter\\n school_filter = [True if agent == 'school' else False for agent in \\\\\\n self.source.data['agent_type']]\\n self.school_view.filters[0] = BooleanFilter(school_filter)\",\n \"def __setitem__(self, query_filter, value):\\n saved_items = []\\n for index, query in enumerate(self.__bound_queries):\\n saved_items.append(query.get(query_filter, None))\\n try:\\n query[query_filter] = value\\n except:\\n for q, old_value in itertools.izip(self.__bound_queries[:index],\\n saved_items):\\n if old_value is not None:\\n q[query_filter] = old_value\\n else:\\n del q[query_filter]\\n raise\",\n \"def apply_fn(self,fn):\\r\\n \\r\\n self.check_Data()\\r\\n for split,data_ in self.processed_data.items():\\r\\n x = data_['x']\\r\\n x = np.array([fn(xi) for xi in x])\\r\\n data_['x'] = x\",\n \"def update(self, iteration):\\n pass\",\n \"def before_each(self, dataset: pydicom.dataset.Dataset) -> None:\\r\\n for a_filter in self.filters:\\r\\n a_filter.before_each(dataset)\",\n \"def update_cards(self, filterdict, values, multiple=True):\\n\\n self._collection.update(self._constrain_keys(filterdict),\\n {'$set': self._constrain_keys(values)},\\n multi=multiple)\",\n \"def fill_obs(self, observation_table, data_store):\\n for obs in observation_table:\\n events = data_store.obs(obs_id=obs['OBS_ID']).events\\n\\n # TODO: filter out (mask) possible sources in the data\\n # for now, the observation table should not contain any\\n # run at or near an existing source\\n\\n self.counts_cube.fill_events([events])\\n self.livetime_cube.data += events.observation_live_time_duration\",\n \"def reset_filter(self):\\n arlen = len(self.variant_list)\\n self.filter = np.zeros((arlen, arlen)) == 0\",\n \"def filter(self, *args):\\n from .elements import EqualClauseElement\\n for a in args:\\n for c in self._criterion:\\n if isinstance(c, EqualClauseElement) and isinstance(a, EqualClauseElement) and \\\\\\n c.attribute.node == a.attribute.node and c.attribute.label == a.attribute.label:\\n c.value = a.value\\n break\\n else:\\n self._criterion.append(a)\\n return self\",\n \"def apply_filters(self, filters):\\n self._data = self.model.objects.filter(**filters)\",\n \"def process_dataset(dataset, func):\\n new_dataset = copy.copy(dataset)\\n del new_dataset[\\\"val\\\"]\\n new_dataset.update(func(dataset))\\n return new_dataset\",\n \"def update(self, observed, axis):\\n\\n # -------------------------------------------------------------------------\\n # YOUR CODE GOES HERE\\n #\\n total = 0\\n \\n # zero out irrelevant data and calculate total for observed sample\\n for each in self._table:\\n if (axis == 1 and each[1] != observed) or (axis == 0 and each[0] != observed):\\n self._table[each] = 0\\n else:\\n total += self._table[each]\\n \\n # normalize the revelant data\\n for each in self._table:\\n if (axis == 1 and each[1] == observed) or (axis == 0 and each[0] == observed):\\n self._table[each] = self._table[each] / total\\n\\n #\\n # END OF YOUR CODE\\n # ------------------------------------------------------------------------- \",\n \"def _modify_updates(self, updates):\\n wxf = self.wxf\\n wyf = self.wyf\\n wxf_updated = updates[wxf]\\n wyf_updated = updates[wyf]\\n nwxf = (wxf_updated.std(0) + SMALL)[numpy.newaxis, :]\\n nwyf = (wyf_updated.std(0) + SMALL)[numpy.newaxis, :]\\n meannxf = nwxf.mean()\\n meannyf = nwyf.mean()\\n # Center filters\\n centered_wxf = wxf_updated - wxf_updated.mean(0)\\n centered_wyf = wyf_updated - wyf_updated.mean(0)\\n # Fix standard deviation\\n wxf_updated = centered_wxf * (meannxf / nwxf)\\n wyf_updated = centered_wyf * (meannyf / nwyf)\\n updates[wxf] = wxf_updated\\n updates[wyf] = wyf_updated\",\n \"def apply_filters(self):\\n hurst_cut = 0\\n coint_cut = 0\\n half_life_cut = 0\\n mean_cross_cut = 0\\n\\n # Create an empty list for pairs that pass the filter tests\\n validated_pairs = []\\n\\n # Create all the pairs combination\\n self.create_pair_differences()\\n\\n # Print the number of potential pairs\\n print(f\\\"Number of potential pairs in before filter: {len(self.__pairs_data)}\\\")\\n\\n for pair in self.__pairs_data:\\n # Select the stocks from the pair\\n stock1 = pair[0]\\n stock2 = pair[1]\\n\\n # Test the hurst filter\\n if self.hurst_filter(self, stock1=stock1, stock2=stock2):\\n hurst_cut += 1\\n if self.engel_filter(self, stock1=stock1, stock2=stock2):\\n coint_cut += 1\\n if self.half_life_filter(self, stock1=stock1, stock2=stock2):\\n half_life_cut += 1\\n if self.mean_cross_filter(self, stock1=stock1, stock2=stock2):\\n mean_cross_cut += 1\\n validated_pairs.append([stock1, stock2])\\n\\n print(f\\\"Hurst filter pass: {hurst_cut}\\\")\\n print(f\\\"Co-integration filter pass: {coint_cut}\\\")\\n print(f\\\"Half-life filter pass: {half_life_cut}\\\")\\n print(f\\\"Mean-cross filter pass: {mean_cross_cut}\\\")\\n print(f\\\"Final Number of validated pairs: {len(validated_pairs)}\\\")\\n print(\\\"The final validated pairs are: \\\")\\n print(validated_pairs)\\n\\n # Save it to the attribute\\n self.__validated_pairs = validated_pairs\\n self.__validated_pairs_diff = self.__pair_diff[self.symbolize_pairs(self.__validated_pairs)]\",\n \"def test_updating_multiple_records_through_filter_with_arg_value(self, test_domain):\\n identifier1 = uuid4()\\n identifier2 = uuid4()\\n identifier3 = uuid4()\\n identifier4 = uuid4()\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier1, first_name=\\\"Athos\\\", last_name=\\\"Musketeer\\\", age=2\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier2, first_name=\\\"Porthos\\\", last_name=\\\"Musketeer\\\", age=3\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier3, first_name=\\\"Aramis\\\", last_name=\\\"Musketeer\\\", age=4\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier4, first_name=\\\"dArtagnan\\\", last_name=\\\"Musketeer\\\", age=5\\n )\\n\\n # Perform update\\n updated_count = (\\n test_domain.repository_for(Person)\\n ._dao.query.filter(age__gt=3)\\n .update_all({\\\"last_name\\\": \\\"Fraud\\\"})\\n )\\n\\n # Query and check if only the relevant records have been updated\\n assert updated_count == 2\\n\\n u_person1 = test_domain.repository_for(Person)._dao.get(identifier1)\\n u_person2 = test_domain.repository_for(Person)._dao.get(identifier2)\\n u_person3 = test_domain.repository_for(Person)._dao.get(identifier3)\\n u_person4 = test_domain.repository_for(Person)._dao.get(identifier4)\\n assert u_person1.last_name == \\\"Musketeer\\\"\\n assert u_person2.last_name == \\\"Musketeer\\\"\\n assert u_person3.last_name == \\\"Fraud\\\"\\n assert u_person4.last_name == \\\"Fraud\\\"\",\n \"def _mutate(self, individuals):\\n for cur in individuals:\\n if random.random() < self.mutation_probability:\\n self.op.mutate(cur['individual'])\\n cur['fitness'] = None\",\n \"def _update_all(self, criteria: Q, *args, **kwargs):\\n raise NotImplementedError\",\n \"def updateIntensities (self,listAtoms):\\r\\n \\r\\n for i in range(len(listAtoms)):\\r\\n for j in range(len(listAtoms[i].spikeArray)):\\r\\n self.mol[i].spikeArray[j].intensity = listAtoms[i].spikeArray[j].intensity\",\n \"def updateFitness(self):\\r\\n for candidate in self.candidates:\\r\\n candidate.updateFitness()\\r\\n return\",\n \"def resample(self):\\n self.particles = ParticleFilter.weighted_values(self.particles,\\n [p.weight for p in self.particles],\\n len(self.particles))\\n for p in self.particles:\\n p.weight = 1./len(self.particles)\",\n \"def update_all(self, stream: Iterator[Mapping[str, np.ndarray]]) -> Self:\\n\\n for element in stream:\\n self.update(element)\\n\\n return self\",\n \"def update_all(self, stream: Iterator[Mapping[str, np.ndarray]]) -> Self:\\n\\n for element in stream:\\n self.update(element)\\n\\n return self\",\n \"def filter(self, observations):\\n\\n (_, _, _, x_filtered, P_filtered) = filter(self.F, self.Q, self.H, self.R, self.x_0, self.P_0, observations)\\n return x_filtered, P_filtered\",\n \"def filter_(self,fltr:torch.tensor):\\n self.container = self.container[:,fltr]\\n self.count_hist = self.count_hist[fltr]\",\n \"def update_individuals(individuals, eval_results):\\n for ind, res in zip(individuals, eval_results):\\n ind.fitness.values = res[0]\\n ind.matching_node_pairs = res[1]\\n ind.gtp_precisions = res[2]\",\n \"def _update_step(self, *, observations: types.ObservationsTorch) -> None:\",\n \"def _applyFilters(self) -> None:\\n self._dataframe = self._source.loc[:, self._visable_columns]\\n for column, value in self._filters.items():\\n if value is not None:\\n self._dataframe = self._dataframe[self._source[column] == value]\\n else:\\n self._dataframe = self._dataframe[self._source[column].isnull()]\\n\\n self.layoutChanged.emit()\",\n \"def filter(self, filters):\",\n \"def filter(self, filter_dict):\\n pass\",\n \"def update_values(self):\\n for key in self.inputs.keys():\\n value = self.inputs[key]['entry'].get()\\n self.inputs[key]['value'] = value\",\n \"def prefilter(self, filt=None, verbose=False):\\n erased = []\\n if verbose:\\n msg = 'Prior to filter, we have {} cells.'.format(len(self.cells))\\n print(msg)\\n # check for supplementary observables to be computed\\n raw_obs, func_obs = set_observable_list(filters=[filt, ])\\n\\n # compute suppl obs for all cells\\n if raw_obs:\\n for cell in self.cells:\\n for obs in raw_obs:\\n cell.build(obs)\\n for cell in self.cells:\\n if filt is not None:\\n if not filt(cell):\\n erased.append(cell)\\n # make Colony.add_cell_recursive non functional\\n cell.bpointer = None\\n if cell.parent:\\n cell.parent.childs.remove(cell)\\n # make daughter cells new roots\\n for ch in cell.childs:\\n ch.bpointer = None\\n if verbose:\\n msg = '{} cells do not pass filter.'.format(len(erased))\\n print(msg)\\n for cell in erased:\\n self.cells.remove(cell) # otherwise would be considered root\\n # clean-up actions for computing extra obs\\n # extra obs computation depends on tree decomposition\\n # this will be done in lineage.get_timeseries()\\n for cell in self.cells:\\n for obs in raw_obs:\\n del cell._sdata[obs.label]\\n if verbose:\\n msg = 'After filtering, we get {} cells.'.format(len(self.cells))\\n print(msg)\\n# self.metadata.filters.append(repr(boofunc))\\n return\",\n \"def run(self, threshold=1e-3, max_iters=100):\\n for msr in self.msrs:\\n # find state transition matrix and propagate state\\n phi_p, state_prop = self._compute_stm(msr.time, self.phis[-1])\\n\\n # compute observation deviation, obs_state matrix\\n y_i, h_tilde = self._msr_resid(msr, state_prop)\\n\\n # update information and N\\n self._update_info_and_n(y_i, h_tilde, phi_p, msr.cov)\\n\\n # add everything to the appropriate lists\\n #new_cov = np.matmul(phi_p, np.matmul(self.cov_list[-1], phi_p))\\n #self.cov_list.append(new_cov)\\n self.prop_state_list.append(state_prop)\\n self.estimates.append(state_prop)\\n self.phis.append(phi_p)\\n self.times.append(msr.time)\\n\\n # compute correction\\n self.iters += 1\\n x_hat_0 = np.linalg.solve(self.fisher_info[-1], self.N[-1])[0]\\n\\n # check for convergence\\n if np.linalg.norm(x_hat_0) <= threshold:\\n print(\\\"Batch filter converged in {} Iterations\\\".format(self.iters))\\n self.cov_batch = np.linalg.inv(self.fisher_info[-1])\\n\\n elif self.iters >= max_iters:\\n raise StopIteration(\\\"max_iters: {} reached without convergence\\\".format(max_iters))\\n\\n else:\\n # reset everything and try again\\n updated_istate = np.add(self.prop_state_list[0], np.transpose(x_hat_0))\\n # fixes a strange bug wher the size of updated istate was changing\\n updated_istate = np.resize(updated_istate, (1, len(self.istate)))[0]\\n\\n self.prop_state_list = [updated_istate]\\n #self.cov_list = [self.apriori]\\n self.fisher_info = [self.fisher_info[0]]\\n self.pert_vec = np.subtract(self.pert_vec, x_hat_0)\\n self.N = [np.matmul(self.apriori, self.pert_vec)]\\n self.phis = [self.phis[0]]\\n self.estimates = [updated_istate]\\n self.times = [0]\\n self.run()\",\n \"def _UpdateDataSetValues( self ):\\n pass\",\n \"def set_filter_for_eval(self, x_filter):\\n self.x_filter = x_filter\\n\\n entity_size = len(self.ent_to_idx)\\n reln_size = len(self.rel_to_idx)\\n\\n first_million_primes_list = []\\n curr_dir, _ = os.path.split(__file__)\\n with open(os.path.join(curr_dir, \\\"prime_number_list.txt\\\"), \\\"r\\\") as f:\\n logger.debug('Reading from prime_number_list.txt.')\\n line = f.readline()\\n i = 0\\n for line in f:\\n p_nums_line = line.split(' ')\\n first_million_primes_list.extend([np.int64(x) for x in p_nums_line if x != '' and x != '\\\\n'])\\n if len(first_million_primes_list) > (2 * entity_size + reln_size):\\n break\\n\\n # subject\\n self.entity_primes_left = first_million_primes_list[:entity_size]\\n # obj\\n self.entity_primes_right = first_million_primes_list[entity_size:2 * entity_size]\\n # reln\\n self.relation_primes = first_million_primes_list[2 * entity_size:(2 * entity_size + reln_size)]\\n\\n self.filter_keys = []\\n # subject\\n self.filter_keys = [self.entity_primes_left[self.x_filter[i, 0]] for i in range(self.x_filter.shape[0])]\\n # obj\\n self.filter_keys = [self.filter_keys[i] * self.entity_primes_right[self.x_filter[i, 2]]\\n for i in range(self.x_filter.shape[0])]\\n # reln\\n self.filter_keys = [self.filter_keys[i] * self.relation_primes[self.x_filter[i, 1]]\\n for i in range(self.x_filter.shape[0])]\\n\\n self.is_filtered = True\",\n \"def test_feature_is_filtered(self):\\n\\n # Duplicate 1st row in var and assigned to 2nd\\n self.validator.adata.var[\\\"feature_is_filtered\\\"][0] = True\\n for i in range(self.validator.adata.X.shape[0]):\\n self.validator.adata.X[i, 0] = 0\\n self.validator.adata.X[0, 0] = 1\\n\\n self.validator.validate_adata()\\n self.assertEqual(\\n self.validator.errors,\\n [\\n \\\"ERROR: Some features are 'True' in 'feature_is_filtered' of dataframe 'var', \\\"\\n \\\"but there are 1 non-zero values in the corresponding columns of the matrix 'X'. \\\"\\n \\\"All values for these features must be 0.\\\"\\n ],\\n )\",\n \"def particle_filter(particle_set_t, measurement_t):\\n global count\\n n_samples, dim = particle_set_t.shape # no of particles and dimension of each particle\\n\\n pred_state = np.zeros((n_samples, dim), dtype=\\\"float64\\\") # store the predicted state \\n weights = np.zeros(n_samples, dtype=\\\"float64\\\") # corresponding weights for resampling\\n\\n particle_set_t1 = np.zeros((n_samples, dim), dtype=\\\"float64\\\") # next iteration of particles\\n\\n\\n # this loop calculates \\\\bar{X_t}, i.e. the predicted belief.\\n for n in range(n_samples):\\n # predicted motion step:\\n xn_t1 = sample_motion_model(particle_set_t[n]) # 3x1 vector: hypothetical state\\n\\n # measurement correction step:\\n weight_xn_t1 = state_likelihood(measurement_t, xn_t1) # scalar value\\n\\n pred_state[n] = xn_t1\\n weights[n] = weight_xn_t1\\n\\n \\n # It was observed that if all weights are 0, the resampling step breaks. \\n # Thus, adding a uniform distribution. This is obviously a very bad idea \\\\ \\n # as the true state can easily be discarded in the resampling step: TODO!\\n if np.sum(weights) > 0.0:\\n weights = weights/np.sum(weights) # normalize array only when sum in not 0\\n else:\\n print(\\\"possile divergence!\\\")\\n weights[:] = 1 / n_samples # if sum is 0 then assign uniform distribution throughout\\n\\n\\n # the resampling step:\\n # indices = monte_carlo.residual_resample(weights)\\n indices = monte_carlo.stratified_resample(weights)\\n count += 1\\n print(count)\\n\\n # new particle set is particles at index locations\\n for i, index in enumerate(indices):\\n particle_set_t1[i] = pred_state[index]\\n\\n return particle_set_t1\",\n \"def filter(self,state0):\\n ok,tchi2 = True,0.\\n state = state0.copy()\\n ii = 0\\n for node in self.nodes:\\n zrun = node.zrun\\n ok,state,F,Q = self.model.propagate(state,zrun)\\n if (not ok):\\n warning(\\\"kfilter.filter not possible to filter at \\\",(ii,zrun))\\n debug(\\\"kfilter.filter i,ok,chi2 \\\",(ii,ok,tchi2))\\n return ok,tchi2\\n node.F = F\\n node.Q = Q\\n node.setstate('pred',state)\\n fstate,fchi2 = node.predict(state)\\n node.setstate('filter',fstate)\\n node.setchi2('filter',fchi2)\\n tchi2+=fchi2\\n self.model.user_filter(node)\\n state = node.getstate('filter').copy()\\n ii+=1\\n self.status='filter'\\n debug(\\\"kfilter.filter ok,chi2 \\\",(ok,tchi2))\\n return ok,tchi2\",\n \"def Filter(self,val):\\n \\n #set th elength of the lis to 0\\n List = [self.InitialList[i] for i in range(0,len(self.InitialList))]\\n FilterValues = [None]\\n Grab = [None]\\n Headers = []\\n \\n #create the quick index\\n for i in range(len(self.Condensensed)):\\n \\n Headers.append([self.Condensensed[i][l][0] for l in range(len(self.Condensensed[i]))])\\n \\n #grab the values...\\n for j in range(len(self.Variables)):\\n \\n FilterValues.append(self.Variables[j].get())\\n\\n if self.Variables[j].get().split(' ')[0] == 'All':\\n \\n Grab.append(False)\\n \\n else:\\n \\n Grab.append(True)\\n \\n #intermediate list to compare\\n ToCompare = []\\n \\n for i in range(1,len(Grab)):\\n \\n if Grab[i]:\\n \\n #find the index\\n l = Headers[i].index(FilterValues[i])\\n \\n #grab it\\n ToCompare.append([self.Condensensed[i][l][m] for m in range(len(self.Condensensed[i][l]))])\\n\\n\\n for i in range(0, len(ToCompare)):\\n \\n List = list(set(List).intersection(ToCompare[i]))\\n\\n #update the interface\\n self.Gatherer(List,list(self.Input))\\n self.BuildTree()\",\n \"def filter_data(self):\\n if(self.filter_classes == []):\\n return\\n \\n filtered_idx = []\\n for id in range(len(self.image_ids)):\\n anns = self.load_annotations(id)\\n found = False\\n for ann in anns:\\n if ann['label'] in self.filter_classes:\\n found = True\\n break\\n if found:\\n filtered_idx.append(id)\\n \\n self.filtered_ids = [self.image_ids[id] for id in filtered_idx]\\n # self.image_ids = self.filtered_ids\\n print(\\\"Number of filtered instances:\\\", len(self.filtered_ids))\",\n \"def update(self, **kwargs):\\n for name, item in itertools.chain(\\n self._cal_objs.items(),\\n self._noise_objs.items()):\\n logger.debug(\\\"update {}\\\".format(item))\\n item.update(**kwargs)\",\n \"def train_filter(filt, U, Y):\\n n = len(U)\\n X = []\\n R = []\\n for i in range(n):\\n x_hat_bar = filt.time_update(U[i])\\n x_hat, P = filt.measurement_update(Y[i], x_hat_bar)\\n print(i, x_hat, P)\\n X.append(x_hat)\\n R.append(P)\\n return np.array(X), R\",\n \"def test_updating_record_through_filter(self, test_domain):\\n identifier1 = uuid4()\\n identifier2 = uuid4()\\n identifier3 = uuid4()\\n identifier4 = uuid4()\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier1, first_name=\\\"Athos\\\", last_name=\\\"Musketeer\\\", age=2\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier2, first_name=\\\"Porthos\\\", last_name=\\\"Musketeer\\\", age=3\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier3, first_name=\\\"Aramis\\\", last_name=\\\"Musketeer\\\", age=4\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier4, first_name=\\\"dArtagnan\\\", last_name=\\\"Musketeer\\\", age=5\\n )\\n\\n # Perform update\\n updated_count = (\\n test_domain.repository_for(Person)\\n ._dao.query.filter(age__gt=3)\\n .update(last_name=\\\"Fraud\\\")\\n )\\n\\n # Query and check if only the relevant records have been updated\\n assert updated_count == 2\\n\\n u_person1 = test_domain.repository_for(Person)._dao.get(identifier1)\\n u_person2 = test_domain.repository_for(Person)._dao.get(identifier2)\\n u_person3 = test_domain.repository_for(Person)._dao.get(identifier3)\\n u_person4 = test_domain.repository_for(Person)._dao.get(identifier4)\\n assert u_person1.last_name == \\\"Musketeer\\\"\\n assert u_person2.last_name == \\\"Musketeer\\\"\\n assert u_person3.last_name == \\\"Fraud\\\"\\n assert u_person4.last_name == \\\"Fraud\\\"\",\n \"def do_filter(self):\\n return np.sum(np.multiply(self._past_values, self._impulse_response))\",\n \"def filter(self):\\n self.data = self.data.loc[~self.data.isnull().any(1),:]\",\n \"def updateValues(self, otherDialog):\\n for index in range(len(self._values)):\\n for oval in otherDialog._values:\\n if self._values[index].getID() == oval.getID():\\n self._values[index].combine( oval, intelligent=False )\\n break\",\n \"def filter_data(self):\\n self.df = self.df[HeatStrokeDataFiller.important_features]\",\n \"def observe(self, observation):\\n # shallow copy observation (deep copy can be expensive)\\n obs = observation.copy()\\n batch_idx = self.opt.get('batchindex', 0)\\n self.observation = obs\\n #self.answers[batch_idx] = None\\n return obs\",\n \"def _update_raw_data(params):\\n from scipy.signal import filtfilt\\n start = params['t_start']\\n stop = params['raw'].time_as_index(start + params['duration'])[0]\\n start = params['raw'].time_as_index(start)[0]\\n data_picks = _pick_data_channels(params['raw'].info)\\n data, times = params['raw'][:, start:stop]\\n if params['projector'] is not None:\\n data = np.dot(params['projector'], data)\\n # remove DC\\n if params['remove_dc'] is True:\\n data -= np.mean(data, axis=1)[:, np.newaxis]\\n if params['ba'] is not None:\\n data[data_picks] = filtfilt(params['ba'][0], params['ba'][1],\\n data[data_picks], axis=1, padlen=0)\\n # scale\\n for di in range(data.shape[0]):\\n data[di] /= params['scalings'][params['types'][di]]\\n # stim channels should be hard limited\\n if params['types'][di] == 'stim':\\n norm = float(max(data[di]))\\n data[di] /= norm if norm > 0 else 1.\\n # clip\\n if params['clipping'] == 'transparent':\\n data[np.logical_or(data > 1, data < -1)] = np.nan\\n elif params['clipping'] == 'clamp':\\n data = np.clip(data, -1, 1, data)\\n params['data'] = data\\n params['times'] = times\",\n \"def _filter_satisfied(self, update_setd=False):\\n\\n model = self.oracle.get_model()\\n setd = set()\\n\\n for i, cl in enumerate(self.soft):\\n if not self.satc[i]:\\n if self._satisfied(cl, model):\\n self.satc[i] = True\\n self.ss_assumps.append(self.sels[i])\\n else:\\n setd = setd.union(set(cl))\\n\\n if update_setd:\\n self.setd = list(setd)\",\n \"def _put_resolved_booleans_into_filter(\\n filter_operator: Operator, model_filters_to_resolved_values: Dict[ModelFilter, BooleanValues]\\n) -> None:\\n for operator in _model_filter_in_operator_generator(filter_operator):\\n model_filter = operator.unresolved_value\\n operator.resolved_value = model_filters_to_resolved_values.get(\\n model_filter, BooleanValues.UNKNOWN\\n )\",\n \"def apply_filters(self, new_filters):\\n\\t\\tself.filters = new_filters\",\n \"def set_observations(self, oseries):\\n self.oseries_index = oseries.index\\n observations_masked = np.ma.array(oseries,\\n mask=(~np.isfinite(oseries)))\\n (n_timesteps, dimobs) = observations_masked.shape\\n self.observation_indices = np.zeros((n_timesteps, dimobs),\\n dtype=np.float64)\\n self.observation_count = np.zeros(n_timesteps, dtype=np.int64)\\n self.observations = np.zeros((n_timesteps, dimobs), dtype=np.float64)\\n\\n for t in range(n_timesteps):\\n observation = observations_masked[t]\\n # add large value to find all finite non-masked values\\n obstmp = observation + 1e10\\n obsindices = obstmp.nonzero()[0]\\n self.observation_count[t] = len(obsindices)\\n\\n if (len(obsindices) > 0):\\n for i, _ in enumerate(obsindices):\\n obsid = int(obsindices[i])\\n self.observations[t, obsid] = observation[obsid]\\n self.observation_indices[t, i] = obsid\",\n \"def updateObservation(self, obs):\\n self.settingsDb.updateObservation(self.observationsTableName(), obs)\",\n \"def update(self):\\n\\n terms_toRemove = []\\n\\n for termIndex, [term_constantFactor, term_unknowns_attributeAddresses] in enumerate(self.LHS):\\n\\n # Check if coefficient is 0 - then no need to process any of the unknowns since term will be 0 anyways\\n if term_constantFactor == 0:\\n terms_toRemove.append(termIndex)\\n continue # continue to next term, no need to resolve the unknowns of this term since the product will be 0 anyways\\n\\n # Check if any unknowns became known\\n unknowns_toRemove = []\\n for unknown_attributeAddress in term_unknowns_attributeAddresses:\\n attribute = getattr_fromAddress(*unknown_attributeAddress)\\n if isNumeric(attribute):\\n # object.attribute which had previously been identified as unknown now has a value, add it to the constant factor product and remove from the unknowns\\n self.LHS[termIndex][0] *= attribute # multiply it with the constant factor product\\n unknowns_toRemove.append([termIndex, unknown_attributeAddress])\\n for termIndex, unknown_attributeAddress in unknowns_toRemove: # remove unknowns which have become known in the end\\n # removing in the end not to tamper with the iteration of the above loop\\n self.LHS[termIndex][1].remove(unknown_attributeAddress)\\n\\n # Move constants to RHS\\n if self.LHS[termIndex][1] == []:\\n # if term has no unknowns, it is a constant, move to RHS\\n self.RHS -= self.LHS[termIndex][0]\\n self.LHS.pop(termIndex)\\n\\n for termIndex in reversed(terms_toRemove): # reversed - otherwise would tamper with indices of items identified for removal\\n self.LHS.pop(termIndex)\\n\\n self._gatherUnknowns()\",\n \"def filter(self):\\n M, p, q = self.M, self.p, self.q\\n x = self.x\\n idx = len(self.x) - (p + 1)\\n x_ = self.x_prev + (x[idx + p] - x[idx - q]) / M\\n self.t_.append(self.t[idx])\\n self.t_filtered.append(self.t[idx])\\n self.x_.append(x_)\\n self.x_filtered.append(x_)\\n self.x_prev = x_\",\n \"def update_filters(self, **kwargs):\\n self._FILTERS = kwargs\",\n \"def update(self, values, train, eta=.1):\\n\\t\\tfor X, y_true in zip(values, train):\\n\\t\\t\\tprediction = self.activate(X)\\n\\t\\t\\terror = y_true - prediction\\n\\t\\t\\tweight_update = error * eta * X\\n\\t\\t\\tself.weights += weight_update\",\n \"def finalize(self):\\n # this forces only some filters will be used for feature computation\\n # this is not ideal, but a necessary stop-gap while we revise\\n # the PropertyTable SQL\\n self._good_filters = ['u', 'g', 'r', 'i', 'z', 'Y']\\n self._good_filter_wave = np.array([3569.5, 4766.5, 6214.5, 7544.5, 8707.5, 10039.5])\\n\\n use_filters = set(self._good_filters) & self.filters\\n if not self.filters.issubset(use_filters):\\n message = 'Number of useful filters ({}) does not equal number available filters ({}) - some filters will not be used'.format(\\n ''.join(use_filters), ''.join(self.filters))\\n warnings.warn(message, RuntimeWarning)\\n self.filters = set(use_filters)\\n mask = np.array([True if x in self.filters else False for x in self.passband])\\n\\n if mask.size: # Not empty arrays\\n self.time = self.time[mask]\\n self.flux = self.flux[mask]\\n self.fluxErr = self.fluxErr[mask]\\n self.obsId = self.obsId[mask]\\n self.passband = self.passband[mask]\\n self.zeropoint = self.zeropoint[mask]\\n for key in self._extra_cols:\\n val = getattr(self, key)\\n setattr(self, key, val[mask])\\n\\n self.nobs = len(self.time)\\n if self.nobs == 0:\\n message = 'Object {} with locus ID {} has no good observations.'.format(self.objectId, self.locusId)\\n raise ValueError(message)\\n\\n return self._remove_flux_extinction()\",\n \"def step_filter(self, v, imu_meas, meas):\\n\\n if (imu_meas is not None) and (meas is None):\\n xp, Pp = self.prediction(v, imu_meas)\\n self.x = xp\\n self.P_t = Pp\\n return xp\\n\\n elif (imu_meas is None) and (meas is not None):\\n z_t = self.transformMeasurement(meas)\\n self.x = z_t\\n return z_t\\n\\n else: # both measurements contain values\\n xp, Pp = self.prediction(v, imu_meas)\\n z_t = self.transformMeasurement(meas)\\n x, P = self.update(z_t, xp, Pp)\\n\\n self.x = x\\n self.P_t = P\\n return x\",\n \"def filter(self, data):\\n self.data = pysap.Image(data=self.flt.filter(data))\",\n \"def _apply_filter(self, fn=lambda ngram, freq: False):\\n tmp_ngram = FreqDist()\\n for ngram, freq in self.ngram_fd.items():\\n if not fn(ngram, freq):\\n tmp_ngram[ngram] = freq\\n self.ngram_fd = tmp_ngram\",\n \"def step_filter(self, v, imu_meas, z_t):\\n # YOUR CODE HERE\\n pass\",\n \"def parameter_update(self, X, X_mask):\\n data_with_mask = np.hstack([X, X_mask])\\n X_pred, X_MC_preds = self.predict(data_with_mask)\\n X[X_mask] *= self.keep_coeff\\n X[X_mask] += self.weight_update_coeff * X_pred[X_mask]\\n return X, X_MC_preds\",\n \"def filter_data(data,filters):\\n final_filter = pd.Series(np.array([True] * data.shape[0]))\\n for attribute, value in filters:\\n final_filter &= data[attribute] == value\\n return data[final_filter]\",\n \"def _set_values(loop_data):\\n \\n # These are the indexes of all the data that are unassigned\\n value_indexes = loop_data.ix[(loop_data.loop==0)&(loop_data.tag==0)&(loop_data.stop==0)].index\\n loop_data[u'value'] = 0\\n loop_data.ix[value_indexes,u'value'] = 1\\n \\n # These are the indexes of data who follow either a loop, label, or stop tag\\n value_indexes_begin = loop_data.ix[(value_indexes-1)].ix[loop_data.value==0].index + 1\\n \\n # The first rows of each data correspond to data for their respective loops\\n loop_max = loop_data.loop.max()\\n loop_range = np.arange(loop_max-1, -1, -1)\\n \\n for idx in value_indexes_begin:\\n loop_data.ix[idx:idx+len(loop_range)-1,u'value'] += loop_range\\n \\n return loop_data\",\n \"def update_with_observation(particles, landmark_list):\\r\\n for p in particles:\\r\\n for i in range(np.shape(landmark_list)[1]):\\r\\n p.update_particle(landmark_list[:, i])\\r\\n\\r\\n return particles\",\n \"def process_observation(self, observation):\\n return observation\",\n \"def process_observation(self, observation):\\n return observation\",\n \"def iterate(self, data):\\n \\n # Append data to self.data\\n self.data = np.append(self.data, data)\\n \\n for i, d in enumerate(data):\\n update = self.current*self.likelihood(d)\\n self.current = self._normalize(update)\\n self.posterior = np.concatenate((self.posterior,[self.current]))\\n \\n print(str(len(data)) + \\\" iterations completed!\\\")\\n \\n return None\",\n \"async def _update_values(self, model: Model):\\n\\n raise NotImplementedError\",\n \"def Iterate(self):\\n\\t\\tfor atom in self.atoms:\\n\\t\\t\\tself.UpdateAtom(atom)\",\n \"def update_state(self):\\n # return initial state if no observation was yet\\n if len(self.obs_history) == 0:\\n return self.kf.initial_state_mean, self.kf.initial_state_covariance\\n\\n hist = np.ma.masked_array(self.obs_history, mask=np.zeros((1,)))\\n for i in range(len(hist)):\\n if hist[i] == -1e8:\\n hist[i] = np.ma.masked\\n\\n # print(hist, hist.shape)\\n return self.kf.filter(hist)\",\n \"def _update_data(self):\\n for attribute in [\\\"flow_rate\\\"]:\\n self._data[attribute] = self._connection.measure\",\n \"def apply_filters(\\n isovar_result,\\n filter_thresholds={},\\n filter_flags=[]):\\n filter_values = OrderedDict(isovar_result.filter_values.items())\\n new_filter_values = evaluate_filters(\\n isovar_result,\\n filter_thresholds=filter_thresholds,\\n filter_flags=filter_flags)\\n filter_values.update(new_filter_values)\\n return isovar_result.clone_with_updates(filter_values=filter_values)\",\n \"def healthcare_filter(df_all): \\n #get requested assets under healthcare tag \\n df_filtered = pandas.DataFrame(columns=['osm_id','asset','geometry']) #create df for saving data\\n for row in range(len(df_all.index)): \\n if 'healthcare' in df_all[\\\"asset\\\"][row]: #check if healthcare key is present\\n df_filtered = df_filtered.append(df_all.loc[row]) #if so, save in df \\n if '\\\"healthcare\\\"=>\\\"doctor\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'doctors' #to be consistent with asset list \\n elif '\\\"healthcare\\\"=>\\\"pharmacy\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'pharmacy'\\n elif '\\\"healthcare\\\"=>\\\"hospital\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'hospital'\\n elif '\\\"healthcare\\\"=>\\\"clinic\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'clinic'\\n elif '\\\"healthcare\\\"=>\\\"dentist\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'dentist'\\n elif '\\\"healthcare\\\"=>\\\"physiotherapist\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'physiotherapist'\\n elif '\\\"healthcare\\\"=>\\\"alternative\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'alternative'\\n elif '\\\"healthcare\\\"=>\\\"laboratory\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'laboratory'\\n elif '\\\"healthcare\\\"=>\\\"optometrist\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'optometrist'\\n elif '\\\"healthcare\\\"=>\\\"rehabilitation\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'rehabilitation'\\n elif '\\\"healthcare\\\"=>\\\"blood_donation\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'blood_donation'\\n elif '\\\"healthcare\\\"=>\\\"birthing_center\\\"' in df_filtered[\\\"asset\\\"][row]:\\n df_filtered[\\\"asset\\\"][row] = 'birthing_center'\\n else:\\n df_filtered = df_filtered.drop(index=row)\\n \\n return df_filtered\",\n \"def filter_patients(self):\\n\\n if self.dataset is None:\\n self.dataset = h5py.File(self.filename, 'r')['dataset']\\n \\n # Find feature indices belonging to specific criteria\\n inclusion_info = self.filter_params['inclusion']\\n # exclusion_info = self.filter_params['exclusion']\\n case_control_info = self.filter_params['case_control']\\n\\n inclusion_inds = self.check_criteria(inclusion_info, case_control=False)\\n # exclusion_inds = self.check_criteria(exclusion_info, case_control=False)\\n case_inds, control_inds = self.check_criteria(case_control_info, case_control=True)\\n\\n filtered_inds = {}\\n # inclusion_exclusion_inds = np.setdiff1d(inclusion_inds, exclusion_inds)\\n filtered_inds['case'] = np.intersect1d(inclusion_inds, case_inds)\\n filtered_inds['control'] = np.intersect1d(inclusion_inds, control_inds)\\n\\n return filtered_inds\",\n \"def test_updating_multiple_records_through_filter_with_kwarg_value(\\n self, test_domain\\n ):\\n identifier1 = uuid4()\\n identifier2 = uuid4()\\n identifier3 = uuid4()\\n identifier4 = uuid4()\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier1, first_name=\\\"Athos\\\", last_name=\\\"Musketeer\\\", age=2\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier2, first_name=\\\"Porthos\\\", last_name=\\\"Musketeer\\\", age=3\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier3, first_name=\\\"Aramis\\\", last_name=\\\"Musketeer\\\", age=4\\n )\\n test_domain.repository_for(Person)._dao.create(\\n id=identifier4, first_name=\\\"dArtagnan\\\", last_name=\\\"Musketeer\\\", age=5\\n )\\n\\n # Perform update\\n updated_count = (\\n test_domain.repository_for(Person)\\n ._dao.query.filter(age__gt=3)\\n .update_all(last_name=\\\"Fraud\\\")\\n )\\n\\n # Query and check if only the relevant records have been updated\\n assert updated_count == 2\\n\\n u_person1 = test_domain.repository_for(Person)._dao.get(identifier1)\\n u_person2 = test_domain.repository_for(Person)._dao.get(identifier2)\\n u_person3 = test_domain.repository_for(Person)._dao.get(identifier3)\\n u_person4 = test_domain.repository_for(Person)._dao.get(identifier4)\\n assert u_person1.last_name == \\\"Musketeer\\\"\\n assert u_person2.last_name == \\\"Musketeer\\\"\\n assert u_person3.last_name == \\\"Fraud\\\"\\n assert u_person4.last_name == \\\"Fraud\\\"\",\n \"def train_filter2(filt, U, Y):\\n n = len(U)\\n X, R = [], []\\n for i in range(n):\\n print(i)\\n filt.predict(fx_args=(U[i],))\\n filt.update(Y[i])\\n X.append(filt.x.copy())\\n R.append(filt.P.copy())\\n X = np.array(X)\\n X, R, K = filt.rts_smoother(X, R, fx_args=(U[i],))\\n return X, R\",\n \"def apply_filter(self, filter_arg):\\n filtered_entries = self.visual.apply_filter(filter_arg, self.get_current_entries())\\n # idxs = self.selector.select_by_objects(filtered_entries, yield_ones_index=True)\\n self.visual.print_entries_enum(filtered_entries, None)\\n # self.list(idxs)\",\n \"def forc_model(self):\\n lag1_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag1_by_store')\\n lag2_loc = self.X[self.model_mask_cols].columns.get_loc('shrink_value_per_day_lag2_by_store')\\n for add in self.X.address1.unique():\\n add_mask = self.X.address1 == add\\n foo = self.X[ add_mask ].sort_values('visit_date', ascending=False)\\n top_index = foo.index[0]\\n clust = int(foo.cluster.values[0])\\n # get values from last visit for store\\n base_input = foo[self.model_mask_cols].values[0]\\n base_actual = self.y[top_index]\\n lag2_val = base_input[lag1_loc]\\n lag1_val = base_actual\\n\\n for i in range(1, self.num_periods + 1):\\n model = self.model_list[clust]\\n inputs = base_input\\n inputs[lag1_loc] = lag1_val\\n inputs[lag2_loc] = lag2_val\\n \\n pred = model.predict(inputs.reshape(1, -1))\\n self._update_cust_table(add, i, pred)\\n \\n lag2_val = lag1_val\\n lag1_val = pred\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63613224","0.62198365","0.6080035","0.60798997","0.60306054","0.58865666","0.5883915","0.58141124","0.5800074","0.5787996","0.5541149","0.5536493","0.55307084","0.5513393","0.55123764","0.5507698","0.54158753","0.5414475","0.5403888","0.53936756","0.5388999","0.53880715","0.5363586","0.5355547","0.5351123","0.534992","0.53498495","0.5347381","0.5285174","0.52653944","0.5261038","0.5215623","0.5215302","0.52017486","0.52017146","0.5200738","0.51895565","0.51877266","0.518692","0.51809186","0.5177147","0.517705","0.517705","0.51713455","0.5161349","0.5150539","0.51468694","0.5144904","0.51423365","0.513975","0.51379853","0.51308095","0.51275927","0.51274836","0.5118717","0.51145047","0.51115704","0.51114297","0.5109708","0.5100439","0.50909036","0.509037","0.5087856","0.5087688","0.50621223","0.5049711","0.5049681","0.5045099","0.5040932","0.50364006","0.5034882","0.50339144","0.5019592","0.5012907","0.50115496","0.50065666","0.50048995","0.5004228","0.49995285","0.49954176","0.4990749","0.49808678","0.49754924","0.4966135","0.49632093","0.49604845","0.4956182","0.4955419","0.4955419","0.49450874","0.49429345","0.4934977","0.49320674","0.49267983","0.49172685","0.49075392","0.48998687","0.48974884","0.48974785","0.48944095","0.48937657"],"string":"[\n \"0.63613224\",\n \"0.62198365\",\n \"0.6080035\",\n \"0.60798997\",\n \"0.60306054\",\n \"0.58865666\",\n \"0.5883915\",\n \"0.58141124\",\n \"0.5800074\",\n \"0.5787996\",\n \"0.5541149\",\n \"0.5536493\",\n \"0.55307084\",\n \"0.5513393\",\n \"0.55123764\",\n \"0.5507698\",\n \"0.54158753\",\n \"0.5414475\",\n \"0.5403888\",\n \"0.53936756\",\n \"0.5388999\",\n \"0.53880715\",\n \"0.5363586\",\n \"0.5355547\",\n \"0.5351123\",\n \"0.534992\",\n \"0.53498495\",\n \"0.5347381\",\n \"0.5285174\",\n \"0.52653944\",\n \"0.5261038\",\n \"0.5215623\",\n \"0.5215302\",\n \"0.52017486\",\n \"0.52017146\",\n \"0.5200738\",\n \"0.51895565\",\n \"0.51877266\",\n \"0.518692\",\n \"0.51809186\",\n \"0.5177147\",\n \"0.517705\",\n \"0.517705\",\n \"0.51713455\",\n \"0.5161349\",\n \"0.5150539\",\n \"0.51468694\",\n \"0.5144904\",\n \"0.51423365\",\n \"0.513975\",\n \"0.51379853\",\n \"0.51308095\",\n \"0.51275927\",\n \"0.51274836\",\n \"0.5118717\",\n \"0.51145047\",\n \"0.51115704\",\n \"0.51114297\",\n \"0.5109708\",\n \"0.5100439\",\n \"0.50909036\",\n \"0.509037\",\n \"0.5087856\",\n \"0.5087688\",\n \"0.50621223\",\n \"0.5049711\",\n \"0.5049681\",\n \"0.5045099\",\n \"0.5040932\",\n \"0.50364006\",\n \"0.5034882\",\n \"0.50339144\",\n \"0.5019592\",\n \"0.5012907\",\n \"0.50115496\",\n \"0.50065666\",\n \"0.50048995\",\n \"0.5004228\",\n \"0.49995285\",\n \"0.49954176\",\n \"0.4990749\",\n \"0.49808678\",\n \"0.49754924\",\n \"0.4966135\",\n \"0.49632093\",\n \"0.49604845\",\n \"0.4956182\",\n \"0.4955419\",\n \"0.4955419\",\n \"0.49450874\",\n \"0.49429345\",\n \"0.4934977\",\n \"0.49320674\",\n \"0.49267983\",\n \"0.49172685\",\n \"0.49075392\",\n \"0.48998687\",\n \"0.48974884\",\n \"0.48974785\",\n \"0.48944095\",\n \"0.48937657\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76680,"cells":{"query":{"kind":"string","value":"Execute the resize_data.sh package script."},"document":{"kind":"string","value":"def AddUnifiedUserDataAssertion(self, input_zip):\n subprocess.call([os.path.join(DEVICE_RELEASE_TOOLS, 'resize_userdata.sh'), str(TARGET_PRODUCT)])\n \"\"\"Include the required binaries in the output zip.\"\"\"\n self.output_zip.write(os.path.join(OUT_DIR, 'resize_userdata.zip'), os.path.join(INSTALL_UNIFY_USERDATA_PATH, 'resize_userdata.zip'))\n self.output_zip.write(os.path.join(RECOVERY_SBIN_DIR, 'sgdisk'), os.path.join(INSTALL_BIN_PATH, 'sgdisk'))\n self.output_zip.write(os.path.join(UTILITIES_DIR, 'toybox'), os.path.join(INSTALL_BIN_PATH, 'toybox'))\n \"\"\"Core script of the UserData Unify Tool.\"\"\"\n self.script.AppendExtra('package_extract_dir(\"install\", \"/tmp/install\");')\n self.script.AppendExtra('set_metadata_recursive(\"/tmp/install/bin\", \"uid\", 0, \"gid\", 0, \"dmode\", 0755, \"fmode\", 0755);')\n self.script.AppendExtra('set_metadata_recursive(\"/tmp/install/unify_userdata\", \"uid\", 0, \"gid\", 0, \"dmode\", 0755, \"fmode\", 0755);')\n self.script.AppendExtra('if run_program(\"/tmp/install/unify_userdata/main.sh\") == 22784 then')\n self.script.AppendExtra('ui_print(\"Unified UserData detected\");')\n self.script.AppendExtra('else')\n self.script.AppendExtra('ui_print(\" \");')\n self.script.AppendExtra('ui_print(\"==================================\");')\n self.script.AppendExtra('ui_print(\" The device needs to be converted\");')\n self.script.AppendExtra('ui_print(\" to Unified UserData partition\");')\n self.script.AppendExtra('ui_print(\"==================================\");')\n self.script.AppendExtra('ui_print(\" \");')\n self.script.AppendExtra('sleep(5);')\n self.script.AppendExtra('assert(abort(\"Please read the informations above...\"););')\n self.script.AppendExtra('endif;')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def script(arch, version, variant, packages, mirror, disk_size, swap_size, image_format, root_password, hostname, no_confirm, output):\n\n # Checking validity of the command-line arguments.\n check_arguments(locals())\n\n # Checking if dependencies for this script are installed.\n check_dependencies(arch)\n\n # Make sure output value is correct and we won't run into any issue at the\n # final step (moving the result to its final location). We check the file\n # does not exist, and if its parent directory does exist.\n output = os.path.abspath(output)\n assert not os.path.exists(output), \"output value is incocrect; destination exists\"\n assert os.path.isdir(os.path.dirname(output)), \"output value is incorrect; parent folder does not exist\"\n\n # Compute partitions info (todo; compute actual infos)\n partitions = [\n ('esp', 512, 'fat32', 42, 42),\n ('root', 512, 'ext4', 42, 42),\n ('swap', 512, 'swap', 42, 42)\n ]\n\n # Compute list of packages that will be explicitly installed.\n packages = compute_packages(packages, locals())\n\n # Printing summary and asking for confirmation.\n summary_mesage = compute_summary_message(locals())\n\n if not no_confirm:\n summary_mesage += \\\n \"\\nPass the --no-confirm flag if you don't want to be prompted for confirmation.\\n\"\n\n print(summary_mesage)\n\n if not no_confirm:\n is_confirmed = input(\"Do you confirm those options ? [y/n] \")\n if not is_confirmed.lower().startswith('y'):\n print(\"Abort!\")\n exit(1)\n\n with tempfile.TemporaryDirectory() as tmp_dir:\n print(f\"Creating a raw disk image of size {disk_size}MiB\")\n disk_path = create_disk_image(tmp_dir, disk_size)\n\n print(\"Partitioning the disk...\")\n partition_disk(disk_path, disk_size, swap_size)\n\n loop_device = '/dev/loop42'\n with attach_to_loop_device(disk_path, loop_device):\n print(\"Formatting partitions...\")\n format_partitions(loop_device)\n\n mount_dir = os.path.join(tmp_dir, 'mnt')\n os.mkdir(mount_dir)\n\n with mount_root_partition(loop_device, mount_dir):\n create_chroot_environment(mount_dir, arch, version, variant, mirror, packages)\n\n # In order to chroot into a filesystem with an architecture\n # different than the host, we need to install a binary\n # interpreter.\n if arch == 'armhf':\n print(\"Copying qemu-arm-static to the chroot environment\")\n shutil.copy2('/usr/bin/qemu-arm-static', os.path.join(mount_dir, 'usr/bin/'))\n elif arch == 'arm64':\n print(\"Copying qemu-aarch64-static to the chroot environment\")\n shutil.copy2('/usr/bin/qemu-aarch64-static', os.path.join(mount_dir, 'usr/bin/'))\n\n with mount_boot_partition(loop_device, mount_dir):\n with mount_transient_files(mount_dir):\n # Configure the GRUB boot loader.\n if arch == 'armhf':\n grub_package = 'grub-efi-arm'\n grub_target = 'arm-efi'\n elif arch == 'arm64':\n grub_package = 'grub-efi-arm64'\n grub_target = 'arm64-efi'\n elif arch == 'amd64':\n grub_package = 'grub-efi-amd64'\n grub_target = 'x86_64-efi'\n\n update_system_cmd = ['apt-get', 'update']\n run_chroot_command(update_system_cmd, mount_dir, arch)\n\n install_grub_pkg_cmd = ['apt-get', 'install', '-y', '--install-recommends', grub_package]\n run_chroot_command(install_grub_pkg_cmd, mount_dir, arch)\n\n purge_osprober_cmd = ['apt-get', '--autoremove', '-y', 'purge', 'os-prober']\n run_chroot_command(purge_osprober_cmd, mount_dir, arch)\n\n # Adjust the '/etc/default/grub' file\n with open(os.path.join(mount_dir, 'etc/default/grub'), 'r') as file:\n text = file.read()\n\n # TODO; adjust text variable\n\n with open(os.path.join(mount_dir, 'etc/default/grub'), 'w') as file:\n file.write(text)\n\n grub_mkconfig_cmd = ['grub-mkconfig', '-o', '/boot/grub/grub.cfg']\n run_chroot_command(grub_mkconfig_cmd, mount_dir, arch)\n\n grub_install_cmd = [\n 'grub-install',\n f'--target={grub_target}',\n '--force-extra-removable',\n '--no-nvram',\n '--no-floppy',\n '--modules=\\\\\"part_msdos part_gpt\\\\\"',\n '--grub-mkdevicemap=/boot/grub/device.map',\n loop_device\n ]\n run_chroot_command(grub_install_cmd, mount_dir, arch)\n\n print(\"Updating /etc/hostname\")\n configure_hostname(mount_dir, 'foo')\n\n print(\"Updating /etc/fstab\")\n configure_fstab(mount_dir)\n\n print(\"Updating /etc/network/interfaces\")\n configure_network_interfaces(mount_dir)\n\n # TODO; run user provided script here...\n\n # Remove the binary interpreter from the chroot environment.\n if arch == 'armhf':\n print(\"Removing qemu-arm-static from the chroot environment\")\n os.remove(os.path.join(mount_dir, 'usr/bin/qemu-arm-static'))\n elif arch == 'arm64':\n print(\"Removing qemu-aarch64-static from the chroot environment\")\n os.remove(os.path.join(mount_dir, 'usr/bin/qemu-aarch64-static'))\n\n # Convert raw disk image to the requested format and and move the result to\n # the requested location.\n if image_format != 'raw':\n print(f\"Converting disk image to {image_format} format...\")\n\n new_disk_path = os.path.join(tmp_dir, 'disk.' + image_format)\n subprocess.run([\n 'qemu-img', 'convert',\n '-f', 'raw',\n '-O', image_format,\n disk_path, new_disk_path\n ])\n\n disk_path = new_disk_path\n\n shutil.move(disk_path, output)\n\n print(\"Done!\")","def do_resize(self, args, opts=None):\n dataset = args[0]\n shape = tuple(int(x) for x in args[1:])\n if opts.axis is not None:\n if len(shape) != 1:\n raise ValueError('Expected exactly one integer when changing axis size')\n self.explorer[dataset].resize(shape[0], axis=opts.axis)\n else:\n self.explorer[dataset].resize(shape)","def main(inputfile, output, size):\n if not output:\n output = join(dirname(inputfile), str(size))\n if not isdir(output):\n os.mkdir(output)\n\n logger.info('Resizing images from: %s' % inputfile)\n inputfile = realpath(inputfile)\n #/usr/share/datasets/KSCGR_Original/data1/boild-egg/0.jpg 0\n nb_lines = count_lines(inputfile)\n pbar = pb.ProgressBar(nb_lines)\n with open(inputfile) as fin:\n for line in fin:\n path, tl = line.strip().split()\n newpath = create_path(path, output)\n img = cv2.imread(path)\n img = resize_image(img, size)\n cv2.imwrite(newpath, img)\n pbar.update()\n logger.info('Total of images resized: %d' % nb_lines)","def main(raw_dir,save_dir,ext,target_size):\r\n try:\r\n msg = \"--target-size must be a tuple of 2 integers\"\r\n assert isinstance(target_size, tuple) and len(target_size) == 2, msg\r\n fnames = glob.glob(os.path.join(raw_dir, \"*.{}\".format(ext)))\r\n os.makedirs(save_dir, exist_ok=True)\r\n print(\"{} files to resize from directory `{}` to target size:{}\".format(len(fnames), raw_dir, target_size))\r\n for i, fname in enumerate(fnames):\r\n print(\".\", end=\"\", flush=True)\r\n img = cv2.imread(fname)\r\n img_small = cv2.resize(img, target_size)\r\n new_fname = \"{}.{}\".format(str(i), ext)\r\n small_fname = os.path.join(save_dir, new_fname)\r\n cv2.imwrite(small_fname, img_small)\r\n print(\"\\nDone resizing {} files.\\nSaved to directory: `{}`\".format(len(fnames), save_dir))\r\n except Exception as ex:\r\n print('Exception:',ex)","def resize_png(width, height, source, destination):\n\n os.system('convert -resize %sx%s \"%s\" \"%s\"' % (width, height, source, destination))","def run_script():\n creator = EnergyScaleCreator(IMP, FONT_SIZE)\n creator.get_scaled_image(WIDTH_MIN)\n creator.extend_image()\n ticks = create_ticks(IMP, TICK_COUNT)\n creator.draw_scale(ticks)\n imp_extended = creator.image\n imp_extended.show()","def exec_resize(self, exec_id, height=None, width=None):\n\n if isinstance(exec_id, dict):\n exec_id = exec_id.get('Id')\n\n params = {'h': height, 'w': width}\n url = self._url(\"/exec/{0}/resize\", exec_id)\n res = self._post(url, params=params)\n self._raise_for_status(res)","def resizeLXCContainer(self,node,vmid,post_data):\n data = self.connect('put',\"nodes/%s/lxc/%s/resize\" % (node,vmid), post_data)\n return data","def resize_action():\n fn = request.args.get(\"filename\")\n size = int(request.args.get(\"size\"))\n resize_file(fn, size)\n return Response(status=200)","def run(basedir, resize_height=288):\n print(\"initialize\")\n\n img0 = [os.path.join(basedir, 'images', f) \\\n for f in sorted(os.listdir(os.path.join(basedir, 'images'))) \\\n if f.endswith('JPG') or f.endswith('jpg') or f.endswith('png')][0]\n sh = cv2.imread(img0).shape\n height = resize_height\n factor = sh[0] / float(height)\n width = int(round(sh[1] / factor))\n _minify(basedir, resolutions=[[height, width]])\n\n print(\"finished\")","def resize(data, number_rows=None, number_columns=None, lower=None, upper=None, categories=None, weights=None, distribution=None, shift=None, scale=None, sample_proportion=None, minimum_rows=None, **kwargs):\n return Component(\n \"Resize\",\n arguments={\n 'data': Component.of(data),\n 'number_rows': Component.of(number_rows),\n 'number_columns': Component.of(number_columns),\n 'lower': Component.of(lower),\n 'upper': Component.of(upper),\n 'categories': Component.of(categories, value_format=\"jagged\"),\n 'weights': Component.of(weights, value_format=\"jagged\"),\n 'distribution': Component.of(distribution),\n 'shift': Component.of(shift),\n 'scale': Component.of(scale),\n 'sample_proportion': Component.of(sample_proportion),\n 'minimum_rows': Component.of(minimum_rows)\n },\n options={\n \n },\n constraints=kwargs)","def test_run_resize_and_crop_png(self):\n self.expect_datatore_lookup('SomeBlobKey', True)\n self.expect_open_image('SomeBlobKey', (1600, 1200), mime_type='PNG')\n self.expect_crop(left_x=0.125, right_x=0.875)\n self.expect_resize(32)\n self.expect_encode_image('SomeImageSize32',\n images_service_pb.OutputSettings.PNG)\n self.mox.ReplayAll()\n self._environ['PATH_INFO'] += '=s32-c'\n self.run_request('image/png', 'SomeImageSize32')","def resize_and_process_image(data: dict, context):\n file_name = data[\"name\"]\n bucket_name = data[\"bucket\"]\n _, temp_local_filename = tempfile.mkstemp(suffix=file_name)\n blob = storage_client.bucket(bucket_name).get_blob(file_name)\n blob_bytes = blob.download_as_bytes()\n output = io.BytesIO(blob_bytes)\n output.seek(0)\n image = Image.open(output)\n print(\"trying to resize image\")\n # resizes image\n resized_image = resize_image(image)\n resized_image.save(fp=temp_local_filename)\n print(\"Image resized\")\n\n # Upload result to second bucket\n print(\"Trying to upload resized image to second bucket\")\n second_bucket_name = os.getenv(\"SECOND_BUCKET\")\n second_bucket = storage_client.bucket(second_bucket_name)\n print(\"second bucket found\")\n new_blob = second_bucket.blob(file_name)\n new_blob.metadata = blob.metadata\n print(\"created new blob\")\n new_blob.upload_from_filename(temp_local_filename)\n print(\"uploaded resized image from file\")\n os.remove(temp_local_filename)","def test_run_resize(self):\n self.expect_datatore_lookup('SomeBlobKey', True)\n self.expect_open_image('SomeBlobKey', (1600, 1200))\n self.expect_resize(32)\n self.expect_encode_image('SomeImageSize32')\n self.mox.ReplayAll()\n self._environ['PATH_INFO'] += '=s32'\n self.run_request('image/jpeg', 'SomeImageSize32')","def resize_image(parent_directory, img_columns,img_rows):\n\t#List of img files in the directory for the specific simulation file\n\tlisting = os.listdir(parent_directory)\n\t#Number of img files in the directory for the specific simulation file\n\tnum_samples = len(listing)\n\t#Create Directory to store the re-sized images\n\tdir_nam = parent_directory + '/resize_col_' + str(img_columns) + '_row_' + str(img_rows)\n\tif not os.path.exists(dir_nam):\n\t\tos.makedirs(dir_nam)\n\t#Open image and resize --> (img_rows,img_columns)\n\tfor file in listing:\n\t\tif file.endswith('.bmp'):\n\t\t\tim = PIL.Image.open(parent_directory + '/' + file)\n\t\t\tim_resized = im.resize((img_columns,img_rows))\n\t\t\tim_resized.save(dir_nam + '/' + file)\n\treturn;","def test_single_resize_er(self):\n to_resize = base_path + '/test_data/rendering_tests/just_resize/original/faulty.jpg'\n to_output = base_path + '/test_data/rendering_tests/just_resize/results/'\n\n for the_file in os.listdir(to_output):\n file_path = os.path.join(to_output, the_file)\n if os.path.isfile(file_path):\n os.unlink(file_path)\n\n\n capturedOutput = io.StringIO() # Create StringIO object\n sys.stdout = capturedOutput # and redirect stdout.\n rb.resize_and_crop(to_resize, to_output+\"faulty.jpg\", 300,300 )\n sys.stdout = sys.__stdout__ # Reset redirect.\n self.assertEqual(\"Image too small to be resized\\n\",capturedOutput.getvalue()) # Now works as before.","def initial_resizing(fr_raw_data_path, fr_data_path, dim=300):\n with h5py.File(fr_raw_data_path, 'r') as data:\n images = resize_array(np.asarray(data['images'].value), dim=dim)\n labels = data['labels'].value\n \n with h5py.File(fr_data_path, 'w') as f:\n f.create_dataset('images', data=images)\n\n with h5py.File(fr_raw_data_path, 'r') as data: \n f.copy(data['fri_data'], 'fri_data')\n f.copy(data['frii_data'], 'frii_data')\n f.copy(data['labels'], 'labels')","def autoscale_cmd(timeout, max_count, min_count, batch_count,\n app_srv_ratio):\n while True:\n create_cnt, extra_servers = autoscale.scale(\n max_servers=max_count,\n min_servers=min_count,\n default_app_srv_ratio=app_srv_ratio,\n max_batch=batch_count)\n if create_cnt > 0:\n autoscale.create_n_servers(create_cnt, partition=None)\n\n if extra_servers:\n autoscale.delete_servers_by_name(extra_servers)\n\n time.sleep(timeout)","def image_resize(job_object):\n try:\n job = json.loads(job_object.arg)\n base64_file = job['image']\n args = job['args'] if 'args' in job else {}\n del job['image']\n logging.info(job)\n \n def write_file(local_path,filename,file_b64):\n logging.debug(\"about to save to \" + \"%s/%s\" % (local_path,filename))\n if not os.path.exists(local_path): os.makedirs(local_path)\n image_file = base64.b64decode(file_b64)\n local_file = open(\"%s/%s\" % (local_path,filename), \"w\")\n local_file.write(image_file)\n local_file.close()\n \n def download_file(url,local_path,filename):\n print \"downloading \" + url\n f = urllib2.urlopen(urllib2.Request(url))\n print \"about to save to \" + \"%s/%s\" % (local_path,filename)\n if not os.path.exists(local_path): os.makedirs(local_path)\n # Open our local file for writing\n local_file = open(\"%s/%s\" % (local_path,filename), \"w\")\n local_file.write(f.read())\n local_file.close()\n \n local_path = '%s/upload/%s' % (options.asset_root,job['path'])\n local_path_wfile = '%s/%s%s' % (local_path,job['file'],job['extension'])\n filename = '%s%s' % (job['file'],job['extension'])\n #download_file(job['url'],local_path,filename)\n write_file(local_path,filename,base64_file)\n \n def resize_and_save(local_file,new_file,maxsize=None,maxh=None,maxw=None,crop=None):\n \"\"\"Resize the image and save\"\"\"\n logging.debug(\"maxw = %s, maxsize=%s, crop=%s\" % (maxw,maxsize,crop))\n img = Image.open(local_file)\n width,height = img.size\n width,height = float(width), float(height)\n ratio = float(1)\n if crop is not None:\n size = float(maxsize)\n if width <= height and width > size:\n ratio = size/width\n elif height < width and height > size:\n ratio = size/height\n else: \n ratio = 1 # too small\n elif maxsize:\n size = float(maxsize)\n if width >= height and width > size:\n ratio = size/width\n elif height > width and height > size:\n ratio = size/height\n else: \n ratio = 1 # too small\n elif maxh:\n size = maxh\n if height > size:\n ratio = size/height\n else:\n # too small\n ratio = 1\n elif maxw:\n size = maxw\n if width > size:\n ratio = size/width\n else:\n # too small\n ratio = 1\n else:\n raise Exception(\"must specify max width, OR max size\")\n \n print(\"old: ratio = %s: size(x,y) = %s,%s\" % (ratio,width,height))\n height = int(height*ratio)\n width = int(width*ratio)\n print(\"new ratio = %s: size(x,y) = %s,%s\" % (ratio,width,height))\n img = img.resize((width, height),Image.ANTIALIAS)\n if crop is not None:\n log.debug(\"in crop %s\" % crop)\n crop = int(crop)\n if width > crop:\n amt = int((int(width) - crop)/2)\n img = img.crop((amt,0,amt + crop, crop))\n elif height > crop:\n amt = int((int(height) - crop)/2)\n img = img.crop((0,amt,crop,amt+crop))\n \n log.debug(\"saving new file %s\" % new_file)\n if img.mode != \"RGB\":\n img = img.convert(\"RGB\")\n img.save(new_file)\n \n \n if os.path.exists(local_path_wfile):\n if args != {}:\n ext = args['extension'] if 'extension' in args else \"_t\"\n resize_and_save(local_path_wfile,\n '%s/%s%s.jpg' % (local_path,job['file'],ext),\n maxsize=args['maxsize'],\n crop=args['crop'])\n else:\n resize_and_save(local_path_wfile,'%s/%s_t.jpg' % (local_path,job['file']),maxsize=100)\n resize_and_save(local_path_wfile,'%s/%s_m.jpg' % (local_path,job['file']),maxw=317)\n resize_and_save(local_path_wfile,'%s/%s_l.jpg' % (local_path,job['file']),maxsize=800)\n keeptrying = False\n else:\n logging.error(\"haven't found file? %s\" % local_path_wfile)\n \n # delete original\n logging.debug(\"About to delete original %s\" % local_path_wfile)\n os.remove(local_path_wfile)\n \n except:\n traceback.print_exc()","def run_image_editor( self ):\n\n # XXX: hardcoded program name and image size.\n subprocess.Popen( [\"gimp\", \"-adfs\", self.record[\"filename\"]] )","def test_run_resize_and_crop(self):\n self.expect_datatore_lookup('SomeBlobKey', True)\n self.expect_open_image('SomeBlobKey', (1600, 1200))\n self.expect_crop(left_x=0.125, right_x=0.875)\n self.expect_resize(32)\n self.expect_encode_image('SomeImageSize32')\n self.mox.ReplayAll()\n self._environ['PATH_INFO'] += '=s32-c'\n self.run_request('image/jpeg', 'SomeImageSize32')","def resize_image_data(data, resize_shape):\n if data is None or len(resize_shape) == 0:\n return data\n\n if len(data.shape) > 1 and np.array_equal(data[0].shape, resize_shape):\n return data\n\n output_data = []\n for im in data:\n output_data.append(zoom(input=im, zoom=np.divide(resize_shape, im.shape)))\n\n return np.array(output_data)","def test_single_resize_ok(self):\n\n to_resize = base_path +'/test_data/rendering_tests/just_resize/original/good.jpg'\n to_output = base_path +'/test_data/rendering_tests/just_resize/results/'\n\n\n rb.resize_and_crop(to_resize, to_output+\"good.jpg\", 300,300 )\n self.assertEqual(1, len(os.listdir(to_output)))","def resize_image(self, nNewSize, nFlags):\n\t\treturn Job(SDK.PrlVmDev_ResizeImage(self.handle, nNewSize, nFlags)[0])","def exec_after_process(app, inp_data, out_data, param_dict, tool=None, stdout=None, stderr=None):\n\n URL = param_dict.get( 'URL', None )\n URL = URL + '&_export=1&GALAXY_URL=0'\n if not URL:\n raise Exception('Datasource has not sent back a URL parameter')\n\n CHUNK_SIZE = 2**20 # 1Mb \n MAX_SIZE = CHUNK_SIZE * 100\n \n try:\n # damn you stupid sanitizer!\n URL = URL.replace('martX', 'mart&')\n URL = URL.replace('0X_', '0&_')\n page = urllib.urlopen(URL)\n except Exception, exc:\n raise Exception('Problems connecting to %s (%s)' % (URL, exc) )\n\n name, data = out_data.items()[0]\n \n fp = open(data.file_name, 'wb')\n size = 0\n while 1:\n chunk = page.read(CHUNK_SIZE)\n if not chunk:\n break\n if size > MAX_SIZE:\n raise Exception('----- maximum datasize exceeded ---')\n size += len(chunk)\n fp.write(chunk)\n\n fp.close()\n data.set_peek()","def main():\n\n\n\n skulls_folder = os.listdir(RAW_IMAGE_DIRECTORY)\n\n # fetch and sort the .mnc and .tag files\n mnc_files = [f for f in skulls_folder if 'mnc' in f]\n tag_files = [f for f in skulls_folder if 'tag' in f]\n mnc_names = [i.split('.mnc')[0] for i in mnc_files]\n \n mnc_files.sort()\n tag_files.sort()\n mnc_names.sort()\n\n # Process and package ndarrays as tuples inside npy file\n package_to_npy(RAW_IMAGE_DIRECTORY, mnc_files, tag_files, mnc_names)\n \n print('\\n' * 5)\n\n # Push the npy files to GCP Cloud Storage\n upload_to_gcp(PROCESSED_IMAGE_DIRECTORY, GCP_PROJECT_NAME, GCP_BUCKET_NAME)","def execute_script(self, script, enterpreter='/bin/sh'):\n destination = '/tmp/' + ''.join(\n random.choice(string.lowercase) for i in range(16))\n\n self.upload(script, destination)\n self.execute('%s %s' % (enterpreter, destination))\n self.execute('rm %s' % destination)","def createbash(self,executable,**keywords):\n\t\timport os\n\t\timport stat\n\n\t\toutputname = os.path.join(\"Results\",self.outputfile.replace(\".root\",\"_${SGE_TASK_ID}.root\"))\n\t\t# Extract the input files\n\t\tinputfiles = \"\"\n\t\tfor f in self.inputfiles:\n\t\t\tinputfiles += f+\",\"\n\t\tinputfiles = inputfiles[:-1]\n\n\t\tlines = \"#!/bin/bash\\n\"\n\t\tlines += \"\\n# Script created automatically by skimfiles.py utility\\n\"\n\t\tlines += \"\\nmkdir -p Results\\n\"\n\t\tlines += \"export PATH=$PATH:\"+os.path.join(self.basedir,\"bin\")+\":\"+os.path.join(self.pkgpath,\"bin\")+\"\\n\"\n\t\tlines += \"export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:\"+self.libsdir+\"\\n\"\n\t\tlines += \"\\n\"\n\t\tlines += \"EVENTFILE=\"+self.eventsfile+\"\\n\"\n\t\tlines += \"EVENTS=$(cat $EVENTFILE | head -n $SGE_TASK_ID | tail -n 1)\\n\"\n\t\tlines += executable+\" \"+self.cutid+\" -i \"+inputfiles+\" -c \"+self.cutfile+\\\n\t\t\t\t\" -e $EVENTS -o \"+outputname+\"\\n\"\n\t\n\t\tfilename = self.nameID+\".sh\"\n\t\tf = open(filename,\"w\")\n\t\tf.writelines(lines)\n\t\tf.close()\n\t\tos.chmod(filename,stat.S_IRWXU+stat.S_IRGRP+stat.S_IXGRP+stat.S_IXOTH)\n\t\t\n\t\treturn filename","def prep(path,date,image):\n \n # run bash code with 'Popen'\n P = Popen('cp '+path+date+'/final/'+image+' ./', shell=True)\n P.wait()\n P = Popen('mv '+image+' '+image+'.fz', shell=True)\n P.wait()\n P = Popen('funpack *.fz', shell=True)\n P.wait()\n P = Popen('rm -rf *.fz', shell=True)\n P.wait()","def main():\r\n original = SimpleImage(\"images/poppy.png\")\r\n original.show()\r\n # shrink function\r\n after_shrink = shrink('images/poppy.png')\r\n after_shrink.show()","def test_full_resize(self):\n number_of_pixels = 300\n destination = base_path +'/test_data/rendering_tests/resized_images/'\n source_folder = base_path + '/test_data/rendering_tests/filter_database/'\n\n\n for the_file in os.listdir(destination):\n file_path = os.path.join(destination, the_file)\n if os.path.isfile(file_path):\n os.unlink(file_path)\n\n\n self.assertEqual(0, len(os.listdir(destination)))\n rb.find_all_files(number_of_pixels,source_folder, destination)\n self.assertEqual(6, len(os.listdir(destination)))\n for the_file in os.listdir(destination):\n file_path = os.path.join(destination,the_file)\n with Image.open(file_path) as f:\n self.assertNotEqual(number_of_pixels+5, f.size[0])\n self.assertNotEqual(number_of_pixels+5, f.size[1])\n # the above checks that the size does not vary as needed\n # probably not necessary\n self.assertEqual(number_of_pixels, f.size[0])\n self.assertEqual(number_of_pixels, f.size[1])","def runUploads():\n os.system(\"chmod u+x uploadCommands.sh\")\n os.system(\"./uploadCommands.sh\")\n os.system(\"rm uploadCommands.sh\")","def rescale_image(self, img_file, new_width, new_height, model_path, file_description):\n cwd = os.getcwd()\n self.new_width = new_width\n self.new_height = new_height\n self.extract_file_name(img_file)\n shutil.copy(img_file, os.path.join('utils_dfn/temp', self.file_name_with_ext))\n self.run_padding()\n self.run_dfn(model_path)\n self.restore_to_correct_size(file_description)\n clean()","def run_image_viewer( self ):\n\n # XXX: hardcoded program name and image size.\n subprocess.Popen( [\"feh\", \"-dZ\", \"-g\", \"800x600\", self.record[\"filename\"]] )","def __resize_to_512(input_data):\n test_size = 720\n\n if DataAugmentor._is_synthtext(input_data):\n input_data['contour'] = [[np.cast['int32'](np.stack([contour[:, :, 0] * 512 / input_data['img'].shape[0],\n contour[:, :, 1] * 512 / input_data['img'].shape[1]],\n axis=-1))\n for contour in contours] for contours in input_data['contour']]\n else:\n input_data['contour'] = [np.cast['int32'](np.stack([contour[:, :, 0] * 512 / input_data['img'].shape[0],\n contour[:, :, 1] * 512 / input_data['img'].shape[1]],\n axis=-1)) for contour in input_data['contour']]\n input_data['center_point'] = [(np.cast['int32'](point[0] * 512 / input_data['img'].shape[0]),\n np.cast['int32'](point[1] * 512 / input_data['img'].shape[1]))\n for point in input_data['center_point']]\n\n seq = iaa.Sequential([\n iaa.Scale({'height': 512, 'width': 512}, 'cubic')\n ])\n input_data['img'] = seq.augment_image(input_data['img'])\n\n return input_data","def main():\n if len(sys.argv) < 3:\n message = \"\"\"\n Usage: python generate_dataset.py <dataset_name> <number of files> <size of each file in bytes>\n \"\"\"\n print(message)\n sys.exit(0)\n dataset_name = sys.argv[1]\n file_number = int(sys.argv[2])\n file_size = int(sys.argv[3])\n\n if not os.path.exists(dataset_name):\n os.makedirs(dataset_name)\n\n for i in range(file_number):\n tmp_file = open('./' + dataset_name + '/' + dataset_name + '.file' + str(i), 'w+')\n tmp_file.write(os.urandom(file_size))\n tmp_file.close()","def bdev_daos_resize(client, name, new_size):\n params = {\n 'name': name,\n 'new_size': new_size,\n }\n return client.call('bdev_daos_resize', params)","def generate_data(dbgen_dir, data_dir, scale, num_streams):\n p = subprocess.Popen([os.path.join(\".\", \"dbgen\"), \"-vf\", \"-s\", str(scale)], cwd = dbgen_dir)\n p.communicate()\n if not p.returncode:\n load_dir = os.path.join(data_dir, LOAD_DIR)\n if inner_generate_data(load_dir, dbgen_dir, \"*.tbl\", \".csv\"):\n print(\"unable to generate data for load phase\")\n return 1\n print(\"generated data for the load phase\")\n else:\n return p.returncode\n\n ## Update/Delete phase data\n ## we generate num_streams + 1 number of updates because 1 is used by the power test\n p = subprocess.Popen([os.path.join(\".\", \"dbgen\"), \"-vf\", \"-s\", str(scale), \"-U\", str(num_streams + 1)],\n cwd = dbgen_dir)\n p.communicate()\n if not p.returncode:\n update_dir = os.path.join(data_dir, UPDATE_DIR)\n delete_dir = os.path.join(data_dir, DELETE_DIR)\n if inner_generate_data(update_dir, dbgen_dir, \"*.tbl.u*\", \".csv\"):\n print(\"unable to generate data for the update phase\")\n return 1\n print(\"generated data for the update phase\")\n if inner_generate_data(delete_dir, dbgen_dir, \"delete.*\", \".csv\"):\n print(\"unable to generate data for the delete phase\")\n return 1\n print(\"generated data for the delete phase\")\n ## All files written successfully. Return success code.\n return 0\n else:\n return p.returncode","def main(passed_arguments):\n\n # use real data as default\n scripts_path = os.path.abspath(os.path.join(PYTHON_PATH, 'scripts'))\n meta_path = os.path.abspath(os.path.join(scripts_path, 'meta.json'))\n manifest_path = os.path.abspath(os.path.join(scripts_path, 'manifest.csv'))\n\n # Locally, we can optionally have sample data\n if passed_arguments.sample and passed_arguments.database != 'remote':\n meta_path = os.path.abspath(os.path.join(scripts_path,\n 'meta_sample.json'))\n manifest_path = os.path.abspath(\n os.path.join(scripts_path, 'manifest_sample.csv'))\n\n # for case of more than one database choice default to the option with\n # the lowest risk if database is updated\n if passed_arguments.database == 'docker':\n database_choice = 'docker_database'\n drop_tables = True\n\n elif passed_arguments.database == 'docker_local':\n database_choice = 'docker_with_local_python'\n drop_tables = True\n\n elif passed_arguments.database == 'remote':\n database_choice = 'remote_database'\n drop_tables = False #TODO this is a hacky way to avoid dropping tables because it's not working with RDS...\n\n # Only users with additional admin privileges can rebuild the\n # remote database\n if not passed_arguments.update_only:\n database_choice = 'remote_database_master'\n\n # TODO: do we want to default to local or docker?\n elif passed_arguments.database == 'local':\n database_choice = 'local_database'\n drop_tables = True\n\n # universal defaults\n keep_temp_files = True\n\n # Instantiate and run the loader\n loader = LoadData(database_choice=database_choice, meta_path=meta_path,\n manifest_path=manifest_path,\n keep_temp_files=keep_temp_files,\n drop_tables=drop_tables)\n\n if passed_arguments.update_only:\n loader.update_database(passed_arguments.update_only)\n else:\n loader.rebuild()\n\n\n\n #TODO add in failures report here e.g. _failed_table_count","def main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"pldmfwuppkgname\", help=\"Name of the PLDM FW update package\"\n )\n parser.add_argument(\"metadatafile\", help=\"Path of metadata JSON file\")\n parser.add_argument(\n \"images\",\n nargs=\"+\",\n help=(\n \"One or more firmware image paths, in the same order as \"\n \" ComponentImageInformationArea entries\"\n ),\n )\n\n args = parser.parse_args()\n image_files = args.images\n with open(args.metadatafile) as file:\n try:\n metadata = json.load(file)\n except ValueError:\n sys.exit(\"ERROR: Invalid metadata JSON file\")\n\n # Validate the number of component images\n if len(image_files) != len(metadata[\"ComponentImageInformationArea\"]):\n sys.exit(\n \"ERROR: number of images passed != number of entries \"\n \" in ComponentImageInformationArea\"\n )\n\n try:\n with open(args.pldmfwuppkgname, \"w+b\") as pldm_fw_up_pkg:\n component_bitmap_bit_length = write_pkg_header_info(\n pldm_fw_up_pkg, metadata\n )\n write_fw_device_identification_area(\n pldm_fw_up_pkg, metadata, component_bitmap_bit_length\n )\n write_component_image_info_area(\n pldm_fw_up_pkg, metadata, image_files\n )\n update_pkg_header_size(pldm_fw_up_pkg)\n write_pkg_header_checksum(pldm_fw_up_pkg)\n append_component_images(pldm_fw_up_pkg, image_files)\n pldm_fw_up_pkg.close()\n except BaseException:\n pldm_fw_up_pkg.close()\n os.remove(args.pldmfwuppkgname)\n raise","def create_sh_script(\n unblur_path, input_image, output_dir,\n input_dir, input_suffix, options\n ):\n strSh = ''\n\n # To make sure it is a bash script\n strSh += '#!/bin/bash\\n\\n'\n\n # Export number of threads\n strSh += 'export OMP_NUM_THREADS={:d}\\n'.format(options.nr_threads)\n\n # The script will abort with non-zero exit values\n strSh += '# The script will abort with non-zero exit values\\n'\n strSh += 'set -e\\n'\n\n # Create a file list of all files\n strSh += '# Create a file list of all files\\n'\n strSh += 'fileList=$(ls {:s})\\n'.format(\n input_image\n )\n\n # Create folders\n strSh += '# Create folders\\n'\n strSh += 'mkdir -p {:s}/Doseuncorrected\\n'.format(output_dir)\n\n strSh += 'mkdir -p {:s}/Shift\\n'.format(output_dir)\n\n strSh += 'mkdir -p {:s}/Temp\\n'.format(output_dir)\n\n if options.filter_sum:\n strSh += 'mkdir -p {:s}/Filtered\\n'.format(output_dir)\n\n if options.dose_filter:\n strSh += 'mkdir -p {:s}/Dosecorrected\\n'.format(output_dir)\n\n if options.expert_mode:\n strSh += 'mkdir -p {:s}/FRC\\n\\n'.format(output_dir)\n\n # Abort script if files in Doseuncorrected already exists\n strSh += '# Abort script if files in Doseuncorrected already exists\\n'\n strSh += 'for f in {:s}/Doseuncorrected/*\\n'.format(output_dir)\n strSh += 'do\\n'\n strSh += 'if [ -e $f ]\\n'\n strSh += 'then\\n'\n strSh += 'echo \"Some files already exists, please choose another output directory\"\\n'\n strSh += 'exit 1\\n'\n strSh += 'break\\n'\n strSh += 'fi\\n'\n strSh += 'done\\n\\n'\n\n # Abort script if files in shift already exists\n strSh += '# Abort script if files in shift already exists\\n'\n strSh += 'for f in {:s}/Shift/*\\n'.format(output_dir)\n strSh += 'do\\n'\n strSh += 'if [ -e $f ]\\n'\n strSh += 'then\\n'\n strSh += 'echo \"Some files already exists, please choose another output directory\"\\n'\n strSh += 'exit 1\\n'\n strSh += 'break\\n'\n strSh += 'fi\\n'\n strSh += 'done\\n\\n'\n\n # Abort script if files in Dosecorrected already exists\n strSh += '# Abort script if files in Dosecorrected already exists\\n'\n strSh += 'for f in {:s}/Dosecorrected/*\\n'.format(output_dir)\n strSh += 'do\\n'\n strSh += 'if [ -e $f ]\\n'\n strSh += 'then\\n'\n strSh += 'echo \"Some files already exists, please choose another output directory\"\\n'\n strSh += 'exit 1\\n'\n strSh += 'break\\n'\n strSh += 'fi\\n'\n strSh += 'done\\n\\n'\n\n # Abort script if files in Filtered already exists\n strSh += '# Abort script if files in Filtered already exists\\n'\n strSh += 'for f in {:s}/Filtered/*\\n'.format(output_dir)\n strSh += 'do\\n'\n strSh += 'if [ -e $f ]\\n'\n strSh += 'then\\n'\n strSh += 'echo \"Some files already exists, please choose another output directory\"\\n'\n strSh += 'exit 1\\n'\n strSh += 'break\\n'\n strSh += 'fi\\n'\n strSh += 'done\\n\\n'\n\n # Abort script if files in FRC already exists\n strSh += '# Abort script if files in FRC already exists\\n'\n strSh += 'for f in {:s}/FRC/*\\n'.format(output_dir)\n strSh += 'do\\n'\n strSh += 'if [ -e $f ]\\n'\n strSh += 'then\\n'\n strSh += 'echo \"Some files already exists, please choose another output directory\"\\n'\n strSh += 'exit 1\\n'\n strSh += 'break\\n'\n strSh += 'fi\\n'\n strSh += 'done\\n\\n'\n\n # Loop over all files\n strSh += '\\nfor file in $fileList\\ndo\\n\\n'\n\n strSh += 'baseName=${{file%{:s}}}\\n'.format(input_suffix)\n strSh += 'baseName=${{baseName#{:s}}}\\n'.format(input_dir)\n\n # Create a temporary file to work with to prevent format issues\n strSh += '# Create a temporary file to work with to prevent format issues\\n'\n strSh += 'e2proc3d.py $file {:s}/Temp/${{baseName}}_temp.mrc\\n\\n'.format(output_dir)\n\n # Remove some temporary files that unblur makes\n strSh += '# Remove some temporary files that unblur makes\\n'\n strSh += 'for f in .UnBlur*\\n'\n strSh += 'do\\n'\n strSh += 'if [ -e $f ]\\n'\n strSh += 'then\\n'\n strSh += 'rm .UnBlur*\\n'\n strSh += 'break\\n'\n strSh += 'else\\n'\n strSh += 'true\\n'\n strSh += 'fi\\n'\n strSh += 'done\\n\\n'\n\n # Start Unblur without dose correction\n strSh += '{:s} << eof\\n'.format(unblur_path)\n\n # Input File\n strSh += '{:s}/Temp/${{baseName}}_temp.mrc\\n'.format(output_dir)\n # Number of Frames\n strSh += '{:d}\\n'.format(options.nr_frames)\n # Sum File\n strSh += '{:s}/Doseuncorrected/${{baseName}}{:s}.mrc\\n'.format(\n output_dir,\n options.sum_suffix\n )\n # Shift File\n strSh += '{:s}/Shift/${{baseName}}{:s}.txt\\n'.format(\n output_dir,\n options.shift_suffix\n )\n # Pixel Size\n strSh += '{:f}\\n'.format(options.pixel_size)\n\n # Say no to Dose Filtering\n strSh += 'NO\\n'\n\n if options.save_frames:\n # Say yes to Save Frames\n strSh += 'YES\\n'\n # Frames file\n strSh += '{:s}/Doseuncorrected/${{baseName}}{:s}{:s}.mrc\\n'.format(\n output_dir,\n options.sum_suffix,\n options.frames_suffix\n )\n else:\n # Say no to Save Frames\n strSh += 'NO\\n'\n\n if options.expert_mode:\n # Say yes to Expert Mode\n strSh += 'YES\\n'\n # FRC File\n strSh += '{:s}/FRC/${{baseName}}{:s}.txt\\n'.format(\n output_dir,\n options.frc_suffix\n )\n # Minimum Shift for initial search\n strSh += '{:f}\\n'.format(options.shift_initial)\n # Outer Radius Shift Limit\n strSh += '{:f}\\n'.format(options.shift_radius)\n # B-Factor to Apply\n strSh += '{:f}\\n'.format(options.b_factor)\n # Half-Width Vertical\n strSh += '{:d}\\n'.format(options.fourier_vertical)\n # Hald-Width Horizontal\n strSh += '{:d}\\n'.format(options.fourier_horizontal)\n # Termination Shift Threshold\n strSh += '{:f}\\n'.format(options.shift_threshold)\n # Maximum Iterations\n strSh += '{:d}\\n'.format(options.iterations)\n # Restore Noise Power\n if options.restore_noise:\n # Say yes to Restore Noise Power\n strSh += 'YES\\n'\n else:\n # Say no to Restore Noise Power\n strSh += 'NO\\n'\n # Verbose Output\n if options.verbose:\n # Say yes to Verbose Output\n strSh += 'YES\\n'\n else:\n # Say no to Verbose Output\n strSh += 'NO\\n'\n else:\n # Say no to Expert Mode\n strSh += 'NO\\n'\n\n # Enf of file reached\n strSh += 'eof\\n\\n'\n\n # Remove some temporary files that unblur makes\n strSh += 'for f in .UnBlur*\\n'\n strSh += 'do\\n'\n strSh += 'if [ -e $f ]\\n'\n strSh += 'then\\n'\n strSh += 'rm .UnBlur*\\n'\n strSh += 'break\\n'\n strSh += 'else\\n'\n strSh += 'true\\n'\n strSh += 'fi\\n'\n strSh += 'done\\n\\n'\n\n # =========== #\n if options.dose_filter:\n\n # Start Unblur with dose correction\n strSh += '{:s} << eof\\n'.format(unblur_path)\n\n # Input File\n strSh += '{:s}/Temp/${{baseName}}_temp.mrc\\n'.format(output_dir)\n # Number of Frames\n strSh += '{:d}\\n'.format(options.nr_frames)\n # Sum File\n strSh += '{:s}/Dosecorrected/${{baseName}}{:s}.mrc\\n'.format(\n output_dir,\n options.sum_suffix\n )\n # Shift File\n strSh += '{:s}/Shift/${{baseName}}{:s}.txt\\n'.format(\n output_dir,\n options.shift_suffix\n )\n # Pixel Size\n strSh += '{:f}\\n'.format(options.pixel_size)\n\n # Say yes to Dose Filtering\n strSh += 'YES\\n'\n # Exposure per Frame\n strSh += '{:f}\\n'.format(options.exposure_per_frame)\n # Acceleration Voltage\n strSh += '{:f}\\n'.format(options.voltage)\n # Pre Exposure\n strSh += '{:f}\\n'.format(options.pre_exposure)\n\n if options.save_frames:\n # Say yes to Save Frames\n strSh += 'YES\\n'\n # Frames file\n strSh += '{:s}/Dosecorrected/${{baseName}}{:s}{:s}.mrc\\n'.format(\n output_dir,\n options.sum_suffix,\n options.frames_suffix\n )\n else:\n # Say no to Save Frames\n strSh += 'NO\\n'\n\n if options.expert_mode:\n # Say yes to Expert Mode\n strSh += 'YES\\n'\n # FRC File\n strSh += '{:s}/FRC/${{baseName}}{:s}.txt\\n'.format(\n output_dir,\n options.frc_suffix\n )\n # Minimum Shift for initial search\n strSh += '{:f}\\n'.format(options.shift_initial)\n # Outer Radius Shift Limit\n strSh += '{:f}\\n'.format(options.shift_radius)\n # B-Factor to Apply\n strSh += '{:f}\\n'.format(options.b_factor)\n # Half-Width Vertical\n strSh += '{:d}\\n'.format(options.fourier_vertical)\n # Hald-Width Horizontal\n strSh += '{:d}\\n'.format(options.fourier_horizontal)\n # Termination Shift Threshold\n strSh += '{:f}\\n'.format(options.shift_threshold)\n # Maximum Iterations\n strSh += '{:d}\\n'.format(options.iterations)\n # Restore Noise Power\n if options.restore_noise:\n # Say yes to Restore Noise Power\n strSh += 'YES\\n'\n else:\n # Say no to Restore Noise Power\n strSh += 'NO\\n'\n # Verbose Output\n if options.verbose:\n # Say yes to Verbose Output\n strSh += 'YES\\n'\n else:\n # Say no to Verbose Output\n strSh += 'NO\\n'\n else:\n # Say no to Expert Mode\n strSh += 'NO\\n'\n\n # Enf of file reached\n strSh += 'eof\\n\\n'\n\n # Remove temporary file\n strSh += 'rm {:s}/Temp/${{baseName}}_temp.mrc\\n'.format(output_dir)\n\n # Remove some temporary files that unblur makes\n # Remove some temporary files that unblur makes\n strSh += 'for f in .UnBlur*\\n'\n strSh += 'do\\n'\n strSh += 'if [ -e $f ]\\n'\n strSh += 'then\\n'\n strSh += 'rm .UnBlur*\\n'\n strSh += 'break\\n'\n strSh += 'else\\n'\n strSh += 'true\\n'\n strSh += 'fi\\n'\n strSh += 'done\\n\\n'\n\n if options.filter_sum:\n # Filter Images\n lowpass_angstrom = options.pixel_size / options.lowpass\n highpass_angstrom = options.pixel_size / options.highpass\n strSh += \\\n 'e2proc3d.py {:s}/Doseuncorrected/${{baseName}}{:s}.mrc '.format(\n output_dir,\n options.sum_suffix\n )\n strSh += '{:s}/Filtered/${{baseName}}{:s}.mrc ' \\\n .format(\n output_dir,\n options.sum_suffix\n )\n strSh += '--process=filter.lowpass.gauss:cutoff_freq={:f} '.format(\n options.lowpass\n )\n strSh += '--process=filter.highpass.gauss:cutoff_freq={:f}\\n\\n' \\\n .format(\n options.highpass\n )\n\n if options.remove_sum:\n # Remove sum files\n strSh += 'rm {:s}/Doseuncorrected/${{baseName}}{:s}.mrc\\n'.format(\n output_dir,\n options.sum_suffix\n )\n\n # Done\n strSh += 'done\\n\\n'\n\n # Remove temp folder\n strSh += 'rm -r {:s}/Temp\\n'.format(output_dir)\n\n strSh += 'echo \"All done!\"'\n\n # Write Output\n with open('{:s}/scriptUnblur.sh'.format(output_dir), 'w') as f:\n f.write(strSh)","def process_image(image):\n image = resize(image)\n return image","def rescale(self, data, width, height, force=True):\n import Image as pil\n from cStringIO import StringIO\n\n max_width = width\n max_height = height\n\n input_file = StringIO(data)\n img = pil.open(input_file)\n img = img.convert('RGB')\n\n if not force:\n img.thumbnail((max_width, max_height), pil.ANTIALIAS)\n else:\n src_width, src_height = img.size\n src_ratio = float(src_width) / float(src_height)\n dst_width, dst_height = max_width, max_height\n dst_ratio = float(dst_width) / float(dst_height)\n\n if dst_ratio < src_ratio:\n crop_height = src_height\n crop_width = crop_height * dst_ratio\n x_offset = float(src_width - crop_width) / 2\n y_offset = 0\n else:\n crop_width = src_width\n crop_height = crop_width / dst_ratio\n x_offset = 0\n y_offset = float(src_height - crop_height) / 3\n img = img.crop(\n (int(x_offset), int(y_offset), int(x_offset) + int(crop_width), int(y_offset) + int(crop_height)))\n img = img.resize((dst_width, dst_height), pil.ANTIALIAS)\n\n tmp = StringIO()\n img.save(tmp, 'JPEG')\n tmp.seek(0)\n output_data = tmp.getvalue()\n input_file.close()\n tmp.close()\n\n return output_data","def resize_all_images(image_dir, width, height, resize_type):\n if width == \"\" and height == \"\":\n return 0\n print(\"Enter resizing image.\")\n print(\"Enter resizing.\", width)\n pool = Pool(1)\n pool.starmap(resize_image, zip(\n image_dir, itertools.repeat(width), itertools.repeat(height), itertools.repeat(resize_type)))\n pool.close()\n pool.join()","def executeMatlabScript(dHeader):\n\t# Now execute the info mat file creator\n\tcmd1 = r'\\rm $HOME/.setupenv 2> /dev/null; . /usr/local/etc/setup.sh; setup matlab71 > /dev/null;'\n\tcmd2 = '%s nohup matlab -nosplash < %s > /tmp/matlabCreateOutput.txt' % (cmd1, dHeader['tempFile'])\n\tres2 = getoutput(cmd2)\n\tinfoFile = dHeader['infoFile']\n\tcmd3 = r'chgrp pimsgrp %s; chmod g+rw %s' % (infoFile, infoFile)\n\tres3 = getoutput(cmd3)","def hxldedup():\n run_script(hxldedup_main)","def cmd_size(args):","def process_files(self):\n for filename in self.temp_directory.iterdir():\n im = Image.open(str(filename))\n scaled = im.resize((640, 480))\n scaled.save(str(filename))","def compute_scaler(args):\n workspace = args.workspace\n data_type = args.data_type\n dir_name = args.dir_name \n # Load data. \n t1 = time.time()\n hdf5_path = os.path.join(workspace, \"packed_features\", \"spectrogram\", data_type, dir_name, \"data.h5\")\n with h5py.File(hdf5_path, 'r') as hf:\n x = hf.get('x') \n x = np.array(x) # (n_segs, n_concat, n_freq)\n \n # Compute scaler. \n (n_segs, n_concat, n_freq) = x.shape\n x2d = x.reshape((n_segs * n_concat, n_freq))\n scaler = preprocessing.StandardScaler(with_mean=True, with_std=True).fit(x2d)\n print(scaler.mean_)\n print(scaler.scale_)\n \n # Write out scaler. \n out_path = os.path.join(workspace, \"packed_features\", \"spectrogram\", data_type, dir_name, \"scaler.p\")\n create_folder(os.path.dirname(out_path))\n pickle.dump(scaler, open(out_path, 'wb'))\n \n print(\"Save scaler to %s\" % out_path)\n print(\"Compute scaler finished! %s s\" % (time.time() - t1,))","def sizes(options):\n # import data\n pixels = dict() # volumes are given in #pixels\n snap_mask = \"/net/astrogate/export/astrodata/jgacon/filex/processing/\" \\\n \"export/f8_h50_v100_objs_snap_%d.csv\"\n snap_ids = np.arange(2,28+1)\n z = snapid2z(snap_ids)\n print z\n\n for id in snap_ids:\n snap = snap_mask % (id - 1) # fix: snap number one too low in filename\n pixels[id] = np.genfromtxt(snap)[1:-1,1] # row 2 contains volumes\n # rm void & halo volumes\n\n # visualise\n if \"err\" in options.keys():\n nums = np.array([pixels[id].size for id in snap_ids])\n avgs = np.array([np.mean(pixels[id]) for id in snap_ids])\n mods = np.array([st.mode(pixels[id])[0][0] for id in snap_ids])\n meds = np.array([np.median(pixels[id]) for id in snap_ids])\n stds = np.array([np.std(pixels[id]) for id in snap_ids])\n\n print mods\n print mods.shape\n\n plt.figure()\n plt.title(\"Sizes of filaments as function of redshift\")\n plt.xlabel(\"Redshift $z$\")\n plt.xticks(snap_ids[::3], z[::3])\n\n plt.ylabel(\"Size in #pixels\")\n\n plt.errorbar(snap_ids, avgs, yerr=stds, label=\"Mean\")\n plt.plot(snap_ids, mods, \"g\", label=\"Mode\")\n plt.plot(snap_ids, meds, \"c\", label=\"Median\")\n plt.legend(loc=\"best\")\n\n plt.twinx()\n plt.ylabel(\"#Filaments\", color=\"r\")\n plt.tick_params(\"y\", colors=\"r\")\n\n plt.plot(snap_ids, nums, \"r--\")\n\n plt.savefig(options[\"err\"])\n\n if \"dist\" in options.keys():\n targets = np.array([5,10,15,20,25])\n plt.figure()\n plt.title(\"Volume distribution of filaments\")\n plt.xlabel(\"Volume $V$ in #pixels\")\n plt.ylabel(\"#Element with $V$ / Total #Elements\")\n plt.xlim([0,1000])\n for target in targets:\n sns.kdeplot(pixels[target], label=\"$z$ = %f\" % snapid2z(target))\n plt.legend(loc=\"best\")\n plt.savefig(options[\"dist\"])\n\n if \"dist_inter\" in options.keys():\n default = snap_ids[-1]\n fig, ax = plt.subplots()\n plt.subplots_adjust(bottom=0.25)\n sns.kdeplot(pixels[int(default - 2)], ax=ax)\n plt.xlim([0, 1000])\n plt.ylim([0, 0.01])\n plt.xlabel(\"Volume $V$ of filaments in #pixels\")\n plt.ylabel(\"#Filaments with volume $V$ / Total #Filaments\")\n\n nums = np.array([pixels[id].size for id in snap_ids])\n ax2 = ax.twinx()\n ax2.set_ylabel(\"#Filaments\", color=\"r\", alpha=0.5)\n ax2.tick_params(axis=\"y\", labelcolor=\"r\")\n ax2_x = np.linspace(0, 1000, nums.size)\n ax2.plot(ax2_x, nums, \"r--\", alpha=0.5)\n point, = ax2.plot(ax2_x[default - 2], nums[default - 2], \"ro\", alpha=0.5)\n\n axcolor = 'lightgoldenrodyellow'\n axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)\n sid = Slider(axfreq, \"ID\", 2, 28, valinit=default, valstep=1)\n ax.set_title(\"$z$ = %f\" % snapid2z(default))\n\n def update(val):\n id = sid.val\n\n print id\n #ax.clear()\n ax.set_ydata()\n ax.set_xdata()\n ax.set_title(\"$z$ = %f\" % snapid2z(int(id)))\n ax.set_xlim([0,1000])\n ax.set_ylim([0, 0.01])\n sns.kdeplot(pixels[int(id)], ax=ax)\n point.set_xdata(ax2_x[int(id) - 2])\n point.set_ydata(nums[int(id) - 2])\n fig.canvas.draw_idle()\n sid.on_changed(update)\n\n plt.show()\n\n\n if \"hist\" in options.keys():\n conc = None\n for id, vols in pixels.iteritems():\n data = np.empty((vols.size, 2))\n data[:,0] = id\n data[:,1] = vols\n\n if conc is None:\n conc = data\n else:\n conc = np.vstack((conc, data))\n\n plt.figure()\n plt.hist2d(conc[:,0], conc[:,1], bins=(snap_ids.size, 1000))\n plt.ylim([100,400])\n plt.savefig(options[\"hist\"])","def execute(args, **kwargs):\n p = set_options()\n a = p.parse_args(args)\n # logging.info(str(a))\n\n ifiles = ImageFiles(a)\n\n if a.info:\n ifiles.describe()\n else:\n ifiles.write()","def main():\n\n parser = argparse.ArgumentParser(description=DESCRIPTION)\n parser.add_argument('-o', '--output', default='output.jpg',\n help=\"Filename to use for the output image\")\n parser.add_argument('INPUT_IMAGE', type=str,\n help=\"Filename for the input content image\")\n parser.add_argument('STYLE_IMAGE', type=str,\n help=\"Filename to for the input style image\")\n parser.add_argument('-s', '--style-weight', default=1e-2, type=float,\n help=\"Weight to apply for the loss associated with the style image. \"\n \"Making this bigger results in a final image more like the style image\")\n parser.add_argument('-c', '--content-weight', default=1e4, type=float,\n help=\"Weight to apply for the loss associated with the content image. \"\n \"Making this bigger results in a final image more like the content image\")\n parser.add_argument('-t', '--total-variation-loss', default=30, type=float,\n help=\"Weight for the total variation loss.\")\n\n args = parser.parse_args()\n\n print(args)","def ResizeVdisk(self, data, vdiskid, headers=None, query_params=None, content_type=\"application/json\"):\n uri = self.client.base_url + \"/vdisks/\"+vdiskid+\"/resize\"\n return self.client.post(uri, data, headers, query_params, content_type)","def make_script(name, data, target, execpath=False):\n exec_path = join('/tmp', name + '-script')\n target_path = join(target, 'tmp', name + '-script')\n sfile = file(target_path, 'w')\n sfile.write(data.read())\n sfile.close()\n os.system('chmod 755 %s' % target_path)\n if not execpath:\n return target_path\n else:\n # for chroot target exec_path\n return exec_path","def run(cls, h5_src, h5_dst, var_attrs, version=None, meta=None,\n process_size=None, check_dset_attrs=False, resolution=None):\n logger.info('Rechunking {} to {} using chunks given in {}'\n .format(h5_src, h5_dst, var_attrs))\n try:\n with cls(h5_src, h5_dst, version=version) as r:\n r.rechunk(var_attrs, meta=meta, process_size=process_size,\n check_dset_attrs=check_dset_attrs,\n resolution=resolution)\n\n logger.info('{} complete'.format(h5_dst))\n except Exception:\n logger.exception(\"Error rechunking {}\".format(h5_src))\n raise","def main():\n\n try:\n worker_id = get_arg(\"--worker-id\", assert_nonnegative_int)\n master_host = get_arg(\"--master-host\", assert_host)\n master_port = get_arg(\"--master-port\", assert_positive_int)\n scale = get_arg(\"--scale\", assert_pos_float)\n method = get_arg(\"--method\", assert_downscaling_method)\n load_backup = get_arg(\"--load-backup\", assert_bool, default=0)\n number_of_random_walkers = get_arg(\n \"--n-random-walkers\", assert_nonnegative_int, default=1)\n backup_size = get_arg(\n \"--backup-size\", assert_nonnegative_int, default=100)\n walking_iterations = get_arg(\n \"--walking-iterations\", assert_positive_int, default=1)\n\n except AssertionError as e:\n print_error(e)\n print_error(\n \"The downscaling worker expects the following arguments:\\n\"\n \"\\t--worker-id: The id of the worker\\n\"\n \"\\t--master-host: The host of the master\\n\"\n \"\\t--master-port: The port of the master\\n\"\n \"\\t--scale: The scale of the downscaled graph w.r.t. the input graph\\n\"\n \"\\t--method: The method to use for downscaling, `random_walk` or `random_edge`\\n\"\n \"\\t--load-backup: Should the worker load from a backup send by the master\\n\"\n \"\\t--n-random-walkers: Number of random walkers to start with\\n\"\n \"\\t--backup-size: Minimum size of the backup before it will be send to the master during a run, 0 if you want no backups\\n\"\n \"\\t--walking-iterations: The number of steps a random walker sets before the queue will be handled\\n\"\n )\n return\n\n Worker(worker_id, master_host, master_port, scale, method, load_backup,\n number_of_random_walkers, backup_size, walking_iterations)","def main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-slices\", type=int, required=True)\n args = parser.parse_args()\n\n upload_model_data(args.slices)","def handle(self, args):\n\n # Ensure there is an uploads directory\n if not os.path.exists(args.uploads) or not os.path.isdir(args.uploads):\n if args.force:\n os.makedirs(args.uploads)\n else:\n raise ConsoleError(\n \"no uploads directory at '{}' use -f to create\".format(args.uploads)\n )\n\n for dataset in args.datasets:\n # Remove the trailing slash\n dataset = dataset.rstrip(os.path.sep)\n\n # Check if the dataset is valid\n if not args.force and not is_valid(dataset, False):\n print(color.format(\"cannot package invalid dataset at {}\", color.LIGHT_RED, dataset))\n continue\n\n name = os.path.basename(dataset)\n out = os.path.join(args.uploads, name)\n\n if not args.force and os.path.exists(out+\".zip\"):\n print(color.format(\"dataset exists at {} use -f to overwrite\", color.LIGHT_YELLOW, out))\n continue\n\n try:\n out = shutil.make_archive(out, \"zip\", root_dir=os.path.dirname(dataset), base_dir=name)\n self.update_manifest(out)\n except Exception as e:\n print(color.format(\"could not package dataset {} at {}: {}\", color.LIGHT_RED, dataset, out.rstrip(\".zip\")+\".zip\", e))\n continue\n\n print(color.format(\"packaged dataset at {}\", color.LIGHT_GREEN, out))","def resize(self, *args):\n return _ida_hexrays.lvar_saved_infos_t_resize(self, *args)","def image_size():\n return eval(subprocess(\"print camera_image_size()\"))","def test_im_file_resize(self):\n self._test_img_resize(IMBackend())","def generateScaled(img):\n\n if not img._scaledfn:\n return\n\n aimgfn = join(opts.root, img._filename)\n if not opts.fast:\n img._size = imageSize(aimgfn)\n\n ascaledfn = join(opts.root, img._scaledfn)\n\n if opts.scaled_force:\n if opts.quiet: print \"forced regeneration of '%s'\" % img._scaledfn\n elif not exists(ascaledfn):\n if opts.quiet: print \"thumbnail absent '%s'\" % img._scaledfn\n else:\n # Check if scaledsize has changed\n if not opts.fast:\n img._scaledsize = imageSize(ascaledfn)\n if not checkThumbSize(img._size, \\\n img._scaledsize, \\\n opts.scaled_size):\n if opts.quiet: print \"thumbnail '%s size has changed\" % img._scaledfn\n try:\n # Clear cache for thumbnail size.\n del imageSizeCache[ ascaledfn ]\n except:\n pass\n else:\n if opts.quiet: print \"thumbnail '%s' already generated (size ok)\" \\\n % img._scaledfn\n return\n else:\n if opts.quiet: print \"thumbnail '%s' already generated\" % img._scaledfn\n return\n\n if opts.no_magick:\n if opts.quiet: print \"ImageMagick tools disabled, can't create thumbnail\"\n return\n\n # create necessary directories\n d = dirname(ascaledfn)\n if not exists(d):\n os.makedirs(d)\n\n if opts.pil:\n\n try:\n im = PilImage.open(aimgfn)\n im.thumbnail((opts.scaled_size, opts.scaled_size), config.ScaledImages[\"Interpolation\"])\n im.save(ascaledfn)\n\n img._scaledsize = im.size\n except IOError, e:\n raise SystemExit(\\\n \"Error: identifying file '%s'\" % aimgfn + str(e))\n\n else:\n\n cmd = getMagickProg('convert') + ' -border 2x2 '\n # FIXME check if this is a problem if not specified\n #cmd += '-interlace NONE '\n\n cmd += '-geometry %dx%d ' % (opts.scaled_size, opts.scaled_size)\n\n if opts.scaled_quality:\n cmd += '-quality %d ' % opts.scaled_quality\n\n # This doesn't add text into the picture itself, just the comment in\n # the header.\n if opts.copyright:\n cmd += '-comment \\\"%s\\\" ' % opts.copyright\n\n # We use [1] to extract the thumbnail when there is one.\n # It is harmless otherwise.\n subimg = \"\"\n if img._ext.lower() in [ \".jpg\", \".tif\", \".tiff\" ]:\n subimg = \"[1]\"\n\n cmd += '\"%s%s\" \"%s\"' % (aimgfn, subimg, ascaledfn)\n\n if opts.quiet: print \"generating thumbnail '%s'\" % img._scaledfn\n\n (chin, chout, cherr) = os.popen3(cmd)\n errs = cherr.readlines()\n chout.close()\n cherr.close()\n if errs:\n print >> sys.stderr, \\\n \"Error: running convert program on %s:\" % aimgfn\n errs = string.join(errs, '\\n')\n print errs\n\n if subimg and \\\n re.compile('Unable to read subimage').search(errs):\n if opts.quiet: print \"retrying without subimage\"\n cmd = string.replace(cmd, subimg, \"\")\n\n (chin, chout, cherr) = os.popen3(cmd)\n errs = cherr.readlines()\n chout.close()\n cherr.close()\n if errs:\n print >> sys.stderr, \\\n \"Error: running convert program on %s:\" % aimgfn\n print string.join(errs, '\\n')\n\n else:\n img._scaledsize = imageSize(ascaledfn)","def resize_multiple_images(src_path, dst_path):\n for filename in os.listdir(src_path):\n img=Image.open(src_path+'/'+filename)\n new_img = img.resize((96,96,))\n #new_img.resize(96,96,1)\n if not os.path.exists(dst_path):\n os.makedirs(dst_path)\n new_img.save(dst_path+'/'+filename)\n print('Resized and saved {} successfully.'.format(filename))","def _run(gridded_eval_dir_names, ungridded_eval_dir_names,\n model_description_strings, output_dir_name):\n\n file_system_utils.mkdir_recursive_if_necessary(\n directory_name=output_dir_name\n )\n\n num_models = len(gridded_eval_dir_names)\n assert len(ungridded_eval_dir_names) == num_models\n assert len(model_description_strings) == num_models\n\n model_description_strings_dashed = [\n s.replace('_', '-') for s in model_description_strings\n ]\n # model_description_strings = [\n # s.replace('_', ' ') for s in model_description_strings\n # ]\n\n pathless_panel_file_names = [\n 'attributes_diagram.jpg', 'performance_diagram.jpg',\n 'mean_forecast_prob.jpg'\n ]\n num_scores = len(pathless_panel_file_names)\n\n panel_file_names = []\n resized_panel_file_names = []\n\n for i in range(num_models):\n for j in range(num_scores):\n this_file_name = '{0:s}/{1:s}'.format(\n gridded_eval_dir_names[i] if j == num_scores - 1\n else ungridded_eval_dir_names[i],\n pathless_panel_file_names[j]\n )\n panel_file_names.append(this_file_name)\n\n this_file_name = '{0:s}/{1:s}_{2:s}.jpg'.format(\n output_dir_name, pathless_panel_file_names[j].split('.')[0],\n model_description_strings_dashed[i]\n )\n resized_panel_file_names.append(this_file_name)\n\n letter_label = None\n\n for k in range(len(panel_file_names)):\n print('Resizing panel and saving to: \"{0:s}\"...'.format(\n resized_panel_file_names[k]\n ))\n\n if letter_label is None:\n letter_label = 'a'\n else:\n letter_label = chr(ord(letter_label) + 1)\n\n imagemagick_utils.trim_whitespace(\n input_file_name=panel_file_names[k],\n output_file_name=resized_panel_file_names[k]\n )\n _overlay_text(\n image_file_name=resized_panel_file_names[k],\n x_offset_from_left_px=0, y_offset_from_top_px=225,\n text_string='({0:s})'.format(letter_label),\n font_size=PANEL_LETTER_FONT_SIZE, use_north_gravity=False\n )\n\n imagemagick_utils.trim_whitespace(\n input_file_name=resized_panel_file_names[k],\n output_file_name=resized_panel_file_names[k]\n )\n imagemagick_utils.resize_image(\n input_file_name=resized_panel_file_names[k],\n output_file_name=resized_panel_file_names[k],\n output_size_pixels=PANEL_SIZE_PX\n )\n\n concat_figure_file_name = '{0:s}/evaluation_comparison.jpg'.format(\n output_dir_name\n )\n print('Concatenating panels to: \"{0:s}\"...'.format(concat_figure_file_name))\n\n imagemagick_utils.concatenate_images(\n input_file_names=resized_panel_file_names,\n output_file_name=concat_figure_file_name,\n num_panel_rows=num_models, num_panel_columns=num_scores\n )\n imagemagick_utils.resize_image(\n input_file_name=concat_figure_file_name,\n output_file_name=concat_figure_file_name,\n output_size_pixels=CONCAT_FIGURE_SIZE_PX\n )\n imagemagick_utils.trim_whitespace(\n input_file_name=concat_figure_file_name,\n output_file_name=concat_figure_file_name\n )","def main():\n\tparser = construct_parser()\n\targs = parser.parse_args()\n\ttiles = slice(args.image, args.num_tiles, save=False)\n\tsave_tiles(tiles, prefix=get_basename(args.image), directory=args.dir,\n\t\t format=args.format)","def dist():\n PackCommandExecutor().pack()\n DistCommandExecutor().dist()","def run(self, dataset_fixture, run_name_prefix=\"test\"):\n self.dataset = dataset_fixture\n self.set_project_shortname(run_name_prefix)\n self.upload_spreadsheet_and_create_submission()\n self.wait_for_ingest_to_process_spreadsheet_files_tab()\n self.get_upload_area_credentials()\n self.stage_data_files()\n self.wait_for_envelope_to_be_validated()\n if self.export_bundles:\n self.complete_submission()\n if run_name_prefix == \"scale\":\n # == Scaling Logic ==\n self.wait_for_primary_bundles_analysis_workflows_and_results_bundles()\n else:\n # == Non-scaling Logic ==\n self.wait_for_primary_bundles()\n self.wait_for_analysis_workflows()\n self.wait_for_secondary_bundles()\n\n self.assert_data_browser_bundles()\n if self.deployment != 'prod':\n self.retrieve_loom_output_from_matrix_service()\n\n if self.failure_reason:\n raise RuntimeError(self.failure_reason)","def run(self, image, replicas, scale_replicas, command=None,\n status_wait=True):\n namespace = self.choose_namespace()\n\n name = self.client.create_statefulset(\n namespace=namespace,\n replicas=replicas,\n image=image,\n command=command,\n status_wait=status_wait\n )\n\n self.client.scale_statefulset(\n name,\n namespace=namespace,\n replicas=scale_replicas,\n status_wait=status_wait\n )\n\n self.client.scale_statefulset(\n name,\n namespace=namespace,\n replicas=replicas,\n status_wait=status_wait\n )\n\n self.client.delete_statefulset(\n name=name,\n namespace=namespace,\n status_wait=status_wait\n )","def gen_vmd_script(\n mole,\n grads,\n filename=\"molecule.xyz\",\n scriptname=\"molecule.vmd\",\n colorid=1,\n scalex=10,\n):\n if colorid > 15:\n colorid = 0\n output = _vmd_script_start()\n output += _vmd_script_molecule(mole, filename)\n output += _vmd_script_vectors(mole, grads, colorid, scalex)\n with open(scriptname, \"w\") as f:\n f.write(output)","def test_resize(dummy_input):\n # Test the 2D image: H, W, C\n image, label = dummy_input(image_size=(512, 512, 3),\n label_size=(512, 512, 1))\n transform = Resize(size=(64, 64))\n _image, _label = transform(image, label, resize_orders=[3, 0])\n assert _image.shape == (64, 64, 3)\n assert _image.dtype == image.dtype\n assert _label.shape == (64, 64, 1)\n assert _label.dtype == label.dtype\n\n # Test the 3D image: H, W, D, C\n image, label = dummy_input(image_size=(512, 512, 20, 3),\n label_size=(512, 512, 20, 1))\n transform = Resize(size=(64, 64, 10))\n _image, _label = transform(image, label, resize_orders=[3, 0])\n assert _image.shape == (64, 64, 10, 3)\n assert _image.dtype == image.dtype\n assert _label.shape == (64, 64, 10, 1)\n assert _label.dtype == label.dtype","def main():\n nb_processed = 0\n for dataset_name in DATASETS:\n print(\"-----------------\")\n print(\"Dataset: '%s'\" % (dataset_name,))\n print(\"-----------------\")\n\n dataset_dir = os.path.join(WRITE_MAIN_DIR, dataset_name)\n if not os.path.isdir(dataset_dir):\n os.makedirs(dataset_dir)\n\n dataset = Dataset([os.path.join(READ_MAIN_DIR, dataset_name)])\n print(\"Found %d images total.\" % (len(dataset.fps),))\n\n errors = []\n\n scale_height, scale_width = SCALES[dataset_name]\n target_aspect_ratio = scale_width / scale_height\n\n # iterate over every image in the current dataset,\n # augment that image N times, add cols/rows until target aspect ratio\n # is reached, resize it (e.g. 64x64), save it\n for img_idx, (image_filepath, image) in enumerate(zip(dataset.fps, dataset.get_images())):\n print(\"[%s] Image %d of %d (%.2f%%)...\" \\\n % (dataset_name, img_idx+1, len(dataset.fps),\n 100*(img_idx+1)/len(dataset.fps)))\n\n # IOErrors during loading of images result here in a None value\n if image is None:\n print(\"Error / None\")\n errors.append((\n image_filepath,\n \"Failed to load image '%s' (idx %d for dataset %s)\" \\\n % (image_filepath, img_idx, dataset_name)\n ))\n else:\n # resize too big images to smaller ones before any augmentation\n # (for performance reasons)\n height = image.shape[0]\n width = image.shape[1]\n aspect_ratio = width / height\n if width > 1000 or height > 1000:\n image = misc.imresize(image, (1000, int(1000 * aspect_ratio)))\n\n # augment image\n # converts augmented versions automatically to float32, 0-1\n augmentations = augment(image, **AUGMENTATIONS[dataset_name])\n\n # create list of original image + augmented versions\n images_aug = [image / 255.0]\n images_aug.extend(augmentations)\n\n # for each augmented version of the images:\n # resize it to target aspect ratio (e.g. same width and height),\n # save it\n for aug_idx, image_aug in enumerate(images_aug):\n image_aug = to_aspect_ratio_add(image_aug, target_aspect_ratio)\n filename = \"{:0>6}_{:0>3}.jpg\".format(img_idx, aug_idx)\n img_scaled = misc.imresize(image_aug, (scale_height, scale_width))\n misc.imsave(os.path.join(dataset_dir, filename), img_scaled)\n\n nb_processed += 1\n\n print(\"Processed %d images with %d errors.\" % (nb_processed, len(errors)))\n for (fp, err) in errors:\n print(\"File %s error:\" % (fp,))\n print(err)\n print(\"Finished.\")","def sizes(self, results_url=None, perf_id=None, platform=None, **kwargs):\n sizes_script = self.package_repo_resource(\n 'scripts', 'slave', 'chromium', 'sizes.py')\n sizes_args = ['--target', self.c.build_config_fs]\n if platform:\n sizes_args.extend(['--platform', platform])\n else:\n sizes_args.extend(['--platform', self.c.TARGET_PLATFORM])\n\n run_tests_args = ['--target', self.c.build_config_fs,\n '--no-xvfb']\n properties_json = self.m.json.dumps(self.m.properties.legacy())\n run_tests_args.extend(['--factory-properties', properties_json,\n '--build-properties', properties_json])\n run_tests_args.extend(['--test-type=sizes',\n '--builder-name=%s' % self.m.properties['buildername'],\n '--slave-name=%s' % self.m.properties['bot_id'],\n '--build-number=%s' % self.m.properties['buildnumber'],\n '--run-python-script'])\n\n if perf_id:\n assert results_url is not None\n run_tests_args.extend(['--annotate=graphing',\n '--results-url=%s' % results_url,\n '--perf-dashboard-id=sizes',\n '--perf-id=%s' % perf_id])\n\n # If we have a clang revision, add that to the perf data point.\n # TODO(hans): We want this for all perf data, not just sizes.\n if hasattr(self, '_clang_version'):\n clang_rev = re.match(r'(\\d+)(-\\d+)?', self._clang_version).group(1)\n run_tests_args.append(\n \"--perf-config={'r_clang_rev': '%s'}\" % clang_rev)\n\n full_args = run_tests_args + [sizes_script] + sizes_args\n\n return self.m.build.python(\n 'sizes', self.package_repo_resource('scripts', 'slave', 'runtest.py'),\n full_args, allow_subannotations=True, **kwargs)","def createRestoreScript(self, wrapper):\n content = textwrap.dedent(\"\"\"\\\n #!/bin/sh\n %(bin)s/pg_restore \\\\\n --host=%(pgdata-directory)s \\\\\n --username postgres \\\\\n --dbname=%(dbname)s \\\\\n --clean \\\\\n --no-owner \\\\\n --no-acl \\\\\n %(backup-directory)s/database.dump\n \"\"\" % self.options)\n self.createExecutable(wrapper, content=content)","def resize(self):\n pass","def run_script(self, params, config_no):\n from dials.algorithms.scaling.algorithm import ScalingAlgorithm\n\n params.scaling_options.__setattr__(\"use_free_set\", True)\n algorithm = ScalingAlgorithm(\n params,\n experiments=deepcopy(self.experiments),\n reflections=deepcopy(self.reflections),\n )\n algorithm.run()\n results = self.get_results_from_script(algorithm)\n self.add_results_to_results_dict(config_no, results)","def run(self, data):\n required = {'token', 'image_id', 'script'}\n api.validate(data, required)\n token = data['token']\n image_id = data['image_id']\n script = data['script']\n detach = data.get('detach', False)\n if not detach:\n detach = False\n host_dir = data.get('host_dir', None)\n docker_dir = data.get('docker_dir', None)\n working_dir = data.get('working_dir', None)\n # cgroup = data.get('cgroup', None)\n # TODO(jorgesece): control image private\n# credentials_module.authorize_image(\n # token,\n # image_id\n # )\n if host_dir:\n self.credentials_module.authorize_directory(token, host_dir)\n job_info = self.credentials_module.get_job_from_token(token)\n cgroup_parent = job_info.get('cgroup', None)\n container_id = self.docker_module.run_container(\n image_id,\n detach,\n script,\n host_dir=host_dir,\n docker_dir=docker_dir,\n working_dir=working_dir,\n cgroup=cgroup_parent\n )\n self.credentials_module.add_container(token, container_id)\n self.docker_module.start_container(container_id)\n if not detach:\n results = self.docker_module.logs_container(container_id)\n else:\n results = container_id\n return results","def prepare_data(oh_id):\n imputer_record = ImputerMember.objects.get(oh_id=oh_id, active=True)\n imputer_record.step = 'prepare_data'\n imputer_record.save()\n os.chdir(settings.BASE_DIR)\n\n command = [\n 'imputer/prepare_genotypes.sh', '{}'.format(oh_id), '{}'.format(imputer_record.variant_length)\n ]\n process = run(command, stdout=PIPE, stderr=PIPE)\n if process.stderr:\n logger.debug(process.stderr)\n logger.info('finished preparing {} plink data'.format(oh_id))","def resize(self, *args):\n return _ida_hexrays.hexwarns_t_resize(self, *args)","def test_08_migrate_vm_live_resize_volume(self):\n global vm\n global data_disk_1\n\n vol = self.helper.resize_volume(apiclient = self.apiclient, volume = data_disk_1, shrinkOk = False, maxiops = 15000)\n # Migrate all volumes and VMs\n destinationHost, vol_list = self.helper.get_destination_pools_hosts(self.apiclient, vm, self.host)\n for v in vol_list:\n self.helper.check_storpool_volume_iops(self.spapi, v)\n vm = self.helper.migrateVm(self.apiclient, self.virtual_machine_live_migration_1, destinationHost)\n\n destinationHost, vol_list = self.helper.get_destination_pools_hosts(self.apiclient, vm, self.host)\n for v in vol_list:\n self.helper.check_storpool_volume_iops(self.spapi, v)","def test_pil_file_resize(self):\n self._test_img_resize(PILBackend())","def run_multinest(shower_name):\n\tos.system('cp '+datafile_dir+'datafile_'+shower_name+' '+multinest_dir\n\t\t+'datafile')\n\n\t# Multinest's mass.sh fails unless actually run from its directory\n\tworking_dir = os.getcwd()\n\tos.chdir(multinest_dir)\n\tos.system('sh mass.sh')\n\tos.chdir(working_dir)\n\n\tos.rename(multinest_dir + 'gr.out', fit_dir + 'fit_' + shower_name + '.out')","def test_resize_machine(self, pretty_print, owner_api_token):\n query_string = setup_data.get('resize_machine', {}).get(\n 'query_string') or [('size', '9417745961a84bffbf6419e5of68faa5')]\n machine = setup_data.get('resize_machine', {}).get('machine') \\\n or setup_data.get('machine') or 'my-machine'\n uri = MIST_URL + \\\n '/api/v2/machines/{machine}/actions/resize'.format(machine=machine)\n request = MistRequests(\n api_token=owner_api_token,\n uri=uri,\n params=query_string)\n request_method = getattr(request, 'POST'.lower())\n response = request_method()\n if 'resize_machine' in REDIRECT_OPERATIONS:\n assert_response_found(response)\n else:\n assert_response_ok(response)\n assert poll(\n api_token=owner_api_token,\n uri=setup_data['amazon_machine_uri'],\n data={'actions': {'start': True}},\n timeout=_setup_module.DEFAULT_TIMEOUT)\n print('Success!!!')","def run(n):\n base_files = os.listdir(DATA_DIR)\n\n # Keep track of files we have uploaded\n uploaded = []\n\n # Upload files\n for _ in range(n):\n files = random.choices(base_files, k=random.randint(1, 15))\n name, checksum = assemble_file(files)\n upload_file(name)\n os.remove(name)\n uploaded.append((name, checksum))\n\n # Download files\n for name, checksum in uploaded:\n download_and_validate_checksum(name, checksum)\n\n # Validation checks\n check_pack_sizes()\n check_pack_checksums()\n\n print(server_stats())\n\n # Delete all files except for the last two. This should force the vacuum to rebalance\n # some packfiles.\n to_delete = uploaded[:-2]\n remaining = uploaded[-2:]\n for name, _ in to_delete:\n delete_file(name)\n\n # Run a vacuum and wait for it to complete\n vacuum_id = vacuum()\n status = None\n for _ in range(10):\n status = vacuum_status(vacuum_id)\n if status != \"RUNNING\":\n break\n time.sleep(1)\n if status != \"SUCCEEDED\":\n raise ValueError(f\"vacuum failed {status}\")\n\n # Check that the remaining files can still be downloaded after the vacuum\n for name, checksum in remaining:\n download_and_validate_checksum(name, checksum)","def photo2web_process_hattenbach():\n\n os.chdir('/Volumes/SSD External/Hattenbach_v2')\n \n dir_base = os.getcwd()\n \n dir_p2w = '/Users/throop/photos/Trips/'\n \n dirs = sorted(glob.glob(os.path.join(dir_base, '*')))\n \n quality_out = '60'\n size_out = '2000x2000'\n \n for i,dir in enumerate(dirs):\n if os.path.isdir(dir):\n os.chdir(dir)\n dir_originals = os.path.join(dir, 'originals')\n dir_originals_fullres = os.path.join(dir, 'originals_fullres')\n\n# For HH files, copy the 'actual' originals into a 'fullres' folder, for safekeeping\n\n if not os.path.isdir(dir_originals_fullres):\n os.rename(dir_originals, dir_originals_fullres)\n os.mkdir(dir_originals)\n \n files = glob.glob(os.path.join(dir_originals_fullres, '*'))\n\n# Get a list of all the images\n\n# For each image, make a low-res, low-quality image. This is just because the scanned files\n# are huge and high-quality, and not useful for online. They are much larger than necessary. \n# So we use 'convert' to shrink them in size and quality, and put the output into 'originals' directory \n# for photo2web.\n\n for file in files:\n file_short = os.path.basename(file)\n file_in = os.path.join(dir_originals_fullres,file_short)\n file_out = os.path.join(dir_originals,file_short)\n if not os.path.isfile(file_out):\n cmd = (f'convert -resize {size_out} -quality {quality_out}' +\n f' {file_in}' +\n f' {file_out}')\n print(f'{cmd}')\n \n subprocess.run(['convert', '-resize', size_out, '-quality', quality_out,\n file_in,\n file_out])\n\n# Now, finally, go thru and do photo2web on all of them.\n \n print(f'\\nProcessing directory {i}/{len(dirs)} {dir}\\n')\n subprocess.run(['cp', '-r', os.path.join(dir_p2w, 'header.txt'), '.'])\n subprocess.run(['cp', '-r', os.path.join(dir_p2w, 'photos.css'), '.'])\n if not os.path.exists('captions.txt'):\n subprocess.run(['captions_photo2web']) \n subprocess.run(['photo2web_old'])\n subprocess.run(['photo2web'])","def test_run_resize_with_padded_blobkey(self):\n padded_blobkey = 'SomeBlobKey==='\n self.expect_datatore_lookup(padded_blobkey, True)\n self.expect_open_image(padded_blobkey, (1600, 1200))\n self.expect_resize(32)\n self.expect_encode_image('SomeImageSize32')\n self.mox.ReplayAll()\n self._environ['PATH_INFO'] += '====s32'\n self.run_request('image/jpeg', 'SomeImageSize32')","def run_populate(rconfig, limit, verbose=False):\n\n output_name = DOCUMENT_PROCESSING_IO[POPULATE]['out']\n dataset = DataSet(POPULATE, output_name, rconfig)\n\n # initialize data set if it does not exist, this is not contingent on\n # anything because --populate is the first step\n if not dataset.exists():\n dataset.initialize_on_disk()\n dataset.load_from_disk()\n\n fspecs = get_lines(rconfig.filenames, dataset.files_processed, limit)\n print \"[--populate] adding %d files to %s\" % (len(fspecs), dataset)\n count = 0\n for fspec in fspecs:\n count += 1\n src_file = fspec.source\n dst_file = os.path.join(rconfig.target_path, 'data', output_name,\n dataset.version_id, 'files', fspec.target)\n # allow for compressed files, while being handed the name without\n # extension\n if not os.path.exists(src_file):\n src_file += \".gz\"\n dst_file += \".gz\"\n if verbose:\n print \"[--populate] %04d %s\" % (count, dst_file)\n ensure_path(os.path.dirname(dst_file))\n try:\n shutil.copyfile(src_file, dst_file)\n except IOError:\n print \" WARNING: source file does not exist, not copying\"\n print \" %s\" % src_file\n # at some point there seemed to be an issue with compressing for Chinese,\n # so added this to do language dependent compressing, there is now no\n # difference for the population phase\n if rconfig.language == 'en': compress(dst_file)\n elif rconfig.language == 'cn': compress(dst_file)\n # TODO: does this mean that you miss some if total_count % STEP != 0\n if count % STEP == 0:\n dataset.update_processed_count(STEP)\n\n return (count % STEP, [dataset])","def main():\n config = configparser.ConfigParser()\n config.read('dwh.cfg')\n try: \n conn = psycopg2.connect(\"host={} dbname={} user={} password={} port={}\".format(*config['CLUSTER'].values()))\n cur = conn.cursor()\n \n # copy data from S3 to staging tables in redshift\n print('Staging song and log data. This takes several minutes!')\n execute_query_list(cur, conn, copy_table_queries)\n \n print('Populating analytics tables.')\n # fill analytical tables from staging tables\n execute_query_list(cur, conn, insert_table_queries)\n finally:\n conn.close()","def Resize_data(data_size):\n\n data_size = data_size\n\n str_taille = 0\n\n data_size_n = data_size - str_taille\n\n taille = data_size_n//3\n\n reste = data_size_n - (taille*3)\n\n taille_sup = taille+reste\n\n return (taille_sup,taille)","def prepare_test_data(args):\n image_dir = args.test_image_dir\n\n files = os.listdir(image_dir)\n files = [f for f in files if f.lower().endswith('.png')]\n\n img_ids = list(range(len(files)))\n img_files = []\n img_heights = []\n img_widths = []\n \n for f in files:\n img_path = os.path.join(image_dir, f)\n img_files.append(img_path)\n img = cv2.imread(img_path)\n img_heights.append(img.shape[0]) \n img_widths.append(img.shape[1]) \n\n print(\"Building the testing dataset...\")\n dataset = DataSet(img_ids, img_files, img_heights, img_widths)\n print(\"Dataset built.\")\n return dataset","def _create_tensor_dataset(self, img_size):\n with tempfile.TemporaryDirectory() as tmpdirname:\n dir_mimic = '/cluster/work/vogtlab/Projects/mimic-cxr/physionet.org/files/mimic-cxr-jpg/2.0.0/files'\n if os.path.exists(dir_mimic):\n # only run test if original data exists\n\n if os.path.exists('/cluster/home/klugh/'):\n tmpdirname = os.path.join(os.path.expandvars('$TMPDIR'), 'test_create_tensor_dataset')\n if not os.path.exists(tmpdirname):\n os.mkdir(tmpdirname)\n dir_out = os.path.expanduser(os.path.join(tmpdirname, 'dir_out'))\n\n dir_base_resized_compressed = os.path.expanduser(os.path.join(tmpdirname))\n self.assertTrue(os.path.exists(tmpdirname))\n\n dir_base_resize = os.path.join(tmpdirname, f'files_small_{img_size[0]}')\n dataset_creator = CreateTensorDataset(dir_base_resize=dir_base_resize, dir_mimic=dir_mimic,\n dir_out=dir_out,\n img_size=img_size,\n dir_base_resized_compressed=dir_base_resized_compressed,\n max_it=10)\n dataset_creator()\n self.assertTrue(os.path.exists(dir_out))\n self.assertTrue(os.path.exists(dir_base_resized_compressed))\n\n assert os.path.exists(os.path.join(dir_base_resized_compressed,\n f'mimic_resized_{img_size[0]}.zip')), \\\n 'dir_resized_compressed {} does not exist \\n {}'.format(\n os.path.join(dir_base_resized_compressed, f'mimic_resized_{img_size[0]}.zip'),\n os.listdir(dir_base_resized_compressed))","def scale_datanode(self, datanode_count):\n call([Command.docker_compose, \"-f\", self.docker_compose_file,\n \"up\", \"-d\", \"--scale\", \"datanode=\" + datanode_count])","def main():\n\n # Just grab all files - we'll use try/except to filter\n images = glob.glob(os.path.join(args.input_dir, '*.*'))\n if not os.path.exists(args.output_dir):\n os.makedirs(args.output_dir)\n for img_file in images:\n print(img_file)\n try:\n np_img = plt.imread(img_file)\n print(np_img.shape)\n img_name = img_file.split(os.sep)[-1]\n new_img_file = os.path.join(args.output_dir, img_name)\n pad_image(np_img, new_img_file)\n except Exception as e:\n print('Warning: {}. Skpping file.'.format(e))\n continue","def main(args):\n args = parse_args(args)\n setup_logging(args.loglevel)\n _logger.debug(\"Starting crazy calculations...\")\n createlookuptable(args.imagefolder)\n _logger.info(\"Script ends here\")","def main():\n logging.info(\"Executing data quality module\")\n\n calculate_quality()","def scale(self, infile: str, outfile: str):\n\n with Image(filename=infile) as src:\n width, height = self.new_dimensions(src)\n with src.clone() as dest:\n dest.resize(width=width,height=height)\n logging.debug(\n f'Converted \"{infile}\" ({src.width}x{src.height})'\n f' -> \"{outfile}\" ({dest.width}x{dest.height})')\n dest.save(filename=outfile)","def run(input, size, engine, resize_method, imagesize, api_key):\n\tengine_list = ['duckgo', 'bing', 'bing_api', 'flickr_api']\n\tclick.clear()\n\n\tif input and engine in engine_list:\n\t\tfactory = SearchEngineFactory(input,size,input,resize_method,\"dataset\",imagesize, engine, api_key)\n\t\t# Remove corrupt files\n\t\tremove_corrupt(\"dataset\")\n\n\telse:\n\t\trich.print(\"Please provide a valid name\")","def execute(in_file, resolution, width, height, raster_mode):\n\n raster_tmpfile = tempfile.NamedTemporaryFile()\n\n # Convert width and height to DPI.\n width = (width/72) * resolution\n height = (height/72) * resolution\n\n args = [config.gs,\n \"-q\",\n \"-dBATCH\",\n \"-dNOPAUSE\",\n \"-r%s\" % resolution,\n \"-g%dx%d\" % (width, height),\n \"-sDEVICE=%s\" % raster_mode,\n \"-sOutputFile=%s\" % raster_tmpfile.name,\n \"-\",\n ]\n # Execute ghostscript\n log.debug(\"Opening ghostscript process with args: %s\" % args)\n process = subprocess.Popen(args, cwd=config.tmp_dir,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n stdin=subprocess.PIPE)\n (stdoutdata, stderrdata) = process.communicate(in_file.read())\n log.debug(\"Waiting on ghostscript to terminate.\")\n process.wait()\n\n # Check that ghostscript functioned correctly.\n if process.returncode != 0:\n log.crit(\"Ghostscript failed during execution with returncode = %s.\"\n % process.returncode)\n\n # Get raster information into a StringIO\n log.debug(\"Rewinding ghostscript raster output and storing it in StringIO\")\n raster_tmpfile.seek(0)\n raster = raster_tmpfile\n\n # Gather the vector information into a StringIO\n log.debug(\"Gathering the vector output from ghostscript.\")\n vector = StringIO()\n vector.write(stdoutdata)\n vector.seek(0)\n\n log.debug(\"Returning ghostscript raster and vector output.\")\n return (raster, vector)","def _make_resized_data(self, odata):\n\n\n nmax = odata['file_id'].shape[1]\n new_nmax = odata['ncutout'].max()\n if new_nmax < 2:\n new_nmax = 2\n temp_obj_data = odata\n\n nobj = temp_obj_data.size\n\n new_data = meds.util.get_meds_output_struct(\n nobj,\n new_nmax,\n extra_fields=self._get_fields(new_nmax),\n )\n new_data = self._add_cat_fields(new_data, copy=False)\n\n for name in new_data.dtype.names:\n if name in temp_obj_data.dtype.names:\n\n shape = new_data[name].shape\n lshape = len(shape)\n\n if lshape > 1 and shape[1] == new_nmax:\n new_data[name][:,:] = temp_obj_data[name][:,0:new_nmax]\n else:\n new_data[name][:] = temp_obj_data[name][:]\n\n del temp_obj_data\n\n return new_data","def resize(file_path, width, height):\n folder, file_name, ext = parse_file_path(file_path)\n url = addr + '/img/resize'\n\n img = cv2.imread(file_path)\n _, img_encoded = cv2.imencode('.jpg', img)\n payload = img_encoded.tostring()\n \n response = requests.post(url, data=payload, params = {'w':width, 'h':height})\n if response.status_code != 200:\n print(json.load(response.content))\n return None\n \n img_array = uncompress_nparr(response.content)\n file_path = os.path.join(folder, file_name, + '_' + random_string() + '-resized' + ext)\n cv2.imwrite(file_path, img_array)\n return file_path","def test_resize_up_confirm(self):\n server_id = self._create_server(flavor=self.flavor.id).id\n # get the starting quota now that we've created a server\n starting_usage = self._get_absolute_limits()\n # now resize up\n alternate_flavor = self._pick_alternate_flavor()\n self.nova('resize',\n params='%s %s --poll' % (server_id, alternate_flavor))\n resize_usage = self._get_absolute_limits()\n # compare the starting usage against the resize usage\n self._compare_quota_usage(starting_usage, resize_usage)\n # now confirm the resize\n self.nova('resize-confirm', params='%s' % server_id)\n # we have to wait for the server to be ACTIVE before we can check quota\n self._wait_for_state_change(server_id, 'active')\n # get the final quota usage which should be the same as the resize\n # usage before confirm\n confirm_usage = self._get_absolute_limits()\n self._compare_quota_usage(\n resize_usage, confirm_usage, expect_diff=False)","def run(self, data_id, script, verbosity=1):\n if verbosity >= 1:\n print('RUN: {} {}'.format(data_id, script))\n\n self.data_id = data_id\n self.process_failed = False\n\n # Fetch data instance to get any executor requirements.\n self.process = Data.objects.get(pk=data_id).process\n requirements = self.process.requirements\n self.requirements = requirements.get('executor', {}).get(self.name, {})\n self.resources = requirements.get('resources', {})\n\n data_dir = settings.FLOW_EXECUTOR['DATA_DIR']\n dir_mode = getattr(settings, 'FLOW_EXECUTOR', {}).get('DATA_DIR_MODE', 0o755)\n\n output_path = os.path.join(data_dir, str(data_id))\n\n os.mkdir(output_path)\n # os.mkdir is not guaranteed to set the given mode\n os.chmod(output_path, dir_mode)\n os.chdir(output_path)\n\n log_file = open('stdout.txt', 'w+')\n json_file = open('jsonout.txt', 'w+')\n\n proc_pid = self.start()\n\n self.update_data_status(\n status=Data.STATUS_PROCESSING,\n started=now(),\n process_pid=proc_pid\n )\n\n # Run processor and handle intermediate results\n self.run_script(script)\n spawn_processors = []\n output = {}\n process_error, process_warning, process_info = [], [], []\n process_progress, process_rc = 0, 0\n\n # read processor output\n try:\n stdout = self.get_stdout()\n while True:\n line = stdout.readline()\n if not line:\n break\n\n try:\n if line.strip().startswith('run'):\n # Save processor and spawn if no errors\n log_file.write(line)\n log_file.flush()\n\n for obj in iterjson(line[3:].strip()):\n spawn_processors.append(obj)\n elif line.strip().startswith('export'):\n file_name = line[6:].strip()\n\n export_folder = settings.FLOW_EXECUTOR['UPLOAD_DIR']\n unique_name = 'export_{}'.format(uuid.uuid4().hex)\n export_path = os.path.join(export_folder, unique_name)\n\n self.exported_files_mapper[self.data_id][file_name] = unique_name\n\n shutil.move(file_name, export_path)\n else:\n # If JSON, save to MongoDB\n updates = {}\n for obj in iterjson(line):\n for key, val in six.iteritems(obj):\n if key.startswith('proc.'):\n if key == 'proc.error':\n process_error.append(val)\n if not process_rc:\n process_rc = 1\n updates['process_rc'] = process_rc\n updates['process_error'] = process_error\n updates['status'] = Data.STATUS_ERROR\n elif key == 'proc.warning':\n process_warning.append(val)\n updates['process_warning'] = process_warning\n elif key == 'proc.info':\n process_info.append(val)\n updates['process_info'] = process_info\n elif key == 'proc.rc':\n process_rc = int(val)\n updates['process_rc'] = process_rc\n if process_rc != 0:\n updates['status'] = Data.STATUS_ERROR\n elif key == 'proc.progress':\n process_progress = int(float(val) * 100)\n updates['process_progress'] = process_progress\n else:\n dict_dot(output, key, val)\n updates['output'] = output\n\n if updates:\n updates['modified'] = now()\n self.update_data_status(**updates)\n\n if process_rc > 0:\n log_file.close()\n json_file.close()\n os.chdir(CWD)\n return\n\n # Debug output\n # Not referenced in Data object\n json_file.write(line)\n json_file.flush()\n\n except ValueError as ex:\n # Ignore if not JSON\n log_file.write(line)\n log_file.flush()\n\n except MemoryError as ex:\n logger.error(__(\"Out of memory: {}\", ex))\n\n except IOError as ex:\n # TODO: if ex.errno == 28: no more free space\n raise ex\n finally:\n # Store results\n log_file.close()\n json_file.close()\n os.chdir(CWD)\n\n return_code = self.end()\n\n if process_rc < return_code:\n process_rc = return_code\n\n # This transaction is needed to make sure that processing of\n # current data object is finished before manager for spawned\n # processes is triggered.\n with transaction.atomic():\n if spawn_processors and process_rc == 0:\n parent_data = Data.objects.get(pk=self.data_id)\n\n # Spawn processors\n for d in spawn_processors:\n d['contributor'] = parent_data.contributor\n d['process'] = Process.objects.filter(slug=d['process']).latest()\n\n for field_schema, fields in iterate_fields(d.get('input', {}), d['process'].input_schema):\n type_ = field_schema['type']\n name = field_schema['name']\n value = fields[name]\n\n if type_ == 'basic:file:':\n fields[name] = self.hydrate_spawned_files(value, data_id)\n elif type_ == 'list:basic:file:':\n fields[name] = [self.hydrate_spawned_files(fn, data_id) for fn in value]\n\n with transaction.atomic():\n d = Data.objects.create(**d)\n DataDependency.objects.create(\n parent=parent_data,\n child=d,\n kind=DataDependency.KIND_SUBPROCESS,\n )\n\n # Copy permissions.\n copy_permissions(parent_data, d)\n\n # Entity is added to the collection only when it is\n # created - when it only contains 1 Data object.\n entities = Entity.objects.filter(data=d).annotate(num_data=Count('data')).filter(num_data=1)\n\n # Copy collections.\n for collection in parent_data.collection_set.all():\n collection.data.add(d)\n\n # Add entities to which data belongs to the collection.\n for entity in entities:\n entity.collections.add(collection)\n\n if process_rc == 0 and not self.process_failed:\n self.update_data_status(\n status=Data.STATUS_DONE,\n process_progress=100,\n finished=now()\n )\n else:\n self.update_data_status(\n status=Data.STATUS_ERROR,\n process_progress=100,\n process_rc=process_rc,\n finished=now()\n )\n\n try:\n # Cleanup after processor\n data_purge(data_ids=[data_id], delete=True, verbosity=verbosity)\n except: # pylint: disable=bare-except\n logger.error(__(\"Purge error:\\n\\n{}\", traceback.format_exc()))\n\n # if not update_data(data): # Data was deleted\n # # Restore original directory\n # os.chdir(settings.PROJECT_ROOT)\n # return\n\n # Restore original directory\n # os.chdir(settings.PROJECT_ROOT)\n\n # return data_id"],"string":"[\n \"def script(arch, version, variant, packages, mirror, disk_size, swap_size, image_format, root_password, hostname, no_confirm, output):\\n\\n # Checking validity of the command-line arguments.\\n check_arguments(locals())\\n\\n # Checking if dependencies for this script are installed.\\n check_dependencies(arch)\\n\\n # Make sure output value is correct and we won't run into any issue at the\\n # final step (moving the result to its final location). We check the file\\n # does not exist, and if its parent directory does exist.\\n output = os.path.abspath(output)\\n assert not os.path.exists(output), \\\"output value is incocrect; destination exists\\\"\\n assert os.path.isdir(os.path.dirname(output)), \\\"output value is incorrect; parent folder does not exist\\\"\\n\\n # Compute partitions info (todo; compute actual infos)\\n partitions = [\\n ('esp', 512, 'fat32', 42, 42),\\n ('root', 512, 'ext4', 42, 42),\\n ('swap', 512, 'swap', 42, 42)\\n ]\\n\\n # Compute list of packages that will be explicitly installed.\\n packages = compute_packages(packages, locals())\\n\\n # Printing summary and asking for confirmation.\\n summary_mesage = compute_summary_message(locals())\\n\\n if not no_confirm:\\n summary_mesage += \\\\\\n \\\"\\\\nPass the --no-confirm flag if you don't want to be prompted for confirmation.\\\\n\\\"\\n\\n print(summary_mesage)\\n\\n if not no_confirm:\\n is_confirmed = input(\\\"Do you confirm those options ? [y/n] \\\")\\n if not is_confirmed.lower().startswith('y'):\\n print(\\\"Abort!\\\")\\n exit(1)\\n\\n with tempfile.TemporaryDirectory() as tmp_dir:\\n print(f\\\"Creating a raw disk image of size {disk_size}MiB\\\")\\n disk_path = create_disk_image(tmp_dir, disk_size)\\n\\n print(\\\"Partitioning the disk...\\\")\\n partition_disk(disk_path, disk_size, swap_size)\\n\\n loop_device = '/dev/loop42'\\n with attach_to_loop_device(disk_path, loop_device):\\n print(\\\"Formatting partitions...\\\")\\n format_partitions(loop_device)\\n\\n mount_dir = os.path.join(tmp_dir, 'mnt')\\n os.mkdir(mount_dir)\\n\\n with mount_root_partition(loop_device, mount_dir):\\n create_chroot_environment(mount_dir, arch, version, variant, mirror, packages)\\n\\n # In order to chroot into a filesystem with an architecture\\n # different than the host, we need to install a binary\\n # interpreter.\\n if arch == 'armhf':\\n print(\\\"Copying qemu-arm-static to the chroot environment\\\")\\n shutil.copy2('/usr/bin/qemu-arm-static', os.path.join(mount_dir, 'usr/bin/'))\\n elif arch == 'arm64':\\n print(\\\"Copying qemu-aarch64-static to the chroot environment\\\")\\n shutil.copy2('/usr/bin/qemu-aarch64-static', os.path.join(mount_dir, 'usr/bin/'))\\n\\n with mount_boot_partition(loop_device, mount_dir):\\n with mount_transient_files(mount_dir):\\n # Configure the GRUB boot loader.\\n if arch == 'armhf':\\n grub_package = 'grub-efi-arm'\\n grub_target = 'arm-efi'\\n elif arch == 'arm64':\\n grub_package = 'grub-efi-arm64'\\n grub_target = 'arm64-efi'\\n elif arch == 'amd64':\\n grub_package = 'grub-efi-amd64'\\n grub_target = 'x86_64-efi'\\n\\n update_system_cmd = ['apt-get', 'update']\\n run_chroot_command(update_system_cmd, mount_dir, arch)\\n\\n install_grub_pkg_cmd = ['apt-get', 'install', '-y', '--install-recommends', grub_package]\\n run_chroot_command(install_grub_pkg_cmd, mount_dir, arch)\\n\\n purge_osprober_cmd = ['apt-get', '--autoremove', '-y', 'purge', 'os-prober']\\n run_chroot_command(purge_osprober_cmd, mount_dir, arch)\\n\\n # Adjust the '/etc/default/grub' file\\n with open(os.path.join(mount_dir, 'etc/default/grub'), 'r') as file:\\n text = file.read()\\n\\n # TODO; adjust text variable\\n\\n with open(os.path.join(mount_dir, 'etc/default/grub'), 'w') as file:\\n file.write(text)\\n\\n grub_mkconfig_cmd = ['grub-mkconfig', '-o', '/boot/grub/grub.cfg']\\n run_chroot_command(grub_mkconfig_cmd, mount_dir, arch)\\n\\n grub_install_cmd = [\\n 'grub-install',\\n f'--target={grub_target}',\\n '--force-extra-removable',\\n '--no-nvram',\\n '--no-floppy',\\n '--modules=\\\\\\\\\\\"part_msdos part_gpt\\\\\\\\\\\"',\\n '--grub-mkdevicemap=/boot/grub/device.map',\\n loop_device\\n ]\\n run_chroot_command(grub_install_cmd, mount_dir, arch)\\n\\n print(\\\"Updating /etc/hostname\\\")\\n configure_hostname(mount_dir, 'foo')\\n\\n print(\\\"Updating /etc/fstab\\\")\\n configure_fstab(mount_dir)\\n\\n print(\\\"Updating /etc/network/interfaces\\\")\\n configure_network_interfaces(mount_dir)\\n\\n # TODO; run user provided script here...\\n\\n # Remove the binary interpreter from the chroot environment.\\n if arch == 'armhf':\\n print(\\\"Removing qemu-arm-static from the chroot environment\\\")\\n os.remove(os.path.join(mount_dir, 'usr/bin/qemu-arm-static'))\\n elif arch == 'arm64':\\n print(\\\"Removing qemu-aarch64-static from the chroot environment\\\")\\n os.remove(os.path.join(mount_dir, 'usr/bin/qemu-aarch64-static'))\\n\\n # Convert raw disk image to the requested format and and move the result to\\n # the requested location.\\n if image_format != 'raw':\\n print(f\\\"Converting disk image to {image_format} format...\\\")\\n\\n new_disk_path = os.path.join(tmp_dir, 'disk.' + image_format)\\n subprocess.run([\\n 'qemu-img', 'convert',\\n '-f', 'raw',\\n '-O', image_format,\\n disk_path, new_disk_path\\n ])\\n\\n disk_path = new_disk_path\\n\\n shutil.move(disk_path, output)\\n\\n print(\\\"Done!\\\")\",\n \"def do_resize(self, args, opts=None):\\n dataset = args[0]\\n shape = tuple(int(x) for x in args[1:])\\n if opts.axis is not None:\\n if len(shape) != 1:\\n raise ValueError('Expected exactly one integer when changing axis size')\\n self.explorer[dataset].resize(shape[0], axis=opts.axis)\\n else:\\n self.explorer[dataset].resize(shape)\",\n \"def main(inputfile, output, size):\\n if not output:\\n output = join(dirname(inputfile), str(size))\\n if not isdir(output):\\n os.mkdir(output)\\n\\n logger.info('Resizing images from: %s' % inputfile)\\n inputfile = realpath(inputfile)\\n #/usr/share/datasets/KSCGR_Original/data1/boild-egg/0.jpg 0\\n nb_lines = count_lines(inputfile)\\n pbar = pb.ProgressBar(nb_lines)\\n with open(inputfile) as fin:\\n for line in fin:\\n path, tl = line.strip().split()\\n newpath = create_path(path, output)\\n img = cv2.imread(path)\\n img = resize_image(img, size)\\n cv2.imwrite(newpath, img)\\n pbar.update()\\n logger.info('Total of images resized: %d' % nb_lines)\",\n \"def main(raw_dir,save_dir,ext,target_size):\\r\\n try:\\r\\n msg = \\\"--target-size must be a tuple of 2 integers\\\"\\r\\n assert isinstance(target_size, tuple) and len(target_size) == 2, msg\\r\\n fnames = glob.glob(os.path.join(raw_dir, \\\"*.{}\\\".format(ext)))\\r\\n os.makedirs(save_dir, exist_ok=True)\\r\\n print(\\\"{} files to resize from directory `{}` to target size:{}\\\".format(len(fnames), raw_dir, target_size))\\r\\n for i, fname in enumerate(fnames):\\r\\n print(\\\".\\\", end=\\\"\\\", flush=True)\\r\\n img = cv2.imread(fname)\\r\\n img_small = cv2.resize(img, target_size)\\r\\n new_fname = \\\"{}.{}\\\".format(str(i), ext)\\r\\n small_fname = os.path.join(save_dir, new_fname)\\r\\n cv2.imwrite(small_fname, img_small)\\r\\n print(\\\"\\\\nDone resizing {} files.\\\\nSaved to directory: `{}`\\\".format(len(fnames), save_dir))\\r\\n except Exception as ex:\\r\\n print('Exception:',ex)\",\n \"def resize_png(width, height, source, destination):\\n\\n os.system('convert -resize %sx%s \\\"%s\\\" \\\"%s\\\"' % (width, height, source, destination))\",\n \"def run_script():\\n creator = EnergyScaleCreator(IMP, FONT_SIZE)\\n creator.get_scaled_image(WIDTH_MIN)\\n creator.extend_image()\\n ticks = create_ticks(IMP, TICK_COUNT)\\n creator.draw_scale(ticks)\\n imp_extended = creator.image\\n imp_extended.show()\",\n \"def exec_resize(self, exec_id, height=None, width=None):\\n\\n if isinstance(exec_id, dict):\\n exec_id = exec_id.get('Id')\\n\\n params = {'h': height, 'w': width}\\n url = self._url(\\\"/exec/{0}/resize\\\", exec_id)\\n res = self._post(url, params=params)\\n self._raise_for_status(res)\",\n \"def resizeLXCContainer(self,node,vmid,post_data):\\n data = self.connect('put',\\\"nodes/%s/lxc/%s/resize\\\" % (node,vmid), post_data)\\n return data\",\n \"def resize_action():\\n fn = request.args.get(\\\"filename\\\")\\n size = int(request.args.get(\\\"size\\\"))\\n resize_file(fn, size)\\n return Response(status=200)\",\n \"def run(basedir, resize_height=288):\\n print(\\\"initialize\\\")\\n\\n img0 = [os.path.join(basedir, 'images', f) \\\\\\n for f in sorted(os.listdir(os.path.join(basedir, 'images'))) \\\\\\n if f.endswith('JPG') or f.endswith('jpg') or f.endswith('png')][0]\\n sh = cv2.imread(img0).shape\\n height = resize_height\\n factor = sh[0] / float(height)\\n width = int(round(sh[1] / factor))\\n _minify(basedir, resolutions=[[height, width]])\\n\\n print(\\\"finished\\\")\",\n \"def resize(data, number_rows=None, number_columns=None, lower=None, upper=None, categories=None, weights=None, distribution=None, shift=None, scale=None, sample_proportion=None, minimum_rows=None, **kwargs):\\n return Component(\\n \\\"Resize\\\",\\n arguments={\\n 'data': Component.of(data),\\n 'number_rows': Component.of(number_rows),\\n 'number_columns': Component.of(number_columns),\\n 'lower': Component.of(lower),\\n 'upper': Component.of(upper),\\n 'categories': Component.of(categories, value_format=\\\"jagged\\\"),\\n 'weights': Component.of(weights, value_format=\\\"jagged\\\"),\\n 'distribution': Component.of(distribution),\\n 'shift': Component.of(shift),\\n 'scale': Component.of(scale),\\n 'sample_proportion': Component.of(sample_proportion),\\n 'minimum_rows': Component.of(minimum_rows)\\n },\\n options={\\n \\n },\\n constraints=kwargs)\",\n \"def test_run_resize_and_crop_png(self):\\n self.expect_datatore_lookup('SomeBlobKey', True)\\n self.expect_open_image('SomeBlobKey', (1600, 1200), mime_type='PNG')\\n self.expect_crop(left_x=0.125, right_x=0.875)\\n self.expect_resize(32)\\n self.expect_encode_image('SomeImageSize32',\\n images_service_pb.OutputSettings.PNG)\\n self.mox.ReplayAll()\\n self._environ['PATH_INFO'] += '=s32-c'\\n self.run_request('image/png', 'SomeImageSize32')\",\n \"def resize_and_process_image(data: dict, context):\\n file_name = data[\\\"name\\\"]\\n bucket_name = data[\\\"bucket\\\"]\\n _, temp_local_filename = tempfile.mkstemp(suffix=file_name)\\n blob = storage_client.bucket(bucket_name).get_blob(file_name)\\n blob_bytes = blob.download_as_bytes()\\n output = io.BytesIO(blob_bytes)\\n output.seek(0)\\n image = Image.open(output)\\n print(\\\"trying to resize image\\\")\\n # resizes image\\n resized_image = resize_image(image)\\n resized_image.save(fp=temp_local_filename)\\n print(\\\"Image resized\\\")\\n\\n # Upload result to second bucket\\n print(\\\"Trying to upload resized image to second bucket\\\")\\n second_bucket_name = os.getenv(\\\"SECOND_BUCKET\\\")\\n second_bucket = storage_client.bucket(second_bucket_name)\\n print(\\\"second bucket found\\\")\\n new_blob = second_bucket.blob(file_name)\\n new_blob.metadata = blob.metadata\\n print(\\\"created new blob\\\")\\n new_blob.upload_from_filename(temp_local_filename)\\n print(\\\"uploaded resized image from file\\\")\\n os.remove(temp_local_filename)\",\n \"def test_run_resize(self):\\n self.expect_datatore_lookup('SomeBlobKey', True)\\n self.expect_open_image('SomeBlobKey', (1600, 1200))\\n self.expect_resize(32)\\n self.expect_encode_image('SomeImageSize32')\\n self.mox.ReplayAll()\\n self._environ['PATH_INFO'] += '=s32'\\n self.run_request('image/jpeg', 'SomeImageSize32')\",\n \"def resize_image(parent_directory, img_columns,img_rows):\\n\\t#List of img files in the directory for the specific simulation file\\n\\tlisting = os.listdir(parent_directory)\\n\\t#Number of img files in the directory for the specific simulation file\\n\\tnum_samples = len(listing)\\n\\t#Create Directory to store the re-sized images\\n\\tdir_nam = parent_directory + '/resize_col_' + str(img_columns) + '_row_' + str(img_rows)\\n\\tif not os.path.exists(dir_nam):\\n\\t\\tos.makedirs(dir_nam)\\n\\t#Open image and resize --> (img_rows,img_columns)\\n\\tfor file in listing:\\n\\t\\tif file.endswith('.bmp'):\\n\\t\\t\\tim = PIL.Image.open(parent_directory + '/' + file)\\n\\t\\t\\tim_resized = im.resize((img_columns,img_rows))\\n\\t\\t\\tim_resized.save(dir_nam + '/' + file)\\n\\treturn;\",\n \"def test_single_resize_er(self):\\n to_resize = base_path + '/test_data/rendering_tests/just_resize/original/faulty.jpg'\\n to_output = base_path + '/test_data/rendering_tests/just_resize/results/'\\n\\n for the_file in os.listdir(to_output):\\n file_path = os.path.join(to_output, the_file)\\n if os.path.isfile(file_path):\\n os.unlink(file_path)\\n\\n\\n capturedOutput = io.StringIO() # Create StringIO object\\n sys.stdout = capturedOutput # and redirect stdout.\\n rb.resize_and_crop(to_resize, to_output+\\\"faulty.jpg\\\", 300,300 )\\n sys.stdout = sys.__stdout__ # Reset redirect.\\n self.assertEqual(\\\"Image too small to be resized\\\\n\\\",capturedOutput.getvalue()) # Now works as before.\",\n \"def initial_resizing(fr_raw_data_path, fr_data_path, dim=300):\\n with h5py.File(fr_raw_data_path, 'r') as data:\\n images = resize_array(np.asarray(data['images'].value), dim=dim)\\n labels = data['labels'].value\\n \\n with h5py.File(fr_data_path, 'w') as f:\\n f.create_dataset('images', data=images)\\n\\n with h5py.File(fr_raw_data_path, 'r') as data: \\n f.copy(data['fri_data'], 'fri_data')\\n f.copy(data['frii_data'], 'frii_data')\\n f.copy(data['labels'], 'labels')\",\n \"def autoscale_cmd(timeout, max_count, min_count, batch_count,\\n app_srv_ratio):\\n while True:\\n create_cnt, extra_servers = autoscale.scale(\\n max_servers=max_count,\\n min_servers=min_count,\\n default_app_srv_ratio=app_srv_ratio,\\n max_batch=batch_count)\\n if create_cnt > 0:\\n autoscale.create_n_servers(create_cnt, partition=None)\\n\\n if extra_servers:\\n autoscale.delete_servers_by_name(extra_servers)\\n\\n time.sleep(timeout)\",\n \"def image_resize(job_object):\\n try:\\n job = json.loads(job_object.arg)\\n base64_file = job['image']\\n args = job['args'] if 'args' in job else {}\\n del job['image']\\n logging.info(job)\\n \\n def write_file(local_path,filename,file_b64):\\n logging.debug(\\\"about to save to \\\" + \\\"%s/%s\\\" % (local_path,filename))\\n if not os.path.exists(local_path): os.makedirs(local_path)\\n image_file = base64.b64decode(file_b64)\\n local_file = open(\\\"%s/%s\\\" % (local_path,filename), \\\"w\\\")\\n local_file.write(image_file)\\n local_file.close()\\n \\n def download_file(url,local_path,filename):\\n print \\\"downloading \\\" + url\\n f = urllib2.urlopen(urllib2.Request(url))\\n print \\\"about to save to \\\" + \\\"%s/%s\\\" % (local_path,filename)\\n if not os.path.exists(local_path): os.makedirs(local_path)\\n # Open our local file for writing\\n local_file = open(\\\"%s/%s\\\" % (local_path,filename), \\\"w\\\")\\n local_file.write(f.read())\\n local_file.close()\\n \\n local_path = '%s/upload/%s' % (options.asset_root,job['path'])\\n local_path_wfile = '%s/%s%s' % (local_path,job['file'],job['extension'])\\n filename = '%s%s' % (job['file'],job['extension'])\\n #download_file(job['url'],local_path,filename)\\n write_file(local_path,filename,base64_file)\\n \\n def resize_and_save(local_file,new_file,maxsize=None,maxh=None,maxw=None,crop=None):\\n \\\"\\\"\\\"Resize the image and save\\\"\\\"\\\"\\n logging.debug(\\\"maxw = %s, maxsize=%s, crop=%s\\\" % (maxw,maxsize,crop))\\n img = Image.open(local_file)\\n width,height = img.size\\n width,height = float(width), float(height)\\n ratio = float(1)\\n if crop is not None:\\n size = float(maxsize)\\n if width <= height and width > size:\\n ratio = size/width\\n elif height < width and height > size:\\n ratio = size/height\\n else: \\n ratio = 1 # too small\\n elif maxsize:\\n size = float(maxsize)\\n if width >= height and width > size:\\n ratio = size/width\\n elif height > width and height > size:\\n ratio = size/height\\n else: \\n ratio = 1 # too small\\n elif maxh:\\n size = maxh\\n if height > size:\\n ratio = size/height\\n else:\\n # too small\\n ratio = 1\\n elif maxw:\\n size = maxw\\n if width > size:\\n ratio = size/width\\n else:\\n # too small\\n ratio = 1\\n else:\\n raise Exception(\\\"must specify max width, OR max size\\\")\\n \\n print(\\\"old: ratio = %s: size(x,y) = %s,%s\\\" % (ratio,width,height))\\n height = int(height*ratio)\\n width = int(width*ratio)\\n print(\\\"new ratio = %s: size(x,y) = %s,%s\\\" % (ratio,width,height))\\n img = img.resize((width, height),Image.ANTIALIAS)\\n if crop is not None:\\n log.debug(\\\"in crop %s\\\" % crop)\\n crop = int(crop)\\n if width > crop:\\n amt = int((int(width) - crop)/2)\\n img = img.crop((amt,0,amt + crop, crop))\\n elif height > crop:\\n amt = int((int(height) - crop)/2)\\n img = img.crop((0,amt,crop,amt+crop))\\n \\n log.debug(\\\"saving new file %s\\\" % new_file)\\n if img.mode != \\\"RGB\\\":\\n img = img.convert(\\\"RGB\\\")\\n img.save(new_file)\\n \\n \\n if os.path.exists(local_path_wfile):\\n if args != {}:\\n ext = args['extension'] if 'extension' in args else \\\"_t\\\"\\n resize_and_save(local_path_wfile,\\n '%s/%s%s.jpg' % (local_path,job['file'],ext),\\n maxsize=args['maxsize'],\\n crop=args['crop'])\\n else:\\n resize_and_save(local_path_wfile,'%s/%s_t.jpg' % (local_path,job['file']),maxsize=100)\\n resize_and_save(local_path_wfile,'%s/%s_m.jpg' % (local_path,job['file']),maxw=317)\\n resize_and_save(local_path_wfile,'%s/%s_l.jpg' % (local_path,job['file']),maxsize=800)\\n keeptrying = False\\n else:\\n logging.error(\\\"haven't found file? %s\\\" % local_path_wfile)\\n \\n # delete original\\n logging.debug(\\\"About to delete original %s\\\" % local_path_wfile)\\n os.remove(local_path_wfile)\\n \\n except:\\n traceback.print_exc()\",\n \"def run_image_editor( self ):\\n\\n # XXX: hardcoded program name and image size.\\n subprocess.Popen( [\\\"gimp\\\", \\\"-adfs\\\", self.record[\\\"filename\\\"]] )\",\n \"def test_run_resize_and_crop(self):\\n self.expect_datatore_lookup('SomeBlobKey', True)\\n self.expect_open_image('SomeBlobKey', (1600, 1200))\\n self.expect_crop(left_x=0.125, right_x=0.875)\\n self.expect_resize(32)\\n self.expect_encode_image('SomeImageSize32')\\n self.mox.ReplayAll()\\n self._environ['PATH_INFO'] += '=s32-c'\\n self.run_request('image/jpeg', 'SomeImageSize32')\",\n \"def resize_image_data(data, resize_shape):\\n if data is None or len(resize_shape) == 0:\\n return data\\n\\n if len(data.shape) > 1 and np.array_equal(data[0].shape, resize_shape):\\n return data\\n\\n output_data = []\\n for im in data:\\n output_data.append(zoom(input=im, zoom=np.divide(resize_shape, im.shape)))\\n\\n return np.array(output_data)\",\n \"def test_single_resize_ok(self):\\n\\n to_resize = base_path +'/test_data/rendering_tests/just_resize/original/good.jpg'\\n to_output = base_path +'/test_data/rendering_tests/just_resize/results/'\\n\\n\\n rb.resize_and_crop(to_resize, to_output+\\\"good.jpg\\\", 300,300 )\\n self.assertEqual(1, len(os.listdir(to_output)))\",\n \"def resize_image(self, nNewSize, nFlags):\\n\\t\\treturn Job(SDK.PrlVmDev_ResizeImage(self.handle, nNewSize, nFlags)[0])\",\n \"def exec_after_process(app, inp_data, out_data, param_dict, tool=None, stdout=None, stderr=None):\\n\\n URL = param_dict.get( 'URL', None )\\n URL = URL + '&_export=1&GALAXY_URL=0'\\n if not URL:\\n raise Exception('Datasource has not sent back a URL parameter')\\n\\n CHUNK_SIZE = 2**20 # 1Mb \\n MAX_SIZE = CHUNK_SIZE * 100\\n \\n try:\\n # damn you stupid sanitizer!\\n URL = URL.replace('martX', 'mart&')\\n URL = URL.replace('0X_', '0&_')\\n page = urllib.urlopen(URL)\\n except Exception, exc:\\n raise Exception('Problems connecting to %s (%s)' % (URL, exc) )\\n\\n name, data = out_data.items()[0]\\n \\n fp = open(data.file_name, 'wb')\\n size = 0\\n while 1:\\n chunk = page.read(CHUNK_SIZE)\\n if not chunk:\\n break\\n if size > MAX_SIZE:\\n raise Exception('----- maximum datasize exceeded ---')\\n size += len(chunk)\\n fp.write(chunk)\\n\\n fp.close()\\n data.set_peek()\",\n \"def main():\\n\\n\\n\\n skulls_folder = os.listdir(RAW_IMAGE_DIRECTORY)\\n\\n # fetch and sort the .mnc and .tag files\\n mnc_files = [f for f in skulls_folder if 'mnc' in f]\\n tag_files = [f for f in skulls_folder if 'tag' in f]\\n mnc_names = [i.split('.mnc')[0] for i in mnc_files]\\n \\n mnc_files.sort()\\n tag_files.sort()\\n mnc_names.sort()\\n\\n # Process and package ndarrays as tuples inside npy file\\n package_to_npy(RAW_IMAGE_DIRECTORY, mnc_files, tag_files, mnc_names)\\n \\n print('\\\\n' * 5)\\n\\n # Push the npy files to GCP Cloud Storage\\n upload_to_gcp(PROCESSED_IMAGE_DIRECTORY, GCP_PROJECT_NAME, GCP_BUCKET_NAME)\",\n \"def execute_script(self, script, enterpreter='/bin/sh'):\\n destination = '/tmp/' + ''.join(\\n random.choice(string.lowercase) for i in range(16))\\n\\n self.upload(script, destination)\\n self.execute('%s %s' % (enterpreter, destination))\\n self.execute('rm %s' % destination)\",\n \"def createbash(self,executable,**keywords):\\n\\t\\timport os\\n\\t\\timport stat\\n\\n\\t\\toutputname = os.path.join(\\\"Results\\\",self.outputfile.replace(\\\".root\\\",\\\"_${SGE_TASK_ID}.root\\\"))\\n\\t\\t# Extract the input files\\n\\t\\tinputfiles = \\\"\\\"\\n\\t\\tfor f in self.inputfiles:\\n\\t\\t\\tinputfiles += f+\\\",\\\"\\n\\t\\tinputfiles = inputfiles[:-1]\\n\\n\\t\\tlines = \\\"#!/bin/bash\\\\n\\\"\\n\\t\\tlines += \\\"\\\\n# Script created automatically by skimfiles.py utility\\\\n\\\"\\n\\t\\tlines += \\\"\\\\nmkdir -p Results\\\\n\\\"\\n\\t\\tlines += \\\"export PATH=$PATH:\\\"+os.path.join(self.basedir,\\\"bin\\\")+\\\":\\\"+os.path.join(self.pkgpath,\\\"bin\\\")+\\\"\\\\n\\\"\\n\\t\\tlines += \\\"export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:\\\"+self.libsdir+\\\"\\\\n\\\"\\n\\t\\tlines += \\\"\\\\n\\\"\\n\\t\\tlines += \\\"EVENTFILE=\\\"+self.eventsfile+\\\"\\\\n\\\"\\n\\t\\tlines += \\\"EVENTS=$(cat $EVENTFILE | head -n $SGE_TASK_ID | tail -n 1)\\\\n\\\"\\n\\t\\tlines += executable+\\\" \\\"+self.cutid+\\\" -i \\\"+inputfiles+\\\" -c \\\"+self.cutfile+\\\\\\n\\t\\t\\t\\t\\\" -e $EVENTS -o \\\"+outputname+\\\"\\\\n\\\"\\n\\t\\n\\t\\tfilename = self.nameID+\\\".sh\\\"\\n\\t\\tf = open(filename,\\\"w\\\")\\n\\t\\tf.writelines(lines)\\n\\t\\tf.close()\\n\\t\\tos.chmod(filename,stat.S_IRWXU+stat.S_IRGRP+stat.S_IXGRP+stat.S_IXOTH)\\n\\t\\t\\n\\t\\treturn filename\",\n \"def prep(path,date,image):\\n \\n # run bash code with 'Popen'\\n P = Popen('cp '+path+date+'/final/'+image+' ./', shell=True)\\n P.wait()\\n P = Popen('mv '+image+' '+image+'.fz', shell=True)\\n P.wait()\\n P = Popen('funpack *.fz', shell=True)\\n P.wait()\\n P = Popen('rm -rf *.fz', shell=True)\\n P.wait()\",\n \"def main():\\r\\n original = SimpleImage(\\\"images/poppy.png\\\")\\r\\n original.show()\\r\\n # shrink function\\r\\n after_shrink = shrink('images/poppy.png')\\r\\n after_shrink.show()\",\n \"def test_full_resize(self):\\n number_of_pixels = 300\\n destination = base_path +'/test_data/rendering_tests/resized_images/'\\n source_folder = base_path + '/test_data/rendering_tests/filter_database/'\\n\\n\\n for the_file in os.listdir(destination):\\n file_path = os.path.join(destination, the_file)\\n if os.path.isfile(file_path):\\n os.unlink(file_path)\\n\\n\\n self.assertEqual(0, len(os.listdir(destination)))\\n rb.find_all_files(number_of_pixels,source_folder, destination)\\n self.assertEqual(6, len(os.listdir(destination)))\\n for the_file in os.listdir(destination):\\n file_path = os.path.join(destination,the_file)\\n with Image.open(file_path) as f:\\n self.assertNotEqual(number_of_pixels+5, f.size[0])\\n self.assertNotEqual(number_of_pixels+5, f.size[1])\\n # the above checks that the size does not vary as needed\\n # probably not necessary\\n self.assertEqual(number_of_pixels, f.size[0])\\n self.assertEqual(number_of_pixels, f.size[1])\",\n \"def runUploads():\\n os.system(\\\"chmod u+x uploadCommands.sh\\\")\\n os.system(\\\"./uploadCommands.sh\\\")\\n os.system(\\\"rm uploadCommands.sh\\\")\",\n \"def rescale_image(self, img_file, new_width, new_height, model_path, file_description):\\n cwd = os.getcwd()\\n self.new_width = new_width\\n self.new_height = new_height\\n self.extract_file_name(img_file)\\n shutil.copy(img_file, os.path.join('utils_dfn/temp', self.file_name_with_ext))\\n self.run_padding()\\n self.run_dfn(model_path)\\n self.restore_to_correct_size(file_description)\\n clean()\",\n \"def run_image_viewer( self ):\\n\\n # XXX: hardcoded program name and image size.\\n subprocess.Popen( [\\\"feh\\\", \\\"-dZ\\\", \\\"-g\\\", \\\"800x600\\\", self.record[\\\"filename\\\"]] )\",\n \"def __resize_to_512(input_data):\\n test_size = 720\\n\\n if DataAugmentor._is_synthtext(input_data):\\n input_data['contour'] = [[np.cast['int32'](np.stack([contour[:, :, 0] * 512 / input_data['img'].shape[0],\\n contour[:, :, 1] * 512 / input_data['img'].shape[1]],\\n axis=-1))\\n for contour in contours] for contours in input_data['contour']]\\n else:\\n input_data['contour'] = [np.cast['int32'](np.stack([contour[:, :, 0] * 512 / input_data['img'].shape[0],\\n contour[:, :, 1] * 512 / input_data['img'].shape[1]],\\n axis=-1)) for contour in input_data['contour']]\\n input_data['center_point'] = [(np.cast['int32'](point[0] * 512 / input_data['img'].shape[0]),\\n np.cast['int32'](point[1] * 512 / input_data['img'].shape[1]))\\n for point in input_data['center_point']]\\n\\n seq = iaa.Sequential([\\n iaa.Scale({'height': 512, 'width': 512}, 'cubic')\\n ])\\n input_data['img'] = seq.augment_image(input_data['img'])\\n\\n return input_data\",\n \"def main():\\n if len(sys.argv) < 3:\\n message = \\\"\\\"\\\"\\n Usage: python generate_dataset.py <dataset_name> <number of files> <size of each file in bytes>\\n \\\"\\\"\\\"\\n print(message)\\n sys.exit(0)\\n dataset_name = sys.argv[1]\\n file_number = int(sys.argv[2])\\n file_size = int(sys.argv[3])\\n\\n if not os.path.exists(dataset_name):\\n os.makedirs(dataset_name)\\n\\n for i in range(file_number):\\n tmp_file = open('./' + dataset_name + '/' + dataset_name + '.file' + str(i), 'w+')\\n tmp_file.write(os.urandom(file_size))\\n tmp_file.close()\",\n \"def bdev_daos_resize(client, name, new_size):\\n params = {\\n 'name': name,\\n 'new_size': new_size,\\n }\\n return client.call('bdev_daos_resize', params)\",\n \"def generate_data(dbgen_dir, data_dir, scale, num_streams):\\n p = subprocess.Popen([os.path.join(\\\".\\\", \\\"dbgen\\\"), \\\"-vf\\\", \\\"-s\\\", str(scale)], cwd = dbgen_dir)\\n p.communicate()\\n if not p.returncode:\\n load_dir = os.path.join(data_dir, LOAD_DIR)\\n if inner_generate_data(load_dir, dbgen_dir, \\\"*.tbl\\\", \\\".csv\\\"):\\n print(\\\"unable to generate data for load phase\\\")\\n return 1\\n print(\\\"generated data for the load phase\\\")\\n else:\\n return p.returncode\\n\\n ## Update/Delete phase data\\n ## we generate num_streams + 1 number of updates because 1 is used by the power test\\n p = subprocess.Popen([os.path.join(\\\".\\\", \\\"dbgen\\\"), \\\"-vf\\\", \\\"-s\\\", str(scale), \\\"-U\\\", str(num_streams + 1)],\\n cwd = dbgen_dir)\\n p.communicate()\\n if not p.returncode:\\n update_dir = os.path.join(data_dir, UPDATE_DIR)\\n delete_dir = os.path.join(data_dir, DELETE_DIR)\\n if inner_generate_data(update_dir, dbgen_dir, \\\"*.tbl.u*\\\", \\\".csv\\\"):\\n print(\\\"unable to generate data for the update phase\\\")\\n return 1\\n print(\\\"generated data for the update phase\\\")\\n if inner_generate_data(delete_dir, dbgen_dir, \\\"delete.*\\\", \\\".csv\\\"):\\n print(\\\"unable to generate data for the delete phase\\\")\\n return 1\\n print(\\\"generated data for the delete phase\\\")\\n ## All files written successfully. Return success code.\\n return 0\\n else:\\n return p.returncode\",\n \"def main(passed_arguments):\\n\\n # use real data as default\\n scripts_path = os.path.abspath(os.path.join(PYTHON_PATH, 'scripts'))\\n meta_path = os.path.abspath(os.path.join(scripts_path, 'meta.json'))\\n manifest_path = os.path.abspath(os.path.join(scripts_path, 'manifest.csv'))\\n\\n # Locally, we can optionally have sample data\\n if passed_arguments.sample and passed_arguments.database != 'remote':\\n meta_path = os.path.abspath(os.path.join(scripts_path,\\n 'meta_sample.json'))\\n manifest_path = os.path.abspath(\\n os.path.join(scripts_path, 'manifest_sample.csv'))\\n\\n # for case of more than one database choice default to the option with\\n # the lowest risk if database is updated\\n if passed_arguments.database == 'docker':\\n database_choice = 'docker_database'\\n drop_tables = True\\n\\n elif passed_arguments.database == 'docker_local':\\n database_choice = 'docker_with_local_python'\\n drop_tables = True\\n\\n elif passed_arguments.database == 'remote':\\n database_choice = 'remote_database'\\n drop_tables = False #TODO this is a hacky way to avoid dropping tables because it's not working with RDS...\\n\\n # Only users with additional admin privileges can rebuild the\\n # remote database\\n if not passed_arguments.update_only:\\n database_choice = 'remote_database_master'\\n\\n # TODO: do we want to default to local or docker?\\n elif passed_arguments.database == 'local':\\n database_choice = 'local_database'\\n drop_tables = True\\n\\n # universal defaults\\n keep_temp_files = True\\n\\n # Instantiate and run the loader\\n loader = LoadData(database_choice=database_choice, meta_path=meta_path,\\n manifest_path=manifest_path,\\n keep_temp_files=keep_temp_files,\\n drop_tables=drop_tables)\\n\\n if passed_arguments.update_only:\\n loader.update_database(passed_arguments.update_only)\\n else:\\n loader.rebuild()\\n\\n\\n\\n #TODO add in failures report here e.g. _failed_table_count\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n \\\"pldmfwuppkgname\\\", help=\\\"Name of the PLDM FW update package\\\"\\n )\\n parser.add_argument(\\\"metadatafile\\\", help=\\\"Path of metadata JSON file\\\")\\n parser.add_argument(\\n \\\"images\\\",\\n nargs=\\\"+\\\",\\n help=(\\n \\\"One or more firmware image paths, in the same order as \\\"\\n \\\" ComponentImageInformationArea entries\\\"\\n ),\\n )\\n\\n args = parser.parse_args()\\n image_files = args.images\\n with open(args.metadatafile) as file:\\n try:\\n metadata = json.load(file)\\n except ValueError:\\n sys.exit(\\\"ERROR: Invalid metadata JSON file\\\")\\n\\n # Validate the number of component images\\n if len(image_files) != len(metadata[\\\"ComponentImageInformationArea\\\"]):\\n sys.exit(\\n \\\"ERROR: number of images passed != number of entries \\\"\\n \\\" in ComponentImageInformationArea\\\"\\n )\\n\\n try:\\n with open(args.pldmfwuppkgname, \\\"w+b\\\") as pldm_fw_up_pkg:\\n component_bitmap_bit_length = write_pkg_header_info(\\n pldm_fw_up_pkg, metadata\\n )\\n write_fw_device_identification_area(\\n pldm_fw_up_pkg, metadata, component_bitmap_bit_length\\n )\\n write_component_image_info_area(\\n pldm_fw_up_pkg, metadata, image_files\\n )\\n update_pkg_header_size(pldm_fw_up_pkg)\\n write_pkg_header_checksum(pldm_fw_up_pkg)\\n append_component_images(pldm_fw_up_pkg, image_files)\\n pldm_fw_up_pkg.close()\\n except BaseException:\\n pldm_fw_up_pkg.close()\\n os.remove(args.pldmfwuppkgname)\\n raise\",\n \"def create_sh_script(\\n unblur_path, input_image, output_dir,\\n input_dir, input_suffix, options\\n ):\\n strSh = ''\\n\\n # To make sure it is a bash script\\n strSh += '#!/bin/bash\\\\n\\\\n'\\n\\n # Export number of threads\\n strSh += 'export OMP_NUM_THREADS={:d}\\\\n'.format(options.nr_threads)\\n\\n # The script will abort with non-zero exit values\\n strSh += '# The script will abort with non-zero exit values\\\\n'\\n strSh += 'set -e\\\\n'\\n\\n # Create a file list of all files\\n strSh += '# Create a file list of all files\\\\n'\\n strSh += 'fileList=$(ls {:s})\\\\n'.format(\\n input_image\\n )\\n\\n # Create folders\\n strSh += '# Create folders\\\\n'\\n strSh += 'mkdir -p {:s}/Doseuncorrected\\\\n'.format(output_dir)\\n\\n strSh += 'mkdir -p {:s}/Shift\\\\n'.format(output_dir)\\n\\n strSh += 'mkdir -p {:s}/Temp\\\\n'.format(output_dir)\\n\\n if options.filter_sum:\\n strSh += 'mkdir -p {:s}/Filtered\\\\n'.format(output_dir)\\n\\n if options.dose_filter:\\n strSh += 'mkdir -p {:s}/Dosecorrected\\\\n'.format(output_dir)\\n\\n if options.expert_mode:\\n strSh += 'mkdir -p {:s}/FRC\\\\n\\\\n'.format(output_dir)\\n\\n # Abort script if files in Doseuncorrected already exists\\n strSh += '# Abort script if files in Doseuncorrected already exists\\\\n'\\n strSh += 'for f in {:s}/Doseuncorrected/*\\\\n'.format(output_dir)\\n strSh += 'do\\\\n'\\n strSh += 'if [ -e $f ]\\\\n'\\n strSh += 'then\\\\n'\\n strSh += 'echo \\\"Some files already exists, please choose another output directory\\\"\\\\n'\\n strSh += 'exit 1\\\\n'\\n strSh += 'break\\\\n'\\n strSh += 'fi\\\\n'\\n strSh += 'done\\\\n\\\\n'\\n\\n # Abort script if files in shift already exists\\n strSh += '# Abort script if files in shift already exists\\\\n'\\n strSh += 'for f in {:s}/Shift/*\\\\n'.format(output_dir)\\n strSh += 'do\\\\n'\\n strSh += 'if [ -e $f ]\\\\n'\\n strSh += 'then\\\\n'\\n strSh += 'echo \\\"Some files already exists, please choose another output directory\\\"\\\\n'\\n strSh += 'exit 1\\\\n'\\n strSh += 'break\\\\n'\\n strSh += 'fi\\\\n'\\n strSh += 'done\\\\n\\\\n'\\n\\n # Abort script if files in Dosecorrected already exists\\n strSh += '# Abort script if files in Dosecorrected already exists\\\\n'\\n strSh += 'for f in {:s}/Dosecorrected/*\\\\n'.format(output_dir)\\n strSh += 'do\\\\n'\\n strSh += 'if [ -e $f ]\\\\n'\\n strSh += 'then\\\\n'\\n strSh += 'echo \\\"Some files already exists, please choose another output directory\\\"\\\\n'\\n strSh += 'exit 1\\\\n'\\n strSh += 'break\\\\n'\\n strSh += 'fi\\\\n'\\n strSh += 'done\\\\n\\\\n'\\n\\n # Abort script if files in Filtered already exists\\n strSh += '# Abort script if files in Filtered already exists\\\\n'\\n strSh += 'for f in {:s}/Filtered/*\\\\n'.format(output_dir)\\n strSh += 'do\\\\n'\\n strSh += 'if [ -e $f ]\\\\n'\\n strSh += 'then\\\\n'\\n strSh += 'echo \\\"Some files already exists, please choose another output directory\\\"\\\\n'\\n strSh += 'exit 1\\\\n'\\n strSh += 'break\\\\n'\\n strSh += 'fi\\\\n'\\n strSh += 'done\\\\n\\\\n'\\n\\n # Abort script if files in FRC already exists\\n strSh += '# Abort script if files in FRC already exists\\\\n'\\n strSh += 'for f in {:s}/FRC/*\\\\n'.format(output_dir)\\n strSh += 'do\\\\n'\\n strSh += 'if [ -e $f ]\\\\n'\\n strSh += 'then\\\\n'\\n strSh += 'echo \\\"Some files already exists, please choose another output directory\\\"\\\\n'\\n strSh += 'exit 1\\\\n'\\n strSh += 'break\\\\n'\\n strSh += 'fi\\\\n'\\n strSh += 'done\\\\n\\\\n'\\n\\n # Loop over all files\\n strSh += '\\\\nfor file in $fileList\\\\ndo\\\\n\\\\n'\\n\\n strSh += 'baseName=${{file%{:s}}}\\\\n'.format(input_suffix)\\n strSh += 'baseName=${{baseName#{:s}}}\\\\n'.format(input_dir)\\n\\n # Create a temporary file to work with to prevent format issues\\n strSh += '# Create a temporary file to work with to prevent format issues\\\\n'\\n strSh += 'e2proc3d.py $file {:s}/Temp/${{baseName}}_temp.mrc\\\\n\\\\n'.format(output_dir)\\n\\n # Remove some temporary files that unblur makes\\n strSh += '# Remove some temporary files that unblur makes\\\\n'\\n strSh += 'for f in .UnBlur*\\\\n'\\n strSh += 'do\\\\n'\\n strSh += 'if [ -e $f ]\\\\n'\\n strSh += 'then\\\\n'\\n strSh += 'rm .UnBlur*\\\\n'\\n strSh += 'break\\\\n'\\n strSh += 'else\\\\n'\\n strSh += 'true\\\\n'\\n strSh += 'fi\\\\n'\\n strSh += 'done\\\\n\\\\n'\\n\\n # Start Unblur without dose correction\\n strSh += '{:s} << eof\\\\n'.format(unblur_path)\\n\\n # Input File\\n strSh += '{:s}/Temp/${{baseName}}_temp.mrc\\\\n'.format(output_dir)\\n # Number of Frames\\n strSh += '{:d}\\\\n'.format(options.nr_frames)\\n # Sum File\\n strSh += '{:s}/Doseuncorrected/${{baseName}}{:s}.mrc\\\\n'.format(\\n output_dir,\\n options.sum_suffix\\n )\\n # Shift File\\n strSh += '{:s}/Shift/${{baseName}}{:s}.txt\\\\n'.format(\\n output_dir,\\n options.shift_suffix\\n )\\n # Pixel Size\\n strSh += '{:f}\\\\n'.format(options.pixel_size)\\n\\n # Say no to Dose Filtering\\n strSh += 'NO\\\\n'\\n\\n if options.save_frames:\\n # Say yes to Save Frames\\n strSh += 'YES\\\\n'\\n # Frames file\\n strSh += '{:s}/Doseuncorrected/${{baseName}}{:s}{:s}.mrc\\\\n'.format(\\n output_dir,\\n options.sum_suffix,\\n options.frames_suffix\\n )\\n else:\\n # Say no to Save Frames\\n strSh += 'NO\\\\n'\\n\\n if options.expert_mode:\\n # Say yes to Expert Mode\\n strSh += 'YES\\\\n'\\n # FRC File\\n strSh += '{:s}/FRC/${{baseName}}{:s}.txt\\\\n'.format(\\n output_dir,\\n options.frc_suffix\\n )\\n # Minimum Shift for initial search\\n strSh += '{:f}\\\\n'.format(options.shift_initial)\\n # Outer Radius Shift Limit\\n strSh += '{:f}\\\\n'.format(options.shift_radius)\\n # B-Factor to Apply\\n strSh += '{:f}\\\\n'.format(options.b_factor)\\n # Half-Width Vertical\\n strSh += '{:d}\\\\n'.format(options.fourier_vertical)\\n # Hald-Width Horizontal\\n strSh += '{:d}\\\\n'.format(options.fourier_horizontal)\\n # Termination Shift Threshold\\n strSh += '{:f}\\\\n'.format(options.shift_threshold)\\n # Maximum Iterations\\n strSh += '{:d}\\\\n'.format(options.iterations)\\n # Restore Noise Power\\n if options.restore_noise:\\n # Say yes to Restore Noise Power\\n strSh += 'YES\\\\n'\\n else:\\n # Say no to Restore Noise Power\\n strSh += 'NO\\\\n'\\n # Verbose Output\\n if options.verbose:\\n # Say yes to Verbose Output\\n strSh += 'YES\\\\n'\\n else:\\n # Say no to Verbose Output\\n strSh += 'NO\\\\n'\\n else:\\n # Say no to Expert Mode\\n strSh += 'NO\\\\n'\\n\\n # Enf of file reached\\n strSh += 'eof\\\\n\\\\n'\\n\\n # Remove some temporary files that unblur makes\\n strSh += 'for f in .UnBlur*\\\\n'\\n strSh += 'do\\\\n'\\n strSh += 'if [ -e $f ]\\\\n'\\n strSh += 'then\\\\n'\\n strSh += 'rm .UnBlur*\\\\n'\\n strSh += 'break\\\\n'\\n strSh += 'else\\\\n'\\n strSh += 'true\\\\n'\\n strSh += 'fi\\\\n'\\n strSh += 'done\\\\n\\\\n'\\n\\n # =========== #\\n if options.dose_filter:\\n\\n # Start Unblur with dose correction\\n strSh += '{:s} << eof\\\\n'.format(unblur_path)\\n\\n # Input File\\n strSh += '{:s}/Temp/${{baseName}}_temp.mrc\\\\n'.format(output_dir)\\n # Number of Frames\\n strSh += '{:d}\\\\n'.format(options.nr_frames)\\n # Sum File\\n strSh += '{:s}/Dosecorrected/${{baseName}}{:s}.mrc\\\\n'.format(\\n output_dir,\\n options.sum_suffix\\n )\\n # Shift File\\n strSh += '{:s}/Shift/${{baseName}}{:s}.txt\\\\n'.format(\\n output_dir,\\n options.shift_suffix\\n )\\n # Pixel Size\\n strSh += '{:f}\\\\n'.format(options.pixel_size)\\n\\n # Say yes to Dose Filtering\\n strSh += 'YES\\\\n'\\n # Exposure per Frame\\n strSh += '{:f}\\\\n'.format(options.exposure_per_frame)\\n # Acceleration Voltage\\n strSh += '{:f}\\\\n'.format(options.voltage)\\n # Pre Exposure\\n strSh += '{:f}\\\\n'.format(options.pre_exposure)\\n\\n if options.save_frames:\\n # Say yes to Save Frames\\n strSh += 'YES\\\\n'\\n # Frames file\\n strSh += '{:s}/Dosecorrected/${{baseName}}{:s}{:s}.mrc\\\\n'.format(\\n output_dir,\\n options.sum_suffix,\\n options.frames_suffix\\n )\\n else:\\n # Say no to Save Frames\\n strSh += 'NO\\\\n'\\n\\n if options.expert_mode:\\n # Say yes to Expert Mode\\n strSh += 'YES\\\\n'\\n # FRC File\\n strSh += '{:s}/FRC/${{baseName}}{:s}.txt\\\\n'.format(\\n output_dir,\\n options.frc_suffix\\n )\\n # Minimum Shift for initial search\\n strSh += '{:f}\\\\n'.format(options.shift_initial)\\n # Outer Radius Shift Limit\\n strSh += '{:f}\\\\n'.format(options.shift_radius)\\n # B-Factor to Apply\\n strSh += '{:f}\\\\n'.format(options.b_factor)\\n # Half-Width Vertical\\n strSh += '{:d}\\\\n'.format(options.fourier_vertical)\\n # Hald-Width Horizontal\\n strSh += '{:d}\\\\n'.format(options.fourier_horizontal)\\n # Termination Shift Threshold\\n strSh += '{:f}\\\\n'.format(options.shift_threshold)\\n # Maximum Iterations\\n strSh += '{:d}\\\\n'.format(options.iterations)\\n # Restore Noise Power\\n if options.restore_noise:\\n # Say yes to Restore Noise Power\\n strSh += 'YES\\\\n'\\n else:\\n # Say no to Restore Noise Power\\n strSh += 'NO\\\\n'\\n # Verbose Output\\n if options.verbose:\\n # Say yes to Verbose Output\\n strSh += 'YES\\\\n'\\n else:\\n # Say no to Verbose Output\\n strSh += 'NO\\\\n'\\n else:\\n # Say no to Expert Mode\\n strSh += 'NO\\\\n'\\n\\n # Enf of file reached\\n strSh += 'eof\\\\n\\\\n'\\n\\n # Remove temporary file\\n strSh += 'rm {:s}/Temp/${{baseName}}_temp.mrc\\\\n'.format(output_dir)\\n\\n # Remove some temporary files that unblur makes\\n # Remove some temporary files that unblur makes\\n strSh += 'for f in .UnBlur*\\\\n'\\n strSh += 'do\\\\n'\\n strSh += 'if [ -e $f ]\\\\n'\\n strSh += 'then\\\\n'\\n strSh += 'rm .UnBlur*\\\\n'\\n strSh += 'break\\\\n'\\n strSh += 'else\\\\n'\\n strSh += 'true\\\\n'\\n strSh += 'fi\\\\n'\\n strSh += 'done\\\\n\\\\n'\\n\\n if options.filter_sum:\\n # Filter Images\\n lowpass_angstrom = options.pixel_size / options.lowpass\\n highpass_angstrom = options.pixel_size / options.highpass\\n strSh += \\\\\\n 'e2proc3d.py {:s}/Doseuncorrected/${{baseName}}{:s}.mrc '.format(\\n output_dir,\\n options.sum_suffix\\n )\\n strSh += '{:s}/Filtered/${{baseName}}{:s}.mrc ' \\\\\\n .format(\\n output_dir,\\n options.sum_suffix\\n )\\n strSh += '--process=filter.lowpass.gauss:cutoff_freq={:f} '.format(\\n options.lowpass\\n )\\n strSh += '--process=filter.highpass.gauss:cutoff_freq={:f}\\\\n\\\\n' \\\\\\n .format(\\n options.highpass\\n )\\n\\n if options.remove_sum:\\n # Remove sum files\\n strSh += 'rm {:s}/Doseuncorrected/${{baseName}}{:s}.mrc\\\\n'.format(\\n output_dir,\\n options.sum_suffix\\n )\\n\\n # Done\\n strSh += 'done\\\\n\\\\n'\\n\\n # Remove temp folder\\n strSh += 'rm -r {:s}/Temp\\\\n'.format(output_dir)\\n\\n strSh += 'echo \\\"All done!\\\"'\\n\\n # Write Output\\n with open('{:s}/scriptUnblur.sh'.format(output_dir), 'w') as f:\\n f.write(strSh)\",\n \"def process_image(image):\\n image = resize(image)\\n return image\",\n \"def rescale(self, data, width, height, force=True):\\n import Image as pil\\n from cStringIO import StringIO\\n\\n max_width = width\\n max_height = height\\n\\n input_file = StringIO(data)\\n img = pil.open(input_file)\\n img = img.convert('RGB')\\n\\n if not force:\\n img.thumbnail((max_width, max_height), pil.ANTIALIAS)\\n else:\\n src_width, src_height = img.size\\n src_ratio = float(src_width) / float(src_height)\\n dst_width, dst_height = max_width, max_height\\n dst_ratio = float(dst_width) / float(dst_height)\\n\\n if dst_ratio < src_ratio:\\n crop_height = src_height\\n crop_width = crop_height * dst_ratio\\n x_offset = float(src_width - crop_width) / 2\\n y_offset = 0\\n else:\\n crop_width = src_width\\n crop_height = crop_width / dst_ratio\\n x_offset = 0\\n y_offset = float(src_height - crop_height) / 3\\n img = img.crop(\\n (int(x_offset), int(y_offset), int(x_offset) + int(crop_width), int(y_offset) + int(crop_height)))\\n img = img.resize((dst_width, dst_height), pil.ANTIALIAS)\\n\\n tmp = StringIO()\\n img.save(tmp, 'JPEG')\\n tmp.seek(0)\\n output_data = tmp.getvalue()\\n input_file.close()\\n tmp.close()\\n\\n return output_data\",\n \"def resize_all_images(image_dir, width, height, resize_type):\\n if width == \\\"\\\" and height == \\\"\\\":\\n return 0\\n print(\\\"Enter resizing image.\\\")\\n print(\\\"Enter resizing.\\\", width)\\n pool = Pool(1)\\n pool.starmap(resize_image, zip(\\n image_dir, itertools.repeat(width), itertools.repeat(height), itertools.repeat(resize_type)))\\n pool.close()\\n pool.join()\",\n \"def executeMatlabScript(dHeader):\\n\\t# Now execute the info mat file creator\\n\\tcmd1 = r'\\\\rm $HOME/.setupenv 2> /dev/null; . /usr/local/etc/setup.sh; setup matlab71 > /dev/null;'\\n\\tcmd2 = '%s nohup matlab -nosplash < %s > /tmp/matlabCreateOutput.txt' % (cmd1, dHeader['tempFile'])\\n\\tres2 = getoutput(cmd2)\\n\\tinfoFile = dHeader['infoFile']\\n\\tcmd3 = r'chgrp pimsgrp %s; chmod g+rw %s' % (infoFile, infoFile)\\n\\tres3 = getoutput(cmd3)\",\n \"def hxldedup():\\n run_script(hxldedup_main)\",\n \"def cmd_size(args):\",\n \"def process_files(self):\\n for filename in self.temp_directory.iterdir():\\n im = Image.open(str(filename))\\n scaled = im.resize((640, 480))\\n scaled.save(str(filename))\",\n \"def compute_scaler(args):\\n workspace = args.workspace\\n data_type = args.data_type\\n dir_name = args.dir_name \\n # Load data. \\n t1 = time.time()\\n hdf5_path = os.path.join(workspace, \\\"packed_features\\\", \\\"spectrogram\\\", data_type, dir_name, \\\"data.h5\\\")\\n with h5py.File(hdf5_path, 'r') as hf:\\n x = hf.get('x') \\n x = np.array(x) # (n_segs, n_concat, n_freq)\\n \\n # Compute scaler. \\n (n_segs, n_concat, n_freq) = x.shape\\n x2d = x.reshape((n_segs * n_concat, n_freq))\\n scaler = preprocessing.StandardScaler(with_mean=True, with_std=True).fit(x2d)\\n print(scaler.mean_)\\n print(scaler.scale_)\\n \\n # Write out scaler. \\n out_path = os.path.join(workspace, \\\"packed_features\\\", \\\"spectrogram\\\", data_type, dir_name, \\\"scaler.p\\\")\\n create_folder(os.path.dirname(out_path))\\n pickle.dump(scaler, open(out_path, 'wb'))\\n \\n print(\\\"Save scaler to %s\\\" % out_path)\\n print(\\\"Compute scaler finished! %s s\\\" % (time.time() - t1,))\",\n \"def sizes(options):\\n # import data\\n pixels = dict() # volumes are given in #pixels\\n snap_mask = \\\"/net/astrogate/export/astrodata/jgacon/filex/processing/\\\" \\\\\\n \\\"export/f8_h50_v100_objs_snap_%d.csv\\\"\\n snap_ids = np.arange(2,28+1)\\n z = snapid2z(snap_ids)\\n print z\\n\\n for id in snap_ids:\\n snap = snap_mask % (id - 1) # fix: snap number one too low in filename\\n pixels[id] = np.genfromtxt(snap)[1:-1,1] # row 2 contains volumes\\n # rm void & halo volumes\\n\\n # visualise\\n if \\\"err\\\" in options.keys():\\n nums = np.array([pixels[id].size for id in snap_ids])\\n avgs = np.array([np.mean(pixels[id]) for id in snap_ids])\\n mods = np.array([st.mode(pixels[id])[0][0] for id in snap_ids])\\n meds = np.array([np.median(pixels[id]) for id in snap_ids])\\n stds = np.array([np.std(pixels[id]) for id in snap_ids])\\n\\n print mods\\n print mods.shape\\n\\n plt.figure()\\n plt.title(\\\"Sizes of filaments as function of redshift\\\")\\n plt.xlabel(\\\"Redshift $z$\\\")\\n plt.xticks(snap_ids[::3], z[::3])\\n\\n plt.ylabel(\\\"Size in #pixels\\\")\\n\\n plt.errorbar(snap_ids, avgs, yerr=stds, label=\\\"Mean\\\")\\n plt.plot(snap_ids, mods, \\\"g\\\", label=\\\"Mode\\\")\\n plt.plot(snap_ids, meds, \\\"c\\\", label=\\\"Median\\\")\\n plt.legend(loc=\\\"best\\\")\\n\\n plt.twinx()\\n plt.ylabel(\\\"#Filaments\\\", color=\\\"r\\\")\\n plt.tick_params(\\\"y\\\", colors=\\\"r\\\")\\n\\n plt.plot(snap_ids, nums, \\\"r--\\\")\\n\\n plt.savefig(options[\\\"err\\\"])\\n\\n if \\\"dist\\\" in options.keys():\\n targets = np.array([5,10,15,20,25])\\n plt.figure()\\n plt.title(\\\"Volume distribution of filaments\\\")\\n plt.xlabel(\\\"Volume $V$ in #pixels\\\")\\n plt.ylabel(\\\"#Element with $V$ / Total #Elements\\\")\\n plt.xlim([0,1000])\\n for target in targets:\\n sns.kdeplot(pixels[target], label=\\\"$z$ = %f\\\" % snapid2z(target))\\n plt.legend(loc=\\\"best\\\")\\n plt.savefig(options[\\\"dist\\\"])\\n\\n if \\\"dist_inter\\\" in options.keys():\\n default = snap_ids[-1]\\n fig, ax = plt.subplots()\\n plt.subplots_adjust(bottom=0.25)\\n sns.kdeplot(pixels[int(default - 2)], ax=ax)\\n plt.xlim([0, 1000])\\n plt.ylim([0, 0.01])\\n plt.xlabel(\\\"Volume $V$ of filaments in #pixels\\\")\\n plt.ylabel(\\\"#Filaments with volume $V$ / Total #Filaments\\\")\\n\\n nums = np.array([pixels[id].size for id in snap_ids])\\n ax2 = ax.twinx()\\n ax2.set_ylabel(\\\"#Filaments\\\", color=\\\"r\\\", alpha=0.5)\\n ax2.tick_params(axis=\\\"y\\\", labelcolor=\\\"r\\\")\\n ax2_x = np.linspace(0, 1000, nums.size)\\n ax2.plot(ax2_x, nums, \\\"r--\\\", alpha=0.5)\\n point, = ax2.plot(ax2_x[default - 2], nums[default - 2], \\\"ro\\\", alpha=0.5)\\n\\n axcolor = 'lightgoldenrodyellow'\\n axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)\\n sid = Slider(axfreq, \\\"ID\\\", 2, 28, valinit=default, valstep=1)\\n ax.set_title(\\\"$z$ = %f\\\" % snapid2z(default))\\n\\n def update(val):\\n id = sid.val\\n\\n print id\\n #ax.clear()\\n ax.set_ydata()\\n ax.set_xdata()\\n ax.set_title(\\\"$z$ = %f\\\" % snapid2z(int(id)))\\n ax.set_xlim([0,1000])\\n ax.set_ylim([0, 0.01])\\n sns.kdeplot(pixels[int(id)], ax=ax)\\n point.set_xdata(ax2_x[int(id) - 2])\\n point.set_ydata(nums[int(id) - 2])\\n fig.canvas.draw_idle()\\n sid.on_changed(update)\\n\\n plt.show()\\n\\n\\n if \\\"hist\\\" in options.keys():\\n conc = None\\n for id, vols in pixels.iteritems():\\n data = np.empty((vols.size, 2))\\n data[:,0] = id\\n data[:,1] = vols\\n\\n if conc is None:\\n conc = data\\n else:\\n conc = np.vstack((conc, data))\\n\\n plt.figure()\\n plt.hist2d(conc[:,0], conc[:,1], bins=(snap_ids.size, 1000))\\n plt.ylim([100,400])\\n plt.savefig(options[\\\"hist\\\"])\",\n \"def execute(args, **kwargs):\\n p = set_options()\\n a = p.parse_args(args)\\n # logging.info(str(a))\\n\\n ifiles = ImageFiles(a)\\n\\n if a.info:\\n ifiles.describe()\\n else:\\n ifiles.write()\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(description=DESCRIPTION)\\n parser.add_argument('-o', '--output', default='output.jpg',\\n help=\\\"Filename to use for the output image\\\")\\n parser.add_argument('INPUT_IMAGE', type=str,\\n help=\\\"Filename for the input content image\\\")\\n parser.add_argument('STYLE_IMAGE', type=str,\\n help=\\\"Filename to for the input style image\\\")\\n parser.add_argument('-s', '--style-weight', default=1e-2, type=float,\\n help=\\\"Weight to apply for the loss associated with the style image. \\\"\\n \\\"Making this bigger results in a final image more like the style image\\\")\\n parser.add_argument('-c', '--content-weight', default=1e4, type=float,\\n help=\\\"Weight to apply for the loss associated with the content image. \\\"\\n \\\"Making this bigger results in a final image more like the content image\\\")\\n parser.add_argument('-t', '--total-variation-loss', default=30, type=float,\\n help=\\\"Weight for the total variation loss.\\\")\\n\\n args = parser.parse_args()\\n\\n print(args)\",\n \"def ResizeVdisk(self, data, vdiskid, headers=None, query_params=None, content_type=\\\"application/json\\\"):\\n uri = self.client.base_url + \\\"/vdisks/\\\"+vdiskid+\\\"/resize\\\"\\n return self.client.post(uri, data, headers, query_params, content_type)\",\n \"def make_script(name, data, target, execpath=False):\\n exec_path = join('/tmp', name + '-script')\\n target_path = join(target, 'tmp', name + '-script')\\n sfile = file(target_path, 'w')\\n sfile.write(data.read())\\n sfile.close()\\n os.system('chmod 755 %s' % target_path)\\n if not execpath:\\n return target_path\\n else:\\n # for chroot target exec_path\\n return exec_path\",\n \"def run(cls, h5_src, h5_dst, var_attrs, version=None, meta=None,\\n process_size=None, check_dset_attrs=False, resolution=None):\\n logger.info('Rechunking {} to {} using chunks given in {}'\\n .format(h5_src, h5_dst, var_attrs))\\n try:\\n with cls(h5_src, h5_dst, version=version) as r:\\n r.rechunk(var_attrs, meta=meta, process_size=process_size,\\n check_dset_attrs=check_dset_attrs,\\n resolution=resolution)\\n\\n logger.info('{} complete'.format(h5_dst))\\n except Exception:\\n logger.exception(\\\"Error rechunking {}\\\".format(h5_src))\\n raise\",\n \"def main():\\n\\n try:\\n worker_id = get_arg(\\\"--worker-id\\\", assert_nonnegative_int)\\n master_host = get_arg(\\\"--master-host\\\", assert_host)\\n master_port = get_arg(\\\"--master-port\\\", assert_positive_int)\\n scale = get_arg(\\\"--scale\\\", assert_pos_float)\\n method = get_arg(\\\"--method\\\", assert_downscaling_method)\\n load_backup = get_arg(\\\"--load-backup\\\", assert_bool, default=0)\\n number_of_random_walkers = get_arg(\\n \\\"--n-random-walkers\\\", assert_nonnegative_int, default=1)\\n backup_size = get_arg(\\n \\\"--backup-size\\\", assert_nonnegative_int, default=100)\\n walking_iterations = get_arg(\\n \\\"--walking-iterations\\\", assert_positive_int, default=1)\\n\\n except AssertionError as e:\\n print_error(e)\\n print_error(\\n \\\"The downscaling worker expects the following arguments:\\\\n\\\"\\n \\\"\\\\t--worker-id: The id of the worker\\\\n\\\"\\n \\\"\\\\t--master-host: The host of the master\\\\n\\\"\\n \\\"\\\\t--master-port: The port of the master\\\\n\\\"\\n \\\"\\\\t--scale: The scale of the downscaled graph w.r.t. the input graph\\\\n\\\"\\n \\\"\\\\t--method: The method to use for downscaling, `random_walk` or `random_edge`\\\\n\\\"\\n \\\"\\\\t--load-backup: Should the worker load from a backup send by the master\\\\n\\\"\\n \\\"\\\\t--n-random-walkers: Number of random walkers to start with\\\\n\\\"\\n \\\"\\\\t--backup-size: Minimum size of the backup before it will be send to the master during a run, 0 if you want no backups\\\\n\\\"\\n \\\"\\\\t--walking-iterations: The number of steps a random walker sets before the queue will be handled\\\\n\\\"\\n )\\n return\\n\\n Worker(worker_id, master_host, master_port, scale, method, load_backup,\\n number_of_random_walkers, backup_size, walking_iterations)\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"-slices\\\", type=int, required=True)\\n args = parser.parse_args()\\n\\n upload_model_data(args.slices)\",\n \"def handle(self, args):\\n\\n # Ensure there is an uploads directory\\n if not os.path.exists(args.uploads) or not os.path.isdir(args.uploads):\\n if args.force:\\n os.makedirs(args.uploads)\\n else:\\n raise ConsoleError(\\n \\\"no uploads directory at '{}' use -f to create\\\".format(args.uploads)\\n )\\n\\n for dataset in args.datasets:\\n # Remove the trailing slash\\n dataset = dataset.rstrip(os.path.sep)\\n\\n # Check if the dataset is valid\\n if not args.force and not is_valid(dataset, False):\\n print(color.format(\\\"cannot package invalid dataset at {}\\\", color.LIGHT_RED, dataset))\\n continue\\n\\n name = os.path.basename(dataset)\\n out = os.path.join(args.uploads, name)\\n\\n if not args.force and os.path.exists(out+\\\".zip\\\"):\\n print(color.format(\\\"dataset exists at {} use -f to overwrite\\\", color.LIGHT_YELLOW, out))\\n continue\\n\\n try:\\n out = shutil.make_archive(out, \\\"zip\\\", root_dir=os.path.dirname(dataset), base_dir=name)\\n self.update_manifest(out)\\n except Exception as e:\\n print(color.format(\\\"could not package dataset {} at {}: {}\\\", color.LIGHT_RED, dataset, out.rstrip(\\\".zip\\\")+\\\".zip\\\", e))\\n continue\\n\\n print(color.format(\\\"packaged dataset at {}\\\", color.LIGHT_GREEN, out))\",\n \"def resize(self, *args):\\n return _ida_hexrays.lvar_saved_infos_t_resize(self, *args)\",\n \"def image_size():\\n return eval(subprocess(\\\"print camera_image_size()\\\"))\",\n \"def test_im_file_resize(self):\\n self._test_img_resize(IMBackend())\",\n \"def generateScaled(img):\\n\\n if not img._scaledfn:\\n return\\n\\n aimgfn = join(opts.root, img._filename)\\n if not opts.fast:\\n img._size = imageSize(aimgfn)\\n\\n ascaledfn = join(opts.root, img._scaledfn)\\n\\n if opts.scaled_force:\\n if opts.quiet: print \\\"forced regeneration of '%s'\\\" % img._scaledfn\\n elif not exists(ascaledfn):\\n if opts.quiet: print \\\"thumbnail absent '%s'\\\" % img._scaledfn\\n else:\\n # Check if scaledsize has changed\\n if not opts.fast:\\n img._scaledsize = imageSize(ascaledfn)\\n if not checkThumbSize(img._size, \\\\\\n img._scaledsize, \\\\\\n opts.scaled_size):\\n if opts.quiet: print \\\"thumbnail '%s size has changed\\\" % img._scaledfn\\n try:\\n # Clear cache for thumbnail size.\\n del imageSizeCache[ ascaledfn ]\\n except:\\n pass\\n else:\\n if opts.quiet: print \\\"thumbnail '%s' already generated (size ok)\\\" \\\\\\n % img._scaledfn\\n return\\n else:\\n if opts.quiet: print \\\"thumbnail '%s' already generated\\\" % img._scaledfn\\n return\\n\\n if opts.no_magick:\\n if opts.quiet: print \\\"ImageMagick tools disabled, can't create thumbnail\\\"\\n return\\n\\n # create necessary directories\\n d = dirname(ascaledfn)\\n if not exists(d):\\n os.makedirs(d)\\n\\n if opts.pil:\\n\\n try:\\n im = PilImage.open(aimgfn)\\n im.thumbnail((opts.scaled_size, opts.scaled_size), config.ScaledImages[\\\"Interpolation\\\"])\\n im.save(ascaledfn)\\n\\n img._scaledsize = im.size\\n except IOError, e:\\n raise SystemExit(\\\\\\n \\\"Error: identifying file '%s'\\\" % aimgfn + str(e))\\n\\n else:\\n\\n cmd = getMagickProg('convert') + ' -border 2x2 '\\n # FIXME check if this is a problem if not specified\\n #cmd += '-interlace NONE '\\n\\n cmd += '-geometry %dx%d ' % (opts.scaled_size, opts.scaled_size)\\n\\n if opts.scaled_quality:\\n cmd += '-quality %d ' % opts.scaled_quality\\n\\n # This doesn't add text into the picture itself, just the comment in\\n # the header.\\n if opts.copyright:\\n cmd += '-comment \\\\\\\"%s\\\\\\\" ' % opts.copyright\\n\\n # We use [1] to extract the thumbnail when there is one.\\n # It is harmless otherwise.\\n subimg = \\\"\\\"\\n if img._ext.lower() in [ \\\".jpg\\\", \\\".tif\\\", \\\".tiff\\\" ]:\\n subimg = \\\"[1]\\\"\\n\\n cmd += '\\\"%s%s\\\" \\\"%s\\\"' % (aimgfn, subimg, ascaledfn)\\n\\n if opts.quiet: print \\\"generating thumbnail '%s'\\\" % img._scaledfn\\n\\n (chin, chout, cherr) = os.popen3(cmd)\\n errs = cherr.readlines()\\n chout.close()\\n cherr.close()\\n if errs:\\n print >> sys.stderr, \\\\\\n \\\"Error: running convert program on %s:\\\" % aimgfn\\n errs = string.join(errs, '\\\\n')\\n print errs\\n\\n if subimg and \\\\\\n re.compile('Unable to read subimage').search(errs):\\n if opts.quiet: print \\\"retrying without subimage\\\"\\n cmd = string.replace(cmd, subimg, \\\"\\\")\\n\\n (chin, chout, cherr) = os.popen3(cmd)\\n errs = cherr.readlines()\\n chout.close()\\n cherr.close()\\n if errs:\\n print >> sys.stderr, \\\\\\n \\\"Error: running convert program on %s:\\\" % aimgfn\\n print string.join(errs, '\\\\n')\\n\\n else:\\n img._scaledsize = imageSize(ascaledfn)\",\n \"def resize_multiple_images(src_path, dst_path):\\n for filename in os.listdir(src_path):\\n img=Image.open(src_path+'/'+filename)\\n new_img = img.resize((96,96,))\\n #new_img.resize(96,96,1)\\n if not os.path.exists(dst_path):\\n os.makedirs(dst_path)\\n new_img.save(dst_path+'/'+filename)\\n print('Resized and saved {} successfully.'.format(filename))\",\n \"def _run(gridded_eval_dir_names, ungridded_eval_dir_names,\\n model_description_strings, output_dir_name):\\n\\n file_system_utils.mkdir_recursive_if_necessary(\\n directory_name=output_dir_name\\n )\\n\\n num_models = len(gridded_eval_dir_names)\\n assert len(ungridded_eval_dir_names) == num_models\\n assert len(model_description_strings) == num_models\\n\\n model_description_strings_dashed = [\\n s.replace('_', '-') for s in model_description_strings\\n ]\\n # model_description_strings = [\\n # s.replace('_', ' ') for s in model_description_strings\\n # ]\\n\\n pathless_panel_file_names = [\\n 'attributes_diagram.jpg', 'performance_diagram.jpg',\\n 'mean_forecast_prob.jpg'\\n ]\\n num_scores = len(pathless_panel_file_names)\\n\\n panel_file_names = []\\n resized_panel_file_names = []\\n\\n for i in range(num_models):\\n for j in range(num_scores):\\n this_file_name = '{0:s}/{1:s}'.format(\\n gridded_eval_dir_names[i] if j == num_scores - 1\\n else ungridded_eval_dir_names[i],\\n pathless_panel_file_names[j]\\n )\\n panel_file_names.append(this_file_name)\\n\\n this_file_name = '{0:s}/{1:s}_{2:s}.jpg'.format(\\n output_dir_name, pathless_panel_file_names[j].split('.')[0],\\n model_description_strings_dashed[i]\\n )\\n resized_panel_file_names.append(this_file_name)\\n\\n letter_label = None\\n\\n for k in range(len(panel_file_names)):\\n print('Resizing panel and saving to: \\\"{0:s}\\\"...'.format(\\n resized_panel_file_names[k]\\n ))\\n\\n if letter_label is None:\\n letter_label = 'a'\\n else:\\n letter_label = chr(ord(letter_label) + 1)\\n\\n imagemagick_utils.trim_whitespace(\\n input_file_name=panel_file_names[k],\\n output_file_name=resized_panel_file_names[k]\\n )\\n _overlay_text(\\n image_file_name=resized_panel_file_names[k],\\n x_offset_from_left_px=0, y_offset_from_top_px=225,\\n text_string='({0:s})'.format(letter_label),\\n font_size=PANEL_LETTER_FONT_SIZE, use_north_gravity=False\\n )\\n\\n imagemagick_utils.trim_whitespace(\\n input_file_name=resized_panel_file_names[k],\\n output_file_name=resized_panel_file_names[k]\\n )\\n imagemagick_utils.resize_image(\\n input_file_name=resized_panel_file_names[k],\\n output_file_name=resized_panel_file_names[k],\\n output_size_pixels=PANEL_SIZE_PX\\n )\\n\\n concat_figure_file_name = '{0:s}/evaluation_comparison.jpg'.format(\\n output_dir_name\\n )\\n print('Concatenating panels to: \\\"{0:s}\\\"...'.format(concat_figure_file_name))\\n\\n imagemagick_utils.concatenate_images(\\n input_file_names=resized_panel_file_names,\\n output_file_name=concat_figure_file_name,\\n num_panel_rows=num_models, num_panel_columns=num_scores\\n )\\n imagemagick_utils.resize_image(\\n input_file_name=concat_figure_file_name,\\n output_file_name=concat_figure_file_name,\\n output_size_pixels=CONCAT_FIGURE_SIZE_PX\\n )\\n imagemagick_utils.trim_whitespace(\\n input_file_name=concat_figure_file_name,\\n output_file_name=concat_figure_file_name\\n )\",\n \"def main():\\n\\tparser = construct_parser()\\n\\targs = parser.parse_args()\\n\\ttiles = slice(args.image, args.num_tiles, save=False)\\n\\tsave_tiles(tiles, prefix=get_basename(args.image), directory=args.dir,\\n\\t\\t format=args.format)\",\n \"def dist():\\n PackCommandExecutor().pack()\\n DistCommandExecutor().dist()\",\n \"def run(self, dataset_fixture, run_name_prefix=\\\"test\\\"):\\n self.dataset = dataset_fixture\\n self.set_project_shortname(run_name_prefix)\\n self.upload_spreadsheet_and_create_submission()\\n self.wait_for_ingest_to_process_spreadsheet_files_tab()\\n self.get_upload_area_credentials()\\n self.stage_data_files()\\n self.wait_for_envelope_to_be_validated()\\n if self.export_bundles:\\n self.complete_submission()\\n if run_name_prefix == \\\"scale\\\":\\n # == Scaling Logic ==\\n self.wait_for_primary_bundles_analysis_workflows_and_results_bundles()\\n else:\\n # == Non-scaling Logic ==\\n self.wait_for_primary_bundles()\\n self.wait_for_analysis_workflows()\\n self.wait_for_secondary_bundles()\\n\\n self.assert_data_browser_bundles()\\n if self.deployment != 'prod':\\n self.retrieve_loom_output_from_matrix_service()\\n\\n if self.failure_reason:\\n raise RuntimeError(self.failure_reason)\",\n \"def run(self, image, replicas, scale_replicas, command=None,\\n status_wait=True):\\n namespace = self.choose_namespace()\\n\\n name = self.client.create_statefulset(\\n namespace=namespace,\\n replicas=replicas,\\n image=image,\\n command=command,\\n status_wait=status_wait\\n )\\n\\n self.client.scale_statefulset(\\n name,\\n namespace=namespace,\\n replicas=scale_replicas,\\n status_wait=status_wait\\n )\\n\\n self.client.scale_statefulset(\\n name,\\n namespace=namespace,\\n replicas=replicas,\\n status_wait=status_wait\\n )\\n\\n self.client.delete_statefulset(\\n name=name,\\n namespace=namespace,\\n status_wait=status_wait\\n )\",\n \"def gen_vmd_script(\\n mole,\\n grads,\\n filename=\\\"molecule.xyz\\\",\\n scriptname=\\\"molecule.vmd\\\",\\n colorid=1,\\n scalex=10,\\n):\\n if colorid > 15:\\n colorid = 0\\n output = _vmd_script_start()\\n output += _vmd_script_molecule(mole, filename)\\n output += _vmd_script_vectors(mole, grads, colorid, scalex)\\n with open(scriptname, \\\"w\\\") as f:\\n f.write(output)\",\n \"def test_resize(dummy_input):\\n # Test the 2D image: H, W, C\\n image, label = dummy_input(image_size=(512, 512, 3),\\n label_size=(512, 512, 1))\\n transform = Resize(size=(64, 64))\\n _image, _label = transform(image, label, resize_orders=[3, 0])\\n assert _image.shape == (64, 64, 3)\\n assert _image.dtype == image.dtype\\n assert _label.shape == (64, 64, 1)\\n assert _label.dtype == label.dtype\\n\\n # Test the 3D image: H, W, D, C\\n image, label = dummy_input(image_size=(512, 512, 20, 3),\\n label_size=(512, 512, 20, 1))\\n transform = Resize(size=(64, 64, 10))\\n _image, _label = transform(image, label, resize_orders=[3, 0])\\n assert _image.shape == (64, 64, 10, 3)\\n assert _image.dtype == image.dtype\\n assert _label.shape == (64, 64, 10, 1)\\n assert _label.dtype == label.dtype\",\n \"def main():\\n nb_processed = 0\\n for dataset_name in DATASETS:\\n print(\\\"-----------------\\\")\\n print(\\\"Dataset: '%s'\\\" % (dataset_name,))\\n print(\\\"-----------------\\\")\\n\\n dataset_dir = os.path.join(WRITE_MAIN_DIR, dataset_name)\\n if not os.path.isdir(dataset_dir):\\n os.makedirs(dataset_dir)\\n\\n dataset = Dataset([os.path.join(READ_MAIN_DIR, dataset_name)])\\n print(\\\"Found %d images total.\\\" % (len(dataset.fps),))\\n\\n errors = []\\n\\n scale_height, scale_width = SCALES[dataset_name]\\n target_aspect_ratio = scale_width / scale_height\\n\\n # iterate over every image in the current dataset,\\n # augment that image N times, add cols/rows until target aspect ratio\\n # is reached, resize it (e.g. 64x64), save it\\n for img_idx, (image_filepath, image) in enumerate(zip(dataset.fps, dataset.get_images())):\\n print(\\\"[%s] Image %d of %d (%.2f%%)...\\\" \\\\\\n % (dataset_name, img_idx+1, len(dataset.fps),\\n 100*(img_idx+1)/len(dataset.fps)))\\n\\n # IOErrors during loading of images result here in a None value\\n if image is None:\\n print(\\\"Error / None\\\")\\n errors.append((\\n image_filepath,\\n \\\"Failed to load image '%s' (idx %d for dataset %s)\\\" \\\\\\n % (image_filepath, img_idx, dataset_name)\\n ))\\n else:\\n # resize too big images to smaller ones before any augmentation\\n # (for performance reasons)\\n height = image.shape[0]\\n width = image.shape[1]\\n aspect_ratio = width / height\\n if width > 1000 or height > 1000:\\n image = misc.imresize(image, (1000, int(1000 * aspect_ratio)))\\n\\n # augment image\\n # converts augmented versions automatically to float32, 0-1\\n augmentations = augment(image, **AUGMENTATIONS[dataset_name])\\n\\n # create list of original image + augmented versions\\n images_aug = [image / 255.0]\\n images_aug.extend(augmentations)\\n\\n # for each augmented version of the images:\\n # resize it to target aspect ratio (e.g. same width and height),\\n # save it\\n for aug_idx, image_aug in enumerate(images_aug):\\n image_aug = to_aspect_ratio_add(image_aug, target_aspect_ratio)\\n filename = \\\"{:0>6}_{:0>3}.jpg\\\".format(img_idx, aug_idx)\\n img_scaled = misc.imresize(image_aug, (scale_height, scale_width))\\n misc.imsave(os.path.join(dataset_dir, filename), img_scaled)\\n\\n nb_processed += 1\\n\\n print(\\\"Processed %d images with %d errors.\\\" % (nb_processed, len(errors)))\\n for (fp, err) in errors:\\n print(\\\"File %s error:\\\" % (fp,))\\n print(err)\\n print(\\\"Finished.\\\")\",\n \"def sizes(self, results_url=None, perf_id=None, platform=None, **kwargs):\\n sizes_script = self.package_repo_resource(\\n 'scripts', 'slave', 'chromium', 'sizes.py')\\n sizes_args = ['--target', self.c.build_config_fs]\\n if platform:\\n sizes_args.extend(['--platform', platform])\\n else:\\n sizes_args.extend(['--platform', self.c.TARGET_PLATFORM])\\n\\n run_tests_args = ['--target', self.c.build_config_fs,\\n '--no-xvfb']\\n properties_json = self.m.json.dumps(self.m.properties.legacy())\\n run_tests_args.extend(['--factory-properties', properties_json,\\n '--build-properties', properties_json])\\n run_tests_args.extend(['--test-type=sizes',\\n '--builder-name=%s' % self.m.properties['buildername'],\\n '--slave-name=%s' % self.m.properties['bot_id'],\\n '--build-number=%s' % self.m.properties['buildnumber'],\\n '--run-python-script'])\\n\\n if perf_id:\\n assert results_url is not None\\n run_tests_args.extend(['--annotate=graphing',\\n '--results-url=%s' % results_url,\\n '--perf-dashboard-id=sizes',\\n '--perf-id=%s' % perf_id])\\n\\n # If we have a clang revision, add that to the perf data point.\\n # TODO(hans): We want this for all perf data, not just sizes.\\n if hasattr(self, '_clang_version'):\\n clang_rev = re.match(r'(\\\\d+)(-\\\\d+)?', self._clang_version).group(1)\\n run_tests_args.append(\\n \\\"--perf-config={'r_clang_rev': '%s'}\\\" % clang_rev)\\n\\n full_args = run_tests_args + [sizes_script] + sizes_args\\n\\n return self.m.build.python(\\n 'sizes', self.package_repo_resource('scripts', 'slave', 'runtest.py'),\\n full_args, allow_subannotations=True, **kwargs)\",\n \"def createRestoreScript(self, wrapper):\\n content = textwrap.dedent(\\\"\\\"\\\"\\\\\\n #!/bin/sh\\n %(bin)s/pg_restore \\\\\\\\\\n --host=%(pgdata-directory)s \\\\\\\\\\n --username postgres \\\\\\\\\\n --dbname=%(dbname)s \\\\\\\\\\n --clean \\\\\\\\\\n --no-owner \\\\\\\\\\n --no-acl \\\\\\\\\\n %(backup-directory)s/database.dump\\n \\\"\\\"\\\" % self.options)\\n self.createExecutable(wrapper, content=content)\",\n \"def resize(self):\\n pass\",\n \"def run_script(self, params, config_no):\\n from dials.algorithms.scaling.algorithm import ScalingAlgorithm\\n\\n params.scaling_options.__setattr__(\\\"use_free_set\\\", True)\\n algorithm = ScalingAlgorithm(\\n params,\\n experiments=deepcopy(self.experiments),\\n reflections=deepcopy(self.reflections),\\n )\\n algorithm.run()\\n results = self.get_results_from_script(algorithm)\\n self.add_results_to_results_dict(config_no, results)\",\n \"def run(self, data):\\n required = {'token', 'image_id', 'script'}\\n api.validate(data, required)\\n token = data['token']\\n image_id = data['image_id']\\n script = data['script']\\n detach = data.get('detach', False)\\n if not detach:\\n detach = False\\n host_dir = data.get('host_dir', None)\\n docker_dir = data.get('docker_dir', None)\\n working_dir = data.get('working_dir', None)\\n # cgroup = data.get('cgroup', None)\\n # TODO(jorgesece): control image private\\n# credentials_module.authorize_image(\\n # token,\\n # image_id\\n # )\\n if host_dir:\\n self.credentials_module.authorize_directory(token, host_dir)\\n job_info = self.credentials_module.get_job_from_token(token)\\n cgroup_parent = job_info.get('cgroup', None)\\n container_id = self.docker_module.run_container(\\n image_id,\\n detach,\\n script,\\n host_dir=host_dir,\\n docker_dir=docker_dir,\\n working_dir=working_dir,\\n cgroup=cgroup_parent\\n )\\n self.credentials_module.add_container(token, container_id)\\n self.docker_module.start_container(container_id)\\n if not detach:\\n results = self.docker_module.logs_container(container_id)\\n else:\\n results = container_id\\n return results\",\n \"def prepare_data(oh_id):\\n imputer_record = ImputerMember.objects.get(oh_id=oh_id, active=True)\\n imputer_record.step = 'prepare_data'\\n imputer_record.save()\\n os.chdir(settings.BASE_DIR)\\n\\n command = [\\n 'imputer/prepare_genotypes.sh', '{}'.format(oh_id), '{}'.format(imputer_record.variant_length)\\n ]\\n process = run(command, stdout=PIPE, stderr=PIPE)\\n if process.stderr:\\n logger.debug(process.stderr)\\n logger.info('finished preparing {} plink data'.format(oh_id))\",\n \"def resize(self, *args):\\n return _ida_hexrays.hexwarns_t_resize(self, *args)\",\n \"def test_08_migrate_vm_live_resize_volume(self):\\n global vm\\n global data_disk_1\\n\\n vol = self.helper.resize_volume(apiclient = self.apiclient, volume = data_disk_1, shrinkOk = False, maxiops = 15000)\\n # Migrate all volumes and VMs\\n destinationHost, vol_list = self.helper.get_destination_pools_hosts(self.apiclient, vm, self.host)\\n for v in vol_list:\\n self.helper.check_storpool_volume_iops(self.spapi, v)\\n vm = self.helper.migrateVm(self.apiclient, self.virtual_machine_live_migration_1, destinationHost)\\n\\n destinationHost, vol_list = self.helper.get_destination_pools_hosts(self.apiclient, vm, self.host)\\n for v in vol_list:\\n self.helper.check_storpool_volume_iops(self.spapi, v)\",\n \"def test_pil_file_resize(self):\\n self._test_img_resize(PILBackend())\",\n \"def run_multinest(shower_name):\\n\\tos.system('cp '+datafile_dir+'datafile_'+shower_name+' '+multinest_dir\\n\\t\\t+'datafile')\\n\\n\\t# Multinest's mass.sh fails unless actually run from its directory\\n\\tworking_dir = os.getcwd()\\n\\tos.chdir(multinest_dir)\\n\\tos.system('sh mass.sh')\\n\\tos.chdir(working_dir)\\n\\n\\tos.rename(multinest_dir + 'gr.out', fit_dir + 'fit_' + shower_name + '.out')\",\n \"def test_resize_machine(self, pretty_print, owner_api_token):\\n query_string = setup_data.get('resize_machine', {}).get(\\n 'query_string') or [('size', '9417745961a84bffbf6419e5of68faa5')]\\n machine = setup_data.get('resize_machine', {}).get('machine') \\\\\\n or setup_data.get('machine') or 'my-machine'\\n uri = MIST_URL + \\\\\\n '/api/v2/machines/{machine}/actions/resize'.format(machine=machine)\\n request = MistRequests(\\n api_token=owner_api_token,\\n uri=uri,\\n params=query_string)\\n request_method = getattr(request, 'POST'.lower())\\n response = request_method()\\n if 'resize_machine' in REDIRECT_OPERATIONS:\\n assert_response_found(response)\\n else:\\n assert_response_ok(response)\\n assert poll(\\n api_token=owner_api_token,\\n uri=setup_data['amazon_machine_uri'],\\n data={'actions': {'start': True}},\\n timeout=_setup_module.DEFAULT_TIMEOUT)\\n print('Success!!!')\",\n \"def run(n):\\n base_files = os.listdir(DATA_DIR)\\n\\n # Keep track of files we have uploaded\\n uploaded = []\\n\\n # Upload files\\n for _ in range(n):\\n files = random.choices(base_files, k=random.randint(1, 15))\\n name, checksum = assemble_file(files)\\n upload_file(name)\\n os.remove(name)\\n uploaded.append((name, checksum))\\n\\n # Download files\\n for name, checksum in uploaded:\\n download_and_validate_checksum(name, checksum)\\n\\n # Validation checks\\n check_pack_sizes()\\n check_pack_checksums()\\n\\n print(server_stats())\\n\\n # Delete all files except for the last two. This should force the vacuum to rebalance\\n # some packfiles.\\n to_delete = uploaded[:-2]\\n remaining = uploaded[-2:]\\n for name, _ in to_delete:\\n delete_file(name)\\n\\n # Run a vacuum and wait for it to complete\\n vacuum_id = vacuum()\\n status = None\\n for _ in range(10):\\n status = vacuum_status(vacuum_id)\\n if status != \\\"RUNNING\\\":\\n break\\n time.sleep(1)\\n if status != \\\"SUCCEEDED\\\":\\n raise ValueError(f\\\"vacuum failed {status}\\\")\\n\\n # Check that the remaining files can still be downloaded after the vacuum\\n for name, checksum in remaining:\\n download_and_validate_checksum(name, checksum)\",\n \"def photo2web_process_hattenbach():\\n\\n os.chdir('/Volumes/SSD External/Hattenbach_v2')\\n \\n dir_base = os.getcwd()\\n \\n dir_p2w = '/Users/throop/photos/Trips/'\\n \\n dirs = sorted(glob.glob(os.path.join(dir_base, '*')))\\n \\n quality_out = '60'\\n size_out = '2000x2000'\\n \\n for i,dir in enumerate(dirs):\\n if os.path.isdir(dir):\\n os.chdir(dir)\\n dir_originals = os.path.join(dir, 'originals')\\n dir_originals_fullres = os.path.join(dir, 'originals_fullres')\\n\\n# For HH files, copy the 'actual' originals into a 'fullres' folder, for safekeeping\\n\\n if not os.path.isdir(dir_originals_fullres):\\n os.rename(dir_originals, dir_originals_fullres)\\n os.mkdir(dir_originals)\\n \\n files = glob.glob(os.path.join(dir_originals_fullres, '*'))\\n\\n# Get a list of all the images\\n\\n# For each image, make a low-res, low-quality image. This is just because the scanned files\\n# are huge and high-quality, and not useful for online. They are much larger than necessary. \\n# So we use 'convert' to shrink them in size and quality, and put the output into 'originals' directory \\n# for photo2web.\\n\\n for file in files:\\n file_short = os.path.basename(file)\\n file_in = os.path.join(dir_originals_fullres,file_short)\\n file_out = os.path.join(dir_originals,file_short)\\n if not os.path.isfile(file_out):\\n cmd = (f'convert -resize {size_out} -quality {quality_out}' +\\n f' {file_in}' +\\n f' {file_out}')\\n print(f'{cmd}')\\n \\n subprocess.run(['convert', '-resize', size_out, '-quality', quality_out,\\n file_in,\\n file_out])\\n\\n# Now, finally, go thru and do photo2web on all of them.\\n \\n print(f'\\\\nProcessing directory {i}/{len(dirs)} {dir}\\\\n')\\n subprocess.run(['cp', '-r', os.path.join(dir_p2w, 'header.txt'), '.'])\\n subprocess.run(['cp', '-r', os.path.join(dir_p2w, 'photos.css'), '.'])\\n if not os.path.exists('captions.txt'):\\n subprocess.run(['captions_photo2web']) \\n subprocess.run(['photo2web_old'])\\n subprocess.run(['photo2web'])\",\n \"def test_run_resize_with_padded_blobkey(self):\\n padded_blobkey = 'SomeBlobKey==='\\n self.expect_datatore_lookup(padded_blobkey, True)\\n self.expect_open_image(padded_blobkey, (1600, 1200))\\n self.expect_resize(32)\\n self.expect_encode_image('SomeImageSize32')\\n self.mox.ReplayAll()\\n self._environ['PATH_INFO'] += '====s32'\\n self.run_request('image/jpeg', 'SomeImageSize32')\",\n \"def run_populate(rconfig, limit, verbose=False):\\n\\n output_name = DOCUMENT_PROCESSING_IO[POPULATE]['out']\\n dataset = DataSet(POPULATE, output_name, rconfig)\\n\\n # initialize data set if it does not exist, this is not contingent on\\n # anything because --populate is the first step\\n if not dataset.exists():\\n dataset.initialize_on_disk()\\n dataset.load_from_disk()\\n\\n fspecs = get_lines(rconfig.filenames, dataset.files_processed, limit)\\n print \\\"[--populate] adding %d files to %s\\\" % (len(fspecs), dataset)\\n count = 0\\n for fspec in fspecs:\\n count += 1\\n src_file = fspec.source\\n dst_file = os.path.join(rconfig.target_path, 'data', output_name,\\n dataset.version_id, 'files', fspec.target)\\n # allow for compressed files, while being handed the name without\\n # extension\\n if not os.path.exists(src_file):\\n src_file += \\\".gz\\\"\\n dst_file += \\\".gz\\\"\\n if verbose:\\n print \\\"[--populate] %04d %s\\\" % (count, dst_file)\\n ensure_path(os.path.dirname(dst_file))\\n try:\\n shutil.copyfile(src_file, dst_file)\\n except IOError:\\n print \\\" WARNING: source file does not exist, not copying\\\"\\n print \\\" %s\\\" % src_file\\n # at some point there seemed to be an issue with compressing for Chinese,\\n # so added this to do language dependent compressing, there is now no\\n # difference for the population phase\\n if rconfig.language == 'en': compress(dst_file)\\n elif rconfig.language == 'cn': compress(dst_file)\\n # TODO: does this mean that you miss some if total_count % STEP != 0\\n if count % STEP == 0:\\n dataset.update_processed_count(STEP)\\n\\n return (count % STEP, [dataset])\",\n \"def main():\\n config = configparser.ConfigParser()\\n config.read('dwh.cfg')\\n try: \\n conn = psycopg2.connect(\\\"host={} dbname={} user={} password={} port={}\\\".format(*config['CLUSTER'].values()))\\n cur = conn.cursor()\\n \\n # copy data from S3 to staging tables in redshift\\n print('Staging song and log data. This takes several minutes!')\\n execute_query_list(cur, conn, copy_table_queries)\\n \\n print('Populating analytics tables.')\\n # fill analytical tables from staging tables\\n execute_query_list(cur, conn, insert_table_queries)\\n finally:\\n conn.close()\",\n \"def Resize_data(data_size):\\n\\n data_size = data_size\\n\\n str_taille = 0\\n\\n data_size_n = data_size - str_taille\\n\\n taille = data_size_n//3\\n\\n reste = data_size_n - (taille*3)\\n\\n taille_sup = taille+reste\\n\\n return (taille_sup,taille)\",\n \"def prepare_test_data(args):\\n image_dir = args.test_image_dir\\n\\n files = os.listdir(image_dir)\\n files = [f for f in files if f.lower().endswith('.png')]\\n\\n img_ids = list(range(len(files)))\\n img_files = []\\n img_heights = []\\n img_widths = []\\n \\n for f in files:\\n img_path = os.path.join(image_dir, f)\\n img_files.append(img_path)\\n img = cv2.imread(img_path)\\n img_heights.append(img.shape[0]) \\n img_widths.append(img.shape[1]) \\n\\n print(\\\"Building the testing dataset...\\\")\\n dataset = DataSet(img_ids, img_files, img_heights, img_widths)\\n print(\\\"Dataset built.\\\")\\n return dataset\",\n \"def _create_tensor_dataset(self, img_size):\\n with tempfile.TemporaryDirectory() as tmpdirname:\\n dir_mimic = '/cluster/work/vogtlab/Projects/mimic-cxr/physionet.org/files/mimic-cxr-jpg/2.0.0/files'\\n if os.path.exists(dir_mimic):\\n # only run test if original data exists\\n\\n if os.path.exists('/cluster/home/klugh/'):\\n tmpdirname = os.path.join(os.path.expandvars('$TMPDIR'), 'test_create_tensor_dataset')\\n if not os.path.exists(tmpdirname):\\n os.mkdir(tmpdirname)\\n dir_out = os.path.expanduser(os.path.join(tmpdirname, 'dir_out'))\\n\\n dir_base_resized_compressed = os.path.expanduser(os.path.join(tmpdirname))\\n self.assertTrue(os.path.exists(tmpdirname))\\n\\n dir_base_resize = os.path.join(tmpdirname, f'files_small_{img_size[0]}')\\n dataset_creator = CreateTensorDataset(dir_base_resize=dir_base_resize, dir_mimic=dir_mimic,\\n dir_out=dir_out,\\n img_size=img_size,\\n dir_base_resized_compressed=dir_base_resized_compressed,\\n max_it=10)\\n dataset_creator()\\n self.assertTrue(os.path.exists(dir_out))\\n self.assertTrue(os.path.exists(dir_base_resized_compressed))\\n\\n assert os.path.exists(os.path.join(dir_base_resized_compressed,\\n f'mimic_resized_{img_size[0]}.zip')), \\\\\\n 'dir_resized_compressed {} does not exist \\\\n {}'.format(\\n os.path.join(dir_base_resized_compressed, f'mimic_resized_{img_size[0]}.zip'),\\n os.listdir(dir_base_resized_compressed))\",\n \"def scale_datanode(self, datanode_count):\\n call([Command.docker_compose, \\\"-f\\\", self.docker_compose_file,\\n \\\"up\\\", \\\"-d\\\", \\\"--scale\\\", \\\"datanode=\\\" + datanode_count])\",\n \"def main():\\n\\n # Just grab all files - we'll use try/except to filter\\n images = glob.glob(os.path.join(args.input_dir, '*.*'))\\n if not os.path.exists(args.output_dir):\\n os.makedirs(args.output_dir)\\n for img_file in images:\\n print(img_file)\\n try:\\n np_img = plt.imread(img_file)\\n print(np_img.shape)\\n img_name = img_file.split(os.sep)[-1]\\n new_img_file = os.path.join(args.output_dir, img_name)\\n pad_image(np_img, new_img_file)\\n except Exception as e:\\n print('Warning: {}. Skpping file.'.format(e))\\n continue\",\n \"def main(args):\\n args = parse_args(args)\\n setup_logging(args.loglevel)\\n _logger.debug(\\\"Starting crazy calculations...\\\")\\n createlookuptable(args.imagefolder)\\n _logger.info(\\\"Script ends here\\\")\",\n \"def main():\\n logging.info(\\\"Executing data quality module\\\")\\n\\n calculate_quality()\",\n \"def scale(self, infile: str, outfile: str):\\n\\n with Image(filename=infile) as src:\\n width, height = self.new_dimensions(src)\\n with src.clone() as dest:\\n dest.resize(width=width,height=height)\\n logging.debug(\\n f'Converted \\\"{infile}\\\" ({src.width}x{src.height})'\\n f' -> \\\"{outfile}\\\" ({dest.width}x{dest.height})')\\n dest.save(filename=outfile)\",\n \"def run(input, size, engine, resize_method, imagesize, api_key):\\n\\tengine_list = ['duckgo', 'bing', 'bing_api', 'flickr_api']\\n\\tclick.clear()\\n\\n\\tif input and engine in engine_list:\\n\\t\\tfactory = SearchEngineFactory(input,size,input,resize_method,\\\"dataset\\\",imagesize, engine, api_key)\\n\\t\\t# Remove corrupt files\\n\\t\\tremove_corrupt(\\\"dataset\\\")\\n\\n\\telse:\\n\\t\\trich.print(\\\"Please provide a valid name\\\")\",\n \"def execute(in_file, resolution, width, height, raster_mode):\\n\\n raster_tmpfile = tempfile.NamedTemporaryFile()\\n\\n # Convert width and height to DPI.\\n width = (width/72) * resolution\\n height = (height/72) * resolution\\n\\n args = [config.gs,\\n \\\"-q\\\",\\n \\\"-dBATCH\\\",\\n \\\"-dNOPAUSE\\\",\\n \\\"-r%s\\\" % resolution,\\n \\\"-g%dx%d\\\" % (width, height),\\n \\\"-sDEVICE=%s\\\" % raster_mode,\\n \\\"-sOutputFile=%s\\\" % raster_tmpfile.name,\\n \\\"-\\\",\\n ]\\n # Execute ghostscript\\n log.debug(\\\"Opening ghostscript process with args: %s\\\" % args)\\n process = subprocess.Popen(args, cwd=config.tmp_dir,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n stdin=subprocess.PIPE)\\n (stdoutdata, stderrdata) = process.communicate(in_file.read())\\n log.debug(\\\"Waiting on ghostscript to terminate.\\\")\\n process.wait()\\n\\n # Check that ghostscript functioned correctly.\\n if process.returncode != 0:\\n log.crit(\\\"Ghostscript failed during execution with returncode = %s.\\\"\\n % process.returncode)\\n\\n # Get raster information into a StringIO\\n log.debug(\\\"Rewinding ghostscript raster output and storing it in StringIO\\\")\\n raster_tmpfile.seek(0)\\n raster = raster_tmpfile\\n\\n # Gather the vector information into a StringIO\\n log.debug(\\\"Gathering the vector output from ghostscript.\\\")\\n vector = StringIO()\\n vector.write(stdoutdata)\\n vector.seek(0)\\n\\n log.debug(\\\"Returning ghostscript raster and vector output.\\\")\\n return (raster, vector)\",\n \"def _make_resized_data(self, odata):\\n\\n\\n nmax = odata['file_id'].shape[1]\\n new_nmax = odata['ncutout'].max()\\n if new_nmax < 2:\\n new_nmax = 2\\n temp_obj_data = odata\\n\\n nobj = temp_obj_data.size\\n\\n new_data = meds.util.get_meds_output_struct(\\n nobj,\\n new_nmax,\\n extra_fields=self._get_fields(new_nmax),\\n )\\n new_data = self._add_cat_fields(new_data, copy=False)\\n\\n for name in new_data.dtype.names:\\n if name in temp_obj_data.dtype.names:\\n\\n shape = new_data[name].shape\\n lshape = len(shape)\\n\\n if lshape > 1 and shape[1] == new_nmax:\\n new_data[name][:,:] = temp_obj_data[name][:,0:new_nmax]\\n else:\\n new_data[name][:] = temp_obj_data[name][:]\\n\\n del temp_obj_data\\n\\n return new_data\",\n \"def resize(file_path, width, height):\\n folder, file_name, ext = parse_file_path(file_path)\\n url = addr + '/img/resize'\\n\\n img = cv2.imread(file_path)\\n _, img_encoded = cv2.imencode('.jpg', img)\\n payload = img_encoded.tostring()\\n \\n response = requests.post(url, data=payload, params = {'w':width, 'h':height})\\n if response.status_code != 200:\\n print(json.load(response.content))\\n return None\\n \\n img_array = uncompress_nparr(response.content)\\n file_path = os.path.join(folder, file_name, + '_' + random_string() + '-resized' + ext)\\n cv2.imwrite(file_path, img_array)\\n return file_path\",\n \"def test_resize_up_confirm(self):\\n server_id = self._create_server(flavor=self.flavor.id).id\\n # get the starting quota now that we've created a server\\n starting_usage = self._get_absolute_limits()\\n # now resize up\\n alternate_flavor = self._pick_alternate_flavor()\\n self.nova('resize',\\n params='%s %s --poll' % (server_id, alternate_flavor))\\n resize_usage = self._get_absolute_limits()\\n # compare the starting usage against the resize usage\\n self._compare_quota_usage(starting_usage, resize_usage)\\n # now confirm the resize\\n self.nova('resize-confirm', params='%s' % server_id)\\n # we have to wait for the server to be ACTIVE before we can check quota\\n self._wait_for_state_change(server_id, 'active')\\n # get the final quota usage which should be the same as the resize\\n # usage before confirm\\n confirm_usage = self._get_absolute_limits()\\n self._compare_quota_usage(\\n resize_usage, confirm_usage, expect_diff=False)\",\n \"def run(self, data_id, script, verbosity=1):\\n if verbosity >= 1:\\n print('RUN: {} {}'.format(data_id, script))\\n\\n self.data_id = data_id\\n self.process_failed = False\\n\\n # Fetch data instance to get any executor requirements.\\n self.process = Data.objects.get(pk=data_id).process\\n requirements = self.process.requirements\\n self.requirements = requirements.get('executor', {}).get(self.name, {})\\n self.resources = requirements.get('resources', {})\\n\\n data_dir = settings.FLOW_EXECUTOR['DATA_DIR']\\n dir_mode = getattr(settings, 'FLOW_EXECUTOR', {}).get('DATA_DIR_MODE', 0o755)\\n\\n output_path = os.path.join(data_dir, str(data_id))\\n\\n os.mkdir(output_path)\\n # os.mkdir is not guaranteed to set the given mode\\n os.chmod(output_path, dir_mode)\\n os.chdir(output_path)\\n\\n log_file = open('stdout.txt', 'w+')\\n json_file = open('jsonout.txt', 'w+')\\n\\n proc_pid = self.start()\\n\\n self.update_data_status(\\n status=Data.STATUS_PROCESSING,\\n started=now(),\\n process_pid=proc_pid\\n )\\n\\n # Run processor and handle intermediate results\\n self.run_script(script)\\n spawn_processors = []\\n output = {}\\n process_error, process_warning, process_info = [], [], []\\n process_progress, process_rc = 0, 0\\n\\n # read processor output\\n try:\\n stdout = self.get_stdout()\\n while True:\\n line = stdout.readline()\\n if not line:\\n break\\n\\n try:\\n if line.strip().startswith('run'):\\n # Save processor and spawn if no errors\\n log_file.write(line)\\n log_file.flush()\\n\\n for obj in iterjson(line[3:].strip()):\\n spawn_processors.append(obj)\\n elif line.strip().startswith('export'):\\n file_name = line[6:].strip()\\n\\n export_folder = settings.FLOW_EXECUTOR['UPLOAD_DIR']\\n unique_name = 'export_{}'.format(uuid.uuid4().hex)\\n export_path = os.path.join(export_folder, unique_name)\\n\\n self.exported_files_mapper[self.data_id][file_name] = unique_name\\n\\n shutil.move(file_name, export_path)\\n else:\\n # If JSON, save to MongoDB\\n updates = {}\\n for obj in iterjson(line):\\n for key, val in six.iteritems(obj):\\n if key.startswith('proc.'):\\n if key == 'proc.error':\\n process_error.append(val)\\n if not process_rc:\\n process_rc = 1\\n updates['process_rc'] = process_rc\\n updates['process_error'] = process_error\\n updates['status'] = Data.STATUS_ERROR\\n elif key == 'proc.warning':\\n process_warning.append(val)\\n updates['process_warning'] = process_warning\\n elif key == 'proc.info':\\n process_info.append(val)\\n updates['process_info'] = process_info\\n elif key == 'proc.rc':\\n process_rc = int(val)\\n updates['process_rc'] = process_rc\\n if process_rc != 0:\\n updates['status'] = Data.STATUS_ERROR\\n elif key == 'proc.progress':\\n process_progress = int(float(val) * 100)\\n updates['process_progress'] = process_progress\\n else:\\n dict_dot(output, key, val)\\n updates['output'] = output\\n\\n if updates:\\n updates['modified'] = now()\\n self.update_data_status(**updates)\\n\\n if process_rc > 0:\\n log_file.close()\\n json_file.close()\\n os.chdir(CWD)\\n return\\n\\n # Debug output\\n # Not referenced in Data object\\n json_file.write(line)\\n json_file.flush()\\n\\n except ValueError as ex:\\n # Ignore if not JSON\\n log_file.write(line)\\n log_file.flush()\\n\\n except MemoryError as ex:\\n logger.error(__(\\\"Out of memory: {}\\\", ex))\\n\\n except IOError as ex:\\n # TODO: if ex.errno == 28: no more free space\\n raise ex\\n finally:\\n # Store results\\n log_file.close()\\n json_file.close()\\n os.chdir(CWD)\\n\\n return_code = self.end()\\n\\n if process_rc < return_code:\\n process_rc = return_code\\n\\n # This transaction is needed to make sure that processing of\\n # current data object is finished before manager for spawned\\n # processes is triggered.\\n with transaction.atomic():\\n if spawn_processors and process_rc == 0:\\n parent_data = Data.objects.get(pk=self.data_id)\\n\\n # Spawn processors\\n for d in spawn_processors:\\n d['contributor'] = parent_data.contributor\\n d['process'] = Process.objects.filter(slug=d['process']).latest()\\n\\n for field_schema, fields in iterate_fields(d.get('input', {}), d['process'].input_schema):\\n type_ = field_schema['type']\\n name = field_schema['name']\\n value = fields[name]\\n\\n if type_ == 'basic:file:':\\n fields[name] = self.hydrate_spawned_files(value, data_id)\\n elif type_ == 'list:basic:file:':\\n fields[name] = [self.hydrate_spawned_files(fn, data_id) for fn in value]\\n\\n with transaction.atomic():\\n d = Data.objects.create(**d)\\n DataDependency.objects.create(\\n parent=parent_data,\\n child=d,\\n kind=DataDependency.KIND_SUBPROCESS,\\n )\\n\\n # Copy permissions.\\n copy_permissions(parent_data, d)\\n\\n # Entity is added to the collection only when it is\\n # created - when it only contains 1 Data object.\\n entities = Entity.objects.filter(data=d).annotate(num_data=Count('data')).filter(num_data=1)\\n\\n # Copy collections.\\n for collection in parent_data.collection_set.all():\\n collection.data.add(d)\\n\\n # Add entities to which data belongs to the collection.\\n for entity in entities:\\n entity.collections.add(collection)\\n\\n if process_rc == 0 and not self.process_failed:\\n self.update_data_status(\\n status=Data.STATUS_DONE,\\n process_progress=100,\\n finished=now()\\n )\\n else:\\n self.update_data_status(\\n status=Data.STATUS_ERROR,\\n process_progress=100,\\n process_rc=process_rc,\\n finished=now()\\n )\\n\\n try:\\n # Cleanup after processor\\n data_purge(data_ids=[data_id], delete=True, verbosity=verbosity)\\n except: # pylint: disable=bare-except\\n logger.error(__(\\\"Purge error:\\\\n\\\\n{}\\\", traceback.format_exc()))\\n\\n # if not update_data(data): # Data was deleted\\n # # Restore original directory\\n # os.chdir(settings.PROJECT_ROOT)\\n # return\\n\\n # Restore original directory\\n # os.chdir(settings.PROJECT_ROOT)\\n\\n # return data_id\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58786595","0.58316845","0.5807729","0.5498334","0.5434105","0.54233754","0.54066753","0.5386028","0.5220113","0.52190685","0.5190704","0.51890326","0.51768124","0.5113704","0.5107601","0.5094947","0.5084976","0.50797784","0.50737107","0.50622976","0.5018568","0.49954957","0.49853355","0.49801832","0.49603394","0.495813","0.49575585","0.4953014","0.49357384","0.49010938","0.4895066","0.48893714","0.4888984","0.48790678","0.48675337","0.48671597","0.48660436","0.48644382","0.48591498","0.48436674","0.48420593","0.48339996","0.48300025","0.4829566","0.4817767","0.47975463","0.47913837","0.476817","0.47623292","0.4747481","0.4743991","0.4741838","0.47368774","0.47223648","0.4722179","0.47007155","0.46942985","0.4687087","0.46744952","0.4662315","0.46555516","0.4653326","0.46507755","0.46445355","0.46425098","0.46374568","0.46354568","0.46327063","0.4623092","0.46200553","0.46185142","0.46177918","0.46132368","0.46126398","0.46115777","0.4606635","0.46050963","0.46048665","0.46042585","0.4600351","0.45914483","0.45849416","0.45791134","0.45789427","0.4578626","0.45726106","0.45705184","0.45658275","0.45655903","0.45634994","0.45633617","0.4562702","0.4548509","0.45459065","0.45441136","0.45378998","0.45375186","0.4534652","0.45338166","0.4525509","0.45230016"],"string":"[\n \"0.58786595\",\n \"0.58316845\",\n \"0.5807729\",\n \"0.5498334\",\n \"0.5434105\",\n \"0.54233754\",\n \"0.54066753\",\n \"0.5386028\",\n \"0.5220113\",\n \"0.52190685\",\n \"0.5190704\",\n \"0.51890326\",\n \"0.51768124\",\n \"0.5113704\",\n \"0.5107601\",\n \"0.5094947\",\n \"0.5084976\",\n \"0.50797784\",\n \"0.50737107\",\n \"0.50622976\",\n \"0.5018568\",\n \"0.49954957\",\n \"0.49853355\",\n \"0.49801832\",\n \"0.49603394\",\n \"0.495813\",\n \"0.49575585\",\n \"0.4953014\",\n \"0.49357384\",\n \"0.49010938\",\n \"0.4895066\",\n \"0.48893714\",\n \"0.4888984\",\n \"0.48790678\",\n \"0.48675337\",\n \"0.48671597\",\n \"0.48660436\",\n \"0.48644382\",\n \"0.48591498\",\n \"0.48436674\",\n \"0.48420593\",\n \"0.48339996\",\n \"0.48300025\",\n \"0.4829566\",\n \"0.4817767\",\n \"0.47975463\",\n \"0.47913837\",\n \"0.476817\",\n \"0.47623292\",\n \"0.4747481\",\n \"0.4743991\",\n \"0.4741838\",\n \"0.47368774\",\n \"0.47223648\",\n \"0.4722179\",\n \"0.47007155\",\n \"0.46942985\",\n \"0.4687087\",\n \"0.46744952\",\n \"0.4662315\",\n \"0.46555516\",\n \"0.4653326\",\n \"0.46507755\",\n \"0.46445355\",\n \"0.46425098\",\n \"0.46374568\",\n \"0.46354568\",\n \"0.46327063\",\n \"0.4623092\",\n \"0.46200553\",\n \"0.46185142\",\n \"0.46177918\",\n \"0.46132368\",\n \"0.46126398\",\n \"0.46115777\",\n \"0.4606635\",\n \"0.46050963\",\n \"0.46048665\",\n \"0.46042585\",\n \"0.4600351\",\n \"0.45914483\",\n \"0.45849416\",\n \"0.45791134\",\n \"0.45789427\",\n \"0.4578626\",\n \"0.45726106\",\n \"0.45705184\",\n \"0.45658275\",\n \"0.45655903\",\n \"0.45634994\",\n \"0.45633617\",\n \"0.4562702\",\n \"0.4548509\",\n \"0.45459065\",\n \"0.45441136\",\n \"0.45378998\",\n \"0.45375186\",\n \"0.4534652\",\n \"0.45338166\",\n \"0.4525509\",\n \"0.45230016\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76681,"cells":{"query":{"kind":"string","value":"Overskriver metoden i en subklasse kun en dummyversjon"},"document":{"kind":"string","value":"def encode(self, text):"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def mezclar_bolsa(self):","def regular(self):","def subectIsSelf():","def lassh():","def setup_class(self):\n\n class SubFLRW(FLRW):\n def w(self, z):\n return super().w(z)\n\n self.cls = SubFLRW\n # H0, Om0, Ode0\n self.cls_args = (70 * u.km / u.s / u.Mpc, 0.27 * u.one, 0.689 * u.one)\n self.cls_kwargs = dict(Tcmb0=3.0 * u.K, name=self.__class__.__name__, meta={\"a\": \"b\"})","def __init__(self):\n super(Hardswish, self).__init__()","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(self):\n self.classes = {}","def primary(self):\n ...","def dummy(self):\n pass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def setup_class(klass):","def setup_class(klass):","def __init__(self, base):\n Category_realization_of_parent.__init__(self, base)","def __init__(self, base):\n Category_realization_of_parent.__init__(self, base)","def test_init_subclass(self, cosmo_cls):\n super().test_init_subclass(cosmo_cls)\n\n with pytest.raises(TypeError, match=\"subclasses of\"):\n\n class HASOde0SubClass(cosmo_cls):\n def __init__(self, Ode0):\n pass\n\n _COSMOLOGY_CLASSES.pop(HASOde0SubClass.__qualname__, None)","def __init__(self):\r\n\t\tpass","def __post_init__(self):\n pass","def __init(self):\n print(\"Welkam tu mobail lejen\")","def __call__(self):\n pass","def __call__(self):\n pass","def test_class_method(self):\n self.assertEqual(self.Test.has_one.im_self.__name__, 'Test')","def __init__():","def __init__(self):\n self.name = \"Kursawe\"\n objectives = [o_ku_1, o_ku_2]\n decisions = [Decision(-5, 5), Decision(-5, 5), Decision(-5, 5)]\n Model.__init__(self, objectives, None, decisions)","def __init__(self, nivel=None):","def setup_class(self):\n class SubCosmology(Cosmology):\n\n H0 = Parameter(unit=u.km / u.s / u.Mpc)\n Tcmb0 = Parameter(unit=u.K)\n\n def __init__(self, H0, Tcmb0=0*u.K, name=None, meta=None):\n super().__init__(name=name, meta=meta)\n self._H0 = H0\n self._Tcmb0 = Tcmb0\n\n self.cls = SubCosmology\n self.cls_args = (70 * (u.km / u.s / u.Mpc), 2.7 * u.K)\n self.cls_kwargs = dict(name=self.__class__.__name__, meta={\"a\": \"b\"})","def __init__(self):\n raise Exception('TODO IMPLEMENT ME !')","def _init(self):","def __init__(self):\n super(ManDaySerie, self).__init__()","def __call__( self ):\n pass","def __init__(self):\n super()","def __init__(self):\n\t\tsuper().__init__()","def __init__(self):\n\t\tsuper().__init__()","def __init__(self):\n super(PlannedSerie, self).__init__()","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass"],"string":"[\n \"def mezclar_bolsa(self):\",\n \"def regular(self):\",\n \"def subectIsSelf():\",\n \"def lassh():\",\n \"def setup_class(self):\\n\\n class SubFLRW(FLRW):\\n def w(self, z):\\n return super().w(z)\\n\\n self.cls = SubFLRW\\n # H0, Om0, Ode0\\n self.cls_args = (70 * u.km / u.s / u.Mpc, 0.27 * u.one, 0.689 * u.one)\\n self.cls_kwargs = dict(Tcmb0=3.0 * u.K, name=self.__class__.__name__, meta={\\\"a\\\": \\\"b\\\"})\",\n \"def __init__(self):\\n super(Hardswish, self).__init__()\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(self):\\n self.classes = {}\",\n \"def primary(self):\\n ...\",\n \"def dummy(self):\\n pass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def setup_class(klass):\",\n \"def setup_class(klass):\",\n \"def __init__(self, base):\\n Category_realization_of_parent.__init__(self, base)\",\n \"def __init__(self, base):\\n Category_realization_of_parent.__init__(self, base)\",\n \"def test_init_subclass(self, cosmo_cls):\\n super().test_init_subclass(cosmo_cls)\\n\\n with pytest.raises(TypeError, match=\\\"subclasses of\\\"):\\n\\n class HASOde0SubClass(cosmo_cls):\\n def __init__(self, Ode0):\\n pass\\n\\n _COSMOLOGY_CLASSES.pop(HASOde0SubClass.__qualname__, None)\",\n \"def __init__(self):\\r\\n\\t\\tpass\",\n \"def __post_init__(self):\\n pass\",\n \"def __init(self):\\n print(\\\"Welkam tu mobail lejen\\\")\",\n \"def __call__(self):\\n pass\",\n \"def __call__(self):\\n pass\",\n \"def test_class_method(self):\\n self.assertEqual(self.Test.has_one.im_self.__name__, 'Test')\",\n \"def __init__():\",\n \"def __init__(self):\\n self.name = \\\"Kursawe\\\"\\n objectives = [o_ku_1, o_ku_2]\\n decisions = [Decision(-5, 5), Decision(-5, 5), Decision(-5, 5)]\\n Model.__init__(self, objectives, None, decisions)\",\n \"def __init__(self, nivel=None):\",\n \"def setup_class(self):\\n class SubCosmology(Cosmology):\\n\\n H0 = Parameter(unit=u.km / u.s / u.Mpc)\\n Tcmb0 = Parameter(unit=u.K)\\n\\n def __init__(self, H0, Tcmb0=0*u.K, name=None, meta=None):\\n super().__init__(name=name, meta=meta)\\n self._H0 = H0\\n self._Tcmb0 = Tcmb0\\n\\n self.cls = SubCosmology\\n self.cls_args = (70 * (u.km / u.s / u.Mpc), 2.7 * u.K)\\n self.cls_kwargs = dict(name=self.__class__.__name__, meta={\\\"a\\\": \\\"b\\\"})\",\n \"def __init__(self):\\n raise Exception('TODO IMPLEMENT ME !')\",\n \"def _init(self):\",\n \"def __init__(self):\\n super(ManDaySerie, self).__init__()\",\n \"def __call__( self ):\\n pass\",\n \"def __init__(self):\\n super()\",\n \"def __init__(self):\\n\\t\\tsuper().__init__()\",\n \"def __init__(self):\\n\\t\\tsuper().__init__()\",\n \"def __init__(self):\\n super(PlannedSerie, self).__init__()\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6570687","0.63065004","0.60361695","0.6011499","0.59461576","0.5945843","0.5942525","0.5942525","0.5942525","0.5942525","0.5942525","0.5942525","0.5942525","0.5942525","0.5942525","0.5942525","0.5895928","0.5890295","0.5882326","0.58123356","0.58123356","0.58123356","0.58123356","0.58123356","0.58123356","0.58123356","0.58123356","0.58123356","0.58123356","0.58123356","0.58123356","0.58123356","0.58123356","0.58085984","0.58085984","0.5796474","0.5796474","0.5786867","0.5783951","0.5758224","0.57279783","0.56919414","0.56919414","0.5689928","0.568746","0.56830364","0.56648093","0.565176","0.5639447","0.56352645","0.5634247","0.5632007","0.56131756","0.560953","0.560953","0.5604652","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676","0.56041676"],"string":"[\n \"0.6570687\",\n \"0.63065004\",\n \"0.60361695\",\n \"0.6011499\",\n \"0.59461576\",\n \"0.5945843\",\n \"0.5942525\",\n \"0.5942525\",\n \"0.5942525\",\n \"0.5942525\",\n \"0.5942525\",\n \"0.5942525\",\n \"0.5942525\",\n \"0.5942525\",\n \"0.5942525\",\n \"0.5942525\",\n \"0.5895928\",\n \"0.5890295\",\n \"0.5882326\",\n \"0.58123356\",\n \"0.58123356\",\n \"0.58123356\",\n \"0.58123356\",\n \"0.58123356\",\n \"0.58123356\",\n \"0.58123356\",\n \"0.58123356\",\n \"0.58123356\",\n \"0.58123356\",\n \"0.58123356\",\n \"0.58123356\",\n \"0.58123356\",\n \"0.58123356\",\n \"0.58085984\",\n \"0.58085984\",\n \"0.5796474\",\n \"0.5796474\",\n \"0.5786867\",\n \"0.5783951\",\n \"0.5758224\",\n \"0.57279783\",\n \"0.56919414\",\n \"0.56919414\",\n \"0.5689928\",\n \"0.568746\",\n \"0.56830364\",\n \"0.56648093\",\n \"0.565176\",\n \"0.5639447\",\n \"0.56352645\",\n \"0.5634247\",\n \"0.5632007\",\n \"0.56131756\",\n \"0.560953\",\n \"0.560953\",\n \"0.5604652\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\",\n \"0.56041676\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76682,"cells":{"query":{"kind":"string","value":"Overskriver metoden i en subklasse kun en dummyversjon"},"document":{"kind":"string","value":"def decode(self, crypto):"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def mezclar_bolsa(self):","def regular(self):","def subectIsSelf():","def lassh():","def __init__(self):\n super(Hardswish, self).__init__()","def setup_class(self):\n\n class SubFLRW(FLRW):\n def w(self, z):\n return super().w(z)\n\n self.cls = SubFLRW\n # H0, Om0, Ode0\n self.cls_args = (70 * u.km / u.s / u.Mpc, 0.27 * u.one, 0.689 * u.one)\n self.cls_kwargs = dict(Tcmb0=3.0 * u.K, name=self.__class__.__name__, meta={\"a\": \"b\"})","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(__self__):\n pass","def __init__(self):\n self.classes = {}","def primary(self):\n ...","def dummy(self):\n pass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def __init__(self):\n\t\tpass","def setup_class(klass):","def setup_class(klass):","def __init__(self, base):\n Category_realization_of_parent.__init__(self, base)","def __init__(self, base):\n Category_realization_of_parent.__init__(self, base)","def test_init_subclass(self, cosmo_cls):\n super().test_init_subclass(cosmo_cls)\n\n with pytest.raises(TypeError, match=\"subclasses of\"):\n\n class HASOde0SubClass(cosmo_cls):\n def __init__(self, Ode0):\n pass\n\n _COSMOLOGY_CLASSES.pop(HASOde0SubClass.__qualname__, None)","def __init__(self):\r\n\t\tpass","def __post_init__(self):\n pass","def __init(self):\n print(\"Welkam tu mobail lejen\")","def test_class_method(self):\n self.assertEqual(self.Test.has_one.im_self.__name__, 'Test')","def __call__(self):\n pass","def __call__(self):\n pass","def __init__():","def __init__(self):\n self.name = \"Kursawe\"\n objectives = [o_ku_1, o_ku_2]\n decisions = [Decision(-5, 5), Decision(-5, 5), Decision(-5, 5)]\n Model.__init__(self, objectives, None, decisions)","def __init__(self, nivel=None):","def setup_class(self):\n class SubCosmology(Cosmology):\n\n H0 = Parameter(unit=u.km / u.s / u.Mpc)\n Tcmb0 = Parameter(unit=u.K)\n\n def __init__(self, H0, Tcmb0=0*u.K, name=None, meta=None):\n super().__init__(name=name, meta=meta)\n self._H0 = H0\n self._Tcmb0 = Tcmb0\n\n self.cls = SubCosmology\n self.cls_args = (70 * (u.km / u.s / u.Mpc), 2.7 * u.K)\n self.cls_kwargs = dict(name=self.__class__.__name__, meta={\"a\": \"b\"})","def __init__(self):\n raise Exception('TODO IMPLEMENT ME !')","def _init(self):","def __init__(self):\n super(ManDaySerie, self).__init__()","def __call__( self ):\n pass","def __init__(self):\n super()","def __init__(self):\n\t\tsuper().__init__()","def __init__(self):\n\t\tsuper().__init__()","def __init__(self):\n super(PlannedSerie, self).__init__()","def test_reset_sequence_subclass_parent(self):\n class SubTestObjectFactory(self.TestObjectFactory):\n pass\n\n o1 = SubTestObjectFactory()\n self.assertEqual(0, o1.one)\n\n o2 = SubTestObjectFactory()\n self.assertEqual(1, o2.one)\n\n self.TestObjectFactory.reset_sequence()\n o3 = SubTestObjectFactory()\n self.assertEqual(0, o3.one)\n\n o4 = self.TestObjectFactory()\n self.assertEqual(1, o4.one)","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass","def __init_subclass__(self, *args, **kwargs): # real signature unknown\n pass"],"string":"[\n \"def mezclar_bolsa(self):\",\n \"def regular(self):\",\n \"def subectIsSelf():\",\n \"def lassh():\",\n \"def __init__(self):\\n super(Hardswish, self).__init__()\",\n \"def setup_class(self):\\n\\n class SubFLRW(FLRW):\\n def w(self, z):\\n return super().w(z)\\n\\n self.cls = SubFLRW\\n # H0, Om0, Ode0\\n self.cls_args = (70 * u.km / u.s / u.Mpc, 0.27 * u.one, 0.689 * u.one)\\n self.cls_kwargs = dict(Tcmb0=3.0 * u.K, name=self.__class__.__name__, meta={\\\"a\\\": \\\"b\\\"})\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(__self__):\\n pass\",\n \"def __init__(self):\\n self.classes = {}\",\n \"def primary(self):\\n ...\",\n \"def dummy(self):\\n pass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def __init__(self):\\n\\t\\tpass\",\n \"def setup_class(klass):\",\n \"def setup_class(klass):\",\n \"def __init__(self, base):\\n Category_realization_of_parent.__init__(self, base)\",\n \"def __init__(self, base):\\n Category_realization_of_parent.__init__(self, base)\",\n \"def test_init_subclass(self, cosmo_cls):\\n super().test_init_subclass(cosmo_cls)\\n\\n with pytest.raises(TypeError, match=\\\"subclasses of\\\"):\\n\\n class HASOde0SubClass(cosmo_cls):\\n def __init__(self, Ode0):\\n pass\\n\\n _COSMOLOGY_CLASSES.pop(HASOde0SubClass.__qualname__, None)\",\n \"def __init__(self):\\r\\n\\t\\tpass\",\n \"def __post_init__(self):\\n pass\",\n \"def __init(self):\\n print(\\\"Welkam tu mobail lejen\\\")\",\n \"def test_class_method(self):\\n self.assertEqual(self.Test.has_one.im_self.__name__, 'Test')\",\n \"def __call__(self):\\n pass\",\n \"def __call__(self):\\n pass\",\n \"def __init__():\",\n \"def __init__(self):\\n self.name = \\\"Kursawe\\\"\\n objectives = [o_ku_1, o_ku_2]\\n decisions = [Decision(-5, 5), Decision(-5, 5), Decision(-5, 5)]\\n Model.__init__(self, objectives, None, decisions)\",\n \"def __init__(self, nivel=None):\",\n \"def setup_class(self):\\n class SubCosmology(Cosmology):\\n\\n H0 = Parameter(unit=u.km / u.s / u.Mpc)\\n Tcmb0 = Parameter(unit=u.K)\\n\\n def __init__(self, H0, Tcmb0=0*u.K, name=None, meta=None):\\n super().__init__(name=name, meta=meta)\\n self._H0 = H0\\n self._Tcmb0 = Tcmb0\\n\\n self.cls = SubCosmology\\n self.cls_args = (70 * (u.km / u.s / u.Mpc), 2.7 * u.K)\\n self.cls_kwargs = dict(name=self.__class__.__name__, meta={\\\"a\\\": \\\"b\\\"})\",\n \"def __init__(self):\\n raise Exception('TODO IMPLEMENT ME !')\",\n \"def _init(self):\",\n \"def __init__(self):\\n super(ManDaySerie, self).__init__()\",\n \"def __call__( self ):\\n pass\",\n \"def __init__(self):\\n super()\",\n \"def __init__(self):\\n\\t\\tsuper().__init__()\",\n \"def __init__(self):\\n\\t\\tsuper().__init__()\",\n \"def __init__(self):\\n super(PlannedSerie, self).__init__()\",\n \"def test_reset_sequence_subclass_parent(self):\\n class SubTestObjectFactory(self.TestObjectFactory):\\n pass\\n\\n o1 = SubTestObjectFactory()\\n self.assertEqual(0, o1.one)\\n\\n o2 = SubTestObjectFactory()\\n self.assertEqual(1, o2.one)\\n\\n self.TestObjectFactory.reset_sequence()\\n o3 = SubTestObjectFactory()\\n self.assertEqual(0, o3.one)\\n\\n o4 = self.TestObjectFactory()\\n self.assertEqual(1, o4.one)\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def __init_subclass__(self, *args, **kwargs): # real signature unknown\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6569991","0.63043004","0.6036007","0.6010795","0.59454274","0.594468","0.5941208","0.5941208","0.5941208","0.5941208","0.5941208","0.5941208","0.5941208","0.5941208","0.5941208","0.5941208","0.5896316","0.588907","0.58811665","0.581134","0.581134","0.581134","0.581134","0.581134","0.581134","0.581134","0.581134","0.581134","0.581134","0.581134","0.581134","0.581134","0.581134","0.58086866","0.58086866","0.5796015","0.5796015","0.5787631","0.5782941","0.57572967","0.572725","0.5690567","0.56901795","0.56901795","0.56858176","0.5683137","0.5664643","0.5650617","0.5638059","0.5633979","0.5633535","0.56302065","0.5612011","0.5608307","0.5608307","0.5603322","0.56031996","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233","0.56031233"],"string":"[\n \"0.6569991\",\n \"0.63043004\",\n \"0.6036007\",\n \"0.6010795\",\n \"0.59454274\",\n \"0.594468\",\n \"0.5941208\",\n \"0.5941208\",\n \"0.5941208\",\n \"0.5941208\",\n \"0.5941208\",\n \"0.5941208\",\n \"0.5941208\",\n \"0.5941208\",\n \"0.5941208\",\n \"0.5941208\",\n \"0.5896316\",\n \"0.588907\",\n \"0.58811665\",\n \"0.581134\",\n \"0.581134\",\n \"0.581134\",\n \"0.581134\",\n \"0.581134\",\n \"0.581134\",\n \"0.581134\",\n \"0.581134\",\n \"0.581134\",\n \"0.581134\",\n \"0.581134\",\n \"0.581134\",\n \"0.581134\",\n \"0.581134\",\n \"0.58086866\",\n \"0.58086866\",\n \"0.5796015\",\n \"0.5796015\",\n \"0.5787631\",\n \"0.5782941\",\n \"0.57572967\",\n \"0.572725\",\n \"0.5690567\",\n \"0.56901795\",\n \"0.56901795\",\n \"0.56858176\",\n \"0.5683137\",\n \"0.5664643\",\n \"0.5650617\",\n \"0.5638059\",\n \"0.5633979\",\n \"0.5633535\",\n \"0.56302065\",\n \"0.5612011\",\n \"0.5608307\",\n \"0.5608307\",\n \"0.5603322\",\n \"0.56031996\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\",\n \"0.56031233\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76683,"cells":{"query":{"kind":"string","value":"klartekst > koder denne > dekoder cipherteksten og sjekker at de er lik"},"document":{"kind":"string","value":"def verify(self):\n\n # tekstlig testing om koden fungerer\n text = self.klar_tekst_start + \" ble sendt til mottaker som krypteringen \" + \\\n self.crypto + \".\\nMottaker dekrypterte dette til \" + self.klar_tekst_slutt\n\n return text"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def operate_cipher(self):","def cipher_feedback(self):","def encrypt():\n\tnull = 0","def decrypt_vigenere(ciphertext: str, keyword: str) -> str:\n plaintext = \"\"\n # PUT YOUR CODE HERE\n key_lenght = len(keyword)\n text_lenght = len(ciphertext)\n\n while key_lenght != text_lenght:\n keyword += keyword\n key_lenght = len(keyword)\n if key_lenght > text_lenght:\n keyword = keyword[:text_lenght]\n key_lenght = len(keyword)\n code_key = []\n ord_a = ord('a')\n ord_A = ord('A')\n\n if ciphertext.islower():\n for i in range(key_lenght):\n if ciphertext[i] == \" \":\n code_key.append(\" \")\n else:\n code_key.append(ord(keyword[i]) - ord_a)\n code_text = []\n for n in range(text_lenght):\n if ciphertext[n] == \" \":\n code_text.append(\" \")\n else:\n code_text.append(ord(ciphertext[n]) - ord_a)\n for u in range(text_lenght):\n if ciphertext[u] == \" \":\n value = ord(\" \")\n else:\n\n value = ((code_text[u] - code_key[u] + 26) % 26) + ord_a\n plaintext += chr(value)\n else:\n for i in range(key_lenght):\n if ciphertext[i] == \" \":\n code_key.append(\" \")\n else:\n code_key.append(ord(keyword[i]) - ord_A)\n code_text = []\n for n in range(text_lenght):\n if ciphertext[n] == \" \":\n code_text.append(\" \")\n else:\n code_text.append(ord(ciphertext[n]) - ord_A)\n for u in range(text_lenght):\n if ciphertext[u] == \" \":\n value = ord(\" \")\n else:\n value = ((code_text[u] - code_key[u] + 26) % 26) + ord_A\n plaintext += chr(value)\n\n return plaintext","def encrypt_vigenere(plaintext: str, keyword: str) -> str:\n ciphertext = \"\"\n # PUT YOUR CODE HERE\n\n key_lenght = len(keyword)\n text_lenght = len(plaintext)\n while key_lenght != text_lenght:\n keyword += keyword\n key_lenght = len(keyword)\n if key_lenght > text_lenght:\n keyword = keyword[:text_lenght]\n key_lenght = len(keyword)\n code_key = []\n ord_A = ord('A')\n ord_a = ord('a')\n\n if plaintext.islower():\n for i in range(key_lenght):\n if plaintext[i] == \" \":\n code_key.append(\" \")\n else:\n code_key.append(ord(keyword[i]) - ord_a)\n code_text = []\n for n in range(text_lenght):\n if plaintext[n] == \" \":\n code_text.append(\" \")\n else:\n code_text.append(ord(plaintext[n]) - ord_a)\n ciphertext = ''\n for u in range(len(plaintext)):\n if plaintext[u] == \" \":\n ciphertext += \" \"\n else:\n value = (code_key[u] + code_text[u]) % 26 + ord_a\n ciphertext += chr(value)\n else:\n for i in range(key_lenght):\n if plaintext[i] == \" \":\n code_key.append(\" \")\n else:\n code_key.append(ord(keyword[i]) - ord_A)\n code_text = []\n for n in range(text_lenght):\n if plaintext[n] == \" \":\n code_text.append(\" \")\n else:\n code_text.append(ord(plaintext[n]) - ord_A)\n ciphertext = ''\n for u in range(len(plaintext)):\n if plaintext[u] == \" \":\n value = ord(\" \")\n else:\n value = (code_key[u] + code_text[u]) % 26 + ord_A\n ciphertext += chr(value)\n return ciphertext","def de_cryptate(self):\r\n\r\n intab1 = \"abcdefghijklomnopqrstuvwxyz !,.\"\r\n outtab1 = \"?2p=o)7i(u9/y&t3%r¤5e#w1q!>*'^;)\"\r\n# Fetching from written in textbox\r\n s = self.textbox.toPlainText()\r\n a = s.lower()\r\n# Changing out the letters/numbers/etc\r\n crypted = (a.translate({ord(x): y for (y, x) in zip(intab1, outtab1)}))\r\n# Clear the textbox\r\n self.textbox.clear()\r\n# Write the Decrypted text\r\n self.textbox.setPlainText(crypted)","def encipher(self):\n ciphertext = \"\"\n for pt, key_char in zip(self.text, self.key):\n char_index = self.char_block.alphabet.index(pt)\n ciphertext += self.char_block.rows[key_char][char_index]\n print(ciphertext)","def test_encryption(e, c):\n message = input(\"Enter word to encrypt: \")\n ciphered = ''\n\n for i in range(0, len(message)):\n ciphered = f'{ciphered}{chr(endecrypt(ord(message[i]), e, c))}'\n\n print(ciphered + ' is the ciphered text')\n d = key_cracker(e, c)\n print(\"Plain text is:\")\n for i in range(0, len(ciphered)):\n print(chr(endecrypt(ord(ciphered[i]), d, c)), end='')","def cryptate(self):\r\n\r\n intab1 = \"abcdefghijklomnopqrstuvwxyz\"\r\n outtab1 = \"?2p=o)7i(u9/y&t3%r¤5e#w1q!>)\"\r\n# Fetching the writing in textbox\r\n s = self.textbox.toPlainText()\r\n a = s.lower()\r\n# The crypting process, replaces letters in intab1 with outtab1\r\n crypted = (a.translate({ord(x): y for (x, y) in zip(intab1, outtab1)}))\r\n# Clear the textbox\r\n self.textbox.clear()\r\n# Write the crypted text within textbox\r\n self.textbox.setPlainText(crypted)","def decipher(self):\n plaintext = \"\"\n for ct, key_char in zip(self.text, self.key):\n char_index = self.char_block.rows[key_char].index(ct)\n plaintext += self.char_block.alphabet[char_index]\n print(plaintext)","def test_encryption(e, c):\n\n#\te = int(raw_input(\"\\nEnter e from public key\\n\"))\n#\tc = int(raw_input(\"\\nEnter c from public key\\n\"))\n\n string = raw_input(\"\\nEnter word to encrpyt\\n\")\n for i in range(0, len(string)):\n print endecrypt(ord(string[i]), e, c)","def encrypt(message, key):\r\n # --- YOU CODE STARTS HERE\r\n if type(message) != str or type(key) != int:\r\n return 'Invalid input'\r\n alpha_lower = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\r\n alpha_upper = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\r\n\r\n new_st = ''\r\n\r\n for x in message:\r\n if (alpha_lower.count(x) != 0) or (alpha_upper.count(x) != 0):\r\n if alpha_lower.count(x) != 0 and alpha_lower.index(x) + key < 26:\r\n new_st += alpha_lower[alpha_lower.index(x) + key]\r\n\r\n if alpha_upper.count(x) != 0 and alpha_upper.index(x) + key < 26:\r\n new_st += alpha_upper[alpha_upper.index(x) + key]\r\n\r\n if alpha_upper.count(x)!= 0 and alpha_upper.index(x) + key >= 26:\r\n new_st += alpha_upper[alpha_upper.index(x) + key - 26]\r\n\r\n if alpha_lower.count(x) != 0 and alpha_lower.index(x) + key >= 26:\r\n new_st += alpha_lower[alpha_lower.index(x) + key - 26]\r\n else:\r\n new_st += x\r\n\r\n return new_st\r\n\r\n # --- CODE ENDS HERE\r","def test_endecrypt():\n\n e, d, c = keygen()\n\n test_encryption(e, c)\n test_decryption(d, c)\n key_cracker(e, c)","def _applyCipher(self, encode):\n pass","def decrypt(self, data):","def verify_decrypt_key(self):\r\n\t\tpercent_english = Dict_Control(self.my_code).check_key()\r\n\t\t#If more than half the words are english, the key will pass. \r\n\t\tif percent_english > 50:\r\n\t\t\tself.right_key = False\r\n\t\t#If the key does not pass, the program will give you a warning and prompt you for another key. \r\n\t\telse: \r\n\t\t\tprint(f\"After decryption, it looks like only {percent_english}% of your words are english, you may have entered the wrong key?\")","def coding():\r\n \r\n key={'reverse_word': False, 'reverse_string': False, 'a': 'a', 'b': 'b', 'c': 'c', 'd': 'd', 'e': 'e', 'f': 'f', 'g': 'g', 'h': 'h',\r\n 'i': 'i', 'j': 'j', 'k': 'j', 'l': 'l', 'm': 'm', 'n': 'n', 'o': 'o', 'p': 'p', 'q': 'q', 'r': 'r', 's': 's', 't': 't', 'u': 'u',\r\n 'v': 'v', 'w': 'w', 'x':'x', 'y': 'y', 'z': 'z'}\r\n x=0 #determine the sliding of the letters\r\n \r\n def isKeyEmpty(k):\r\n \"\"\"Utility Function that checks if key is empty\"\"\"\r\n if k=={'reverse_word': False, 'reverse_string': False, 'a': 'a', 'b': 'b', 'c': 'c', 'd': 'd', 'e': 'e', 'f': 'f', 'g': 'g', 'h': 'h',\r\n 'i': 'i', 'j': 'j', 'k': 'j', 'l': 'l', 'm': 'm', 'n': 'n', 'o': 'o', 'p': 'p', 'q': 'q', 'r': 'r', 's': 's', 't': 't', 'u': 'u',\r\n 'v': 'v', 'w': 'w', 'x':'x', 'y': 'y', 'z': 'z'}:\r\n return True\r\n return False\r\n \r\n def set_key(vars): #vars=[0]num,[1]rWord,[2]rString\r\n \"\"\"Function that set the new key\"\"\"\r\n nonlocal key\r\n nonlocal x\r\n x=vars[0]\r\n if (vars[1]=='yes'):\r\n key['reverse_word']=True\r\n if (vars[2]=='yes'):\r\n key['reverse_string']=True\r\n if (x<-26 or x>26):\r\n x=x%26 #makes x to be in range\r\n if (x==0):\r\n x=random.randrange(-26,26) #random number\r\n for i in range (97,123): #26 ABC letters, ASCII value of 'a' is 97 97+26=123\r\n if(i+x>122):\r\n key[chr(i)]=chr(i-25+x)\r\n elif (i+x<97):\r\n key[chr(i)]=chr(i+26+x)\r\n else:\r\n key[chr(i)]=chr(i+x)\r\n print(\"done\")\r\n \r\n def empty_key():\r\n \"\"\"Function makes current key empty\"\"\"\r\n nonlocal key\r\n nonlocal x\r\n x=0\r\n key={'reverse_word': False, 'reverse_string': False, 'a': 'a', 'b': 'b', 'c': 'c', 'd': 'd', 'e': 'e', 'f': 'f', 'g': 'g', 'h': 'h',\r\n 'i': 'i', 'j': 'j', 'k': 'j', 'l': 'l', 'm': 'm', 'n': 'n', 'o': 'o', 'p': 'p', 'q': 'q', 'r': 'r', 's': 's', 't': 't', 'u': 'u',\r\n 'v': 'v', 'w': 'w', 'x':'x', 'y': 'y', 'z': 'z'}\r\n print(\"done\")\r\n \r\n def export_key():\r\n \"\"\"Function export key\"\"\"\r\n if(isKeyEmpty(key)):\r\n print(\"key empty\")\r\n else:\r\n return key\r\n \r\n def import_key(key2):\r\n \"\"\"Function import key\"\"\"\r\n nonlocal key\r\n if(isKeyEmpty(key2)):\r\n print(\"key is empty\")\r\n else:\r\n key=key2\r\n print(\"done\")\r\n \r\n def encoding(sentence):\r\n \"\"\"function encoding given string with the key\"\"\"\r\n sentence=list(sentence)\r\n for i in range(len(sentence)):\r\n if (sentence[i]!=' '):\r\n sentence[i]=key[sentence[i]]\r\n sentence=''.join(sentence)\r\n if(key['reverse_word']==True):\r\n splitT=tuple(sentence.split(' '))\r\n splitT=map(lambda x:x[::-1],splitT)\r\n sentence=' '.join(splitT)\r\n if(key['reverse_string']==True):\r\n splitList=sentence.split(' ')\r\n splitList=splitList[-1::-1]\r\n sentence=' '.join(splitList)\r\n return sentence\r\n \r\n def decoding(sentence):\r\n \"\"\"function decoding given string with the key\"\"\"\r\n if(isKeyEmpty(key)):\r\n return \"key empty\"\r\n helpKey=dict((y,x) for x,y in key.items())\r\n if(key['reverse_word']==True):\r\n splitT=tuple(sentence.split(' '))\r\n splitT=map(lambda x:x[::-1],splitT)\r\n sentence=' '.join(splitT)\r\n if(key['reverse_string']==True):\r\n splitList=sentence.split(' ')\r\n splitList=splitList[-1::-1]\r\n sentence=' '.join(splitList)\r\n sentence=list(sentence)\r\n for i in range(len(sentence)):\r\n if(sentence[i]!=' '):\r\n sentence[i]=helpKey[sentence[i]]\r\n sentence=''.join(sentence)\r\n return sentence\r\n\r\n def dispatch(message,var=None):\r\n \"\"\"dispatch with message passing\"\"\"\r\n if message=='set_key':\r\n set_key(var)\r\n elif message=='empty_key':\r\n empty_key()\r\n elif message=='export_key':\r\n return export_key()\r\n elif message=='import_key':\r\n import_key(var)\r\n elif message=='encoding':\r\n return encoding(var)\r\n elif message=='decoding':\r\n return decoding(var)\r\n else:\r\n print(\"Unknown message\") \r\n return dispatch","def encode(key, plain):\n print(\"ciphertext: \", end=\"\")\n\n # used variables\n pos = 0\n key_len = len(key)\n\n # loop over every character in the text\n for char in plain:\n key_pos = pos % key_len\n # leave non-alphabetical characters alone\n if not char.isalpha():\n print(char, end=\"\")\n # cipher characters\n elif char.isupper():\n cipher = chr((char_to_number(char) + char_to_number(key[key_pos])) \\\n % 26 + ord(\"A\"))\n \n print(cipher, end=\"\")\n pos += 1\n else:\n cipher = chr((char_to_number(char) + char_to_number(key[key_pos])) \\\n % 26 + ord(\"a\"))\n \n print(cipher, end=\"\")\n pos += 1\n\n print()","def encript(self): \n if (len(sys.argv) == Cconfiguration_caesar.DUAL_PARAMETER) and (int(sys.argv[Cconfiguration_caesar.INCREMENTAL_PARAMETER])>=Cconfiguration_caesar.INITIAL_INT_PARAMETER):\n result = \"\"\n k = int(sys.argv[Cconfiguration_caesar.INCREMENTAL_PARAMETER])\n plaintext = input(\"plaintext: \")\n for i in range(len(plaintext)):\n char = plaintext[i]\n if ((Cconfiguration_caesar.ALPHABET_LOWER_INDEX>ord(char)) or (Cconfiguration_caesar.ALPHABET_LOWER_LIMIT<ord(char))) and ((Cconfiguration_caesar.ALPHABET_UPPER_INDEX>ord(char)) or (Cconfiguration_caesar.ALPHABET_UPPER_LIMIT<ord(char))):\n result += char\n elif (char.isupper()):\n result += chr((ord(char) + k-Cconfiguration_caesar.ALPHABET_UPPER_INDEX) % Cconfiguration_caesar.ALPHABET_LIMIT + Cconfiguration_caesar.ALPHABET_UPPER_INDEX)\n else:\n result += chr((ord(char) + k - Cconfiguration_caesar.ALPHABET_LOWER_INDEX) % Cconfiguration_caesar.ALPHABET_LIMIT + Cconfiguration_caesar.ALPHABET_LOWER_INDEX)\n print(f\"ciphertext: {result}\")\n else:\n print(CextraStatusDefinition.COMMAND_LINE_EERROR)\n exit(Cconfiguration_caesar.INCREMENTAL_PARAMETER)","def encipher(self,string): \n string = self.remove_punctuation(string)\n ret = ''\n for (i,c) in enumerate(string):\n if i<len(self.key): offset = self.a2i(self.key[i])\n else: offset = self.a2i(string[i-len(self.key)]) \n ret += self.i2a(self.a2i(c)+offset)\n return ret","def encrypt_railfence(plaintext, num_rails):\n if num_rails == 1:\n return plaintext\n\n lists = []\n for i in range(0, num_rails):\n lists.append([])\n row = -1\n dir = 1\n for char in plaintext:\n row += dir\n lists[row].append(char)\n if row == 0:\n dir = 1\n elif row == num_rails - 1:\n dir = -1\n encrypted = \"\"\n for list in lists:\n #print(list)\n encrypted += ''.join(list)\n return encrypted","def decrypt_vigenere(ciphertext: str, keyword: str) -> str:\r\n ciphertext = \"LXFOPVEFRNHR\"\r\n keyword = \"LEMON\"\r\n plaintext = \"\"\r\n nonaplhacount = 0 \r\n for index, char in enumerate(ciphertext):\r\n if not char.isalpha():\r\n plaintext = plaintext + char\r\n nonalphacount += 1\r\n d = 'A' if char.isupper() else 'a'\r\n keyword_char_index = (index - nonalphacount)%len(keyword)\r\n keyword_char = keyword[keyword_char_index].upper() if char.isupper() else keyword[keyword_char_index]\r\n key = ord(keyword_char) - ord(d)\r\n key = 26 - key\r\n cipher = (((ord(char) + key) - ord(d)) % 26) + ord(d)\r\n plaintext = plaintext + chr(cipher)\r\n print(plaintext)\r\n return plaintext","def encodeVigenere(self, key):\n\n key = key.upper().replace(\" \", \"\")\n encode = Vig(key)\n cipherText = encode.encode(self.planeText)\n \n if (self.verbose == 1):\n print(cipherText)\n \n return(cipherText)","def eul59():\n cipher = [c.rstrip('\\n') for c in open('resources/p059_cipher.txt')]\n cipher = [int(c) for c in cipher[0].split(\",\")]\n for key in itertools.product(range(97, 123), repeat=3):\n msg = [x ^ y for x, y in zip(cipher, itertools.cycle(key))]\n if ' the ' in ''.join(map(chr, msg)):\n return sum(msg)","def decrypt(message, key):\r\n\r\n # --- YOU CODE STARTS HERE\r\n if type(message) != str or type(key) != int:\r\n return 'Invalid input'\r\n new_st = ''\r\n alpha_lower = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\r\n alpha_upper = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\r\n\r\n for x in message:\r\n if (alpha_lower.count(x) != 0) or (alpha_upper.count(x) != 0):\r\n if alpha_lower.count(x) != 0:\r\n new_st += alpha_lower[alpha_lower.index(x) - key]\r\n if alpha_upper.count(x) != 0:\r\n new_st += alpha_upper[alpha_upper.index(x) - key]\r\n else:\r\n new_st += x\r\n\r\n return new_st\r\n\r\n\r\n # --- CODE ENDS HERE\r","def decrypt_vigenere(cipehrtext: str, keyword: str) -> str:\n plaintext = \"\"\n if len(keyword) < len(cipehrtext):\n for j in range(len(cipehrtext) - len(keyword)):\n keyword += keyword[j]\n for i in range(len(cipehrtext)):\n n = ord(cipehrtext[i])\n m = ord(keyword[i])\n if (n >= ord('A')) and (n <= ord('Z')):\n if n >= m:\n plaintext += chr(n - m + ord('A'))\n else:\n plaintext += chr(ord('Z') + 1 - (m - n))\n else:\n if n >= m:\n plaintext += chr(n - m + ord('a'))\n else:\n plaintext += chr(ord('Z') + 1 - (m - n))\n return plaintext","def decrypt_vigenere(plaintext, keyword):\n ciphertext = \"\"\n for i in range(len(plaintext)):\n if 64 < ord(plaintext[i]) < 65 + 26:\n shift = (ord(keyword[i % len(keyword)]) - 65)\n if ord(plaintext[i]) < 64 + shift:\n ciphertext += chr(ord(plaintext[i]) + 26 - shift)\n else:\n ciphertext += chr(ord(plaintext[i]) - shift)\n elif 96 < ord(plaintext[i]) < 97 + 26:\n shift = (ord(keyword[i % len(keyword)]) - 97)\n if ord(plaintext[i]) < 96 + shift:\n ciphertext += chr(ord(plaintext[i]) + shift - 26)\n else:\n ciphertext += chr(ord(plaintext[i]) + shift)\n else:\n ciphertext += i\n return ciphertext","def encrypt(self, message):\n # message = message.upper().split()\n # message = \"\".join(message)\n message = message.upper()\n message_list = []\n for ch in message:\n message_list.append(self.main_dict[ch][0])\n\n # Generate a random key\n random_otp = [random.choice(self.alphabet_list) for _ in range(len(message))]\n print(\"Your OTP is: \" + str(\"\".join(random_otp)))\n print(\"Use the OTP to unlock the message.\")\n\n # Convert Random key to integers\n for i, item in enumerate(random_otp):\n random_otp[i] = self.main_dict[item][0]\n\n # Do the math with Random Key and the message\n math_list = []\n for i, item in enumerate(message_list):\n try:\n result = message_list[i] + random_otp[i]\n math_list.append(result)\n except:\n print(\"The message and OTP does not have the same length\")\n continue\n\n # Logic to do mod27\n for i, item in enumerate(math_list):\n for key, value in self.main_dict.items():\n if item > 26:\n if value[1] == item:\n math_list[i] = key\n else:\n if value[0] == item:\n math_list[i] = key\n\n # Algorithm for 5 block characters\n padding = input(\"Would you like to use block 5 characters? y/n \")\n if padding == \"y\":\n math_list = self.salt_with_random(math_list)\n return \" \".join(math_list)\n else:\n math_list = \"\".join(math_list)\n return math_list","def obfuscate(self):\n r = self.public_key.get_random_lt_n()\n r_pow_n = powmod(r, self.public_key.n, self.public_key.nsquare)\n self.__ciphertext = self.__ciphertext * r_pow_n % self.public_key.nsquare\n self.__is_obfuscated = True","def FtEncrypt(self,text):\n \n self.text = text\n EncryptText = []\n characters = \"abcdefghijklmnopqrstuvwxyz \"\n\n #attempt to append my_list and update my_dict\n #using a random set of alphabet and a random made_key \n try:\n for char in random.sample(characters,27):\n Code_Fouad_Teniou.my_list.append(char)\n \n Code_Fouad_Teniou.my_dict.update(zip(Code_Fouad_Teniou.my_key,Code_Fouad_Teniou.my_list))\n\n for item in text.lower():\n for i in Code_Fouad_Teniou.my_dict.items():\n if item == i[1]:\n EncryptText.append(i[0])\n \n return EncryptText\n \n #Raise AttributeError if text is not a string \n except AttributeError:\n raise AttributeError, \"\\n<Please re-enter your text as a 'string'\"","def decrypt():\n plaintext = \"\"\n i = 0\n while i < len(ciphertext):\n if i%2==1:\n try:\n plaintext += key[ ciphertext[i-1]+ciphertext[i] ]\n except KeyError:\n plaintext += ciphertext[i-1]+ciphertext[i]\n i += 1\n return plaintext","def run():\n key = input(\"Enter a 26 letter key: \")\n if not isValidKey(key):\n print(\"Invalid key.\")\n return\n plainText = input(\"Plain Text: \")\n cipherText = substitution(plainText, key)\n print(f\"Cipher Text: {cipherText}\")\n return","def encrypt(self,string=\"vrorqjdqgwdqnviruwkhilvk\",key=3):\r\n return \"\".join([chr((ord(ch)-key-ord('a'))%(ord('z')-ord('a')+1)+ord('a')) for ch in string])","def mode_crypt_cramershoup(stdscr, message=None):\n loop = True\n cursor = 0\n while loop:\n show_key_choices(stdscr, cursor, message)\n key = stdscr.getkey()\n loop = False\n cs = CramerShoup()\n if key == '1' or (key == '\\n' and cursor == 1):\n key_size = choose_keys_size(stdscr)# choose the size of key [256,512,1024]\n stdscr.clear()\n stdscr.addstr(\"Création des clés de chiffrement ...\\n\\n\")\n stdscr.refresh()\n cs.generate_keys(key_size)\n stdscr.addstr(\"Vos clés ont été générés dans keys/\\n\")\n stdscr.refresh()\n napms(2000)\n mode_crypt_cramershoup(stdscr, \"Les clés ont été générés\\n\")\n \n elif key == '2' or (key == '\\n' and cursor == 2):\n # chiffre avec la clé privé (la clé privé contient la clé publique)\n key_file_name = input_user(stdscr, \"Veuiller entrer l'enplacement de la clé public. Ctrl + G pour confirmer\")\n try:\n cs.read_key(key_file_name)\n except IOError:\n # cannot open the file\n mode_crypt_cramershoup(stdscr, \"Impossible de lire la clé dans le fichier {}\".format(key_file_name))\n return\n file_name = input_user(stdscr, \"Clé chargé avec succès.\\n Veuillez entrer le nom du fichier à chiffrer\")\n try:\n file = open(file_name)\n file.close()\n except IOError:\n mode_crypt_cramershoup(stdscr, \"Impossible d'ouvrir le fichier {}\".format(file_name))\n return\n # si le fichier est un pgm, on laisse le choix à l'utilisateur\n pgm = False\n if re.match('.+\\.pgm.*', file_name) is not None:\n pgm = choix_mode_PGM(stdscr)\n \n # on chiffre le fichier\n stdscr.clear()\n stdscr.addstr(\"En cours de chiffrement ...\\n\")\n stdscr.refresh()\n wrap = None\n if pgm:\n wrap = PGMEncrypter(file_name, cs, cs.bit_size//(2*8), file_name + \".crypted\", 4*cs.bit_size//8)\n else:\n wrap = BlockFileEncrypter(file_name, cs, cs.bit_size//(2*8), file_name + \".crypted\", 4*cs.bit_size//8)\n wrap.crypt_to_out()\n stdscr.addstr(\"Votre fichier {} a été chiffré :) !\".format(file_name), curses.color_pair(3))\n stdscr.refresh()\n napms(1000)\n menu(stdscr)\n elif key == 'm' or (key == '\\n' and cursor == 3):\n menu(stdscr)\n elif key == 'KEY_UP' and cursor > 1:\n cursor -= 1\n loop = True\n elif key == 'KEY_DOWN' and cursor < 3:\n cursor += 1\n loop = True\n else:\n loop = True","def call_cipher(self):\n if self.cipher_choice == \"affine\":\n\n if self.crypt == \"encrypt\":\n encrypted_message = Affine().encrypt(self.message.upper())\n otp_encrypted = OneTimePad().encrypt(\n encrypted_message, self.otp.upper())\n return (otp_encrypted)\n\n elif self.crypt == \"decrypt\":\n otp_decrypted = OneTimePad().decrypt(\n self.message.upper(), self.otp.upper())\n decrypted_message = Affine().decrypt(otp_decrypted)\n return (decrypted_message.lower())\n\n elif self.cipher_choice == \"atbash\":\n\n if self.crypt == \"encrypt\":\n encrypted_message = Atbash().encrypt(self.message.upper())\n otp_encrypted = OneTimePad().encrypt(\n encrypted_message, self.otp.upper())\n return (otp_encrypted)\n\n elif self.crypt == \"decrypt\":\n otp_decrypted = OneTimePad().decrypt(\n self.message.upper(), self.otp.upper())\n decrypted_message = Atbash().decrypt(otp_decrypted)\n return (decrypted_message.lower())\n\n elif self.cipher_choice == \"keyword\":\n\n if self.crypt == \"encrypt\":\n encrypted_message = KeywordCipher().encrypt(self.message.upper())\n otp_encrypted = OneTimePad().encrypt(\n encrypted_message, self.otp.upper())\n return (otp_encrypted)\n\n elif self.crypt == \"decrypt\":\n otp_decrypted = OneTimePad().decrypt(\n self.message.upper(), self.otp.upper())\n decrypted_message = KeywordCipher().decrypt(otp_decrypted)\n return (decrypted_message.lower())","def main():\n # key = random(1024)\n # ciphertexts = [encrypt(key, msg) for msg in MSGS]\n\n # Get key and secret message\n knownPlain2 = \"The nice thing about Keeyloq is now we cryptographers can drive a lot of fancy cars - Dan Boneh\"\n key = strxor(ciphertexts[2], knownPlain2)\n secret = strxor(target, key)\n\n print \"Key: \" + key\n print \"Key (Hex): \" + key.encode(\"hex\")\n print \"Secret: \" + secret","def encrypt_vigenere(plaintext, keyword):\n ciphertext = \"\"\n for i in range(len(plaintext)):\n if 64 < ord(plaintext[i]) < 65 + 26:\n shift = (ord(keyword[i % len(keyword)]) - 65)\n if ord(plaintext[i]) > 64 + 26 - shift:\n ciphertext += chr(ord(plaintext[i]) + shift - 26)\n else:\n ciphertext += chr(ord(plaintext[i]) + shift)\n elif 96 < ord(plaintext[i]) < 97 + 26:\n shift = (ord(keyword[i % len(keyword)]) - 97)\n if ord(plaintext[i]) > 96 + 26 - shift:\n ciphertext += chr(ord(plaintext[i]) + shift - 26)\n else:\n ciphertext += chr(ord(plaintext[i]) + shift)\n else:\n ciphertext += i\n return ciphertext","def caesar_encryption(text):\n result = ''\n for char in text:\n if char.isdigit():\n i = (num_key.index(char) - 4) % 10\n result += num_key[i]\n elif not char.isdigit() and char.lower() in alpha_key:\n i = (alpha_key.index(char.lower()) - 4) % 26\n result += alpha_key[i]\n else:\n result += char\n return result","def decrypt_vigenere(ciphertext, keyword):\n plaintext = ''\n new_keyword = ''\n for i in range(len(ciphertext)):\n num_word = ord(ciphertext[i])\n while len(ciphertext) > len(new_keyword):\n new_keyword += keyword\n num_key = ord(new_keyword[i])\n if num_key <= 90:\n num_key -= 65\n elif num_key >= 97:\n num_key -= 97\n if num_word <= 90:\n if (num_word-num_key) < 65:\n num_word += 26\n else:\n if (num_word-num_key) < 97:\n num_word += 26\n plaintext += chr(num_word-num_key)\n return plaintext","def encrypt_vigenere(plaintext, keyword):\n ciphertext = '' # Обнуляем строку\n keyword = length_equal(plaintext, keyword)\n for i in range(len(plaintext)):\n if (ord(plaintext[i].upper()) + ord(keyword[i].upper()) - 65) <= 90:\n ciphertext += chr(ord(plaintext[i].upper()) + (ord(keyword[i].upper()) - 65))\n else:\n ciphertext += chr(ord(keyword[i].upper()) - 91 + ord(plaintext[i].upper()))\n return ciphertext","def encrypt_vigenere(plaintext, keyword):\n ciphertext = ''\n new_keyword = ''\n for i in range(len(plaintext)):\n num_word = ord(plaintext[i])\n while len(plaintext) > len(new_keyword):\n new_keyword += keyword\n num_key = ord(new_keyword[i])\n if num_key <= 90:\n num_key -= 65\n elif num_key >= 97:\n num_key -= 97\n if num_word <= 90:\n if (num_key+num_word) > 90:\n num_word -= 26\n else:\n if (num_key+num_word) > 122:\n num_word -= 26\n ciphertext += chr(num_word+num_key)\n return ciphertext","def main():\n\n # performs crib dragging using initial values\n plaintext1, plaintext2 = crib_drag('', '', 0, 0)\n\n if plaintext1 is None or plaintext2 is None:\n print('No possible English decryption using the current dictionary')\n return\n\n # find the key and creates file with results\n plaintext1 = plaintext1[:CIPHER_LEN]\n plaintext2 = plaintext2[:CIPHER_LEN]\n key = find_key(plaintext1, plaintext2)\n\n with open('plaintext1.txt', 'w') as plain_file:\n plain_file.write(plaintext1)\n with open('plaintext2.txt', 'w') as plain_file:\n plain_file.write(plaintext2)\n with open('key.txt', 'wb') as plain_file:\n plain_file.write(key)","def brute_force_decrypt(text):\n for n in range(26):\n print(f\"Using a shift value of {n}\")\n print(decrypt(text, n))\n print(\"\\n***\\n\")","def encrypt(word_e):\r\n\r\n translated_word = \"\"\r\n\r\n # For every character in word_e, translate and add to translated_word\r\n for char in word_e:\r\n translated_word += values[keys.index(char)]\r\n # Returns output\r\n return translated_word","def CipherRun():\r\n ChosenData = UserInput() # retieving user input\r\n Msg, Shift = ChosenData # assigning the message text and shift amount\r\n Cipher = \"\" # placeholder for the encrypted text\r\n\r\n for char in Msg:\r\n if char.isalpha(): # checks if the charcter is a letter\r\n code = ord(char) + Shift # shifts the ordinal position by the requested shift amount\r\n if code > ord('z') and char.islower()==True: # if the char is lowercase and shift is beyound the letter z\r\n code = ord('a') + ((code-ord('z'))-1) # wrap back around to the beginning but take account that you are starting by 1 shift\r\n elif code > ord('Z') and char.isupper() == True: # if the char is upppercase and shift is beyound the letter Z\r\n code = ord('A') + ((code -ord('Z'))-1) # wrap back around to the beginning but take account that you are starting by 1 shift\r\n Cipher += chr(code) # add the character you land on b/c of the shift to the Cipher text\r\n else:\r\n Cipher +=char # add the non letter character to the cipher text\r\n print(Cipher)","def decrypt_vigenere(ciphertext, keyword):\n list = []\n index = 0\n for char in ciphertext:\n new_char_val = ord(char) - (ord(keyword[index]) - ord('A'))\n if new_char_val < ord('A'):\n new_char_val += 26\n list.append(chr(new_char_val))\n index += 1\n index %= len(keyword)\n return ''.join(list)","def __encrypt_content__(self, content):\n ciphertext_bundle = self.__encrypt_text_aes__(content, self.password)\n decrypt_form = Template(DECRYPT_FORM_TPL).render({\n # custom message and template rendering\n 'summary': self.summary,\n 'placeholder': self.placeholder,\n 'password_button': self.password_button,\n 'password_button_text': self.password_button_text,\n 'decryption_failure_message': self.decryption_failure_message,\n 'encryption_info_message': self.encryption_info_message,\n # this benign decoding is necessary before writing to the template, \n # otherwise the output string will be wrapped with b''\n 'ciphertext_bundle': b';'.join(ciphertext_bundle).decode('ascii'),\n 'js_libraries': JS_LIBRARIES,\n # enable / disable features\n 'arithmatex': self.arithmatex,\n 'hljs': self.hljs,\n 'remember_password': self.remember_password,\n 'disable_cookie_protection': self.disable_cookie_protection,\n 'encrypted_something': self.encrypted_something,\n })\n return decrypt_form","def caesar_cipher():\n string = input(\"Type a string: \")\n key = int(input(\"Type a key: \"))\n print(\"Transforming\", end = '')\n result = ''\n for char in string:\n if ord(char) > 64 and ord(char) < 91:\n if (ord(char) + key) > 90:\n result += chr(ord(char) + key - 26)\n elif (ord(char) + key) < 65:\n result += chr(ord(char) + key + 26)\n else:\n result += chr(ord(char) + key) \n elif ord(char) > 96 and ord(char) < 123:\n if (ord(char) + key) > 122:\n result += chr(ord(char) + key - 26)\n elif (ord(char) + key) < 97:\n result += chr(ord(char) + key + 26)\n else:\n result += chr(ord(char) + key)\n else:\n result += char\n print(\".\", end = '')\n time.sleep(0.5)\n print(\".\", end = '')\n time.sleep(0.5)\n print(\".\")\n time.sleep(0.5)\n return result","def caesar_cipher_decrept(string, key):\n try:\n string_to_return = \"\"\n for l in string:\n if not(l >= 'A'and l <= 'Z' or l >= 'a'and l <= 'z'):\n string_to_return += l\n elif ord(l.upper()) - key < ord('A'):\n string_to_return += chr(ord('Z') - ord('A') + ord(l.upper()))\n else:\n string_to_return += chr(ord(l.upper())-key)\n return string_to_return\n except (ValueError, IndexError, Exception) as ex:\n print(EXCEPTION_MESSAGE, ex)","def encryptionRotate(text):\n s = text\n transformedChar = \"\"\n transformedChar = s[-1] + s[:-1]\n\n print(\"Single Rotation Encrypted text : \" )\n return transformedChar","def decrypt_railfence(ciphertext, num_rails):\n if num_rails == 1:\n return ciphertext\n\n lists = slice_ciphertext(ciphertext, num_rails) # could use queue to simply the implementation once we got to OOP\n\n #print(lists)\n rows_indices = [0] * num_rails \n\n decrypted = ''\n row = -1\n dir = 1\n cipher_len = len(ciphertext)\n for i in range(cipher_len):\n row += dir\n decrypted += lists[row][rows_indices[row]]\n rows_indices[row] += 1\n if row == 0:\n dir = 1\n elif row == num_rails - 1:\n dir = -1\n return decrypted","def encrypt(message):\r\n\r\n assert isinstance(message, str), 'Only encrypt strings only!'\r\n\r\n code = first_code_word()\r\n encrypted_message = ''\r\n message = message.lower()\r\n final_dot = False\r\n final_question = False\r\n final_comma = False\r\n final_dot_counter = 0\r\n final_question_counter = 0\r\n final_comma_counter = 0\r\n\r\n splitted_code = list(code)\r\n\r\n\r\n message = code + \" \" + message\r\n\r\n while message[-1] == '.':\r\n message = message[:-1]\r\n final_dot = True\r\n final_dot_counter += 1\r\n\r\n while message[-1] == '?':\r\n message = message[:-1]\r\n final_question = True\r\n final_question_counter += 1\r\n\r\n\r\n while message[-1] == ',':\r\n message = message[:-1]\r\n final_comma = True\r\n final_comma_counter += 1\r\n\r\n counter = 0\r\n for message_word in message.split():\r\n counter += 1\r\n checked = False\r\n middle_dot = False\r\n middle_question = False\r\n middle_comma = False\r\n middle_dot_counter = 0\r\n middle_question_counter = 0\r\n middle_comma_counter = 0\r\n\r\n if counter == 2:\r\n for i in range(0, int(splitted_code[0])):\r\n word = create_encryptors()\r\n encrypted_message += str(word) + \" \"\r\n\r\n while message_word[-1] == '.':\r\n message_word = message_word[:-1]\r\n middle_dot = True\r\n middle_dot_counter += 1\r\n\r\n while message_word[-1] == '?':\r\n message_word = message_word[:-1]\r\n middle_question = True\r\n middle_question_counter += 1\r\n\r\n\r\n while message_word[-1] == ',':\r\n message_word = message_word[:-1]\r\n middle_comma = True\r\n middle_comma_counter += 1\r\n\r\n text = open(\"words.txt\", \"r\")\r\n for line in text:\r\n text_word = line.split()[0]\r\n if message_word[0] == text_word[0]:\r\n if message_word == text_word:\r\n encrypted_message += line.split()[2] + \" \"\r\n if middle_dot == True: encrypted_message = encrypted_message[:-1] + \".\"*middle_dot_counter + \" \"\r\n if middle_question == True: encrypted_message = encrypted_message[:-1] + \"?\"*middle_question_counter + \" \"\r\n if middle_comma == True: encrypted_message = encrypted_message[:-1] + \",\"*middle_comma_counter + \" \"\r\n checked = True\r\n break\r\n\r\n if checked != True:\r\n encrypted_message += message_word + \" \"\r\n if middle_dot == True: encrypted_message = encrypted_message[:-1] + \".\" * middle_dot_counter + \" \"\r\n if middle_question == True: encrypted_message = encrypted_message[:-1] + \"?\" * middle_question_counter + \" \"\r\n if middle_comma == True: encrypted_message = encrypted_message[:-1] + \",\" * middle_comma_counter + \" \"\r\n\r\n text.close()\r\n\r\n encrypted_message = encrypted_message[:-1]\r\n if final_dot == True:\r\n encrypted_message += '.'*final_dot_counter + ' '\r\n elif final_question == True:\r\n encrypted_message += '?'*final_question_counter + ' '\r\n elif final_comma == True:\r\n encrypted_message += ','*final_comma_counter + ' '\r\n else: encrypted_message += ' '\r\n\r\n for i in range(0, int(splitted_code[2])):\r\n word = create_encryptors()\r\n encrypted_message += word + \" \"\r\n encrypted_message = encrypted_message[:-1]\r\n return encrypted_message","def hack_message(self):\r\n\t\t#Will not let user input useless messages that cannot be hacked.\r\n\t\twhile True:\r\n\t\t\tself.message = input(\"Please enter a message you would like to hack. --> \")\r\n\t\t\tif self.message != \"\" and len(self.message) > 4:\r\n\t\t\t\tbreak\t\t\t\r\n\t\tmax_key = len(self.message)\r\n\t\tself.i = 1\r\n\t\tpotential_hits = []\r\n\t\t#Runs through all potential keys. \r\n\t\tfor self.i in range(1, max_key):\r\n\t\t\tprint(f\"Trying key #{self.i}\")\t\t\t\r\n\t\t\tself.my_code = Decryptor(self.message, self.i).transfer_decrypt()\r\n\t\t\tself.hack_plausible = False\r\n\t\t\tself.verify_hack_key()\r\n\t\t\tif self.hack_plausible:\r\n\t\t\t\tpotential_hits.append(f\"Key #{self.i} yeilded {self.percent_english}% english words after decryption.\\n\" + \"\\t\" + self.my_code[:50])\r\n\t\tprint(\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n\")\r\n\t\tprint(\"Hacking results:\\n\")\r\n\t\tfor hit in potential_hits:\r\n\t\t\tprint(\"\\t\" + hit + \"|\\n\")","def encode(keyFile: str, string: str=\"\", inputFile: str=\"\", outputFile:str=\"\") -> str:\n print(\"Encoding message ...\")\n print(\"Is public key file ok ?\", checkKeyFile(keyFile,\"public\"))\n\n if (checkKeyFile(keyFile,\"public\")): \n f = open(keyFile)\n keyData = extractParamsFromKey(f.readlines()[1]) # read the second line of the file and extract the param\n if args.verbose : print(\"keydata (publ) :\", keyData)\n \n #open a file if the string is empty\n if(string == \"\"):\n string = str(readFile(inputFile))\n else:\n string = string\n\n # transform the ascii string into a series of numbers\n asciiToInt = \"\"\n for char in string :\n asciiToInt += str(ord(char)).zfill(3)\n if args.verbose : print(\"ascii to int\", asciiToInt)\n\n # calculate the block length\n blocklen = len(str(keyData[0])) -1\n if args.verbose : print(\"block size is\", blocklen)\n \n # split the string into blocks\n # start bu reversing the string so we can start left to right\n tmp = asciiToInt[::-1]\n # cut them\n blocks = wrap(tmp, blocklen)\n # reverse the lsit of cut\n blocks.reverse()\n # inside eecaht cut reserve the characters\n for i in range(len(blocks)):\n blocks[i] = blocks[i][::-1]\n if args.verbose : print(blocks)\n \n # make sur that every block is the corect length, overwise add padding\n for i in range(len(blocks)):\n blocks[i] = blocks[i].zfill(blocklen)\n if args.verbose : print(\"blocks after padding :\", blocks)\n \n # crypt everyblock\n tempCryptString = \"\"\n if args.verbose : print(\"encrypted blocks:\")\n for i in range(len(blocks)): \n blockEncrypted = str(calculateCrypt(blocks[i], keyData[1], keyData[0]))\n if args.verbose : print(blockEncrypted)\n blockEncrypted = blockEncrypted.zfill(blocklen+1)\n if args.verbose : print(blockEncrypted)\n tempCryptString += blockEncrypted\n if args.verbose : print(\"encrypted string :\",tempCryptString)\n \n # write the contentes to a file\n hexstr = intToHexToBase64(tempCryptString)\n if(outputFile == \"\"):\n print(\"Encrypted :\")\n print(hexstr)\n else :\n print(\"writing to file\", outputFile)\n writeToFile(outputFile, hexstr)\n return hexstr\n else: \n print(\"keyfile is incorrect\")\n return","def encryptCaesar(message, key):\r\n newMessage = \"\"\r\n for char in message:\r\n if char in alphaLower:\r\n newLetterPosition = (alphaLower.index(char) + key) % 26\r\n newMessage += alphaLower[newLetterPosition]\r\n elif char in alphaUpper:\r\n newLetterPosition = (alphaUpper.index(char) + key) % 26\r\n newMessage += alphaUpper[newLetterPosition]\r\n else: newMessage += char\r\n return newMessage","def __init__(self, key, msg0503):\n enkey1 = map(ord, AES.new(key).encrypt(msg0503[:16]))\n self.cipher = AES.new(\"\".join(\n map(chr, (enkey1[i] ^ ord(msg0503[i + 16]) for i in range(16)))))\n self.encrypt_seq = random.randint(0, 0xffff)","def encrypt_vigenere(plaintext: str, keyword: str) -> str:","def decrypt_vigenere(ciphertext, keyword):\n plaintext = ''\n keyword = length_equal(ciphertext, keyword)\n for i in range(len(ciphertext)):\n if (ord(ciphertext[i].upper()) - (ord(keyword[i].upper()) - 65)) >= 65:\n plaintext += chr(ord(ciphertext[i].upper()) - (ord(keyword[i].upper()) - 65))\n else:\n plaintext += chr(ord(ciphertext[i].upper()) - ord(keyword[i].upper()) + 91)\n return plaintext","def decrypt_vigenere(ciphertext: str, keyword: str) -> str:\r\n while len(ciphertext) > len(keyword):\r\n keyword += keyword\r\n plaintext = str()\r\n if keyword.isupper():\r\n for i in range(len(ciphertext)):\r\n n = ord(ciphertext[i])\r\n x = ord(keyword[i])\r\n print(n, x)\r\n if n >= x:\r\n plaintext += chr(n - (x % 65))\r\n else:\r\n plaintext += chr(n + 26 - (x % 65))\r\n else:\r\n for i in range(len(ciphertext)):\r\n n = ord(ciphertext[i])\r\n x = ord(keyword[i])\r\n if n >= x:\r\n plaintext += chr(n - (x % 97))\r\n else:\r\n plaintext += chr(n + 26 - (x % 97))\r\n\r\n return (plaintext)","def encrypt_caesar(plaintext):\n ciphertext = \"\"\n for c in plaintext:\n if (ord(c) <= 64) or (91 <= ord(c) <= 96) or (123 <= ord(c)):\n ciphertext += chr(ord(c))\n elif (97 <= ord(c) + 3 <= 122) or (65 <= ord(c) + 3 <= 90):\n ciphertext += chr(ord(c) + 3)\n else:\n ciphertext += chr(ord(c) + 3 - 26)\n return ciphertext","def decrypt(self, message):\n # message = message.upper().split()\n # message = \"\".join(message)\n # desalting the message to remove 5 characters blocks\n padding = input(\"Have you used 5 characters blocks? y/n \")\n if padding == \"y\":\n message = message.replace(\" \", \"\")\n message = self.desalt_random(message)\n message = \"\".join(message)\n\n message = message.upper()\n message_list = []\n for ch in message:\n message_list.append(self.main_dict[ch][0])\n\n # OTP Encryption / process the message with OTP\n otp = input(\"What is the OTP that was generated for you during \"\n \"encryption process?: \")\n otp = otp.upper()\n random_otp = []\n for ch in otp:\n random_otp.append(self.main_dict[ch][0])\n\n # If OTP is correct, decrypt the message with mod27\n if len(message_list) != len(random_otp):\n print(\"You typed a wrong OTP.\")\n return None\n else:\n math_list = []\n for i, item in enumerate(message_list):\n if message_list[i] >= random_otp[i]:\n x = message_list[i] - random_otp[i]\n for key, value in self.main_dict.items():\n if value[0] == x:\n math_list.append(key)\n else:\n for key, value in self.main_dict.items():\n if item == value[0]:\n x = value[1] - random_otp[i]\n for key, value in self.main_dict.items():\n if value[0] == x:\n math_list.append(key)\n return \"\".join(math_list)","def decryptionRotate(text):\n s = text;\n transformedChar = \"\"\n transformedChar = s[1:] + s[0]\n\n print(\"Single Rotation Decrypted text : \" )\n return transformedChar","def encrypt_vigenere(plaintext: str, keyword: str) -> str:\n ciphertext = \"\"\n if len(keyword) < len(plaintext):\n for j in range(len(plaintext) - len(keyword)):\n keyword += keyword[j]\n for i in range(len(plaintext)):\n n = ord(plaintext[i])\n m = ord(keyword[i])\n if (n >= ord('A')) and (n <= ord('Z')):\n if (m - ord('A') + n) <= ord('Z'):\n ciphertext += chr(m - ord('А') + n)\n else:\n ciphertext += chr((m + n) % (1 + ord('Z')))\n else:\n if (m - ord('a') + n) <= ord('z'):\n ciphertext += chr(m - ord('a') + n)\n else:\n ciphertext += chr((m + n) % ord('a'))\n return ciphertext","def fernet_encript(key,message):\n\tf = Fernet(key)\n\treturn f.encrypt(message)","def applyCoder(text, coder):\n ciphertext = str()\n #for each letter in the text find it, and grab shifted letter\n for letter in text:\n ciphertext += coder.get(letter, letter)\n return ciphertext","def encryptionMultipleRotate(text, power):\n s = text\n transformedChar = \"\"\n transformedChar = s[-power:] + s[:-(power)]\n\n print(\"Multiple Rotation Encrypted text : \" )\n return transformedChar","def test_decryption(d, c):\n\n#\td = int(raw_input(\"\\nEnter d from public key\\n\"))\n#\tc = int(raw_input(\"\\nEnter c from public key\\n\"))\n\n x = int(raw_input(\"\\nEnter number to decrypt\\n\"))\n decode(endecrypt(x, d, c))","def find_key(plaintext1, plaintext2):\n\n # if plain1 XOR cipher1 == plain2 XOR cipher2\n xor1 = strings_xor(list(plaintext1.encode()), list(CIPHERTEXT1))\n xor2 = strings_xor(list(plaintext2.encode()), list(CIPHERTEXT2))\n if xor1 == xor2:\n return xor1.encode()\n\n # if plain1 XOR cipher2 == plain2 XOR cipher1\n xor1 = strings_xor(list(plaintext1.encode()), list(CIPHERTEXT2))\n xor2 = strings_xor(list(plaintext2.encode()), list(CIPHERTEXT1))\n if xor1 == xor2:\n return xor1.encode()\n\n return None","def decrypt_caesar(ciphertext: str) -> str:\n plaintext = ''\n for ab in ciphertext:\n if ('a' <= ab <= 'z') or ('A' <= ab <= 'Z'):\n ans = ord(ab) - 3\n if (ans < ord('a')) and (ans > ord('Z')) or (ans < ord('A')):\n ans += 26\n plaintext += chr(ans)\n else:\n plaintext += ab\n return plaintext","def recover_encrypt_pass(self):\n with open(self.key_path) as input_file:\n key = input_file.readlines()\n cipher_suite = Fernet(key[0])\n bin_passwd = bytes(self.password, 'utf-8')\n ciphered_text = cipher_suite.encrypt(bin_passwd)\n return ciphered_text","def encrypt(self, sentence):\r\n sentence = list(sentence)\r\n encrypted_sentence = []\r\n x = list(\"abcdefghijklmnopqrstuvwxyz\")\r\n l = len(x)\r\n y = random.sample(x, l)\r\n z = list(zip(x, y))\r\n z.append((\" \", \" \"))\r\n for i in sentence:\r\n if i.lower() not in x:\r\n encrypted_sentence.append(i)\r\n continue \r\n for a in z:\r\n if i.lower() == a[0]:\r\n if i.isupper():\r\n encrypted_sentence.append(a[1].upper())\r\n else:\r\n encrypted_sentence.append(a[1])\r\n encrypted_sentence = \"\".join(encrypted_sentence)\r\n y = \"\".join(y)\r\n cls()\r\n print(\"Your one time use pad is: {}\".format(y))\r\n print(\"\")\r\n return encrypted_sentence","def main():\n key, plain = get_key_plain()\n encode(key, plain)","def test_rekey(self):\n old_key = b'0' * 32\n new_key = b'1' * 32\n\n old_encrypted = encrypt('message', key=old_key)\n new_encrypted = rekey(old_encrypted, old_key=old_key, new_key=new_key)\n\n assert decrypt(new_encrypted, key=new_key) == 'message'","def cipherFromExternalContact(self, char):\n self.incrementRotor() #Incements the rotor as the key is 'pressed' on the enigma machine\n char = self.cipherToReflector(char) #Ciphers a character in the forward direction to the reflector\n return char #Returns character","def _post_decrypt_checks(self, aad, plaintext, protected_message, request_id):","def test(k, p, c):\n P, S = generate(key_arr(k))\n crypt = encrypt(p, P, S)\n plain = decrypt(crypt, P, S)\n\n res = True\n\n if crypt != c:\n print('Crypt:', hex(crypt), '!=', hex(c))\n res = False\n\n if plain != p:\n print('Decrypt:', hex(plain), '!=', hex(p))\n res = False\n\n return res","def decryptionMultipleRotate(text, power):\n s = text;\n transformedChar = \"\"\n transformedChar = s[power:] + s[0:power]\n\n print(\"Multiple Rotation Decrypted text : \" )\n return transformedChar","def encryptionSelfMadeFunction(text,index):\n s = text\n transformedChar = \"\"\n\n transformedChar = s[0:index] + s[index+1:] +s[index]\n\n print(\"Encrypted Transformed text : \" )\n return transformedChar","def cipher_feedback_mode_encode(msg, CEK, IV = int(0).to_bytes(8, 'big')):\n assert(len(CEK) == 32)\n assert(len(IV) == 8)\n last_block = IV\n res = b''\n for i in range(0, len(msg), 8):\n gamma = GOST2814789ECB_encode(last_block, CEK)\n block = msg[i: min(i + 8, len(msg))]\n encrypted_block = b''\n for j in range(len(block)):\n encrypted_block += int(block[j] ^ gamma[j]).to_bytes(1, 'big')\n res += encrypted_block\n last_block = encrypted_block\n return res","def FtDecrypt(self,EncryptText):\n \n self.EncryptText = EncryptText\n characters = \"abcdefghijklmnopqrstuvwxyz \"\n DecripText = ''\n\n #attempt to decrypt the text using the made_key and EncryptText \n try:\n for item in self.EncryptText:\n DecripText += Code_Fouad_Teniou.my_dict[item]\n\n return DecripText\n \n #Raise KeyError if a different key was used to encrypt the text \n except KeyError:\n print \"\\n<Please use the right code(made_key) to decrypt your text\"","def encrypt_caesar(plaintext: str) -> str:\n ciphertext = ''\n for ab in plaintext:\n if ('a' <= ab <= 'z') or ('A' <= ab <= 'Z'):\n ans = ord(ab) + 3\n if (ans > ord('Z')) and (ans < ord('a')) or (ans > ord('z')):\n ans -= 26\n ciphertext += chr(ans)\n else:\n ciphertext += ab\n return ciphertext","def encode(plain):\n # Remove whitespace and punctionation\n encoded = remove_punctuation(plain.lower())\n encoded = remove_whitespace(encoded)\n \n # Add space after every 5 characters\n encoded = add_space(encoded, 5)\n \n # Use the cipher translation\n encoded = encoded.translate(cipher)\n \n return encoded","def vigenere(inputString, mode, key):\n\n\t# Sanitize inputs\n\tkey = key.lower()\n\tkey.replace(\" \", \"\")\n\tif mode not in ['e', 'd']:\n\t\traise Exception(\"'mode' must be either 'e' for encryption or 'd' for decryption\")\n\n\t# Initialize local variables\n\tkeystreamCounter = 0\n\toutString = \"\"\n\n\t# Iterate on each character of the input string.\n\tfor inputChar in inputString:\n\t\t# If this character is in the alphabet, encode it\n\t\tif inputChar.lower() in alphabet:\n\t\t\t# Find the number of positions to move and the starting position\n\t\t\tkeyChar = key[keystreamCounter % len(key)]\n\t\t\tnumMoves = alphabet.index(keyChar)\n\t\t\tstartPosition = alphabet.index(inputChar.lower())\n\n\t\t\t# Encode or decode the input character by shifting the alphabet\n\t\t\tif mode == 'e': # encode\n\t\t\t\toutputChar = alphabet[(startPosition + numMoves) % len(alphabet)]\n\t\t\telif mode == 'd': # decode\n\t\t\t\toutputChar = alphabet[(startPosition - numMoves) % len(alphabet)]\n\n\t\t\t# Preserve capitalization\n\t\t\tif inputChar.isupper():\n\t\t\t\toutputChar = outputChar.upper()\n\n\t\t\t# Increment counter on each loop.\n\t\t\tkeystreamCounter += 1\n\n\t\t# If character is not in alphabet, preserve punctutation\n\t\telse:\n\t\t\toutputChar = inputChar\n\n\t\t# Append to output string\n\t\toutString += outputChar\n\n\t# Write to file\n\treturn(outString)","def encryption_oracle(unknown_encryptor: Callable[[bytes, bytes], bytes]) -> str:\n my_bytes = b'This is 16 bytes'*3\n encrypted_bytes = unknown_encryptor(my_bytes, os.urandom(16))\n hamming_distance = average_hamming_distance_between_blocks(encrypted_bytes[16:], 16, 2)\n return 'ECB' if hamming_distance == 0 else 'Not ECB'","def decrypt_cesar(new_word, number): #3\n decrypted_num = []\n for char in new_word:\n char = ord(char)\n decrypted_num.append(char)\n\n decrypted_word = []\n for num in decrypted_num:\n num = num - number\n decrypted_word.append(chr(num))\n\n return \"\".join(decrypted_word)","def decrypt_fable():\n ### TODO.\n fable_string = get_fable_string()\n print \"fable_string = \", fable_string\n fable_best_shifts = find_best_shifts(wordlist, fable_string)\n print \"fable_best_shifts =\", fable_best_shifts\n fable_decoded = apply_shifts(fable_string, fable_best_shifts)\n return fable_decoded","def encrypt_vigenere(plaintext, keyword):\n list = []\n index = 0\n for char in plaintext:\n new_char_val = ord(char) + ord(keyword[index]) - ord('A')\n if new_char_val > ord('Z'):\n new_char_val -= 26\n list.append(chr(new_char_val))\n index += 1\n index %= len(keyword)\n return ''.join(list)","def caesar_cipher_encrept(string, key):\n try:\n string_to_return = \"\"\n for l in string:\n if not(l >= 'A'and l <= 'Z' or l >= 'a'and l <= 'z'):\n string_to_return += l\n elif key + ord(l.upper()) > ord('Z'):\n string_to_return += chr(ord('A') + ord('Z') - ord(l.upper()))\n else:\n string_to_return += chr(ord(l.upper())+key)\n return string_to_return\n except (ValueError, IndexError, Exception) as ex:\n print(EXCEPTION_MESSAGE, ex)","def test_secretbox_enc_dec(self):\n # Encrypt with sk\n encrypted_data = nacl.secretbox_encrypt(data=self.unencrypted_data, sk=self.sk)\n\n # Decrypt with sk\n decrypted_data = nacl.secretbox_decrypt(data=encrypted_data, sk=self.sk)\n\n self.assertEqual(self.unencrypted_data, decrypted_data)","def break_cipher( self, ciphertexts ):\n\n\t\ttruncated = self.__truncate(ciphertexts)\n\t\tciphertexstring = b''.join( truncated )\n\n\t\tkeystream_size = len( truncated[0] )\n\n\t\ttransposed = self.__transpose( ciphertexstring, keystream_size )\n\n\t\txor_key = b''\n\t\tfor block in transposed:\n\t\t\thex_block = hexlify( block )\n\t\t\txor_key += bytes( [ ord( break_xor_single_byte_by_freq(hex_block) ) ] )\n\n\t\treturn xor_key","def decrypt_caesar(ciphertext):\n plaintext = \"\"\n for c in ciphertext:\n if (ord(c) <= 64) or (91 <= ord(c) <= 96) or (123 <= ord(c)):\n plaintext += chr(ord(c))\n elif (97 <= ord(c) - 3 <= 122) or (65 <= ord(c) - 3 <= 90):\n plaintext += chr(ord(c) - 3)\n else:\n plaintext += chr(ord(c) - 3 + 26)\n return plaintext","def decrypt_caesar(ciphertext: str, shift: int = 3) -> str:\n plaintext = \"\"\n low_first = ord(\"a\")\n low_last = ord(\"z\")\n high_first = ord(\"A\")\n high_last = ord(\"Z\")\n eng_alp = 26\n for i in ciphertext:\n if i.isalpha():\n if low_first <= ord(i) <= low_last:\n a = chr((((ord(i) - low_first) - shift) % eng_alp) + low_first)\n plaintext += a\n elif high_first <= ord(i) <= high_last:\n a = chr((((ord(i) - high_first) - shift) % eng_alp) + high_first)\n plaintext += a\n else:\n plaintext += i\n return plaintext","def key():","def rail_fence_encrypt(string, key):\n try:\n cols_size = len(string)\n arr_of_words = [[0 for i in range(cols_size)] for j in range(key)]\n pos = 0\n direction = 1\n for j in range(0, cols_size):\n # direction variable sets the moving direction\n arr_of_words[pos][j] = string[j]\n pos += direction\n if pos == 0 or pos == key-1:\n direction *= (-1)\n\n str_to_return = \"\" # concat the new string\n for i in range(key):\n for j in range(cols_size):\n if arr_of_words[i][j] != 0:\n str_to_return += arr_of_words[i][j]\n return str_to_return\n except (ValueError, IndexError) as ex:\n print(EXCEPTION_MESSAGE, ex)","def encrypt(text, key):\r\n\trail = [['\\n' for i in range(len(text))] for j in range(key)] \r\n\t\r\n\tdir_down = False\r\n\trow, col = 0, 0\r\n\t\r\n\tfor i in range(len(text)): \r\n\t\tif (row == 0) or (row == key - 1): \r\n\t\t\tdir_down = not dir_down \r\n\t\t\r\n\t\trail[row][col] = text[i] \r\n\t\tcol += 1\r\n\t\t\r\n\t\tif dir_down: \r\n\t\t\trow += 1\r\n\t\telse: \r\n\t\t\trow -= 1\r\n\t \r\n\tresult = [] \r\n\tfor i in range(key): \r\n\t\tfor j in range(len(text)): \r\n\t\t\tif rail[i][j] != '\\n': \r\n\t\t\t\tresult.append(rail[i][j]) \r\n\treturn(\"\" . join(result))","def setup_key_decrypt(self):\r\n\t\tself.max_key = math.floor(len(self.message) / 2)\r\n\t\twhile True:\r\n\t\t\tkey = input(\"Please enter the key that was used to encrypt your message.--> \")\r\n\t\t\ttry:\r\n\t\t\t\tself.key = int(key)\r\n\t\t\texcept ValueError:\r\n\t\t\t\tprint(\"Key needs to be a number.\")\r\n\t\t\t\tcontinue\r\n\t\t\tif self.key > self.max_key: \t\t\t\r\n\t\t\t\tprint(f\"{key} is too big of a number.\")\r\n\t\t\telif self.key == 0:\r\n\t\t\t\tprint(\"0 cannot be a key.\")\t\t\t\t\r\n\t\t\telse:\t\t\t\r\n\t\t\t\tbreak","def decipher(self,string): \n string = self.remove_punctuation(string)\n ret = ''\n for (i,c) in enumerate(string):\n if i<len(self.key): offset = self.a2i(self.key[i])\n else: offset = self.a2i(ret[i-len(self.key)]) \n ret += self.i2a(self.a2i(c)-offset)\n return ret","def test_16(self):\n plain = b';admin=True'\n cipher = bytearray(encrypt16(plain))\n cipher[16] = cu.XOR_bytes(b'?', cu.XOR_bytes(bytes([cipher[16]]), b';'))[0]\n cipher[22] = cu.XOR_bytes(b'?', cu.XOR_bytes(bytes([cipher[22]]), b'='))[0]\n self.assertTrue(decrypt16(cipher))","def decrypt_message(encrypted_message):","def test_rekey_defaults(self, settings):\n old_key = b'0' * 32\n new_key = b'1' * 32\n\n settings.CHITON_ENCRYPTION_KEY = new_key\n settings.CHITON_PREVIOUS_ENCRYPTION_KEY = old_key\n\n encrypted = encrypt('message', key=old_key)\n rekeyed = rekey(encrypted)\n\n assert decrypt(rekeyed) == 'message'"],"string":"[\n \"def operate_cipher(self):\",\n \"def cipher_feedback(self):\",\n \"def encrypt():\\n\\tnull = 0\",\n \"def decrypt_vigenere(ciphertext: str, keyword: str) -> str:\\n plaintext = \\\"\\\"\\n # PUT YOUR CODE HERE\\n key_lenght = len(keyword)\\n text_lenght = len(ciphertext)\\n\\n while key_lenght != text_lenght:\\n keyword += keyword\\n key_lenght = len(keyword)\\n if key_lenght > text_lenght:\\n keyword = keyword[:text_lenght]\\n key_lenght = len(keyword)\\n code_key = []\\n ord_a = ord('a')\\n ord_A = ord('A')\\n\\n if ciphertext.islower():\\n for i in range(key_lenght):\\n if ciphertext[i] == \\\" \\\":\\n code_key.append(\\\" \\\")\\n else:\\n code_key.append(ord(keyword[i]) - ord_a)\\n code_text = []\\n for n in range(text_lenght):\\n if ciphertext[n] == \\\" \\\":\\n code_text.append(\\\" \\\")\\n else:\\n code_text.append(ord(ciphertext[n]) - ord_a)\\n for u in range(text_lenght):\\n if ciphertext[u] == \\\" \\\":\\n value = ord(\\\" \\\")\\n else:\\n\\n value = ((code_text[u] - code_key[u] + 26) % 26) + ord_a\\n plaintext += chr(value)\\n else:\\n for i in range(key_lenght):\\n if ciphertext[i] == \\\" \\\":\\n code_key.append(\\\" \\\")\\n else:\\n code_key.append(ord(keyword[i]) - ord_A)\\n code_text = []\\n for n in range(text_lenght):\\n if ciphertext[n] == \\\" \\\":\\n code_text.append(\\\" \\\")\\n else:\\n code_text.append(ord(ciphertext[n]) - ord_A)\\n for u in range(text_lenght):\\n if ciphertext[u] == \\\" \\\":\\n value = ord(\\\" \\\")\\n else:\\n value = ((code_text[u] - code_key[u] + 26) % 26) + ord_A\\n plaintext += chr(value)\\n\\n return plaintext\",\n \"def encrypt_vigenere(plaintext: str, keyword: str) -> str:\\n ciphertext = \\\"\\\"\\n # PUT YOUR CODE HERE\\n\\n key_lenght = len(keyword)\\n text_lenght = len(plaintext)\\n while key_lenght != text_lenght:\\n keyword += keyword\\n key_lenght = len(keyword)\\n if key_lenght > text_lenght:\\n keyword = keyword[:text_lenght]\\n key_lenght = len(keyword)\\n code_key = []\\n ord_A = ord('A')\\n ord_a = ord('a')\\n\\n if plaintext.islower():\\n for i in range(key_lenght):\\n if plaintext[i] == \\\" \\\":\\n code_key.append(\\\" \\\")\\n else:\\n code_key.append(ord(keyword[i]) - ord_a)\\n code_text = []\\n for n in range(text_lenght):\\n if plaintext[n] == \\\" \\\":\\n code_text.append(\\\" \\\")\\n else:\\n code_text.append(ord(plaintext[n]) - ord_a)\\n ciphertext = ''\\n for u in range(len(plaintext)):\\n if plaintext[u] == \\\" \\\":\\n ciphertext += \\\" \\\"\\n else:\\n value = (code_key[u] + code_text[u]) % 26 + ord_a\\n ciphertext += chr(value)\\n else:\\n for i in range(key_lenght):\\n if plaintext[i] == \\\" \\\":\\n code_key.append(\\\" \\\")\\n else:\\n code_key.append(ord(keyword[i]) - ord_A)\\n code_text = []\\n for n in range(text_lenght):\\n if plaintext[n] == \\\" \\\":\\n code_text.append(\\\" \\\")\\n else:\\n code_text.append(ord(plaintext[n]) - ord_A)\\n ciphertext = ''\\n for u in range(len(plaintext)):\\n if plaintext[u] == \\\" \\\":\\n value = ord(\\\" \\\")\\n else:\\n value = (code_key[u] + code_text[u]) % 26 + ord_A\\n ciphertext += chr(value)\\n return ciphertext\",\n \"def de_cryptate(self):\\r\\n\\r\\n intab1 = \\\"abcdefghijklomnopqrstuvwxyz !,.\\\"\\r\\n outtab1 = \\\"?2p=o)7i(u9/y&t3%r¤5e#w1q!>*'^;)\\\"\\r\\n# Fetching from written in textbox\\r\\n s = self.textbox.toPlainText()\\r\\n a = s.lower()\\r\\n# Changing out the letters/numbers/etc\\r\\n crypted = (a.translate({ord(x): y for (y, x) in zip(intab1, outtab1)}))\\r\\n# Clear the textbox\\r\\n self.textbox.clear()\\r\\n# Write the Decrypted text\\r\\n self.textbox.setPlainText(crypted)\",\n \"def encipher(self):\\n ciphertext = \\\"\\\"\\n for pt, key_char in zip(self.text, self.key):\\n char_index = self.char_block.alphabet.index(pt)\\n ciphertext += self.char_block.rows[key_char][char_index]\\n print(ciphertext)\",\n \"def test_encryption(e, c):\\n message = input(\\\"Enter word to encrypt: \\\")\\n ciphered = ''\\n\\n for i in range(0, len(message)):\\n ciphered = f'{ciphered}{chr(endecrypt(ord(message[i]), e, c))}'\\n\\n print(ciphered + ' is the ciphered text')\\n d = key_cracker(e, c)\\n print(\\\"Plain text is:\\\")\\n for i in range(0, len(ciphered)):\\n print(chr(endecrypt(ord(ciphered[i]), d, c)), end='')\",\n \"def cryptate(self):\\r\\n\\r\\n intab1 = \\\"abcdefghijklomnopqrstuvwxyz\\\"\\r\\n outtab1 = \\\"?2p=o)7i(u9/y&t3%r¤5e#w1q!>)\\\"\\r\\n# Fetching the writing in textbox\\r\\n s = self.textbox.toPlainText()\\r\\n a = s.lower()\\r\\n# The crypting process, replaces letters in intab1 with outtab1\\r\\n crypted = (a.translate({ord(x): y for (x, y) in zip(intab1, outtab1)}))\\r\\n# Clear the textbox\\r\\n self.textbox.clear()\\r\\n# Write the crypted text within textbox\\r\\n self.textbox.setPlainText(crypted)\",\n \"def decipher(self):\\n plaintext = \\\"\\\"\\n for ct, key_char in zip(self.text, self.key):\\n char_index = self.char_block.rows[key_char].index(ct)\\n plaintext += self.char_block.alphabet[char_index]\\n print(plaintext)\",\n \"def test_encryption(e, c):\\n\\n#\\te = int(raw_input(\\\"\\\\nEnter e from public key\\\\n\\\"))\\n#\\tc = int(raw_input(\\\"\\\\nEnter c from public key\\\\n\\\"))\\n\\n string = raw_input(\\\"\\\\nEnter word to encrpyt\\\\n\\\")\\n for i in range(0, len(string)):\\n print endecrypt(ord(string[i]), e, c)\",\n \"def encrypt(message, key):\\r\\n # --- YOU CODE STARTS HERE\\r\\n if type(message) != str or type(key) != int:\\r\\n return 'Invalid input'\\r\\n alpha_lower = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\\r\\n alpha_upper = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\\r\\n\\r\\n new_st = ''\\r\\n\\r\\n for x in message:\\r\\n if (alpha_lower.count(x) != 0) or (alpha_upper.count(x) != 0):\\r\\n if alpha_lower.count(x) != 0 and alpha_lower.index(x) + key < 26:\\r\\n new_st += alpha_lower[alpha_lower.index(x) + key]\\r\\n\\r\\n if alpha_upper.count(x) != 0 and alpha_upper.index(x) + key < 26:\\r\\n new_st += alpha_upper[alpha_upper.index(x) + key]\\r\\n\\r\\n if alpha_upper.count(x)!= 0 and alpha_upper.index(x) + key >= 26:\\r\\n new_st += alpha_upper[alpha_upper.index(x) + key - 26]\\r\\n\\r\\n if alpha_lower.count(x) != 0 and alpha_lower.index(x) + key >= 26:\\r\\n new_st += alpha_lower[alpha_lower.index(x) + key - 26]\\r\\n else:\\r\\n new_st += x\\r\\n\\r\\n return new_st\\r\\n\\r\\n # --- CODE ENDS HERE\\r\",\n \"def test_endecrypt():\\n\\n e, d, c = keygen()\\n\\n test_encryption(e, c)\\n test_decryption(d, c)\\n key_cracker(e, c)\",\n \"def _applyCipher(self, encode):\\n pass\",\n \"def decrypt(self, data):\",\n \"def verify_decrypt_key(self):\\r\\n\\t\\tpercent_english = Dict_Control(self.my_code).check_key()\\r\\n\\t\\t#If more than half the words are english, the key will pass. \\r\\n\\t\\tif percent_english > 50:\\r\\n\\t\\t\\tself.right_key = False\\r\\n\\t\\t#If the key does not pass, the program will give you a warning and prompt you for another key. \\r\\n\\t\\telse: \\r\\n\\t\\t\\tprint(f\\\"After decryption, it looks like only {percent_english}% of your words are english, you may have entered the wrong key?\\\")\",\n \"def coding():\\r\\n \\r\\n key={'reverse_word': False, 'reverse_string': False, 'a': 'a', 'b': 'b', 'c': 'c', 'd': 'd', 'e': 'e', 'f': 'f', 'g': 'g', 'h': 'h',\\r\\n 'i': 'i', 'j': 'j', 'k': 'j', 'l': 'l', 'm': 'm', 'n': 'n', 'o': 'o', 'p': 'p', 'q': 'q', 'r': 'r', 's': 's', 't': 't', 'u': 'u',\\r\\n 'v': 'v', 'w': 'w', 'x':'x', 'y': 'y', 'z': 'z'}\\r\\n x=0 #determine the sliding of the letters\\r\\n \\r\\n def isKeyEmpty(k):\\r\\n \\\"\\\"\\\"Utility Function that checks if key is empty\\\"\\\"\\\"\\r\\n if k=={'reverse_word': False, 'reverse_string': False, 'a': 'a', 'b': 'b', 'c': 'c', 'd': 'd', 'e': 'e', 'f': 'f', 'g': 'g', 'h': 'h',\\r\\n 'i': 'i', 'j': 'j', 'k': 'j', 'l': 'l', 'm': 'm', 'n': 'n', 'o': 'o', 'p': 'p', 'q': 'q', 'r': 'r', 's': 's', 't': 't', 'u': 'u',\\r\\n 'v': 'v', 'w': 'w', 'x':'x', 'y': 'y', 'z': 'z'}:\\r\\n return True\\r\\n return False\\r\\n \\r\\n def set_key(vars): #vars=[0]num,[1]rWord,[2]rString\\r\\n \\\"\\\"\\\"Function that set the new key\\\"\\\"\\\"\\r\\n nonlocal key\\r\\n nonlocal x\\r\\n x=vars[0]\\r\\n if (vars[1]=='yes'):\\r\\n key['reverse_word']=True\\r\\n if (vars[2]=='yes'):\\r\\n key['reverse_string']=True\\r\\n if (x<-26 or x>26):\\r\\n x=x%26 #makes x to be in range\\r\\n if (x==0):\\r\\n x=random.randrange(-26,26) #random number\\r\\n for i in range (97,123): #26 ABC letters, ASCII value of 'a' is 97 97+26=123\\r\\n if(i+x>122):\\r\\n key[chr(i)]=chr(i-25+x)\\r\\n elif (i+x<97):\\r\\n key[chr(i)]=chr(i+26+x)\\r\\n else:\\r\\n key[chr(i)]=chr(i+x)\\r\\n print(\\\"done\\\")\\r\\n \\r\\n def empty_key():\\r\\n \\\"\\\"\\\"Function makes current key empty\\\"\\\"\\\"\\r\\n nonlocal key\\r\\n nonlocal x\\r\\n x=0\\r\\n key={'reverse_word': False, 'reverse_string': False, 'a': 'a', 'b': 'b', 'c': 'c', 'd': 'd', 'e': 'e', 'f': 'f', 'g': 'g', 'h': 'h',\\r\\n 'i': 'i', 'j': 'j', 'k': 'j', 'l': 'l', 'm': 'm', 'n': 'n', 'o': 'o', 'p': 'p', 'q': 'q', 'r': 'r', 's': 's', 't': 't', 'u': 'u',\\r\\n 'v': 'v', 'w': 'w', 'x':'x', 'y': 'y', 'z': 'z'}\\r\\n print(\\\"done\\\")\\r\\n \\r\\n def export_key():\\r\\n \\\"\\\"\\\"Function export key\\\"\\\"\\\"\\r\\n if(isKeyEmpty(key)):\\r\\n print(\\\"key empty\\\")\\r\\n else:\\r\\n return key\\r\\n \\r\\n def import_key(key2):\\r\\n \\\"\\\"\\\"Function import key\\\"\\\"\\\"\\r\\n nonlocal key\\r\\n if(isKeyEmpty(key2)):\\r\\n print(\\\"key is empty\\\")\\r\\n else:\\r\\n key=key2\\r\\n print(\\\"done\\\")\\r\\n \\r\\n def encoding(sentence):\\r\\n \\\"\\\"\\\"function encoding given string with the key\\\"\\\"\\\"\\r\\n sentence=list(sentence)\\r\\n for i in range(len(sentence)):\\r\\n if (sentence[i]!=' '):\\r\\n sentence[i]=key[sentence[i]]\\r\\n sentence=''.join(sentence)\\r\\n if(key['reverse_word']==True):\\r\\n splitT=tuple(sentence.split(' '))\\r\\n splitT=map(lambda x:x[::-1],splitT)\\r\\n sentence=' '.join(splitT)\\r\\n if(key['reverse_string']==True):\\r\\n splitList=sentence.split(' ')\\r\\n splitList=splitList[-1::-1]\\r\\n sentence=' '.join(splitList)\\r\\n return sentence\\r\\n \\r\\n def decoding(sentence):\\r\\n \\\"\\\"\\\"function decoding given string with the key\\\"\\\"\\\"\\r\\n if(isKeyEmpty(key)):\\r\\n return \\\"key empty\\\"\\r\\n helpKey=dict((y,x) for x,y in key.items())\\r\\n if(key['reverse_word']==True):\\r\\n splitT=tuple(sentence.split(' '))\\r\\n splitT=map(lambda x:x[::-1],splitT)\\r\\n sentence=' '.join(splitT)\\r\\n if(key['reverse_string']==True):\\r\\n splitList=sentence.split(' ')\\r\\n splitList=splitList[-1::-1]\\r\\n sentence=' '.join(splitList)\\r\\n sentence=list(sentence)\\r\\n for i in range(len(sentence)):\\r\\n if(sentence[i]!=' '):\\r\\n sentence[i]=helpKey[sentence[i]]\\r\\n sentence=''.join(sentence)\\r\\n return sentence\\r\\n\\r\\n def dispatch(message,var=None):\\r\\n \\\"\\\"\\\"dispatch with message passing\\\"\\\"\\\"\\r\\n if message=='set_key':\\r\\n set_key(var)\\r\\n elif message=='empty_key':\\r\\n empty_key()\\r\\n elif message=='export_key':\\r\\n return export_key()\\r\\n elif message=='import_key':\\r\\n import_key(var)\\r\\n elif message=='encoding':\\r\\n return encoding(var)\\r\\n elif message=='decoding':\\r\\n return decoding(var)\\r\\n else:\\r\\n print(\\\"Unknown message\\\") \\r\\n return dispatch\",\n \"def encode(key, plain):\\n print(\\\"ciphertext: \\\", end=\\\"\\\")\\n\\n # used variables\\n pos = 0\\n key_len = len(key)\\n\\n # loop over every character in the text\\n for char in plain:\\n key_pos = pos % key_len\\n # leave non-alphabetical characters alone\\n if not char.isalpha():\\n print(char, end=\\\"\\\")\\n # cipher characters\\n elif char.isupper():\\n cipher = chr((char_to_number(char) + char_to_number(key[key_pos])) \\\\\\n % 26 + ord(\\\"A\\\"))\\n \\n print(cipher, end=\\\"\\\")\\n pos += 1\\n else:\\n cipher = chr((char_to_number(char) + char_to_number(key[key_pos])) \\\\\\n % 26 + ord(\\\"a\\\"))\\n \\n print(cipher, end=\\\"\\\")\\n pos += 1\\n\\n print()\",\n \"def encript(self): \\n if (len(sys.argv) == Cconfiguration_caesar.DUAL_PARAMETER) and (int(sys.argv[Cconfiguration_caesar.INCREMENTAL_PARAMETER])>=Cconfiguration_caesar.INITIAL_INT_PARAMETER):\\n result = \\\"\\\"\\n k = int(sys.argv[Cconfiguration_caesar.INCREMENTAL_PARAMETER])\\n plaintext = input(\\\"plaintext: \\\")\\n for i in range(len(plaintext)):\\n char = plaintext[i]\\n if ((Cconfiguration_caesar.ALPHABET_LOWER_INDEX>ord(char)) or (Cconfiguration_caesar.ALPHABET_LOWER_LIMIT<ord(char))) and ((Cconfiguration_caesar.ALPHABET_UPPER_INDEX>ord(char)) or (Cconfiguration_caesar.ALPHABET_UPPER_LIMIT<ord(char))):\\n result += char\\n elif (char.isupper()):\\n result += chr((ord(char) + k-Cconfiguration_caesar.ALPHABET_UPPER_INDEX) % Cconfiguration_caesar.ALPHABET_LIMIT + Cconfiguration_caesar.ALPHABET_UPPER_INDEX)\\n else:\\n result += chr((ord(char) + k - Cconfiguration_caesar.ALPHABET_LOWER_INDEX) % Cconfiguration_caesar.ALPHABET_LIMIT + Cconfiguration_caesar.ALPHABET_LOWER_INDEX)\\n print(f\\\"ciphertext: {result}\\\")\\n else:\\n print(CextraStatusDefinition.COMMAND_LINE_EERROR)\\n exit(Cconfiguration_caesar.INCREMENTAL_PARAMETER)\",\n \"def encipher(self,string): \\n string = self.remove_punctuation(string)\\n ret = ''\\n for (i,c) in enumerate(string):\\n if i<len(self.key): offset = self.a2i(self.key[i])\\n else: offset = self.a2i(string[i-len(self.key)]) \\n ret += self.i2a(self.a2i(c)+offset)\\n return ret\",\n \"def encrypt_railfence(plaintext, num_rails):\\n if num_rails == 1:\\n return plaintext\\n\\n lists = []\\n for i in range(0, num_rails):\\n lists.append([])\\n row = -1\\n dir = 1\\n for char in plaintext:\\n row += dir\\n lists[row].append(char)\\n if row == 0:\\n dir = 1\\n elif row == num_rails - 1:\\n dir = -1\\n encrypted = \\\"\\\"\\n for list in lists:\\n #print(list)\\n encrypted += ''.join(list)\\n return encrypted\",\n \"def decrypt_vigenere(ciphertext: str, keyword: str) -> str:\\r\\n ciphertext = \\\"LXFOPVEFRNHR\\\"\\r\\n keyword = \\\"LEMON\\\"\\r\\n plaintext = \\\"\\\"\\r\\n nonaplhacount = 0 \\r\\n for index, char in enumerate(ciphertext):\\r\\n if not char.isalpha():\\r\\n plaintext = plaintext + char\\r\\n nonalphacount += 1\\r\\n d = 'A' if char.isupper() else 'a'\\r\\n keyword_char_index = (index - nonalphacount)%len(keyword)\\r\\n keyword_char = keyword[keyword_char_index].upper() if char.isupper() else keyword[keyword_char_index]\\r\\n key = ord(keyword_char) - ord(d)\\r\\n key = 26 - key\\r\\n cipher = (((ord(char) + key) - ord(d)) % 26) + ord(d)\\r\\n plaintext = plaintext + chr(cipher)\\r\\n print(plaintext)\\r\\n return plaintext\",\n \"def encodeVigenere(self, key):\\n\\n key = key.upper().replace(\\\" \\\", \\\"\\\")\\n encode = Vig(key)\\n cipherText = encode.encode(self.planeText)\\n \\n if (self.verbose == 1):\\n print(cipherText)\\n \\n return(cipherText)\",\n \"def eul59():\\n cipher = [c.rstrip('\\\\n') for c in open('resources/p059_cipher.txt')]\\n cipher = [int(c) for c in cipher[0].split(\\\",\\\")]\\n for key in itertools.product(range(97, 123), repeat=3):\\n msg = [x ^ y for x, y in zip(cipher, itertools.cycle(key))]\\n if ' the ' in ''.join(map(chr, msg)):\\n return sum(msg)\",\n \"def decrypt(message, key):\\r\\n\\r\\n # --- YOU CODE STARTS HERE\\r\\n if type(message) != str or type(key) != int:\\r\\n return 'Invalid input'\\r\\n new_st = ''\\r\\n alpha_lower = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\\r\\n alpha_upper = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\\r\\n\\r\\n for x in message:\\r\\n if (alpha_lower.count(x) != 0) or (alpha_upper.count(x) != 0):\\r\\n if alpha_lower.count(x) != 0:\\r\\n new_st += alpha_lower[alpha_lower.index(x) - key]\\r\\n if alpha_upper.count(x) != 0:\\r\\n new_st += alpha_upper[alpha_upper.index(x) - key]\\r\\n else:\\r\\n new_st += x\\r\\n\\r\\n return new_st\\r\\n\\r\\n\\r\\n # --- CODE ENDS HERE\\r\",\n \"def decrypt_vigenere(cipehrtext: str, keyword: str) -> str:\\n plaintext = \\\"\\\"\\n if len(keyword) < len(cipehrtext):\\n for j in range(len(cipehrtext) - len(keyword)):\\n keyword += keyword[j]\\n for i in range(len(cipehrtext)):\\n n = ord(cipehrtext[i])\\n m = ord(keyword[i])\\n if (n >= ord('A')) and (n <= ord('Z')):\\n if n >= m:\\n plaintext += chr(n - m + ord('A'))\\n else:\\n plaintext += chr(ord('Z') + 1 - (m - n))\\n else:\\n if n >= m:\\n plaintext += chr(n - m + ord('a'))\\n else:\\n plaintext += chr(ord('Z') + 1 - (m - n))\\n return plaintext\",\n \"def decrypt_vigenere(plaintext, keyword):\\n ciphertext = \\\"\\\"\\n for i in range(len(plaintext)):\\n if 64 < ord(plaintext[i]) < 65 + 26:\\n shift = (ord(keyword[i % len(keyword)]) - 65)\\n if ord(plaintext[i]) < 64 + shift:\\n ciphertext += chr(ord(plaintext[i]) + 26 - shift)\\n else:\\n ciphertext += chr(ord(plaintext[i]) - shift)\\n elif 96 < ord(plaintext[i]) < 97 + 26:\\n shift = (ord(keyword[i % len(keyword)]) - 97)\\n if ord(plaintext[i]) < 96 + shift:\\n ciphertext += chr(ord(plaintext[i]) + shift - 26)\\n else:\\n ciphertext += chr(ord(plaintext[i]) + shift)\\n else:\\n ciphertext += i\\n return ciphertext\",\n \"def encrypt(self, message):\\n # message = message.upper().split()\\n # message = \\\"\\\".join(message)\\n message = message.upper()\\n message_list = []\\n for ch in message:\\n message_list.append(self.main_dict[ch][0])\\n\\n # Generate a random key\\n random_otp = [random.choice(self.alphabet_list) for _ in range(len(message))]\\n print(\\\"Your OTP is: \\\" + str(\\\"\\\".join(random_otp)))\\n print(\\\"Use the OTP to unlock the message.\\\")\\n\\n # Convert Random key to integers\\n for i, item in enumerate(random_otp):\\n random_otp[i] = self.main_dict[item][0]\\n\\n # Do the math with Random Key and the message\\n math_list = []\\n for i, item in enumerate(message_list):\\n try:\\n result = message_list[i] + random_otp[i]\\n math_list.append(result)\\n except:\\n print(\\\"The message and OTP does not have the same length\\\")\\n continue\\n\\n # Logic to do mod27\\n for i, item in enumerate(math_list):\\n for key, value in self.main_dict.items():\\n if item > 26:\\n if value[1] == item:\\n math_list[i] = key\\n else:\\n if value[0] == item:\\n math_list[i] = key\\n\\n # Algorithm for 5 block characters\\n padding = input(\\\"Would you like to use block 5 characters? y/n \\\")\\n if padding == \\\"y\\\":\\n math_list = self.salt_with_random(math_list)\\n return \\\" \\\".join(math_list)\\n else:\\n math_list = \\\"\\\".join(math_list)\\n return math_list\",\n \"def obfuscate(self):\\n r = self.public_key.get_random_lt_n()\\n r_pow_n = powmod(r, self.public_key.n, self.public_key.nsquare)\\n self.__ciphertext = self.__ciphertext * r_pow_n % self.public_key.nsquare\\n self.__is_obfuscated = True\",\n \"def FtEncrypt(self,text):\\n \\n self.text = text\\n EncryptText = []\\n characters = \\\"abcdefghijklmnopqrstuvwxyz \\\"\\n\\n #attempt to append my_list and update my_dict\\n #using a random set of alphabet and a random made_key \\n try:\\n for char in random.sample(characters,27):\\n Code_Fouad_Teniou.my_list.append(char)\\n \\n Code_Fouad_Teniou.my_dict.update(zip(Code_Fouad_Teniou.my_key,Code_Fouad_Teniou.my_list))\\n\\n for item in text.lower():\\n for i in Code_Fouad_Teniou.my_dict.items():\\n if item == i[1]:\\n EncryptText.append(i[0])\\n \\n return EncryptText\\n \\n #Raise AttributeError if text is not a string \\n except AttributeError:\\n raise AttributeError, \\\"\\\\n<Please re-enter your text as a 'string'\\\"\",\n \"def decrypt():\\n plaintext = \\\"\\\"\\n i = 0\\n while i < len(ciphertext):\\n if i%2==1:\\n try:\\n plaintext += key[ ciphertext[i-1]+ciphertext[i] ]\\n except KeyError:\\n plaintext += ciphertext[i-1]+ciphertext[i]\\n i += 1\\n return plaintext\",\n \"def run():\\n key = input(\\\"Enter a 26 letter key: \\\")\\n if not isValidKey(key):\\n print(\\\"Invalid key.\\\")\\n return\\n plainText = input(\\\"Plain Text: \\\")\\n cipherText = substitution(plainText, key)\\n print(f\\\"Cipher Text: {cipherText}\\\")\\n return\",\n \"def encrypt(self,string=\\\"vrorqjdqgwdqnviruwkhilvk\\\",key=3):\\r\\n return \\\"\\\".join([chr((ord(ch)-key-ord('a'))%(ord('z')-ord('a')+1)+ord('a')) for ch in string])\",\n \"def mode_crypt_cramershoup(stdscr, message=None):\\n loop = True\\n cursor = 0\\n while loop:\\n show_key_choices(stdscr, cursor, message)\\n key = stdscr.getkey()\\n loop = False\\n cs = CramerShoup()\\n if key == '1' or (key == '\\\\n' and cursor == 1):\\n key_size = choose_keys_size(stdscr)# choose the size of key [256,512,1024]\\n stdscr.clear()\\n stdscr.addstr(\\\"Création des clés de chiffrement ...\\\\n\\\\n\\\")\\n stdscr.refresh()\\n cs.generate_keys(key_size)\\n stdscr.addstr(\\\"Vos clés ont été générés dans keys/\\\\n\\\")\\n stdscr.refresh()\\n napms(2000)\\n mode_crypt_cramershoup(stdscr, \\\"Les clés ont été générés\\\\n\\\")\\n \\n elif key == '2' or (key == '\\\\n' and cursor == 2):\\n # chiffre avec la clé privé (la clé privé contient la clé publique)\\n key_file_name = input_user(stdscr, \\\"Veuiller entrer l'enplacement de la clé public. Ctrl + G pour confirmer\\\")\\n try:\\n cs.read_key(key_file_name)\\n except IOError:\\n # cannot open the file\\n mode_crypt_cramershoup(stdscr, \\\"Impossible de lire la clé dans le fichier {}\\\".format(key_file_name))\\n return\\n file_name = input_user(stdscr, \\\"Clé chargé avec succès.\\\\n Veuillez entrer le nom du fichier à chiffrer\\\")\\n try:\\n file = open(file_name)\\n file.close()\\n except IOError:\\n mode_crypt_cramershoup(stdscr, \\\"Impossible d'ouvrir le fichier {}\\\".format(file_name))\\n return\\n # si le fichier est un pgm, on laisse le choix à l'utilisateur\\n pgm = False\\n if re.match('.+\\\\.pgm.*', file_name) is not None:\\n pgm = choix_mode_PGM(stdscr)\\n \\n # on chiffre le fichier\\n stdscr.clear()\\n stdscr.addstr(\\\"En cours de chiffrement ...\\\\n\\\")\\n stdscr.refresh()\\n wrap = None\\n if pgm:\\n wrap = PGMEncrypter(file_name, cs, cs.bit_size//(2*8), file_name + \\\".crypted\\\", 4*cs.bit_size//8)\\n else:\\n wrap = BlockFileEncrypter(file_name, cs, cs.bit_size//(2*8), file_name + \\\".crypted\\\", 4*cs.bit_size//8)\\n wrap.crypt_to_out()\\n stdscr.addstr(\\\"Votre fichier {} a été chiffré :) !\\\".format(file_name), curses.color_pair(3))\\n stdscr.refresh()\\n napms(1000)\\n menu(stdscr)\\n elif key == 'm' or (key == '\\\\n' and cursor == 3):\\n menu(stdscr)\\n elif key == 'KEY_UP' and cursor > 1:\\n cursor -= 1\\n loop = True\\n elif key == 'KEY_DOWN' and cursor < 3:\\n cursor += 1\\n loop = True\\n else:\\n loop = True\",\n \"def call_cipher(self):\\n if self.cipher_choice == \\\"affine\\\":\\n\\n if self.crypt == \\\"encrypt\\\":\\n encrypted_message = Affine().encrypt(self.message.upper())\\n otp_encrypted = OneTimePad().encrypt(\\n encrypted_message, self.otp.upper())\\n return (otp_encrypted)\\n\\n elif self.crypt == \\\"decrypt\\\":\\n otp_decrypted = OneTimePad().decrypt(\\n self.message.upper(), self.otp.upper())\\n decrypted_message = Affine().decrypt(otp_decrypted)\\n return (decrypted_message.lower())\\n\\n elif self.cipher_choice == \\\"atbash\\\":\\n\\n if self.crypt == \\\"encrypt\\\":\\n encrypted_message = Atbash().encrypt(self.message.upper())\\n otp_encrypted = OneTimePad().encrypt(\\n encrypted_message, self.otp.upper())\\n return (otp_encrypted)\\n\\n elif self.crypt == \\\"decrypt\\\":\\n otp_decrypted = OneTimePad().decrypt(\\n self.message.upper(), self.otp.upper())\\n decrypted_message = Atbash().decrypt(otp_decrypted)\\n return (decrypted_message.lower())\\n\\n elif self.cipher_choice == \\\"keyword\\\":\\n\\n if self.crypt == \\\"encrypt\\\":\\n encrypted_message = KeywordCipher().encrypt(self.message.upper())\\n otp_encrypted = OneTimePad().encrypt(\\n encrypted_message, self.otp.upper())\\n return (otp_encrypted)\\n\\n elif self.crypt == \\\"decrypt\\\":\\n otp_decrypted = OneTimePad().decrypt(\\n self.message.upper(), self.otp.upper())\\n decrypted_message = KeywordCipher().decrypt(otp_decrypted)\\n return (decrypted_message.lower())\",\n \"def main():\\n # key = random(1024)\\n # ciphertexts = [encrypt(key, msg) for msg in MSGS]\\n\\n # Get key and secret message\\n knownPlain2 = \\\"The nice thing about Keeyloq is now we cryptographers can drive a lot of fancy cars - Dan Boneh\\\"\\n key = strxor(ciphertexts[2], knownPlain2)\\n secret = strxor(target, key)\\n\\n print \\\"Key: \\\" + key\\n print \\\"Key (Hex): \\\" + key.encode(\\\"hex\\\")\\n print \\\"Secret: \\\" + secret\",\n \"def encrypt_vigenere(plaintext, keyword):\\n ciphertext = \\\"\\\"\\n for i in range(len(plaintext)):\\n if 64 < ord(plaintext[i]) < 65 + 26:\\n shift = (ord(keyword[i % len(keyword)]) - 65)\\n if ord(plaintext[i]) > 64 + 26 - shift:\\n ciphertext += chr(ord(plaintext[i]) + shift - 26)\\n else:\\n ciphertext += chr(ord(plaintext[i]) + shift)\\n elif 96 < ord(plaintext[i]) < 97 + 26:\\n shift = (ord(keyword[i % len(keyword)]) - 97)\\n if ord(plaintext[i]) > 96 + 26 - shift:\\n ciphertext += chr(ord(plaintext[i]) + shift - 26)\\n else:\\n ciphertext += chr(ord(plaintext[i]) + shift)\\n else:\\n ciphertext += i\\n return ciphertext\",\n \"def caesar_encryption(text):\\n result = ''\\n for char in text:\\n if char.isdigit():\\n i = (num_key.index(char) - 4) % 10\\n result += num_key[i]\\n elif not char.isdigit() and char.lower() in alpha_key:\\n i = (alpha_key.index(char.lower()) - 4) % 26\\n result += alpha_key[i]\\n else:\\n result += char\\n return result\",\n \"def decrypt_vigenere(ciphertext, keyword):\\n plaintext = ''\\n new_keyword = ''\\n for i in range(len(ciphertext)):\\n num_word = ord(ciphertext[i])\\n while len(ciphertext) > len(new_keyword):\\n new_keyword += keyword\\n num_key = ord(new_keyword[i])\\n if num_key <= 90:\\n num_key -= 65\\n elif num_key >= 97:\\n num_key -= 97\\n if num_word <= 90:\\n if (num_word-num_key) < 65:\\n num_word += 26\\n else:\\n if (num_word-num_key) < 97:\\n num_word += 26\\n plaintext += chr(num_word-num_key)\\n return plaintext\",\n \"def encrypt_vigenere(plaintext, keyword):\\n ciphertext = '' # Обнуляем строку\\n keyword = length_equal(plaintext, keyword)\\n for i in range(len(plaintext)):\\n if (ord(plaintext[i].upper()) + ord(keyword[i].upper()) - 65) <= 90:\\n ciphertext += chr(ord(plaintext[i].upper()) + (ord(keyword[i].upper()) - 65))\\n else:\\n ciphertext += chr(ord(keyword[i].upper()) - 91 + ord(plaintext[i].upper()))\\n return ciphertext\",\n \"def encrypt_vigenere(plaintext, keyword):\\n ciphertext = ''\\n new_keyword = ''\\n for i in range(len(plaintext)):\\n num_word = ord(plaintext[i])\\n while len(plaintext) > len(new_keyword):\\n new_keyword += keyword\\n num_key = ord(new_keyword[i])\\n if num_key <= 90:\\n num_key -= 65\\n elif num_key >= 97:\\n num_key -= 97\\n if num_word <= 90:\\n if (num_key+num_word) > 90:\\n num_word -= 26\\n else:\\n if (num_key+num_word) > 122:\\n num_word -= 26\\n ciphertext += chr(num_word+num_key)\\n return ciphertext\",\n \"def main():\\n\\n # performs crib dragging using initial values\\n plaintext1, plaintext2 = crib_drag('', '', 0, 0)\\n\\n if plaintext1 is None or plaintext2 is None:\\n print('No possible English decryption using the current dictionary')\\n return\\n\\n # find the key and creates file with results\\n plaintext1 = plaintext1[:CIPHER_LEN]\\n plaintext2 = plaintext2[:CIPHER_LEN]\\n key = find_key(plaintext1, plaintext2)\\n\\n with open('plaintext1.txt', 'w') as plain_file:\\n plain_file.write(plaintext1)\\n with open('plaintext2.txt', 'w') as plain_file:\\n plain_file.write(plaintext2)\\n with open('key.txt', 'wb') as plain_file:\\n plain_file.write(key)\",\n \"def brute_force_decrypt(text):\\n for n in range(26):\\n print(f\\\"Using a shift value of {n}\\\")\\n print(decrypt(text, n))\\n print(\\\"\\\\n***\\\\n\\\")\",\n \"def encrypt(word_e):\\r\\n\\r\\n translated_word = \\\"\\\"\\r\\n\\r\\n # For every character in word_e, translate and add to translated_word\\r\\n for char in word_e:\\r\\n translated_word += values[keys.index(char)]\\r\\n # Returns output\\r\\n return translated_word\",\n \"def CipherRun():\\r\\n ChosenData = UserInput() # retieving user input\\r\\n Msg, Shift = ChosenData # assigning the message text and shift amount\\r\\n Cipher = \\\"\\\" # placeholder for the encrypted text\\r\\n\\r\\n for char in Msg:\\r\\n if char.isalpha(): # checks if the charcter is a letter\\r\\n code = ord(char) + Shift # shifts the ordinal position by the requested shift amount\\r\\n if code > ord('z') and char.islower()==True: # if the char is lowercase and shift is beyound the letter z\\r\\n code = ord('a') + ((code-ord('z'))-1) # wrap back around to the beginning but take account that you are starting by 1 shift\\r\\n elif code > ord('Z') and char.isupper() == True: # if the char is upppercase and shift is beyound the letter Z\\r\\n code = ord('A') + ((code -ord('Z'))-1) # wrap back around to the beginning but take account that you are starting by 1 shift\\r\\n Cipher += chr(code) # add the character you land on b/c of the shift to the Cipher text\\r\\n else:\\r\\n Cipher +=char # add the non letter character to the cipher text\\r\\n print(Cipher)\",\n \"def decrypt_vigenere(ciphertext, keyword):\\n list = []\\n index = 0\\n for char in ciphertext:\\n new_char_val = ord(char) - (ord(keyword[index]) - ord('A'))\\n if new_char_val < ord('A'):\\n new_char_val += 26\\n list.append(chr(new_char_val))\\n index += 1\\n index %= len(keyword)\\n return ''.join(list)\",\n \"def __encrypt_content__(self, content):\\n ciphertext_bundle = self.__encrypt_text_aes__(content, self.password)\\n decrypt_form = Template(DECRYPT_FORM_TPL).render({\\n # custom message and template rendering\\n 'summary': self.summary,\\n 'placeholder': self.placeholder,\\n 'password_button': self.password_button,\\n 'password_button_text': self.password_button_text,\\n 'decryption_failure_message': self.decryption_failure_message,\\n 'encryption_info_message': self.encryption_info_message,\\n # this benign decoding is necessary before writing to the template, \\n # otherwise the output string will be wrapped with b''\\n 'ciphertext_bundle': b';'.join(ciphertext_bundle).decode('ascii'),\\n 'js_libraries': JS_LIBRARIES,\\n # enable / disable features\\n 'arithmatex': self.arithmatex,\\n 'hljs': self.hljs,\\n 'remember_password': self.remember_password,\\n 'disable_cookie_protection': self.disable_cookie_protection,\\n 'encrypted_something': self.encrypted_something,\\n })\\n return decrypt_form\",\n \"def caesar_cipher():\\n string = input(\\\"Type a string: \\\")\\n key = int(input(\\\"Type a key: \\\"))\\n print(\\\"Transforming\\\", end = '')\\n result = ''\\n for char in string:\\n if ord(char) > 64 and ord(char) < 91:\\n if (ord(char) + key) > 90:\\n result += chr(ord(char) + key - 26)\\n elif (ord(char) + key) < 65:\\n result += chr(ord(char) + key + 26)\\n else:\\n result += chr(ord(char) + key) \\n elif ord(char) > 96 and ord(char) < 123:\\n if (ord(char) + key) > 122:\\n result += chr(ord(char) + key - 26)\\n elif (ord(char) + key) < 97:\\n result += chr(ord(char) + key + 26)\\n else:\\n result += chr(ord(char) + key)\\n else:\\n result += char\\n print(\\\".\\\", end = '')\\n time.sleep(0.5)\\n print(\\\".\\\", end = '')\\n time.sleep(0.5)\\n print(\\\".\\\")\\n time.sleep(0.5)\\n return result\",\n \"def caesar_cipher_decrept(string, key):\\n try:\\n string_to_return = \\\"\\\"\\n for l in string:\\n if not(l >= 'A'and l <= 'Z' or l >= 'a'and l <= 'z'):\\n string_to_return += l\\n elif ord(l.upper()) - key < ord('A'):\\n string_to_return += chr(ord('Z') - ord('A') + ord(l.upper()))\\n else:\\n string_to_return += chr(ord(l.upper())-key)\\n return string_to_return\\n except (ValueError, IndexError, Exception) as ex:\\n print(EXCEPTION_MESSAGE, ex)\",\n \"def encryptionRotate(text):\\n s = text\\n transformedChar = \\\"\\\"\\n transformedChar = s[-1] + s[:-1]\\n\\n print(\\\"Single Rotation Encrypted text : \\\" )\\n return transformedChar\",\n \"def decrypt_railfence(ciphertext, num_rails):\\n if num_rails == 1:\\n return ciphertext\\n\\n lists = slice_ciphertext(ciphertext, num_rails) # could use queue to simply the implementation once we got to OOP\\n\\n #print(lists)\\n rows_indices = [0] * num_rails \\n\\n decrypted = ''\\n row = -1\\n dir = 1\\n cipher_len = len(ciphertext)\\n for i in range(cipher_len):\\n row += dir\\n decrypted += lists[row][rows_indices[row]]\\n rows_indices[row] += 1\\n if row == 0:\\n dir = 1\\n elif row == num_rails - 1:\\n dir = -1\\n return decrypted\",\n \"def encrypt(message):\\r\\n\\r\\n assert isinstance(message, str), 'Only encrypt strings only!'\\r\\n\\r\\n code = first_code_word()\\r\\n encrypted_message = ''\\r\\n message = message.lower()\\r\\n final_dot = False\\r\\n final_question = False\\r\\n final_comma = False\\r\\n final_dot_counter = 0\\r\\n final_question_counter = 0\\r\\n final_comma_counter = 0\\r\\n\\r\\n splitted_code = list(code)\\r\\n\\r\\n\\r\\n message = code + \\\" \\\" + message\\r\\n\\r\\n while message[-1] == '.':\\r\\n message = message[:-1]\\r\\n final_dot = True\\r\\n final_dot_counter += 1\\r\\n\\r\\n while message[-1] == '?':\\r\\n message = message[:-1]\\r\\n final_question = True\\r\\n final_question_counter += 1\\r\\n\\r\\n\\r\\n while message[-1] == ',':\\r\\n message = message[:-1]\\r\\n final_comma = True\\r\\n final_comma_counter += 1\\r\\n\\r\\n counter = 0\\r\\n for message_word in message.split():\\r\\n counter += 1\\r\\n checked = False\\r\\n middle_dot = False\\r\\n middle_question = False\\r\\n middle_comma = False\\r\\n middle_dot_counter = 0\\r\\n middle_question_counter = 0\\r\\n middle_comma_counter = 0\\r\\n\\r\\n if counter == 2:\\r\\n for i in range(0, int(splitted_code[0])):\\r\\n word = create_encryptors()\\r\\n encrypted_message += str(word) + \\\" \\\"\\r\\n\\r\\n while message_word[-1] == '.':\\r\\n message_word = message_word[:-1]\\r\\n middle_dot = True\\r\\n middle_dot_counter += 1\\r\\n\\r\\n while message_word[-1] == '?':\\r\\n message_word = message_word[:-1]\\r\\n middle_question = True\\r\\n middle_question_counter += 1\\r\\n\\r\\n\\r\\n while message_word[-1] == ',':\\r\\n message_word = message_word[:-1]\\r\\n middle_comma = True\\r\\n middle_comma_counter += 1\\r\\n\\r\\n text = open(\\\"words.txt\\\", \\\"r\\\")\\r\\n for line in text:\\r\\n text_word = line.split()[0]\\r\\n if message_word[0] == text_word[0]:\\r\\n if message_word == text_word:\\r\\n encrypted_message += line.split()[2] + \\\" \\\"\\r\\n if middle_dot == True: encrypted_message = encrypted_message[:-1] + \\\".\\\"*middle_dot_counter + \\\" \\\"\\r\\n if middle_question == True: encrypted_message = encrypted_message[:-1] + \\\"?\\\"*middle_question_counter + \\\" \\\"\\r\\n if middle_comma == True: encrypted_message = encrypted_message[:-1] + \\\",\\\"*middle_comma_counter + \\\" \\\"\\r\\n checked = True\\r\\n break\\r\\n\\r\\n if checked != True:\\r\\n encrypted_message += message_word + \\\" \\\"\\r\\n if middle_dot == True: encrypted_message = encrypted_message[:-1] + \\\".\\\" * middle_dot_counter + \\\" \\\"\\r\\n if middle_question == True: encrypted_message = encrypted_message[:-1] + \\\"?\\\" * middle_question_counter + \\\" \\\"\\r\\n if middle_comma == True: encrypted_message = encrypted_message[:-1] + \\\",\\\" * middle_comma_counter + \\\" \\\"\\r\\n\\r\\n text.close()\\r\\n\\r\\n encrypted_message = encrypted_message[:-1]\\r\\n if final_dot == True:\\r\\n encrypted_message += '.'*final_dot_counter + ' '\\r\\n elif final_question == True:\\r\\n encrypted_message += '?'*final_question_counter + ' '\\r\\n elif final_comma == True:\\r\\n encrypted_message += ','*final_comma_counter + ' '\\r\\n else: encrypted_message += ' '\\r\\n\\r\\n for i in range(0, int(splitted_code[2])):\\r\\n word = create_encryptors()\\r\\n encrypted_message += word + \\\" \\\"\\r\\n encrypted_message = encrypted_message[:-1]\\r\\n return encrypted_message\",\n \"def hack_message(self):\\r\\n\\t\\t#Will not let user input useless messages that cannot be hacked.\\r\\n\\t\\twhile True:\\r\\n\\t\\t\\tself.message = input(\\\"Please enter a message you would like to hack. --> \\\")\\r\\n\\t\\t\\tif self.message != \\\"\\\" and len(self.message) > 4:\\r\\n\\t\\t\\t\\tbreak\\t\\t\\t\\r\\n\\t\\tmax_key = len(self.message)\\r\\n\\t\\tself.i = 1\\r\\n\\t\\tpotential_hits = []\\r\\n\\t\\t#Runs through all potential keys. \\r\\n\\t\\tfor self.i in range(1, max_key):\\r\\n\\t\\t\\tprint(f\\\"Trying key #{self.i}\\\")\\t\\t\\t\\r\\n\\t\\t\\tself.my_code = Decryptor(self.message, self.i).transfer_decrypt()\\r\\n\\t\\t\\tself.hack_plausible = False\\r\\n\\t\\t\\tself.verify_hack_key()\\r\\n\\t\\t\\tif self.hack_plausible:\\r\\n\\t\\t\\t\\tpotential_hits.append(f\\\"Key #{self.i} yeilded {self.percent_english}% english words after decryption.\\\\n\\\" + \\\"\\\\t\\\" + self.my_code[:50])\\r\\n\\t\\tprint(\\\"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\\\n\\\")\\r\\n\\t\\tprint(\\\"Hacking results:\\\\n\\\")\\r\\n\\t\\tfor hit in potential_hits:\\r\\n\\t\\t\\tprint(\\\"\\\\t\\\" + hit + \\\"|\\\\n\\\")\",\n \"def encode(keyFile: str, string: str=\\\"\\\", inputFile: str=\\\"\\\", outputFile:str=\\\"\\\") -> str:\\n print(\\\"Encoding message ...\\\")\\n print(\\\"Is public key file ok ?\\\", checkKeyFile(keyFile,\\\"public\\\"))\\n\\n if (checkKeyFile(keyFile,\\\"public\\\")): \\n f = open(keyFile)\\n keyData = extractParamsFromKey(f.readlines()[1]) # read the second line of the file and extract the param\\n if args.verbose : print(\\\"keydata (publ) :\\\", keyData)\\n \\n #open a file if the string is empty\\n if(string == \\\"\\\"):\\n string = str(readFile(inputFile))\\n else:\\n string = string\\n\\n # transform the ascii string into a series of numbers\\n asciiToInt = \\\"\\\"\\n for char in string :\\n asciiToInt += str(ord(char)).zfill(3)\\n if args.verbose : print(\\\"ascii to int\\\", asciiToInt)\\n\\n # calculate the block length\\n blocklen = len(str(keyData[0])) -1\\n if args.verbose : print(\\\"block size is\\\", blocklen)\\n \\n # split the string into blocks\\n # start bu reversing the string so we can start left to right\\n tmp = asciiToInt[::-1]\\n # cut them\\n blocks = wrap(tmp, blocklen)\\n # reverse the lsit of cut\\n blocks.reverse()\\n # inside eecaht cut reserve the characters\\n for i in range(len(blocks)):\\n blocks[i] = blocks[i][::-1]\\n if args.verbose : print(blocks)\\n \\n # make sur that every block is the corect length, overwise add padding\\n for i in range(len(blocks)):\\n blocks[i] = blocks[i].zfill(blocklen)\\n if args.verbose : print(\\\"blocks after padding :\\\", blocks)\\n \\n # crypt everyblock\\n tempCryptString = \\\"\\\"\\n if args.verbose : print(\\\"encrypted blocks:\\\")\\n for i in range(len(blocks)): \\n blockEncrypted = str(calculateCrypt(blocks[i], keyData[1], keyData[0]))\\n if args.verbose : print(blockEncrypted)\\n blockEncrypted = blockEncrypted.zfill(blocklen+1)\\n if args.verbose : print(blockEncrypted)\\n tempCryptString += blockEncrypted\\n if args.verbose : print(\\\"encrypted string :\\\",tempCryptString)\\n \\n # write the contentes to a file\\n hexstr = intToHexToBase64(tempCryptString)\\n if(outputFile == \\\"\\\"):\\n print(\\\"Encrypted :\\\")\\n print(hexstr)\\n else :\\n print(\\\"writing to file\\\", outputFile)\\n writeToFile(outputFile, hexstr)\\n return hexstr\\n else: \\n print(\\\"keyfile is incorrect\\\")\\n return\",\n \"def encryptCaesar(message, key):\\r\\n newMessage = \\\"\\\"\\r\\n for char in message:\\r\\n if char in alphaLower:\\r\\n newLetterPosition = (alphaLower.index(char) + key) % 26\\r\\n newMessage += alphaLower[newLetterPosition]\\r\\n elif char in alphaUpper:\\r\\n newLetterPosition = (alphaUpper.index(char) + key) % 26\\r\\n newMessage += alphaUpper[newLetterPosition]\\r\\n else: newMessage += char\\r\\n return newMessage\",\n \"def __init__(self, key, msg0503):\\n enkey1 = map(ord, AES.new(key).encrypt(msg0503[:16]))\\n self.cipher = AES.new(\\\"\\\".join(\\n map(chr, (enkey1[i] ^ ord(msg0503[i + 16]) for i in range(16)))))\\n self.encrypt_seq = random.randint(0, 0xffff)\",\n \"def encrypt_vigenere(plaintext: str, keyword: str) -> str:\",\n \"def decrypt_vigenere(ciphertext, keyword):\\n plaintext = ''\\n keyword = length_equal(ciphertext, keyword)\\n for i in range(len(ciphertext)):\\n if (ord(ciphertext[i].upper()) - (ord(keyword[i].upper()) - 65)) >= 65:\\n plaintext += chr(ord(ciphertext[i].upper()) - (ord(keyword[i].upper()) - 65))\\n else:\\n plaintext += chr(ord(ciphertext[i].upper()) - ord(keyword[i].upper()) + 91)\\n return plaintext\",\n \"def decrypt_vigenere(ciphertext: str, keyword: str) -> str:\\r\\n while len(ciphertext) > len(keyword):\\r\\n keyword += keyword\\r\\n plaintext = str()\\r\\n if keyword.isupper():\\r\\n for i in range(len(ciphertext)):\\r\\n n = ord(ciphertext[i])\\r\\n x = ord(keyword[i])\\r\\n print(n, x)\\r\\n if n >= x:\\r\\n plaintext += chr(n - (x % 65))\\r\\n else:\\r\\n plaintext += chr(n + 26 - (x % 65))\\r\\n else:\\r\\n for i in range(len(ciphertext)):\\r\\n n = ord(ciphertext[i])\\r\\n x = ord(keyword[i])\\r\\n if n >= x:\\r\\n plaintext += chr(n - (x % 97))\\r\\n else:\\r\\n plaintext += chr(n + 26 - (x % 97))\\r\\n\\r\\n return (plaintext)\",\n \"def encrypt_caesar(plaintext):\\n ciphertext = \\\"\\\"\\n for c in plaintext:\\n if (ord(c) <= 64) or (91 <= ord(c) <= 96) or (123 <= ord(c)):\\n ciphertext += chr(ord(c))\\n elif (97 <= ord(c) + 3 <= 122) or (65 <= ord(c) + 3 <= 90):\\n ciphertext += chr(ord(c) + 3)\\n else:\\n ciphertext += chr(ord(c) + 3 - 26)\\n return ciphertext\",\n \"def decrypt(self, message):\\n # message = message.upper().split()\\n # message = \\\"\\\".join(message)\\n # desalting the message to remove 5 characters blocks\\n padding = input(\\\"Have you used 5 characters blocks? y/n \\\")\\n if padding == \\\"y\\\":\\n message = message.replace(\\\" \\\", \\\"\\\")\\n message = self.desalt_random(message)\\n message = \\\"\\\".join(message)\\n\\n message = message.upper()\\n message_list = []\\n for ch in message:\\n message_list.append(self.main_dict[ch][0])\\n\\n # OTP Encryption / process the message with OTP\\n otp = input(\\\"What is the OTP that was generated for you during \\\"\\n \\\"encryption process?: \\\")\\n otp = otp.upper()\\n random_otp = []\\n for ch in otp:\\n random_otp.append(self.main_dict[ch][0])\\n\\n # If OTP is correct, decrypt the message with mod27\\n if len(message_list) != len(random_otp):\\n print(\\\"You typed a wrong OTP.\\\")\\n return None\\n else:\\n math_list = []\\n for i, item in enumerate(message_list):\\n if message_list[i] >= random_otp[i]:\\n x = message_list[i] - random_otp[i]\\n for key, value in self.main_dict.items():\\n if value[0] == x:\\n math_list.append(key)\\n else:\\n for key, value in self.main_dict.items():\\n if item == value[0]:\\n x = value[1] - random_otp[i]\\n for key, value in self.main_dict.items():\\n if value[0] == x:\\n math_list.append(key)\\n return \\\"\\\".join(math_list)\",\n \"def decryptionRotate(text):\\n s = text;\\n transformedChar = \\\"\\\"\\n transformedChar = s[1:] + s[0]\\n\\n print(\\\"Single Rotation Decrypted text : \\\" )\\n return transformedChar\",\n \"def encrypt_vigenere(plaintext: str, keyword: str) -> str:\\n ciphertext = \\\"\\\"\\n if len(keyword) < len(plaintext):\\n for j in range(len(plaintext) - len(keyword)):\\n keyword += keyword[j]\\n for i in range(len(plaintext)):\\n n = ord(plaintext[i])\\n m = ord(keyword[i])\\n if (n >= ord('A')) and (n <= ord('Z')):\\n if (m - ord('A') + n) <= ord('Z'):\\n ciphertext += chr(m - ord('А') + n)\\n else:\\n ciphertext += chr((m + n) % (1 + ord('Z')))\\n else:\\n if (m - ord('a') + n) <= ord('z'):\\n ciphertext += chr(m - ord('a') + n)\\n else:\\n ciphertext += chr((m + n) % ord('a'))\\n return ciphertext\",\n \"def fernet_encript(key,message):\\n\\tf = Fernet(key)\\n\\treturn f.encrypt(message)\",\n \"def applyCoder(text, coder):\\n ciphertext = str()\\n #for each letter in the text find it, and grab shifted letter\\n for letter in text:\\n ciphertext += coder.get(letter, letter)\\n return ciphertext\",\n \"def encryptionMultipleRotate(text, power):\\n s = text\\n transformedChar = \\\"\\\"\\n transformedChar = s[-power:] + s[:-(power)]\\n\\n print(\\\"Multiple Rotation Encrypted text : \\\" )\\n return transformedChar\",\n \"def test_decryption(d, c):\\n\\n#\\td = int(raw_input(\\\"\\\\nEnter d from public key\\\\n\\\"))\\n#\\tc = int(raw_input(\\\"\\\\nEnter c from public key\\\\n\\\"))\\n\\n x = int(raw_input(\\\"\\\\nEnter number to decrypt\\\\n\\\"))\\n decode(endecrypt(x, d, c))\",\n \"def find_key(plaintext1, plaintext2):\\n\\n # if plain1 XOR cipher1 == plain2 XOR cipher2\\n xor1 = strings_xor(list(plaintext1.encode()), list(CIPHERTEXT1))\\n xor2 = strings_xor(list(plaintext2.encode()), list(CIPHERTEXT2))\\n if xor1 == xor2:\\n return xor1.encode()\\n\\n # if plain1 XOR cipher2 == plain2 XOR cipher1\\n xor1 = strings_xor(list(plaintext1.encode()), list(CIPHERTEXT2))\\n xor2 = strings_xor(list(plaintext2.encode()), list(CIPHERTEXT1))\\n if xor1 == xor2:\\n return xor1.encode()\\n\\n return None\",\n \"def decrypt_caesar(ciphertext: str) -> str:\\n plaintext = ''\\n for ab in ciphertext:\\n if ('a' <= ab <= 'z') or ('A' <= ab <= 'Z'):\\n ans = ord(ab) - 3\\n if (ans < ord('a')) and (ans > ord('Z')) or (ans < ord('A')):\\n ans += 26\\n plaintext += chr(ans)\\n else:\\n plaintext += ab\\n return plaintext\",\n \"def recover_encrypt_pass(self):\\n with open(self.key_path) as input_file:\\n key = input_file.readlines()\\n cipher_suite = Fernet(key[0])\\n bin_passwd = bytes(self.password, 'utf-8')\\n ciphered_text = cipher_suite.encrypt(bin_passwd)\\n return ciphered_text\",\n \"def encrypt(self, sentence):\\r\\n sentence = list(sentence)\\r\\n encrypted_sentence = []\\r\\n x = list(\\\"abcdefghijklmnopqrstuvwxyz\\\")\\r\\n l = len(x)\\r\\n y = random.sample(x, l)\\r\\n z = list(zip(x, y))\\r\\n z.append((\\\" \\\", \\\" \\\"))\\r\\n for i in sentence:\\r\\n if i.lower() not in x:\\r\\n encrypted_sentence.append(i)\\r\\n continue \\r\\n for a in z:\\r\\n if i.lower() == a[0]:\\r\\n if i.isupper():\\r\\n encrypted_sentence.append(a[1].upper())\\r\\n else:\\r\\n encrypted_sentence.append(a[1])\\r\\n encrypted_sentence = \\\"\\\".join(encrypted_sentence)\\r\\n y = \\\"\\\".join(y)\\r\\n cls()\\r\\n print(\\\"Your one time use pad is: {}\\\".format(y))\\r\\n print(\\\"\\\")\\r\\n return encrypted_sentence\",\n \"def main():\\n key, plain = get_key_plain()\\n encode(key, plain)\",\n \"def test_rekey(self):\\n old_key = b'0' * 32\\n new_key = b'1' * 32\\n\\n old_encrypted = encrypt('message', key=old_key)\\n new_encrypted = rekey(old_encrypted, old_key=old_key, new_key=new_key)\\n\\n assert decrypt(new_encrypted, key=new_key) == 'message'\",\n \"def cipherFromExternalContact(self, char):\\n self.incrementRotor() #Incements the rotor as the key is 'pressed' on the enigma machine\\n char = self.cipherToReflector(char) #Ciphers a character in the forward direction to the reflector\\n return char #Returns character\",\n \"def _post_decrypt_checks(self, aad, plaintext, protected_message, request_id):\",\n \"def test(k, p, c):\\n P, S = generate(key_arr(k))\\n crypt = encrypt(p, P, S)\\n plain = decrypt(crypt, P, S)\\n\\n res = True\\n\\n if crypt != c:\\n print('Crypt:', hex(crypt), '!=', hex(c))\\n res = False\\n\\n if plain != p:\\n print('Decrypt:', hex(plain), '!=', hex(p))\\n res = False\\n\\n return res\",\n \"def decryptionMultipleRotate(text, power):\\n s = text;\\n transformedChar = \\\"\\\"\\n transformedChar = s[power:] + s[0:power]\\n\\n print(\\\"Multiple Rotation Decrypted text : \\\" )\\n return transformedChar\",\n \"def encryptionSelfMadeFunction(text,index):\\n s = text\\n transformedChar = \\\"\\\"\\n\\n transformedChar = s[0:index] + s[index+1:] +s[index]\\n\\n print(\\\"Encrypted Transformed text : \\\" )\\n return transformedChar\",\n \"def cipher_feedback_mode_encode(msg, CEK, IV = int(0).to_bytes(8, 'big')):\\n assert(len(CEK) == 32)\\n assert(len(IV) == 8)\\n last_block = IV\\n res = b''\\n for i in range(0, len(msg), 8):\\n gamma = GOST2814789ECB_encode(last_block, CEK)\\n block = msg[i: min(i + 8, len(msg))]\\n encrypted_block = b''\\n for j in range(len(block)):\\n encrypted_block += int(block[j] ^ gamma[j]).to_bytes(1, 'big')\\n res += encrypted_block\\n last_block = encrypted_block\\n return res\",\n \"def FtDecrypt(self,EncryptText):\\n \\n self.EncryptText = EncryptText\\n characters = \\\"abcdefghijklmnopqrstuvwxyz \\\"\\n DecripText = ''\\n\\n #attempt to decrypt the text using the made_key and EncryptText \\n try:\\n for item in self.EncryptText:\\n DecripText += Code_Fouad_Teniou.my_dict[item]\\n\\n return DecripText\\n \\n #Raise KeyError if a different key was used to encrypt the text \\n except KeyError:\\n print \\\"\\\\n<Please use the right code(made_key) to decrypt your text\\\"\",\n \"def encrypt_caesar(plaintext: str) -> str:\\n ciphertext = ''\\n for ab in plaintext:\\n if ('a' <= ab <= 'z') or ('A' <= ab <= 'Z'):\\n ans = ord(ab) + 3\\n if (ans > ord('Z')) and (ans < ord('a')) or (ans > ord('z')):\\n ans -= 26\\n ciphertext += chr(ans)\\n else:\\n ciphertext += ab\\n return ciphertext\",\n \"def encode(plain):\\n # Remove whitespace and punctionation\\n encoded = remove_punctuation(plain.lower())\\n encoded = remove_whitespace(encoded)\\n \\n # Add space after every 5 characters\\n encoded = add_space(encoded, 5)\\n \\n # Use the cipher translation\\n encoded = encoded.translate(cipher)\\n \\n return encoded\",\n \"def vigenere(inputString, mode, key):\\n\\n\\t# Sanitize inputs\\n\\tkey = key.lower()\\n\\tkey.replace(\\\" \\\", \\\"\\\")\\n\\tif mode not in ['e', 'd']:\\n\\t\\traise Exception(\\\"'mode' must be either 'e' for encryption or 'd' for decryption\\\")\\n\\n\\t# Initialize local variables\\n\\tkeystreamCounter = 0\\n\\toutString = \\\"\\\"\\n\\n\\t# Iterate on each character of the input string.\\n\\tfor inputChar in inputString:\\n\\t\\t# If this character is in the alphabet, encode it\\n\\t\\tif inputChar.lower() in alphabet:\\n\\t\\t\\t# Find the number of positions to move and the starting position\\n\\t\\t\\tkeyChar = key[keystreamCounter % len(key)]\\n\\t\\t\\tnumMoves = alphabet.index(keyChar)\\n\\t\\t\\tstartPosition = alphabet.index(inputChar.lower())\\n\\n\\t\\t\\t# Encode or decode the input character by shifting the alphabet\\n\\t\\t\\tif mode == 'e': # encode\\n\\t\\t\\t\\toutputChar = alphabet[(startPosition + numMoves) % len(alphabet)]\\n\\t\\t\\telif mode == 'd': # decode\\n\\t\\t\\t\\toutputChar = alphabet[(startPosition - numMoves) % len(alphabet)]\\n\\n\\t\\t\\t# Preserve capitalization\\n\\t\\t\\tif inputChar.isupper():\\n\\t\\t\\t\\toutputChar = outputChar.upper()\\n\\n\\t\\t\\t# Increment counter on each loop.\\n\\t\\t\\tkeystreamCounter += 1\\n\\n\\t\\t# If character is not in alphabet, preserve punctutation\\n\\t\\telse:\\n\\t\\t\\toutputChar = inputChar\\n\\n\\t\\t# Append to output string\\n\\t\\toutString += outputChar\\n\\n\\t# Write to file\\n\\treturn(outString)\",\n \"def encryption_oracle(unknown_encryptor: Callable[[bytes, bytes], bytes]) -> str:\\n my_bytes = b'This is 16 bytes'*3\\n encrypted_bytes = unknown_encryptor(my_bytes, os.urandom(16))\\n hamming_distance = average_hamming_distance_between_blocks(encrypted_bytes[16:], 16, 2)\\n return 'ECB' if hamming_distance == 0 else 'Not ECB'\",\n \"def decrypt_cesar(new_word, number): #3\\n decrypted_num = []\\n for char in new_word:\\n char = ord(char)\\n decrypted_num.append(char)\\n\\n decrypted_word = []\\n for num in decrypted_num:\\n num = num - number\\n decrypted_word.append(chr(num))\\n\\n return \\\"\\\".join(decrypted_word)\",\n \"def decrypt_fable():\\n ### TODO.\\n fable_string = get_fable_string()\\n print \\\"fable_string = \\\", fable_string\\n fable_best_shifts = find_best_shifts(wordlist, fable_string)\\n print \\\"fable_best_shifts =\\\", fable_best_shifts\\n fable_decoded = apply_shifts(fable_string, fable_best_shifts)\\n return fable_decoded\",\n \"def encrypt_vigenere(plaintext, keyword):\\n list = []\\n index = 0\\n for char in plaintext:\\n new_char_val = ord(char) + ord(keyword[index]) - ord('A')\\n if new_char_val > ord('Z'):\\n new_char_val -= 26\\n list.append(chr(new_char_val))\\n index += 1\\n index %= len(keyword)\\n return ''.join(list)\",\n \"def caesar_cipher_encrept(string, key):\\n try:\\n string_to_return = \\\"\\\"\\n for l in string:\\n if not(l >= 'A'and l <= 'Z' or l >= 'a'and l <= 'z'):\\n string_to_return += l\\n elif key + ord(l.upper()) > ord('Z'):\\n string_to_return += chr(ord('A') + ord('Z') - ord(l.upper()))\\n else:\\n string_to_return += chr(ord(l.upper())+key)\\n return string_to_return\\n except (ValueError, IndexError, Exception) as ex:\\n print(EXCEPTION_MESSAGE, ex)\",\n \"def test_secretbox_enc_dec(self):\\n # Encrypt with sk\\n encrypted_data = nacl.secretbox_encrypt(data=self.unencrypted_data, sk=self.sk)\\n\\n # Decrypt with sk\\n decrypted_data = nacl.secretbox_decrypt(data=encrypted_data, sk=self.sk)\\n\\n self.assertEqual(self.unencrypted_data, decrypted_data)\",\n \"def break_cipher( self, ciphertexts ):\\n\\n\\t\\ttruncated = self.__truncate(ciphertexts)\\n\\t\\tciphertexstring = b''.join( truncated )\\n\\n\\t\\tkeystream_size = len( truncated[0] )\\n\\n\\t\\ttransposed = self.__transpose( ciphertexstring, keystream_size )\\n\\n\\t\\txor_key = b''\\n\\t\\tfor block in transposed:\\n\\t\\t\\thex_block = hexlify( block )\\n\\t\\t\\txor_key += bytes( [ ord( break_xor_single_byte_by_freq(hex_block) ) ] )\\n\\n\\t\\treturn xor_key\",\n \"def decrypt_caesar(ciphertext):\\n plaintext = \\\"\\\"\\n for c in ciphertext:\\n if (ord(c) <= 64) or (91 <= ord(c) <= 96) or (123 <= ord(c)):\\n plaintext += chr(ord(c))\\n elif (97 <= ord(c) - 3 <= 122) or (65 <= ord(c) - 3 <= 90):\\n plaintext += chr(ord(c) - 3)\\n else:\\n plaintext += chr(ord(c) - 3 + 26)\\n return plaintext\",\n \"def decrypt_caesar(ciphertext: str, shift: int = 3) -> str:\\n plaintext = \\\"\\\"\\n low_first = ord(\\\"a\\\")\\n low_last = ord(\\\"z\\\")\\n high_first = ord(\\\"A\\\")\\n high_last = ord(\\\"Z\\\")\\n eng_alp = 26\\n for i in ciphertext:\\n if i.isalpha():\\n if low_first <= ord(i) <= low_last:\\n a = chr((((ord(i) - low_first) - shift) % eng_alp) + low_first)\\n plaintext += a\\n elif high_first <= ord(i) <= high_last:\\n a = chr((((ord(i) - high_first) - shift) % eng_alp) + high_first)\\n plaintext += a\\n else:\\n plaintext += i\\n return plaintext\",\n \"def key():\",\n \"def rail_fence_encrypt(string, key):\\n try:\\n cols_size = len(string)\\n arr_of_words = [[0 for i in range(cols_size)] for j in range(key)]\\n pos = 0\\n direction = 1\\n for j in range(0, cols_size):\\n # direction variable sets the moving direction\\n arr_of_words[pos][j] = string[j]\\n pos += direction\\n if pos == 0 or pos == key-1:\\n direction *= (-1)\\n\\n str_to_return = \\\"\\\" # concat the new string\\n for i in range(key):\\n for j in range(cols_size):\\n if arr_of_words[i][j] != 0:\\n str_to_return += arr_of_words[i][j]\\n return str_to_return\\n except (ValueError, IndexError) as ex:\\n print(EXCEPTION_MESSAGE, ex)\",\n \"def encrypt(text, key):\\r\\n\\trail = [['\\\\n' for i in range(len(text))] for j in range(key)] \\r\\n\\t\\r\\n\\tdir_down = False\\r\\n\\trow, col = 0, 0\\r\\n\\t\\r\\n\\tfor i in range(len(text)): \\r\\n\\t\\tif (row == 0) or (row == key - 1): \\r\\n\\t\\t\\tdir_down = not dir_down \\r\\n\\t\\t\\r\\n\\t\\trail[row][col] = text[i] \\r\\n\\t\\tcol += 1\\r\\n\\t\\t\\r\\n\\t\\tif dir_down: \\r\\n\\t\\t\\trow += 1\\r\\n\\t\\telse: \\r\\n\\t\\t\\trow -= 1\\r\\n\\t \\r\\n\\tresult = [] \\r\\n\\tfor i in range(key): \\r\\n\\t\\tfor j in range(len(text)): \\r\\n\\t\\t\\tif rail[i][j] != '\\\\n': \\r\\n\\t\\t\\t\\tresult.append(rail[i][j]) \\r\\n\\treturn(\\\"\\\" . join(result))\",\n \"def setup_key_decrypt(self):\\r\\n\\t\\tself.max_key = math.floor(len(self.message) / 2)\\r\\n\\t\\twhile True:\\r\\n\\t\\t\\tkey = input(\\\"Please enter the key that was used to encrypt your message.--> \\\")\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tself.key = int(key)\\r\\n\\t\\t\\texcept ValueError:\\r\\n\\t\\t\\t\\tprint(\\\"Key needs to be a number.\\\")\\r\\n\\t\\t\\t\\tcontinue\\r\\n\\t\\t\\tif self.key > self.max_key: \\t\\t\\t\\r\\n\\t\\t\\t\\tprint(f\\\"{key} is too big of a number.\\\")\\r\\n\\t\\t\\telif self.key == 0:\\r\\n\\t\\t\\t\\tprint(\\\"0 cannot be a key.\\\")\\t\\t\\t\\t\\r\\n\\t\\t\\telse:\\t\\t\\t\\r\\n\\t\\t\\t\\tbreak\",\n \"def decipher(self,string): \\n string = self.remove_punctuation(string)\\n ret = ''\\n for (i,c) in enumerate(string):\\n if i<len(self.key): offset = self.a2i(self.key[i])\\n else: offset = self.a2i(ret[i-len(self.key)]) \\n ret += self.i2a(self.a2i(c)-offset)\\n return ret\",\n \"def test_16(self):\\n plain = b';admin=True'\\n cipher = bytearray(encrypt16(plain))\\n cipher[16] = cu.XOR_bytes(b'?', cu.XOR_bytes(bytes([cipher[16]]), b';'))[0]\\n cipher[22] = cu.XOR_bytes(b'?', cu.XOR_bytes(bytes([cipher[22]]), b'='))[0]\\n self.assertTrue(decrypt16(cipher))\",\n \"def decrypt_message(encrypted_message):\",\n \"def test_rekey_defaults(self, settings):\\n old_key = b'0' * 32\\n new_key = b'1' * 32\\n\\n settings.CHITON_ENCRYPTION_KEY = new_key\\n settings.CHITON_PREVIOUS_ENCRYPTION_KEY = old_key\\n\\n encrypted = encrypt('message', key=old_key)\\n rekeyed = rekey(encrypted)\\n\\n assert decrypt(rekeyed) == 'message'\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74144083","0.7249195","0.6612421","0.65690863","0.65647596","0.6559704","0.6540644","0.65369374","0.651486","0.6376439","0.62908095","0.6285373","0.6259204","0.6240055","0.62363446","0.6201683","0.6181846","0.6151049","0.6124505","0.60732675","0.60697204","0.6065968","0.60388803","0.599317","0.59856737","0.5936403","0.5910717","0.58978254","0.5896904","0.5882527","0.587293","0.5855944","0.58119905","0.580981","0.57978225","0.57879466","0.5773561","0.5773127","0.5757466","0.5756697","0.5755636","0.57490796","0.5746585","0.57421535","0.57342285","0.5732275","0.57260025","0.5719978","0.5718053","0.5707275","0.57041025","0.56818193","0.5673045","0.5671318","0.56658715","0.5637086","0.56367064","0.56357276","0.5632801","0.56304365","0.56302","0.5627692","0.56227064","0.5615809","0.56129986","0.561009","0.56099427","0.5605245","0.55826133","0.5581921","0.558","0.5579596","0.5579552","0.55793196","0.5577564","0.5574766","0.55661196","0.55580664","0.55440634","0.55372155","0.55336016","0.5524916","0.552","0.5519211","0.5515136","0.5514551","0.5512507","0.5508761","0.55069095","0.550562","0.54997814","0.54994774","0.54918456","0.5489323","0.5489038","0.5486015","0.54804665","0.5476672","0.5473963","0.5472235"],"string":"[\n \"0.74144083\",\n \"0.7249195\",\n \"0.6612421\",\n \"0.65690863\",\n \"0.65647596\",\n \"0.6559704\",\n \"0.6540644\",\n \"0.65369374\",\n \"0.651486\",\n \"0.6376439\",\n \"0.62908095\",\n \"0.6285373\",\n \"0.6259204\",\n \"0.6240055\",\n \"0.62363446\",\n \"0.6201683\",\n \"0.6181846\",\n \"0.6151049\",\n \"0.6124505\",\n \"0.60732675\",\n \"0.60697204\",\n \"0.6065968\",\n \"0.60388803\",\n \"0.599317\",\n \"0.59856737\",\n \"0.5936403\",\n \"0.5910717\",\n \"0.58978254\",\n \"0.5896904\",\n \"0.5882527\",\n \"0.587293\",\n \"0.5855944\",\n \"0.58119905\",\n \"0.580981\",\n \"0.57978225\",\n \"0.57879466\",\n \"0.5773561\",\n \"0.5773127\",\n \"0.5757466\",\n \"0.5756697\",\n \"0.5755636\",\n \"0.57490796\",\n \"0.5746585\",\n \"0.57421535\",\n \"0.57342285\",\n \"0.5732275\",\n \"0.57260025\",\n \"0.5719978\",\n \"0.5718053\",\n \"0.5707275\",\n \"0.57041025\",\n \"0.56818193\",\n \"0.5673045\",\n \"0.5671318\",\n \"0.56658715\",\n \"0.5637086\",\n \"0.56367064\",\n \"0.56357276\",\n \"0.5632801\",\n \"0.56304365\",\n \"0.56302\",\n \"0.5627692\",\n \"0.56227064\",\n \"0.5615809\",\n \"0.56129986\",\n \"0.561009\",\n \"0.56099427\",\n \"0.5605245\",\n \"0.55826133\",\n \"0.5581921\",\n \"0.558\",\n \"0.5579596\",\n \"0.5579552\",\n \"0.55793196\",\n \"0.5577564\",\n \"0.5574766\",\n \"0.55661196\",\n \"0.55580664\",\n \"0.55440634\",\n \"0.55372155\",\n \"0.55336016\",\n \"0.5524916\",\n \"0.552\",\n \"0.5519211\",\n \"0.5515136\",\n \"0.5514551\",\n \"0.5512507\",\n \"0.5508761\",\n \"0.55069095\",\n \"0.550562\",\n \"0.54997814\",\n \"0.54994774\",\n \"0.54918456\",\n \"0.5489323\",\n \"0.5489038\",\n \"0.5486015\",\n \"0.54804665\",\n \"0.5476672\",\n \"0.5473963\",\n \"0.5472235\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5520573"},"document_rank":{"kind":"string","value":"82"}}},{"rowIdx":76684,"cells":{"query":{"kind":"string","value":"multiplying a number by 5 function"},"document":{"kind":"string","value":"def five_mult(x):\r\n return 5 * x"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def multiply_numbers(first_number, second_number):","def multiply(value, multiplier):\n return value*multiplier","def helper(num):\r\n \r\n return lambda x: num * product(x)","def mod_5(x):\r\n return x%5","def multiplier(self) -> global___Expression:","def multiply_by_4(x):\n\treturn int(x) * 4","def multiply(num):\n print(int(num * 4))","def mod_5(x):\n return x % 5","def mod_5(x):\n return x % 5","def mul_by_num(num):\n def h(x):\n return num * x\n\n\n return h","def multiplication_total_of(num_list):","def multiply(t):\n return mul(*t)","def mult(value, arg):\n return int(value)*int(arg)","def multiply(value, arg):\n return value * arg","def make_multiplier(factor):\n return lambda x: factor * x","def sum_mult(number, limit):\n limit = limit - 1\n limit = (limit - (limit%number))/number\n return (number * limit * (limit+1))/2","def _add_five(param):\n return param + 5","def multiply(m, n):\n if n == 1:\n return m\n else:\n return m + multiply(m, n - 1)","def foo_4(x):\n\tresult=1\n\tfor i in range(1, x+1):\n\t\tresult=result * i\n\treturn result","def square_and_multiply(x, exponent, n):\n result = 1\n while exponent > 0:\n if exponent % 2:\n result = (result * x) % n\n x = (x * x) % n\n exponent = exponent // 2\n return result","def foo_5(x): ## a recursive function that calculates the factorial of x\n\tif x == 1:\n\t\treturn 1\n\treturn x * foo_5(x-1)","def multiplication(number1, number2):\n return number1 * number2","def multiplication(number1, number2):\n return number1 * number2","def get_3_5_sum(num):\n nums = range(num)\n mults = [num for num in nums if (num % 3 == 0 or num % 5 == 0)]\n return sum(mults)","def mul_numbers(a: int, b: int) -> int:\n return a * b","def power(x, n):\n value = 1\n for i in range(n):\n value = multiply(value, x)\n return value","def doubler(number):\n return number * 2","def sq(self, x):\n\t\treturn x * x","def multiply(num1, num2):\n return num1 * num2","def multiply(numbers):\n prod = 1\n for i in numbers:\n prod = prod*i\n return prod","def sum_multiples(num):\n pass","def square_number(number: int) -> int:\n return number * number","def multiples_sum_3_5(n):\n\tsum = 0\n\tfor x in range(1,n):\n\t\tif (x%3==0) or (x%5==0):\n\t\t\tsum += x\n\treturn sum","def multiply(num1, num2):\n return num1 * num2","def multiply(num1, num2):\n return num1 * num2","def mul(num1, num2):\n return num1 * num2","def mul(num1, num2):\n return num1 * num2","def mul(num1, num2):\n return num1 * num2","def mult_numbers(numbers):\n product = 1\n for number in numbers:\n product = product * number\n\n return product","def __mul__(self, value):\n out = self.copy()\n out.addMath(Query.Math.Multiply, value)\n return out","def skip_mul(n):\n if n <= 0:\n return 1\n else:\n return n * skip_mul(n - 2)","def square(value):\n return value ** 2","def skip_mul(n):\n if n == 0:\n return 0\n else:\n return n * skip_mul(n - 2)","def square(num):\n return num * num","def multiply(number, word):\n return int(number) * word","def calculate_automation(f, t):\n return round(5 * f * t)","def power(x, n):\n power = 1\n for i in range(abs(n)):\n power = multiply(power, x) \n return power","def foo_1(x):\n\treturn x ** .5","def multiply(n1, n2):\n return n1 * n2","def multiply(first, second):\n return first * second","def multiply_nums(n1, n2):\n\n result = n1 * n2\n return result","def multiply(numbers):\n \n result = numbers[0]\n for n in numbers[1:]:\n result = n * result\n return result","def fun4(a,b):\n mul=a*b\n return mul","def mult2(x):\n return(x * 2)","def multiply(self):\n return self._do_calc(self.multiplier)","def multiply(self):\n return self._do_calc(self.multiplier)","def multiply(self):\n return self._do_calc(self.multiplier)","def multiplesof3and5(to):\n sum = 0\n for i in range(1, to):\n if (i % 3 == 0) or (i % 5 == 0):\n sum += i\n return sum","def multiplesof3and5optimized(to):\n return sum_of_divisible(to - 1, 5) - sum_of_divisible(to - 1, 15) + sum_of_divisible(to - 1, 3)","def double(n):\n return 2 * n","def test_multiply(self):\n self.assertEqual(work_file.multiply(10, 5), 50)\n self.assertEqual(work_file.multiply(-1, 1), -1)\n self.assertEqual(work_file.multiply(-1, -1), 1)","def multiply(numbers):\n counter = 0\n for num in numbers:\n counter *= num\n return counter","def Z(n):\n count5 = 0\n i = 1\n while 1:\n a = pow(5, i)\n if a > n:\n return count5\n else:\n count5 += n/a\n i += 1","def __mul__(self, number):\n if not isinstance(number, (int, float)):\n return NotImplemented\n new_gene = self.copy()\n new_gene.weight *= number\n return new_gene","def res_plus_five(unit: ActiveUnit) -> None:\n unit.mod_res += 5","def sq(x):\n\n return x ** x","def multiplication(a, b):\n pass","def web_mult():\n a = int(request.args.get('a'))\n b = int(request.args.get('b'))\n return str(mult(a,b))","def multiply(*args):\n\n # TODO: Fill sum with the correct value, based on the\n # args provided.\n multiplier = str(args[0] * args[1])\n return multiplier","def mul(a,b):\r\n return a*b","def __mul__(self, n):\n assert isinstance(n, int)\n assert n >= 0\n\n \n if n == 0:\n return Inf(self.curve)\n \n else:\n Q = self\n R = Inf(self.curve)\n \n i = 1\n while i <= n:\n if n & i == i:\n R = R + Q\n \n Q = Q + Q\n \n i = i << 1\n \n return R","def multiply(a, b):\n return a*b","def f5(x):\n return 2* sin(x) + sin(2*x)","def factorial(x):\n value = 1\n for i in range(2, add(x, 1)):\n value = multiply(value, i)\n return value","def multiply_string(message, n):\r\n return message*n","def square(x):\n return x * x","def multiply(a,b):\n return a*b","def multiply(num1, num2):\n product = num1 * num2\n return product","def multiply(num1, num2):\n product = num1 * num2\n return product","def ExpU(x):\n\treturn sum(x * lambdas)","def my_square(x):\n return x ** 2","def square( x ):\n return x * x","def multiply(self, a, b):\n return a * b","def __imul__(self,l):\r\n\t\t\r\n\t\treturn self.multiply(l)","def multiplicacion(x, y):\n return x * y","def multiply(a, b):\n return a * b","def multiply(a, b):\n return a * b","def multiply(a, b):\n return a * b","def multiply(a, b):\n return a * b","def multiply(a, b):\n return a * b","def mul(a, b):\n c = Calculator()\n result = c.mul(a, b)\n click.echo('{} * {} = {}'.format(a, b, result))","def square_nums(number_list):","def square(x):\n return x*x","def TestFunc1(x):\r\n return 12*x[0]*x[0] + 4*x[1]*x[1] - 12*x[0]*x[1] + 2*x[1]","def __mul__(self,l):\r\n\t\t\r\n\t\t# multiply\r\n\t\tm = self.multiply(l)\r\n\t\t\r\n\t\treturn m","def m(w):\n t = 3\n a = w * t\n return a","def test_mul():\n l = [1, 2, 3, 4]\n assert s7.mul(*l) == 1 * 2 * 3 * 4\n assert s7.mul(10, 20) == 200\n assert s7.mul(1.0, 2.0, 100.0) == 200.0","def lessthan_5(num_list):","def square(x):\n\n\treturn x * x","def ramp5n(params, phase, args=dict(n=5, guess=[1., 20, 83, 8.1, -0.1])): #-0.16, 4.4, -0.16, 0.43])):\n # 2013-12-07 14:08 IJMC: Created.\n\n return params[0] * (1. - np.exp(-params[1]*phase + params[2]) - \\\n np.exp(-params[3]*phase + params[4]))"],"string":"[\n \"def multiply_numbers(first_number, second_number):\",\n \"def multiply(value, multiplier):\\n return value*multiplier\",\n \"def helper(num):\\r\\n \\r\\n return lambda x: num * product(x)\",\n \"def mod_5(x):\\r\\n return x%5\",\n \"def multiplier(self) -> global___Expression:\",\n \"def multiply_by_4(x):\\n\\treturn int(x) * 4\",\n \"def multiply(num):\\n print(int(num * 4))\",\n \"def mod_5(x):\\n return x % 5\",\n \"def mod_5(x):\\n return x % 5\",\n \"def mul_by_num(num):\\n def h(x):\\n return num * x\\n\\n\\n return h\",\n \"def multiplication_total_of(num_list):\",\n \"def multiply(t):\\n return mul(*t)\",\n \"def mult(value, arg):\\n return int(value)*int(arg)\",\n \"def multiply(value, arg):\\n return value * arg\",\n \"def make_multiplier(factor):\\n return lambda x: factor * x\",\n \"def sum_mult(number, limit):\\n limit = limit - 1\\n limit = (limit - (limit%number))/number\\n return (number * limit * (limit+1))/2\",\n \"def _add_five(param):\\n return param + 5\",\n \"def multiply(m, n):\\n if n == 1:\\n return m\\n else:\\n return m + multiply(m, n - 1)\",\n \"def foo_4(x):\\n\\tresult=1\\n\\tfor i in range(1, x+1):\\n\\t\\tresult=result * i\\n\\treturn result\",\n \"def square_and_multiply(x, exponent, n):\\n result = 1\\n while exponent > 0:\\n if exponent % 2:\\n result = (result * x) % n\\n x = (x * x) % n\\n exponent = exponent // 2\\n return result\",\n \"def foo_5(x): ## a recursive function that calculates the factorial of x\\n\\tif x == 1:\\n\\t\\treturn 1\\n\\treturn x * foo_5(x-1)\",\n \"def multiplication(number1, number2):\\n return number1 * number2\",\n \"def multiplication(number1, number2):\\n return number1 * number2\",\n \"def get_3_5_sum(num):\\n nums = range(num)\\n mults = [num for num in nums if (num % 3 == 0 or num % 5 == 0)]\\n return sum(mults)\",\n \"def mul_numbers(a: int, b: int) -> int:\\n return a * b\",\n \"def power(x, n):\\n value = 1\\n for i in range(n):\\n value = multiply(value, x)\\n return value\",\n \"def doubler(number):\\n return number * 2\",\n \"def sq(self, x):\\n\\t\\treturn x * x\",\n \"def multiply(num1, num2):\\n return num1 * num2\",\n \"def multiply(numbers):\\n prod = 1\\n for i in numbers:\\n prod = prod*i\\n return prod\",\n \"def sum_multiples(num):\\n pass\",\n \"def square_number(number: int) -> int:\\n return number * number\",\n \"def multiples_sum_3_5(n):\\n\\tsum = 0\\n\\tfor x in range(1,n):\\n\\t\\tif (x%3==0) or (x%5==0):\\n\\t\\t\\tsum += x\\n\\treturn sum\",\n \"def multiply(num1, num2):\\n return num1 * num2\",\n \"def multiply(num1, num2):\\n return num1 * num2\",\n \"def mul(num1, num2):\\n return num1 * num2\",\n \"def mul(num1, num2):\\n return num1 * num2\",\n \"def mul(num1, num2):\\n return num1 * num2\",\n \"def mult_numbers(numbers):\\n product = 1\\n for number in numbers:\\n product = product * number\\n\\n return product\",\n \"def __mul__(self, value):\\n out = self.copy()\\n out.addMath(Query.Math.Multiply, value)\\n return out\",\n \"def skip_mul(n):\\n if n <= 0:\\n return 1\\n else:\\n return n * skip_mul(n - 2)\",\n \"def square(value):\\n return value ** 2\",\n \"def skip_mul(n):\\n if n == 0:\\n return 0\\n else:\\n return n * skip_mul(n - 2)\",\n \"def square(num):\\n return num * num\",\n \"def multiply(number, word):\\n return int(number) * word\",\n \"def calculate_automation(f, t):\\n return round(5 * f * t)\",\n \"def power(x, n):\\n power = 1\\n for i in range(abs(n)):\\n power = multiply(power, x) \\n return power\",\n \"def foo_1(x):\\n\\treturn x ** .5\",\n \"def multiply(n1, n2):\\n return n1 * n2\",\n \"def multiply(first, second):\\n return first * second\",\n \"def multiply_nums(n1, n2):\\n\\n result = n1 * n2\\n return result\",\n \"def multiply(numbers):\\n \\n result = numbers[0]\\n for n in numbers[1:]:\\n result = n * result\\n return result\",\n \"def fun4(a,b):\\n mul=a*b\\n return mul\",\n \"def mult2(x):\\n return(x * 2)\",\n \"def multiply(self):\\n return self._do_calc(self.multiplier)\",\n \"def multiply(self):\\n return self._do_calc(self.multiplier)\",\n \"def multiply(self):\\n return self._do_calc(self.multiplier)\",\n \"def multiplesof3and5(to):\\n sum = 0\\n for i in range(1, to):\\n if (i % 3 == 0) or (i % 5 == 0):\\n sum += i\\n return sum\",\n \"def multiplesof3and5optimized(to):\\n return sum_of_divisible(to - 1, 5) - sum_of_divisible(to - 1, 15) + sum_of_divisible(to - 1, 3)\",\n \"def double(n):\\n return 2 * n\",\n \"def test_multiply(self):\\n self.assertEqual(work_file.multiply(10, 5), 50)\\n self.assertEqual(work_file.multiply(-1, 1), -1)\\n self.assertEqual(work_file.multiply(-1, -1), 1)\",\n \"def multiply(numbers):\\n counter = 0\\n for num in numbers:\\n counter *= num\\n return counter\",\n \"def Z(n):\\n count5 = 0\\n i = 1\\n while 1:\\n a = pow(5, i)\\n if a > n:\\n return count5\\n else:\\n count5 += n/a\\n i += 1\",\n \"def __mul__(self, number):\\n if not isinstance(number, (int, float)):\\n return NotImplemented\\n new_gene = self.copy()\\n new_gene.weight *= number\\n return new_gene\",\n \"def res_plus_five(unit: ActiveUnit) -> None:\\n unit.mod_res += 5\",\n \"def sq(x):\\n\\n return x ** x\",\n \"def multiplication(a, b):\\n pass\",\n \"def web_mult():\\n a = int(request.args.get('a'))\\n b = int(request.args.get('b'))\\n return str(mult(a,b))\",\n \"def multiply(*args):\\n\\n # TODO: Fill sum with the correct value, based on the\\n # args provided.\\n multiplier = str(args[0] * args[1])\\n return multiplier\",\n \"def mul(a,b):\\r\\n return a*b\",\n \"def __mul__(self, n):\\n assert isinstance(n, int)\\n assert n >= 0\\n\\n \\n if n == 0:\\n return Inf(self.curve)\\n \\n else:\\n Q = self\\n R = Inf(self.curve)\\n \\n i = 1\\n while i <= n:\\n if n & i == i:\\n R = R + Q\\n \\n Q = Q + Q\\n \\n i = i << 1\\n \\n return R\",\n \"def multiply(a, b):\\n return a*b\",\n \"def f5(x):\\n return 2* sin(x) + sin(2*x)\",\n \"def factorial(x):\\n value = 1\\n for i in range(2, add(x, 1)):\\n value = multiply(value, i)\\n return value\",\n \"def multiply_string(message, n):\\r\\n return message*n\",\n \"def square(x):\\n return x * x\",\n \"def multiply(a,b):\\n return a*b\",\n \"def multiply(num1, num2):\\n product = num1 * num2\\n return product\",\n \"def multiply(num1, num2):\\n product = num1 * num2\\n return product\",\n \"def ExpU(x):\\n\\treturn sum(x * lambdas)\",\n \"def my_square(x):\\n return x ** 2\",\n \"def square( x ):\\n return x * x\",\n \"def multiply(self, a, b):\\n return a * b\",\n \"def __imul__(self,l):\\r\\n\\t\\t\\r\\n\\t\\treturn self.multiply(l)\",\n \"def multiplicacion(x, y):\\n return x * y\",\n \"def multiply(a, b):\\n return a * b\",\n \"def multiply(a, b):\\n return a * b\",\n \"def multiply(a, b):\\n return a * b\",\n \"def multiply(a, b):\\n return a * b\",\n \"def multiply(a, b):\\n return a * b\",\n \"def mul(a, b):\\n c = Calculator()\\n result = c.mul(a, b)\\n click.echo('{} * {} = {}'.format(a, b, result))\",\n \"def square_nums(number_list):\",\n \"def square(x):\\n return x*x\",\n \"def TestFunc1(x):\\r\\n return 12*x[0]*x[0] + 4*x[1]*x[1] - 12*x[0]*x[1] + 2*x[1]\",\n \"def __mul__(self,l):\\r\\n\\t\\t\\r\\n\\t\\t# multiply\\r\\n\\t\\tm = self.multiply(l)\\r\\n\\t\\t\\r\\n\\t\\treturn m\",\n \"def m(w):\\n t = 3\\n a = w * t\\n return a\",\n \"def test_mul():\\n l = [1, 2, 3, 4]\\n assert s7.mul(*l) == 1 * 2 * 3 * 4\\n assert s7.mul(10, 20) == 200\\n assert s7.mul(1.0, 2.0, 100.0) == 200.0\",\n \"def lessthan_5(num_list):\",\n \"def square(x):\\n\\n\\treturn x * x\",\n \"def ramp5n(params, phase, args=dict(n=5, guess=[1., 20, 83, 8.1, -0.1])): #-0.16, 4.4, -0.16, 0.43])):\\n # 2013-12-07 14:08 IJMC: Created.\\n\\n return params[0] * (1. - np.exp(-params[1]*phase + params[2]) - \\\\\\n np.exp(-params[3]*phase + params[4]))\"\n]","isConversational":false},"negative_scores":{"kind":"list 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tri_recursion(k):\r\n if(k>0):\r\n result = k + tri_recursion(k-1)\r\n # print(result)\r\n else:\r\n result = 0\r\n\r\n return result"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def recur(number):\n if number == 1 or number == 0:\n return number\n else:\n check = recur(max(number - 1, 0))+recur(max(number - 2, 0))\n return check","def recursive(input):\n\n # Base Case: Argument input greater than 0.\n if input <= 0:\n return 0\n else:\n output = recursive(input - 1)\n print(output)","def foo_5(x): ## a recursive function that calculates the factorial of x\n\tif x == 1:\n\t\treturn 1\n\treturn x * foo_5(x-1)","def factorial_recursion(n):\n if n == 1:\n return 1\n\n return factorial_recursion(n - 1) * n","def factorial_recursion(n):\n pass # @todo -fix this","def fn(node, val):\n if not node: return 0\n val = 10*val + node.val\n if not node.left and not node.right: return val \n return fn(node.left, val) + fn(node.right, val)","def lucas_recur(n):\n if n == 0:\n return 2\n elif n == 1:\n return 1\n return lucas_recur(n - 1) + lucas_recur(n - 2)","def fn(node, x):\n if not node: return \n stack.append(node.val)\n if not node.left and not node.right and node.val == x: ans.append(stack.copy())\n fn(node.left, x-node.val) or fn(node.right, x-node.val)\n stack.pop()","def factorial(x):\n if x == 1:\n return x\n else:\n return x * factorial(x - 1)","def factorial(number):\n\n if number == 1:\n return number\n\n return number * factorial(number-1)","def recur(num):\n if num == 1:\n return 1\n # Lookup righthand side of the recurrence using dictionary\n rhs = LHS_DICT[INDEX]\n return rhs(num)","def fn(node):\n if not node: return 0 \n ans = node.val + fn(node.left) + fn(node.right)\n vals.append(ans)\n return ans","def factorial(number):\n if number == 0:\n return 1\n return number * factorial(number - 1)","def factorial(num):\n if num == 1:\n return num\n else:\n return num * factorial(num - 1)","def factorial(num):\n if num == 0:\n return 1\n else:\n return num * factorial(num - 1)","def count_value(tree,val):\r\n if (tree==None):\r\n return 0\r\n elif(value(tree)==val):\r\n return 1+count_value(left(tree), val)+count_value(right(tree), val)\r\n else:\r\n return count_value(left(tree), val)+count_value(right(tree), val)","def factorial(n):\n if n == 1:\n return n\n else:\n return n*factorial(n-1)","def recursive_power(base, power):\n if power == 0:\n return 1\n else:\n return base*recursive_power(base, power-1)","def factorial(num):\n if num==1:\n return 1\n else:\n return(num*factorial(num-1))","def fact(x):\n\n if x == 1:\n return 1\n else:\n return (x * fact(x-1))","def recursive():\n with Local() as tun:\n tun.call(recursive)","def factorial(num):\n if num <= 1:\n return 1\n\n if num > 1:\n return num * factorial(num-1)","def step(tree):\n if type(tree) == list and type(tree[0]) == tuple:#This basically looks for any applications it can do directly. These applications are the ones where the function is already defined through abstraction. That's why it checks whether the first element of the list (for application) is an abstraction\n func = tree[0]#The whole function with parameter and body\n name = func[0][1]#Only the parameter\n func = func[1]#Only the body\n arg = tree[1]\n nfunc = replace(name, arg, func)#The replacement of all occurences of the parameter in the body with the argument\n return nfunc\n elif type(tree) == list:\n return [step(tree[0]), step(tree[1])]#recursive checking, again\n elif type(tree) == tuple:\n return (tree[0], step(tree[1]))\n else:\n return tree","def getrecursionlimit(): # real signature unknown; restored from __doc__\n pass","def recursive(nums, left, right, target):\n return","def factorial (n):\n if n == 1:\n return 1\n \n \n else:\n return n * factorial(n - 1)","def factorial(n):\n\n if n == 1:\n return 1\n else:\n return n * factorial(n-1)","def fn(k):\n if k <= 1: return 1\n return fn(k-1) + fn(k-2)","def fact(n):\n if n < 1:\n return 1\n else:\n return n * fact(n - 1)","def fact(n):\n if n < 1:\n return 1\n else:\n return n * fact(n - 1)","def fact(n):\n if n < 1:\n return 1\n else:\n return n * fact(n - 1)","def fact(n):\n if n < 1:\n return 1\n else:\n return n * fact(n - 1)","def factorial(n):\n if n == 0:\n return 1\n return n * factorial(n - 1)","def factorial(n):\n if n == 0:\n return 1\n return n * factorial(n - 1)","def factorial(n):\n if n == 0:\n return 1\n else:\n return n * factorial(n - 1)","def factorial(n):\n if n == 0:\n return 1\n else:\n return n * factorial(n - 1)","def calculate_factorial(num: int, factorial_value: int):\n if num <= 1:\n return factorial_value\n\n return calculate_factorial(num - 1, num * factorial_value)","def factorial(n):\n if n == 0 or n == 1:\n return n\n return n * factorial(n-1)","def recursive_mult(x, y):\n\t# base case, if x == 0 or y == 0, return 0\n\t# or if a = 1\n\tif x == 0 or y == 0:\n\t\treturn 0\n\telif x == 1:\n\t\treturn y\n\telse:\n\t\treturn recursive_mult(x-1, y) + y","def depth_from_match(function):\n def wrap(start, values):\n #print 'Depth %d | %d %s' %(self._depth, start, values)\n #print self._current_node\n self._depth = start\n self._current_node = function(values)\n #print self._current_node\n return ''\n\n return wrap","def factR(n):\n if n == 1:\n return n\n return n*factR(n-1)","def factorial(n): \n def base(): \n return 1 \n def recursive(): \n return n * factorial(n-1)\n return ____________________________________________________________________","def summationRecursion(lower, upper):\r\n if lower > upper:\r\n return 0\r\n else:\r\n return lower + summationRecursion(lower + 1, upper)","def count_recursively(lst):\n\n if len(lst) == 0: \n return 0\n else: \n return 1 + count_recursively(lst[1:])","def recursion(n, m):\n try:\n return 'Input whole positive numbers!' if n <= 0 or m <= 0 else n**m \\\n if n == 1 else n**m + recursion(n-1, m)\n except TypeError:\n return 'Input whole positive numbers!'","def test_fibonacci_recursive(self):\r\n result = fib.fibonacci_recursive(5)\r\n self.assertEqual(result, 8)","def fibonacci_recur(n):\n if n == 0 or n == 1:\n return n\n return fibonacci_recur(n - 1) + fibonacci_recur(n - 2)","def main(num):\n print(recur(num))","def fact(n):\n if n == 0: return 1\n else: return n*fact(n-1)","def foo(x):\n if x==0:\n return x\n return Apply(foo, x)","def lucas(n):\n\n if n <= 0:\n return None\n # exit statements for recursive function\n elif n == 1:\n return 2\n elif n == 2:\n return 1\n\n return lucas(n-2) + lucas(n-1)","def factorial(n):\n if n == 1:\n return 1\n\n return n * factorial(n - 1)","def depth_first_recursion(graph, current=\"a\"):\n print(current)\n for node in graph.get(current):\n depth_first_recursion(graph, node)","def _parenthesize_recur(self, p, result):\n if self.is_leaf(p):\n result.append(str(p.element())) # leaf value as a string\n else:\n result.append('(') # opening parenthesis\n self._parenthesize_recur(self.left(p), result) # left subtree\n result.append(p.element()) # operator\n self._parenthesize_recur(self.right(p), result) # right subtree\n result.append(')') # closing parenthesis","def _parenthesize_recur(self, p, result):\n if self.is_leaf(p):\n result.append(str(p.element())) # leaf value as a string\n else:\n result.append('(') # opening parenthesis\n self._parenthesize_recur(self.left(p), result) # left subtree\n result.append(p.element()) # operator\n self._parenthesize_recur(self.right(p), result) # right subtree\n result.append(')') # closing parenthesis","def fact(n):\n if n <= 1:\n return 1\n else:\n return n * fact(n-1)","def value(d,o):\n # return memoized value if possible\n if (d,o) in v:\n return v[(d,o)]\n\n thisitem = int(t[d][o])\n # the total of a subtree that starts at the leaf, is just the value of the leaf\n if d == maxdepth:\n val = thisitem\n else:\n val = thisitem + max(value(d+1,o),value(d+1,o+1))\n\n v[(d,o)]=val\n return val","def factorial(x):\n\n if x == 1:\n return 1\n else:\n fact = x * factorial(x-1)\n #print(x, fact, ' is fact')\n return fact","def fib(x):\r\n if x == 0 or x == 1: #two base cases \r\n return 1\r\n else:\r\n return fib(x-1) + fib(x-2) #two recursive cases\r\n #so basically if there are two base cases then I need two recursive cases\r","def fn(node):\n if not node: return #null node\n stack.append(node.val)\n if node.left is node.right: ans.append(\"->\".join(map(str, stack))) #leaf node\n fn(node.left) or fn(node.right)\n stack.pop()","def recurse(n, s):\n print(f\"recurse n -> {n}\")\n print(f\"recurse s -> {s}\")\n if n == 0:\n print(s)\n else:\n recurse(n-1, n+s)","def fn(node, x):\n if not node: return x\n x = fn(node.right, x) # sum of right subtree\n x += node.val \n node.val = x\n return fn(node.left, x)","def fib_recursive(n: int) -> int:\n\n if n < 0:\n raise ValueError\n if n == 0 or n == 1:\n return n\n return fib_recursive(n - 1) + fib_recursive(n - 2)","def fn(val):\n if val < x: return min(2*val-1, 2*(x-val))\n k = int(log(val)//log(x))\n ans = k + fn(val - x**k)\n if x**(k+1) < 2*val: \n ans = min(ans, k + 1 + fn(x**(k+1) - val))\n return ans","def recursive_factorial(num):\n if num == 0:\n answer = 1\n assert answer == math.factorial(num)\n return answer\n else:\n rec_part = recursive_factorial(num - 1)\n answer = num * rec_part\n assert answer == math.factorial(num)\n return answer","def factorial(n):\r\n if n == 1:\r\n return 1\r\n return n * factorial(n - 1)","def fn(node):\n if not node: return 0, 0 # null node \n if node.left is node.right: return node.val, 0 # leaf node \n left, right = fn(node.left), fn(node.right) # post-order traversal \n return node.val+left[1]+right[1], max(left)+max(right)","def fn(n, k):\n if n == 1: return k # base case \n return sum(fn(n-1, kk) for kk in range(1, k+1))","def count_recursively(lst):\n\n if lst == []:\n return 0\n \n return 1 + count_recursively(lst[1:])","def recursive_sum(lst):\n\n if lst == []:\n return 0\n\n else:\n\n return lst[0] + recursive_sum(lst[1:])","def get_factorial(number):\n if number == 1:\n return 1\n else:\n return number * get_factorial(number - 1)","def recursion_step(value_n, r_grid, discount):\n\n n = value_n.shape[0]\n r_len = r_grid.shape[0]\n value_n_minus_1 = np.zeros([n - 1, r_len]) # Value function length reduced by 1\n gittins_n_minus_1 = np.zeros(n - 1) # Value function length reduced by 1\n for k in range(0, n - 1):\n a = k + 1 # a in range [1,n-1]\n b = n - k - 1 # b in range [1,n-1]\n value_n_minus_1[k, :] = np.maximum((r_grid / float(1 - discount)),\n (a / float(n)) * (1 + discount * value_n[k + 1, :]) +\n (b / float(n)) * discount * value_n[k, :]\n )\n try:\n # Find first index where Value = (Value of Safe Arm)\n idx_git = np.argwhere((r_grid / float(1 - discount)) == value_n_minus_1[k, :]).flatten()\n gittins_n_minus_1[k] = 0.5 * (r_grid[idx_git[0]] + r_grid[idx_git[0] - 1]) # Take average\n except:\n print(\"Error in finding Gittins index\")\n\n return gittins_n_minus_1, value_n_minus_1","def a(n):\r\n if n == 0:\r\n return 1\r\n else:\r\n return n*((-1)**n) +a(n-1)","def fn(node):\n ans, stack = [], []\n while stack or node: \n if node: \n stack.append(node)\n node = node.left\n else: \n node = stack.pop()\n ans.append(node.val)\n node = node.right \n return ans","def fn(node):\n lc = rc = 0\n lv = rv = node.val \n if node.left: lc, lv = fn(node.left)\n if node.right: rc, rv = fn(node.right)\n if lv == rv == node.val: return lc + rc + 1, node.val \n return lc + rc, None","def linear_search_recursive(array, value):\n # Base case for empty list\n n = len(array)\n if n == 0:\n return False\n\n # Recursive case\n if compare(array[0], value) == 0:\n return True\n else:\n return linear_search_recursive(array[1:], value)","def soma_recursiva(lista):\n print(lista)\n if lista == []:\n return 0\n else:\n return lista[0] + soma_recursiva(lista[1:])","def g(n):\n \"*** YOUR CODE HERE ***\"\n if n < 4:\n return n\n else:\n return g(n-1) + 2*g(n-2) + 3*g(n-3)","def naive_recursive(k, n):\n # If no floors remaining, no more attempts need to be made.\n if n == 0:\n return 0\n # It will take n attempts to find the correct floor if there is only one egg remaining.\n if k == 1:\n return n\n # Solve the problem recursively.\n return min((max(naive_recursive(k, n - x), naive_recursive(k - 1, x - 1)) + 1 for x in range(1, n + 1)))","def factorial(n: int):\n if n == 0:\n return 1\n else:\n return n * factorial(n - 1)","def get_change_recursive(m):\n if m < 0:\n raise ValueError(\"Invalid negative amount\")\n if m == 0:\n raise ValueError(\"ok 0 moves, but this shouldn't happen either.\")\n\n if m in [1, 3, 4]:\n return 1\n\n alt_paths = [get_change_recursive(m-1)]\n\n if m > 3:\n alt_paths.append(get_change_recursive(m-3))\n if m > 4:\n alt_paths.append(get_change_recursive(m-4))\n\n return min(alt_paths) + 1","def base(x):\r\n return x(0)","def fn(n):\n s, c = value[n], 1\n for nn in tree.get(n, []): \n ss, cc = fn(nn)\n s += ss\n c += cc \n return (s, c) if s != 0 else (0, 0)","def recursion(array, left, right, if_random=True):\n # base case if the sub array contains only 1 element\n if left >= right:\n return\n pos = helper(array, left, right)\n # call left side\n recursion(array, left, pos - 1)\n recursion(array, pos + 1, right)\n return array","def pow(a, n, depth=1):\n if n == 1:\n print(\"depth=\", depth)\n return a\n if n % 2 == 1:\n return pow(a, n-1, depth + 1) * a\n else: # n % 2 = 0\n return pow(a * a, n // 2, depth + 1)","def factorial(n: int):\n return 1 if n == 0 else factorial(n-1)*n","def fib(n):\n if n == 1:\n return 1\n else:\n return n + fib(n-1)","def fib(n):\n if n == 0: # base case\n return 0\n elif n == 1: # base case\n return 1\n else:\n return fib(n - 1) + fib(n - 2) # recursiive call","def question_29(x: int) -> int:\n # Base case below:\n if x == 0:\n return 1\n # Recursive function below:\n else:\n result = 1\n while x > 0:\n for i in range(x):\n result *= question_29(i)\n x -= 1\n return result * 2 * 1","def lis_recursive(array):\n\n #TODO","def fibonacci(number, next_value=1, prev_value=0):\n\n if number == 0:\n return prev_value\n if number == 1:\n return next_value\n\n return fibonacci(number - 1, next_value + prev_value, next_value) # Function is recursive","def expand(value):\n return (2 * value) - 1","def fn(n, k):\n if n == k: return 1\n if k == 0: return 0\n return ((n-1)*fn(n-1, k) + fn(n-1, k-1)) % 1_000_000_007","def fact(x):\n return 1 if x == 0 else x*fact(x-1)","def fact(n: int) -> int:\n if n == 0:\n return 1\n else:\n return n*fact(n-1)","def g(n):\n \"*** YOUR CODE HERE ***\"\n if n <= 3:\n return n\n else:\n return g(n - 1) + 2 * g(n - 2) + 3 * g(n - 3)","def g(n):\n \"*** YOUR CODE HERE ***\"\n if n <= 3:\n return n\n else:\n return g(n - 1) + 2 * g(n - 2) + 3 * g(n - 3)","def cascade(n):\n print(n)\n if n >= 10:\n cascade(n//10)\n print(n)","def question_28(x: int) -> int:\n # Base cases below:\n if x == 0:\n return 0\n if x == 1:\n return 1\n if x == 2:\n return 1\n # Recursive function below:\n else:\n return question_28(x-1) + question_28(x-3)","def fib(n):\n if n <= 0 :\n return None\n # conditions for recursive function to exit.\n elif n == 1:\n return 0\n elif n == 2:\n return 1\n\n return fib(n-2) + fib(n-1)"],"string":"[\n \"def recur(number):\\n if number == 1 or number == 0:\\n return number\\n else:\\n check = recur(max(number - 1, 0))+recur(max(number - 2, 0))\\n return check\",\n \"def recursive(input):\\n\\n # Base Case: Argument input greater than 0.\\n if input <= 0:\\n return 0\\n else:\\n output = recursive(input - 1)\\n print(output)\",\n \"def foo_5(x): ## a recursive function that calculates the factorial of x\\n\\tif x == 1:\\n\\t\\treturn 1\\n\\treturn x * foo_5(x-1)\",\n \"def factorial_recursion(n):\\n if n == 1:\\n return 1\\n\\n return factorial_recursion(n - 1) * n\",\n \"def factorial_recursion(n):\\n pass # @todo -fix this\",\n \"def fn(node, val):\\n if not node: return 0\\n val = 10*val + node.val\\n if not node.left and not node.right: return val \\n return fn(node.left, val) + fn(node.right, val)\",\n \"def lucas_recur(n):\\n if n == 0:\\n return 2\\n elif n == 1:\\n return 1\\n return lucas_recur(n - 1) + lucas_recur(n - 2)\",\n \"def fn(node, x):\\n if not node: return \\n stack.append(node.val)\\n if not node.left and not node.right and node.val == x: ans.append(stack.copy())\\n fn(node.left, x-node.val) or fn(node.right, x-node.val)\\n stack.pop()\",\n \"def factorial(x):\\n if x == 1:\\n return x\\n else:\\n return x * factorial(x - 1)\",\n \"def factorial(number):\\n\\n if number == 1:\\n return number\\n\\n return number * factorial(number-1)\",\n \"def recur(num):\\n if num == 1:\\n return 1\\n # Lookup righthand side of the recurrence using dictionary\\n rhs = LHS_DICT[INDEX]\\n return rhs(num)\",\n \"def fn(node):\\n if not node: return 0 \\n ans = node.val + fn(node.left) + fn(node.right)\\n vals.append(ans)\\n return ans\",\n \"def factorial(number):\\n if number == 0:\\n return 1\\n return number * factorial(number - 1)\",\n \"def factorial(num):\\n if num == 1:\\n return num\\n else:\\n return num * factorial(num - 1)\",\n \"def factorial(num):\\n if num == 0:\\n return 1\\n else:\\n return num * factorial(num - 1)\",\n \"def count_value(tree,val):\\r\\n if (tree==None):\\r\\n return 0\\r\\n elif(value(tree)==val):\\r\\n return 1+count_value(left(tree), val)+count_value(right(tree), val)\\r\\n else:\\r\\n return count_value(left(tree), val)+count_value(right(tree), val)\",\n \"def factorial(n):\\n if n == 1:\\n return n\\n else:\\n return n*factorial(n-1)\",\n \"def recursive_power(base, power):\\n if power == 0:\\n return 1\\n else:\\n return base*recursive_power(base, power-1)\",\n \"def factorial(num):\\n if num==1:\\n return 1\\n else:\\n return(num*factorial(num-1))\",\n \"def fact(x):\\n\\n if x == 1:\\n return 1\\n else:\\n return (x * fact(x-1))\",\n \"def recursive():\\n with Local() as tun:\\n tun.call(recursive)\",\n \"def factorial(num):\\n if num <= 1:\\n return 1\\n\\n if num > 1:\\n return num * factorial(num-1)\",\n \"def step(tree):\\n if type(tree) == list and type(tree[0]) == tuple:#This basically looks for any applications it can do directly. These applications are the ones where the function is already defined through abstraction. That's why it checks whether the first element of the list (for application) is an abstraction\\n func = tree[0]#The whole function with parameter and body\\n name = func[0][1]#Only the parameter\\n func = func[1]#Only the body\\n arg = tree[1]\\n nfunc = replace(name, arg, func)#The replacement of all occurences of the parameter in the body with the argument\\n return nfunc\\n elif type(tree) == list:\\n return [step(tree[0]), step(tree[1])]#recursive checking, again\\n elif type(tree) == tuple:\\n return (tree[0], step(tree[1]))\\n else:\\n return tree\",\n \"def getrecursionlimit(): # real signature unknown; restored from __doc__\\n pass\",\n \"def recursive(nums, left, right, target):\\n return\",\n \"def factorial (n):\\n if n == 1:\\n return 1\\n \\n \\n else:\\n return n * factorial(n - 1)\",\n \"def factorial(n):\\n\\n if n == 1:\\n return 1\\n else:\\n return n * factorial(n-1)\",\n \"def fn(k):\\n if k <= 1: return 1\\n return fn(k-1) + fn(k-2)\",\n \"def fact(n):\\n if n < 1:\\n return 1\\n else:\\n return n * fact(n - 1)\",\n \"def fact(n):\\n if n < 1:\\n return 1\\n else:\\n return n * fact(n - 1)\",\n \"def fact(n):\\n if n < 1:\\n return 1\\n else:\\n return n * fact(n - 1)\",\n \"def fact(n):\\n if n < 1:\\n return 1\\n else:\\n return n * fact(n - 1)\",\n \"def factorial(n):\\n if n == 0:\\n return 1\\n return n * factorial(n - 1)\",\n \"def factorial(n):\\n if n == 0:\\n return 1\\n return n * factorial(n - 1)\",\n \"def factorial(n):\\n if n == 0:\\n return 1\\n else:\\n return n * factorial(n - 1)\",\n \"def factorial(n):\\n if n == 0:\\n return 1\\n else:\\n return n * factorial(n - 1)\",\n \"def calculate_factorial(num: int, factorial_value: int):\\n if num <= 1:\\n return factorial_value\\n\\n return calculate_factorial(num - 1, num * factorial_value)\",\n \"def factorial(n):\\n if n == 0 or n == 1:\\n return n\\n return n * factorial(n-1)\",\n \"def recursive_mult(x, y):\\n\\t# base case, if x == 0 or y == 0, return 0\\n\\t# or if a = 1\\n\\tif x == 0 or y == 0:\\n\\t\\treturn 0\\n\\telif x == 1:\\n\\t\\treturn y\\n\\telse:\\n\\t\\treturn recursive_mult(x-1, y) + y\",\n \"def depth_from_match(function):\\n def wrap(start, values):\\n #print 'Depth %d | %d %s' %(self._depth, start, values)\\n #print self._current_node\\n self._depth = start\\n self._current_node = function(values)\\n #print self._current_node\\n return ''\\n\\n return wrap\",\n \"def factR(n):\\n if n == 1:\\n return n\\n return n*factR(n-1)\",\n \"def factorial(n): \\n def base(): \\n return 1 \\n def recursive(): \\n return n * factorial(n-1)\\n return ____________________________________________________________________\",\n \"def summationRecursion(lower, upper):\\r\\n if lower > upper:\\r\\n return 0\\r\\n else:\\r\\n return lower + summationRecursion(lower + 1, upper)\",\n \"def count_recursively(lst):\\n\\n if len(lst) == 0: \\n return 0\\n else: \\n return 1 + count_recursively(lst[1:])\",\n \"def recursion(n, m):\\n try:\\n return 'Input whole positive numbers!' if n <= 0 or m <= 0 else n**m \\\\\\n if n == 1 else n**m + recursion(n-1, m)\\n except TypeError:\\n return 'Input whole positive numbers!'\",\n \"def test_fibonacci_recursive(self):\\r\\n result = fib.fibonacci_recursive(5)\\r\\n self.assertEqual(result, 8)\",\n \"def fibonacci_recur(n):\\n if n == 0 or n == 1:\\n return n\\n return fibonacci_recur(n - 1) + fibonacci_recur(n - 2)\",\n \"def main(num):\\n print(recur(num))\",\n \"def fact(n):\\n if n == 0: return 1\\n else: return n*fact(n-1)\",\n \"def foo(x):\\n if x==0:\\n return x\\n return Apply(foo, x)\",\n \"def lucas(n):\\n\\n if n <= 0:\\n return None\\n # exit statements for recursive function\\n elif n == 1:\\n return 2\\n elif n == 2:\\n return 1\\n\\n return lucas(n-2) + lucas(n-1)\",\n \"def factorial(n):\\n if n == 1:\\n return 1\\n\\n return n * factorial(n - 1)\",\n \"def depth_first_recursion(graph, current=\\\"a\\\"):\\n print(current)\\n for node in graph.get(current):\\n depth_first_recursion(graph, node)\",\n \"def _parenthesize_recur(self, p, result):\\n if self.is_leaf(p):\\n result.append(str(p.element())) # leaf value as a string\\n else:\\n result.append('(') # opening parenthesis\\n self._parenthesize_recur(self.left(p), result) # left subtree\\n result.append(p.element()) # operator\\n self._parenthesize_recur(self.right(p), result) # right subtree\\n result.append(')') # closing parenthesis\",\n \"def _parenthesize_recur(self, p, result):\\n if self.is_leaf(p):\\n result.append(str(p.element())) # leaf value as a string\\n else:\\n result.append('(') # opening parenthesis\\n self._parenthesize_recur(self.left(p), result) # left subtree\\n result.append(p.element()) # operator\\n self._parenthesize_recur(self.right(p), result) # right subtree\\n result.append(')') # closing parenthesis\",\n \"def fact(n):\\n if n <= 1:\\n return 1\\n else:\\n return n * fact(n-1)\",\n \"def value(d,o):\\n # return memoized value if possible\\n if (d,o) in v:\\n return v[(d,o)]\\n\\n thisitem = int(t[d][o])\\n # the total of a subtree that starts at the leaf, is just the value of the leaf\\n if d == maxdepth:\\n val = thisitem\\n else:\\n val = thisitem + max(value(d+1,o),value(d+1,o+1))\\n\\n v[(d,o)]=val\\n return val\",\n \"def factorial(x):\\n\\n if x == 1:\\n return 1\\n else:\\n fact = x * factorial(x-1)\\n #print(x, fact, ' is fact')\\n return fact\",\n \"def fib(x):\\r\\n if x == 0 or x == 1: #two base cases \\r\\n return 1\\r\\n else:\\r\\n return fib(x-1) + fib(x-2) #two recursive cases\\r\\n #so basically if there are two base cases then I need two recursive cases\\r\",\n \"def fn(node):\\n if not node: return #null node\\n stack.append(node.val)\\n if node.left is node.right: ans.append(\\\"->\\\".join(map(str, stack))) #leaf node\\n fn(node.left) or fn(node.right)\\n stack.pop()\",\n \"def recurse(n, s):\\n print(f\\\"recurse n -> {n}\\\")\\n print(f\\\"recurse s -> {s}\\\")\\n if n == 0:\\n print(s)\\n else:\\n recurse(n-1, n+s)\",\n \"def fn(node, x):\\n if not node: return x\\n x = fn(node.right, x) # sum of right subtree\\n x += node.val \\n node.val = x\\n return fn(node.left, x)\",\n \"def fib_recursive(n: int) -> int:\\n\\n if n < 0:\\n raise ValueError\\n if n == 0 or n == 1:\\n return n\\n return fib_recursive(n - 1) + fib_recursive(n - 2)\",\n \"def fn(val):\\n if val < x: return min(2*val-1, 2*(x-val))\\n k = int(log(val)//log(x))\\n ans = k + fn(val - x**k)\\n if x**(k+1) < 2*val: \\n ans = min(ans, k + 1 + fn(x**(k+1) - val))\\n return ans\",\n \"def recursive_factorial(num):\\n if num == 0:\\n answer = 1\\n assert answer == math.factorial(num)\\n return answer\\n else:\\n rec_part = recursive_factorial(num - 1)\\n answer = num * rec_part\\n assert answer == math.factorial(num)\\n return answer\",\n \"def factorial(n):\\r\\n if n == 1:\\r\\n return 1\\r\\n return n * factorial(n - 1)\",\n \"def fn(node):\\n if not node: return 0, 0 # null node \\n if node.left is node.right: return node.val, 0 # leaf node \\n left, right = fn(node.left), fn(node.right) # post-order traversal \\n return node.val+left[1]+right[1], max(left)+max(right)\",\n \"def fn(n, k):\\n if n == 1: return k # base case \\n return sum(fn(n-1, kk) for kk in range(1, k+1))\",\n \"def count_recursively(lst):\\n\\n if lst == []:\\n return 0\\n \\n return 1 + count_recursively(lst[1:])\",\n \"def recursive_sum(lst):\\n\\n if lst == []:\\n return 0\\n\\n else:\\n\\n return lst[0] + recursive_sum(lst[1:])\",\n \"def get_factorial(number):\\n if number == 1:\\n return 1\\n else:\\n return number * get_factorial(number - 1)\",\n \"def recursion_step(value_n, r_grid, discount):\\n\\n n = value_n.shape[0]\\n r_len = r_grid.shape[0]\\n value_n_minus_1 = np.zeros([n - 1, r_len]) # Value function length reduced by 1\\n gittins_n_minus_1 = np.zeros(n - 1) # Value function length reduced by 1\\n for k in range(0, n - 1):\\n a = k + 1 # a in range [1,n-1]\\n b = n - k - 1 # b in range [1,n-1]\\n value_n_minus_1[k, :] = np.maximum((r_grid / float(1 - discount)),\\n (a / float(n)) * (1 + discount * value_n[k + 1, :]) +\\n (b / float(n)) * discount * value_n[k, :]\\n )\\n try:\\n # Find first index where Value = (Value of Safe Arm)\\n idx_git = np.argwhere((r_grid / float(1 - discount)) == value_n_minus_1[k, :]).flatten()\\n gittins_n_minus_1[k] = 0.5 * (r_grid[idx_git[0]] + r_grid[idx_git[0] - 1]) # Take average\\n except:\\n print(\\\"Error in finding Gittins index\\\")\\n\\n return gittins_n_minus_1, value_n_minus_1\",\n \"def a(n):\\r\\n if n == 0:\\r\\n return 1\\r\\n else:\\r\\n return n*((-1)**n) +a(n-1)\",\n \"def fn(node):\\n ans, stack = [], []\\n while stack or node: \\n if node: \\n stack.append(node)\\n node = node.left\\n else: \\n node = stack.pop()\\n ans.append(node.val)\\n node = node.right \\n return ans\",\n \"def fn(node):\\n lc = rc = 0\\n lv = rv = node.val \\n if node.left: lc, lv = fn(node.left)\\n if node.right: rc, rv = fn(node.right)\\n if lv == rv == node.val: return lc + rc + 1, node.val \\n return lc + rc, None\",\n \"def linear_search_recursive(array, value):\\n # Base case for empty list\\n n = len(array)\\n if n == 0:\\n return False\\n\\n # Recursive case\\n if compare(array[0], value) == 0:\\n return True\\n else:\\n return linear_search_recursive(array[1:], value)\",\n \"def soma_recursiva(lista):\\n print(lista)\\n if lista == []:\\n return 0\\n else:\\n return lista[0] + soma_recursiva(lista[1:])\",\n \"def g(n):\\n \\\"*** YOUR CODE HERE ***\\\"\\n if n < 4:\\n return n\\n else:\\n return g(n-1) + 2*g(n-2) + 3*g(n-3)\",\n \"def naive_recursive(k, n):\\n # If no floors remaining, no more attempts need to be made.\\n if n == 0:\\n return 0\\n # It will take n attempts to find the correct floor if there is only one egg remaining.\\n if k == 1:\\n return n\\n # Solve the problem recursively.\\n return min((max(naive_recursive(k, n - x), naive_recursive(k - 1, x - 1)) + 1 for x in range(1, n + 1)))\",\n \"def factorial(n: int):\\n if n == 0:\\n return 1\\n else:\\n return n * factorial(n - 1)\",\n \"def get_change_recursive(m):\\n if m < 0:\\n raise ValueError(\\\"Invalid negative amount\\\")\\n if m == 0:\\n raise ValueError(\\\"ok 0 moves, but this shouldn't happen either.\\\")\\n\\n if m in [1, 3, 4]:\\n return 1\\n\\n alt_paths = [get_change_recursive(m-1)]\\n\\n if m > 3:\\n alt_paths.append(get_change_recursive(m-3))\\n if m > 4:\\n alt_paths.append(get_change_recursive(m-4))\\n\\n return min(alt_paths) + 1\",\n \"def base(x):\\r\\n return x(0)\",\n \"def fn(n):\\n s, c = value[n], 1\\n for nn in tree.get(n, []): \\n ss, cc = fn(nn)\\n s += ss\\n c += cc \\n return (s, c) if s != 0 else (0, 0)\",\n \"def recursion(array, left, right, if_random=True):\\n # base case if the sub array contains only 1 element\\n if left >= right:\\n return\\n pos = helper(array, left, right)\\n # call left side\\n recursion(array, left, pos - 1)\\n recursion(array, pos + 1, right)\\n return array\",\n \"def pow(a, n, depth=1):\\n if n == 1:\\n print(\\\"depth=\\\", depth)\\n return a\\n if n % 2 == 1:\\n return pow(a, n-1, depth + 1) * a\\n else: # n % 2 = 0\\n return pow(a * a, n // 2, depth + 1)\",\n \"def factorial(n: int):\\n return 1 if n == 0 else factorial(n-1)*n\",\n \"def fib(n):\\n if n == 1:\\n return 1\\n else:\\n return n + fib(n-1)\",\n \"def fib(n):\\n if n == 0: # base case\\n return 0\\n elif n == 1: # base case\\n return 1\\n else:\\n return fib(n - 1) + fib(n - 2) # recursiive call\",\n \"def question_29(x: int) -> int:\\n # Base case below:\\n if x == 0:\\n return 1\\n # Recursive function below:\\n else:\\n result = 1\\n while x > 0:\\n for i in range(x):\\n result *= question_29(i)\\n x -= 1\\n return result * 2 * 1\",\n \"def lis_recursive(array):\\n\\n #TODO\",\n \"def fibonacci(number, next_value=1, prev_value=0):\\n\\n if number == 0:\\n return prev_value\\n if number == 1:\\n return next_value\\n\\n return fibonacci(number - 1, next_value + prev_value, next_value) # Function is recursive\",\n \"def expand(value):\\n return (2 * value) - 1\",\n \"def fn(n, k):\\n if n == k: return 1\\n if k == 0: return 0\\n return ((n-1)*fn(n-1, k) + fn(n-1, k-1)) % 1_000_000_007\",\n \"def fact(x):\\n return 1 if x == 0 else x*fact(x-1)\",\n \"def fact(n: int) -> int:\\n if n == 0:\\n return 1\\n else:\\n return n*fact(n-1)\",\n \"def g(n):\\n \\\"*** YOUR CODE HERE ***\\\"\\n if n <= 3:\\n return n\\n else:\\n return g(n - 1) + 2 * g(n - 2) + 3 * g(n - 3)\",\n \"def g(n):\\n \\\"*** YOUR CODE HERE ***\\\"\\n if n <= 3:\\n return n\\n else:\\n return g(n - 1) + 2 * g(n - 2) + 3 * g(n - 3)\",\n \"def cascade(n):\\n print(n)\\n if n >= 10:\\n cascade(n//10)\\n print(n)\",\n \"def question_28(x: int) -> int:\\n # Base cases below:\\n if x == 0:\\n return 0\\n if x == 1:\\n return 1\\n if x == 2:\\n return 1\\n # Recursive function below:\\n else:\\n return question_28(x-1) + question_28(x-3)\",\n \"def fib(n):\\n if n <= 0 :\\n return None\\n # conditions for recursive function to exit.\\n elif n == 1:\\n return 0\\n elif n == 2:\\n return 1\\n\\n return fib(n-2) + fib(n-1)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6645191","0.6535834","0.6381994","0.63048947","0.6251828","0.6248429","0.61911374","0.6188937","0.6067438","0.60306674","0.5995112","0.59597176","0.594877","0.59247464","0.5921001","0.5912498","0.5909545","0.58961886","0.5894752","0.5893231","0.58901757","0.587433","0.58582085","0.58294684","0.58159834","0.58013654","0.57986844","0.57928807","0.57902724","0.57902724","0.57902724","0.57902724","0.5784519","0.5784519","0.5780283","0.5780283","0.5777712","0.5776419","0.577567","0.5769516","0.57659","0.5755209","0.5754271","0.57481945","0.5742366","0.5738974","0.57342273","0.57240695","0.5719853","0.5716893","0.571658","0.57155436","0.5714433","0.57082087","0.5705084","0.5694588","0.5686831","0.56826776","0.56821406","0.56748515","0.5669319","0.56684124","0.56645185","0.56548506","0.5653266","0.5650977","0.56501377","0.5638669","0.5636111","0.5633168","0.5631955","0.56302124","0.5602711","0.55982876","0.5584137","0.557617","0.5568308","0.556598","0.555274","0.5544986","0.55383533","0.55303705","0.55286545","0.552454","0.5505307","0.55009115","0.54979265","0.5481165","0.54769456","0.54749376","0.5473886","0.545969","0.54592407","0.545818","0.5457675","0.54521817","0.54521817","0.5449407","0.5446118","0.54456687"],"string":"[\n \"0.6645191\",\n \"0.6535834\",\n \"0.6381994\",\n \"0.63048947\",\n \"0.6251828\",\n \"0.6248429\",\n \"0.61911374\",\n \"0.6188937\",\n \"0.6067438\",\n \"0.60306674\",\n \"0.5995112\",\n \"0.59597176\",\n \"0.594877\",\n \"0.59247464\",\n \"0.5921001\",\n \"0.5912498\",\n \"0.5909545\",\n \"0.58961886\",\n \"0.5894752\",\n \"0.5893231\",\n \"0.58901757\",\n \"0.587433\",\n \"0.58582085\",\n \"0.58294684\",\n \"0.58159834\",\n \"0.58013654\",\n \"0.57986844\",\n \"0.57928807\",\n \"0.57902724\",\n \"0.57902724\",\n \"0.57902724\",\n \"0.57902724\",\n \"0.5784519\",\n \"0.5784519\",\n \"0.5780283\",\n \"0.5780283\",\n \"0.5777712\",\n \"0.5776419\",\n \"0.577567\",\n \"0.5769516\",\n \"0.57659\",\n \"0.5755209\",\n \"0.5754271\",\n \"0.57481945\",\n \"0.5742366\",\n \"0.5738974\",\n \"0.57342273\",\n \"0.57240695\",\n \"0.5719853\",\n \"0.5716893\",\n \"0.571658\",\n \"0.57155436\",\n \"0.5714433\",\n \"0.57082087\",\n \"0.5705084\",\n \"0.5694588\",\n \"0.5686831\",\n \"0.56826776\",\n \"0.56821406\",\n \"0.56748515\",\n \"0.5669319\",\n \"0.56684124\",\n \"0.56645185\",\n \"0.56548506\",\n \"0.5653266\",\n \"0.5650977\",\n \"0.56501377\",\n \"0.5638669\",\n \"0.5636111\",\n \"0.5633168\",\n \"0.5631955\",\n \"0.56302124\",\n \"0.5602711\",\n \"0.55982876\",\n \"0.5584137\",\n \"0.557617\",\n \"0.5568308\",\n \"0.556598\",\n \"0.555274\",\n \"0.5544986\",\n \"0.55383533\",\n \"0.55303705\",\n \"0.55286545\",\n \"0.552454\",\n \"0.5505307\",\n \"0.55009115\",\n \"0.54979265\",\n \"0.5481165\",\n \"0.54769456\",\n \"0.54749376\",\n \"0.5473886\",\n \"0.545969\",\n \"0.54592407\",\n \"0.545818\",\n \"0.5457675\",\n \"0.54521817\",\n \"0.54521817\",\n \"0.5449407\",\n \"0.5446118\",\n \"0.54456687\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.686639"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":76686,"cells":{"query":{"kind":"string","value":"returns a list of all lines encountered in start, stop, start, stop form distances are from self.center_of_mass along angle"},"document":{"kind":"string","value":"def find_points(self, angle):\n angle_rads = angle * 1.0 / 360 * 2 * pi\n points = []\n self.pic.setPenColor(255, 0, 0)\n cur_pos_x = self.xcenter\n cur_pos_y = self.ycenter\n in_line = False\n while self.in_bounds(cur_pos_x, cur_pos_y):\n if in_line:\n if not is_dark(self.pic, cur_pos_x, cur_pos_y):\n in_line = False\n points.append(self.distance(cur_pos_x, cur_pos_y))\n # self.pic.drawCircleFill(cur_pos_x, cur_pos_y, 2)\n else:\n if is_dark(self.pic, cur_pos_x, cur_pos_y):\n in_line = True\n points.append(self.distance(cur_pos_x, cur_pos_y))\n # self.pic.drawCircleFill(cur_pos_x, cur_pos_y, 2)\n cur_pos_x += 3 * cos(angle_rads)\n cur_pos_y += 3 * sin(angle_rads)\n return points"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def vary_distance_lines(self, n_lines,\n start_theta=-180.0, end_theta=+180.0):\n delta_theta = end_theta - start_theta\n thetas = [start_theta + ((float(i) / (n_lines - 1)) * delta_theta)\n for i in range(n_lines)]\n thetas = list(map(np.radians, thetas))\n\n lines = []\n for theta in thetas:\n low = self.build_input(0.0, theta)\n high = self.build_input(self.intersection_distance(theta), theta)\n lines.append((low, high))\n return lines","def lines(self):\n return lines(self.startEA, self.endEA)","def marker_lines(self) -> list[Line]:\n upper_point = (\n self.leaf_center_px - self.leaf_width_px / 2 * self._analysis_ratio\n )\n lower_point = (\n self.leaf_center_px + self.leaf_width_px / 2 * self._analysis_ratio\n )\n\n lines = []\n for mlc_position in self.position:\n if self._orientation == Orientation.UP_DOWN:\n line = Line((mlc_position, upper_point), (mlc_position, lower_point))\n else:\n line = Line((upper_point, mlc_position), (lower_point, mlc_position))\n lines.append(line)\n return lines","def angles(self) -> list[npt.NDArray[np.float_]]:\n result = []\n a = cast(Segment, self.edges[-1])\n for b in self.edges:\n b = cast(Segment, b)\n result.append(angle(a.vertices[1], a.vertices[0], b.vertices[1]))\n a = b\n\n return result","def get_lat_offsets(self):\n\n startlat = self.parameters['startlatitude']\n stoplat = self.parameters['stoplatitude']\n\n #Given the start and stops,\n startidx, startvalue = utils.getnearest(self.latitudes, startlat)\n stopidx, stopvalue = utils.getnearest(self.latitudes, stoplat)\n startidx -= 2\n stopidx += 2\n latslice = np.arange(startidx, stopidx + 1)\n if utils.checkmonotonic(latslice):\n latslice = latslice\n else:\n #TODO: Support pole crossing images\n logger.error('Image is pole crossing, not currently supported.')\n '''\n print \"NOT MONOTONIC\"\n #Handle wraps around the poles\n latslice = np.arange(start_idx, stop_idx + 1)\n nlats = self.startlookup.shape[1]\n greatermask = np.where(latslice >= nlats)\n latslice[greatermask] -= nlats\n lessmask = np.where(latslice < 0)\n latslice[lessmask] += self.startlookup.shape[1]\n\n self.latsort = np.argsort(latslice)\n self.latslice = latslice[self.latsort]\n self.latsort = np.argsort(self.latsort)\n '''\n latslice = None\n logger.debug('Start latitude node is {}. Nearest lookup node is {}.'.format(startlat, startidx))\n logger.debug('Stop latitude node is {}. Nearest lookup node is {}.'.format(stoplat, stopidx))\n return latslice","def direction_coordinates(self, gc_lines):\n lins = [(_line[0][mid], _line[0][mid + 1], _line[1][mid], _line[1][mid + 1])\n for _line, mid in zip(gc_lines, [len(_line[0]) // 2 for _line in gc_lines])\n if len(_line[0]) > 2]\n lens = [np.hypot(_line[0][0] - _line[0][-1], _line[0][0] - _line[0][-1]) * 110.\n for _line in gc_lines\n if len(_line[0]) > 2]\n lins = [(x0 * np.cos(np.deg2rad(np.mean([y0, y1]))), x1 * np.cos(np.deg2rad(np.mean([y0, y1]))), y0, y1)\n for x0, x1, y0, y1 in lins]\n lins = [_x for _x, _l in zip(lins, lens) if _l > 10]\n\n direction = [(0.5 * (x0 + x1), 0.5 * (y0 + y1), x1 - x0, y1 - y0) for x0, x1, y0, y1 in lins]\n direction = [(_u, _v, _x / np.hypot(_x, _y), _y / np.hypot(_x, _y))\n for _u, _v, _x, _y in direction]\n los = [rotate_point(point[2:], -self.dsbObsAngleAzimuth.value()) for point in direction]\n\n dist = 1.\n tp_dir = (np.array(los).T * dist).T\n\n tps = [(x0, y0, x0 + tp_x, y0 + tp_y) for\n ((x0, y0, _, _), (tp_x, tp_y)) in zip(direction, tp_dir)]\n tps = [[(x0 / np.cos(np.deg2rad(y0)), y0), (x1 / np.cos(np.deg2rad(y0)), y1)] for (x0, y0, x1, y1) in tps]\n return tps","def __get_pts_lines(self, lines, arcs):\n # store unique points\n points_dict = {}\n all_lines = []\n for ind, line in enumerate(lines):\n tup = self.__fix_tuple((line.start[0], line.start[1]))\n start = self.__find_make_pt(tup, points_dict)\n tup = self.__fix_tuple((line.end[0], line.end[1]))\n end = self.__find_make_pt(tup, points_dict)\n line = geometry.Line(start, end)\n all_lines.append(line)\n for ind, arc in enumerate(arcs):\n # dxfgrabber arcs are stored ccw when looking at xy plane\n # x horizontal\n # y vertical\n tup = self.__fix_tuple((arc.center[0], arc.center[1]))\n center = self.__find_make_pt(tup, points_dict)\n sign = -1\n if self.__swapxy:\n sign = 1\n startangle = arc.start_angle*sign\n endangle = arc.end_angle*sign\n angle = endangle - startangle\n if arc.end_angle < arc.start_angle:\n angle = angle + 360*sign\n \"\"\"\n print('---------------------------------------')\n print('| ARC')\n print('center: %s' % center)\n print('startangle: %f' % startangle)\n print('endangle: %f' % endangle)\n print('traversed_angle: %f' % angle)\n \"\"\"\n start_vect = geometry.Point(0, arc.radius)\n if self.__swapxy == False:\n start_vect = geometry.Point(arc.radius, 0)\n start_vect.rot_ccw_deg(arc.start_angle*sign)\n end_vect = geometry.Point(0, arc.radius)\n if self.__swapxy == False:\n end_vect = geometry.Point(arc.radius, 0)\n end_vect.rot_ccw_deg(arc.end_angle*sign)\n start = center + start_vect\n start_tup = (start.x, start.y)\n end = center + end_vect\n end_tup = (end.x, end.y)\n start = self.__find_make_pt(start_tup, points_dict)\n end = self.__find_make_pt(end_tup, points_dict)\n rvect = start - center\n if abs(angle) <= 90:\n arc = geometry.Arc(start, end, center)\n all_lines.append(arc)\n print('1 arc made')\n continue\n #print(' %s' % arc)\n pieces = math.ceil(abs(angle)/90)\n print('%i arcs being made' % pieces)\n points = [start, end]\n # 2 pieces need 3 points, we have start + end already --> 1 pt\n inserts = pieces + 1 - 2\n piece_ang = angle/pieces\n #print('piece_ang = %f' % piece_ang)\n while inserts > 0:\n rvect.rot_ccw_deg(piece_ang)\n point = center + rvect\n tup = (point.x, point.y)\n point = self.__find_make_pt(tup, points_dict)\n points.insert(-1, point)\n inserts = inserts - 1\n for ind in range(len(points)-1):\n #print(' %s' % arc)\n arc = geometry.Arc(points[ind], points[ind+1], center)\n all_lines.append(arc)\n for line in all_lines:\n line.save_to_points()\n return [list(points_dict.values()), all_lines]","def _gen_segments(self, markers, distances, threshold):\n coordinates = list(itertools.combinations(range(len(markers)), 2))\n self.logger.debug(\n f\"{self.id}:Distances: {distances} [threshold = {threshold}]\")\n indexes, = np.where(distances < threshold)\n\n segments = []\n for idx in indexes:\n i, j = coordinates[idx]\n segments.append((markers[i], markers[j]))\n\n self.logger.debug(f\"{self.id}:Segments: {segments}\")\n return segments","def _calculate_distances(self):\n all_dists = []\n for ref in range(len(self.atoms)):\n if self.atoms[ref].symbol in self.exclude:\n continue\n indices = list(range(ref+1, len(self.atoms)))\n indices = self._filter_excluded(indices)\n if len(indices) == 0:\n continue\n dists = self.atoms.get_distances(ref, indices, mic=True)\n all_dists += list(dists)\n \n # Normalize by the mean distance\n return np.array(all_dists)/np.mean(all_dists)","def _get_lines(self) -> tuple[VGroup, VGroup]:\n center = self.get_origin()\n ratio_faded_lines = self.faded_line_ratio\n offset = self.azimuth_offset\n\n if ratio_faded_lines == 0: # don't show faded lines\n ratio_faded_lines = 1 # i.e. set ratio to 1\n rstep = (1 / ratio_faded_lines) * self.x_axis.x_range[2]\n astep = (1 / ratio_faded_lines) * (TAU * (1 / self.azimuth_step))\n rlines1 = VGroup()\n rlines2 = VGroup()\n alines1 = VGroup()\n alines2 = VGroup()\n\n rinput = np.arange(0, self.x_axis.x_range[1] + rstep, rstep)\n ainput = np.arange(0, TAU, astep)\n\n unit_vector = self.x_axis.get_unit_vector()[0]\n\n for k, x in enumerate(rinput):\n new_line = Circle(radius=x * unit_vector)\n if k % ratio_faded_lines == 0:\n alines1.add(new_line)\n else:\n alines2.add(new_line)\n\n line = Line(center, self.get_x_axis().get_end())\n\n for k, x in enumerate(ainput):\n new_line = line.copy()\n new_line.rotate(x + offset, about_point=center)\n if k % ratio_faded_lines == 0:\n rlines1.add(new_line)\n else:\n rlines2.add(new_line)\n\n lines1 = VGroup(*rlines1, *alines1)\n lines2 = VGroup(*rlines2, *alines2)\n return lines1, lines2","def segment(self):\n start = self.alignment.matching_function_startpoint(self.idx)\n end = self.alignment.matching_function_endpoint(self.idx)\n return [start, end]","def find_lines(self):\n return []","def points(self):\n angles_positions = [a.position[0] for a in self.angles]\n x, y = 0, 0\n points = [[x, y]]\n for angle, length in zip(angles_positions, self.lengths):\n x += length * math.sin(angle)\n y += -length * math.cos(angle)\n points.append([x, y])\n return points","def _gather_points(self):\n # This is just a stub for now. We should really find the lines only\n # inside the screen range here.\n\n x = self.index.get_data()\n y = self.value.get_data()\n rad= min(self.width/2.0,self.height/2.0)\n sx = x*rad+ self.x + self.width/2.0\n sy = y*rad+ self.y + self.height/2.0\n\n points = transpose(array((sx,sy)))\n self._cached_data_pts = points\n self._cache_valid = True\n return","def find_isolated_endpoints(lines):\n \n isolated_endpoints = []\n count = len(lines)\n print(\"Finding isolated end points 2/3\")\n pb = pbar.ProgressBar(count)\n for i, line in enumerate(lines):\n pb += 1\n other_lines = lines[:i] + lines[i+1:]\n for q in [0,-1]:\n endpoint = Point(line.coords[q])\n if any(endpoint.touches(another_line) \n for another_line in other_lines):\n continue\n else:\n isolated_endpoints.append(endpoint)\n del pb\n return isolated_endpoints","def _get_distances(self):\n for molecule in self.values():\n molecule.get_distances()\n\n # for atom in self.atoms:\n # atom.get_distances()","def generate_lines(self):\n x = self.square[0]\n y = self.square[1]\n lines = [[]]\n \n lines.append( ( (x, y+a) for a in range(1,8) ) )\n lines.append( ( (x+a, y) for a in range(1,8) ) )\n lines.append( ( (x, y-a) for a in range(1,8) ) )\n lines.append( ( (x-a, y) for a in range(1,8) ) )\n \n return lines","def compute_start_end_points(linestrings):\n starts = []\n stops = []\n for ls in linestrings:\n pt = Point(ls.coords[0])\n starts.append(round(CONUS[\"poly\"].exterior.project(pt), 2))\n pt = Point(ls.coords[-1])\n stops.append(round(CONUS[\"poly\"].exterior.project(pt), 2))\n return starts, stops","def get_station_lines(self):\n station_lines = []\n for wrapper in self.soup.find_all(\"div\", {\"class\": \"stop-wrapper\"}):\n lines = wrapper.find(\"h3\").text.split(' ')[-1][1:-1]\n station_lines.append(lines)\n return np.array(station_lines).T","def vary_theta_lines(self, n_lines):\n max_rho = np.sqrt(\n max(self.min_x**2, self.max_x**2) +\n max(self.min_y**2, self.max_y**2))\n\n distances = [(float(i + 1) / n_lines) * max_rho\n for i in range(n_lines)]\n lines = []\n for distance in distances:\n for start, end in self.theta_breaks(distance):\n low = self.build_input(distance, start)\n high = self.build_input(distance, end)\n lines.append((low, high))\n return lines","def getArcsFrom(self):\n return [ arc for arc in sorted(self._arcsFrom) ]","def detect_lines_from_geometry(\n masked_derivative,\n positions,\n normals,\n start_threshold,\n continuation_threshold,\n max_lines,\n angle_weight,\n force_dir,\n):\n\n def to_absolute_threshold(filtered_image, threshold):\n f = -filtered_image[filtered_image < 0]\n mn = np.min(f)\n mx = np.max(f)\n\n return -((mx - mn) * threshold + mn)\n\n thresh = to_absolute_threshold(masked_derivative, start_threshold)\n proceed = (\n thresh\n if not continuation_threshold\n else to_absolute_threshold(masked_derivative, continuation_threshold)\n )\n\n # Generate lookup table which convert normal angle into table of points to be trialed\n candidates = get_candidate_generator()\n\n lines = []\n for flat_idx in np.argsort(masked_derivative.flatten()):\n idx = np.unravel_index(flat_idx, masked_derivative.shape)\n\n if masked_derivative[idx[0], idx[1]] == KymoCode.seen:\n continue\n\n if masked_derivative[idx[0], idx[1]] >= thresh or len(lines) >= max_lines:\n break\n\n # Traverse the line. Note that traverse_line modifies the masked_derivative image by marking some as seen.\n line = traverse_line(\n idx, masked_derivative, positions, normals, proceed, candidates, angle_weight, force_dir\n )\n\n if line:\n lines.append(line)\n\n return lines","def findLines(self, sequence): \n size = self.size\n lines_in_seq = {'Vertical': {}, 'Horizontal': {}, 'D-pos': {}, 'D-neg': {} }\n\n ###############\n # Evaluate and append a line in a particular direction at a particular coordinate \n def tallyLine(direction, coordinate, move):\n tic = 0\n tac = move[1]\n current_line = lines_in_seq[direction].get(coordinate, 'Empty')\n\n if current_line is not None and current_line is 'Empty':\n lines_in_seq[direction][coordinate] = [tac, move[0]]\n\n elif current_line is not None and current_line[tic] == tac:\n lines_in_seq[direction][coordinate].append(move[0]) if move[0] not in lines_in_seq[direction][coordinate] else None\n\n else:\n lines_in_seq[direction][coordinate] = None\n \n ###############\n # Check if each point in the game sequence belongs to a line or not\n for move in sequence:\n x , y = self.getCoordinates(move[0])\n\n # Tally of horizontal and vertical lines\n for direction in ['Horizontal', 'Vertical']:\n coordinate = {'Vertical': x, 'Horizontal': y}[direction]\n tallyLine(direction, coordinate, move)\n \n # Tally of the two possible diagonal lines\n if x == y: \n tallyLine('D-pos', 0, move)\n if x + y == size - 1:\n tallyLine('D-neg', 0, move)\n\n return lines_in_seq","def list_all_lines(self):\n lines = self.list_lines_gen(self.go_forward, start=self.get_oldest_index())\n self.read_pos = self.write_pos\n return lines","def linelist(self):\n line_list = Marker()\n line_list.header = self._header\n line_list.type = Marker.LINE_LIST\n line_list.action = Marker.ADD\n line_list.scale.x = 0.005\n line_list.color = self.YELLOW\n line_list.pose = deepcopy(self.POSE)\n\n line_list.points.extend((self._p1, self._p2))\n line_list.points.extend((self._p2, self._p3))\n line_list.points.extend((self._p3, self._p4))\n line_list.points.extend((self._p4, self._p1))\n line_list.points.extend((self._p5, self._p6))\n line_list.points.extend((self._p6, self._p7))\n line_list.points.extend((self._p7, self._p8))\n line_list.points.extend((self._p8, self._p5))\n line_list.points.extend((self._p1, self._p5))\n line_list.points.extend((self._p2, self._p6))\n line_list.points.extend((self._p3, self._p7))\n line_list.points.extend((self._p4, self._p8))\n\n return line_list","def center(self):\n return [self.position[i]+self.radius for i in range(2)]","def get_lines(self):\n\n\t\t# create a copy of original picture\n\t\tcolor_image = np.copy(self.original)\n\n\t\t# remove colors channel\n\t\tgrey_image = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)\n\n\t\t# apply threshold to distinguish lines better\n\t\tret, thresh = cv2.threshold(grey_image, THRESHOLD, WHITE, BLACK)\n\n\t\t# define edges\n\t\tedges = cv2.Canny(thresh, THRESHOLD_CANNY, WHITE)\n\n\t\t# get lines from all the edges\n\t\tlines = cv2.HoughLines(edges, RHO, THETA, THRESHOLD)\n\n\t\t# lists to store coordinates of edges\n\t\tX, Y = [], []\n\n\t\t# calculate coordinates\n\t\tfor line in lines:\n\t\t\tfor rho,theta in line:\n\t\t\t\ta = np.cos(theta)\n\t\t\t\tb = np.sin(theta)\n\t\t\t\tx0 = a*rho\n\t\t\t\ty0 = b*rho\n\t\t\t\tX.append(int(x0))\n\t\t\t\tY.append(int(y0))\n\n\t\tX = self.reduce_lines(X)\n\t\tY = self.reduce_lines(Y)\n\n\t\treturn X, Y","def _get_spiral_points(self, start_distance, distance_step, num_legs=10):\n start_loc = self.swarmie.get_odom_location().get_pose()\n points = []\n distance = start_distance\n angle = math.pi / 2\n\n prev_point = Point()\n prev_point.x = start_loc.x + distance * math.cos(start_loc.theta)\n prev_point.y = start_loc.y + distance * math.sin(start_loc.theta)\n points.append(prev_point)\n\n # todo: is this big enough, or too big?\n for i in range(1, num_legs):\n if i % 2 == 0:\n distance += distance_step\n\n point = Point()\n point.x = prev_point.x + distance * math.cos(start_loc.theta\n + angle)\n point.y = prev_point.y + distance * math.sin(start_loc.theta\n + angle)\n points.append(point)\n prev_point = point\n angle += math.pi / 2\n\n return points","def get_file_lines(self, filepath, start, stop):\n\n if self._file_lines is None:\n with open(os.path.join(self.repo_path, filepath)) as f:\n self._file_lines = f.readlines()\n\n if stop is None:\n lines = self._file_lines[start - 1:]\n else:\n lines = self._file_lines[start - 1:stop]\n for i, line in enumerate(lines):\n lines[i] = [start + i, True, line.rstrip()]\n while lines and self.is_empty_line(lines[-1][-1]):\n lines.pop()\n\n if not lines:\n return []\n\n stop = lines[0][0]\n start = max(1, stop - self.surround_by)\n prefix_lines = []\n for i, line in enumerate(self._file_lines[start - 1:stop - 1], start=start):\n prefix_lines.append([i, False, line.rstrip()])\n\n start = lines[-1][0] + 1\n stop = start + self.surround_by\n suffix_lines = []\n for i, line in enumerate(self._file_lines[start - 1:stop - 1], start=start):\n suffix_lines.append([i, False, line.rstrip()])\n\n return prefix_lines + lines + suffix_lines","def get_links(self, thetas):\n\n cum_theta = np.cumsum(thetas)\n\n results = np.zeros((self.arm_lengths.shape[0] + 1, 2))\n\n results[1:, 0] = np.cumsum(self.arm_lengths * np.cos(cum_theta))\n results[1:, 1] = np.cumsum(self.arm_lengths * np.sin(cum_theta))\n links = [Link(start, end) for start, end in zip(results[:-1], results[1:])]\n\n return links","def get_internal_angles(self):\n\n angles = []\n\n for elx, elz in zip(self.grid['x'], self.grid['z']):\n el_angles = []\n xy = np.vstack((elx, elz))\n for i in range(0, elx.size):\n i1 = (i - 1) % elx.size\n i2 = (i + 1) % elx.size\n\n a = (xy[:, i] - xy[:, i1])\n b = (xy[:, i2] - xy[:, i])\n # note that nodes are ordered counter-clockwise!\n angle = np.pi - np.arctan2(\n a[0] * b[1] - a[1] * b[0],\n a[0] * b[0] + a[1] * b[1]\n )\n el_angles.append(angle * 180 / np.pi)\n angles.append(el_angles)\n return np.array(angles)","def _getPath(self):\r\n (startRow, startCol) = self.startCell.position\r\n (endRow, endCol) = self.endCell.position\r\n if(startRow - endRow == 0):\r\n for col in range(min(endCol, startCol) + 1,\r\n max(endCol, startCol)):\r\n yield self.game.getCell(startRow, col)\r\n elif(startCol - endCol == 0):\r\n for row in range(min(startRow, endRow) + 1,\r\n max(startRow, endRow)):\r\n yield self.game.getCell(row, startCol)\r\n else:\r\n raise ValueError(\"Move is not straight line\")","def inters_segment(self, s):\r\n x1 = s.start[0] - self.center[0]\r\n y1 = s.start[1] - self.center[1]\r\n x2 = s.end[0] - self.center[0]\r\n y2 = s.end[1] - self.center[1]\r\n dx = x2 - x1\r\n dy = y2 - y1\r\n dr = math.sqrt(dx * dx + dy * dy)\r\n D = x1 * y2 - x2 * y1\r\n dr2 = dr * dr\r\n d = self.radius * self.radius * dr2 - D * D \r\n \r\n if d < 0:\r\n return []\r\n else: \r\n if dy < 0:\r\n sgndy = -1\r\n else:\r\n sgndy = 1 \r\n \r\n Ddy = D * dy\r\n mDdx = -D * dx\r\n sgndydxsqrtd = sgndy * dx * math.sqrt(d)\r\n absdysqrtd = abs(dy) * math.sqrt(d) \r\n \r\n xa = float(Ddy + sgndydxsqrtd) / dr2 + self.center[0]\r\n ya = float(mDdx + absdysqrtd) / dr2 + self.center[1]\r\n \r\n xb = (Ddy - sgndydxsqrtd) / dr2 + self.center[0]\r\n yb = (mDdx - absdysqrtd) / dr2 + self.center[1]\r\n \r\n if (d == 0) or not s.contains_point(xb, yb):\r\n if s.contains_point(xa, ya):\r\n return [(int(xa), int(ya))]\r\n else:\r\n return []\r\n else:\r\n if s.contains_point(xa, ya):\r\n return [(int(xa), int(ya)), (int(xb), int(yb))]\r\n else:\r\n return [(int(xb), int(yb))]","def getArcs(self):\n return self.getArcsFrom()","def getStarts(self) -> List[int]:\n ...","def findPathSegments(self, startTime, startPoint, startSpeed, startUnitVelocity, legalRotDirection):\n pass","def lines(self):\n\t\tlines = []\n\t\tfor t in self.trigrams:\n\t\t\tfor line in t.lines:\n\t\t\t\tlines.append(line)\n\t\treturn lines","def mergeLines(lines):\r\n\r\n result = []\r\n\r\n for i in range(len(lines)):\r\n\r\n line = lines[i]\r\n\r\n rho = line[0][0] # Distance of line\r\n theta = line[0][1] # Angle of line\r\n\r\n if rho == 0 and theta == -100: continue # Line has been previously merged\r\n\r\n for j in range(i + 1, len(lines)):\r\n\r\n line2 = lines[j]\r\n\r\n rho2 = line2[0][0]\r\n theta2 = line2[0][1]\r\n\r\n # Difference in distance is less than 50 pixels\r\n # Difference in angle is less than 10 degrees\r\n if abs(rho2 - rho) < 50 and abs(theta2 - theta) < np.pi * 10 / 180:\r\n lines[j][0][0] = 0\r\n lines[j][0][1] = -100\r\n\r\n result.append(line)\r\n\r\n return result","def build_steps(self) -> List[Coordinate]:\n if self.orientation == Orientation.HORIZONTAL:\n fill = self.start.y\n else:\n fill = self.start.x\n\n if self.end.x < self.start.x:\n x_range = range(self.start.x, self.end.x - 1, -1)\n else:\n x_range = range(self.start.x, self.end.x + 1)\n\n if self.end.y < self.start.y:\n y_range = range(self.start.y, self.end.y - 1, -1)\n else:\n y_range = range(self.start.y, self.end.y + 1)\n\n return list(zip_longest(x_range, y_range, fillvalue=fill))","def trajectory(self):\n traj = np.zeros((2, self.times.size))\n for t, time in enumerate(self.times):\n traj[:, t] = self.center_of_mass(time)\n return traj","def create_lines(self) -> None:\n res = []\n for connection in self.connections:\n start_component = self.components[connection.start_entity]\n end_component = self.components[connection.end_entity]\n start_pin_location = (\n start_component.location\n + start_component.pin_locations[connection.start_pin]\n )\n end_pin_location = (\n end_component.location + end_component.pin_locations[connection.end_pin]\n )\n\n x_midpoint = (start_pin_location.x + end_pin_location.x) / 2\n bend_start = Point(x_midpoint, start_pin_location.y)\n bend_end = Point(x_midpoint, end_pin_location.y)\n bends = [bend_start, bend_end]\n res.append(Line(connection, start_pin_location, *bends, end_pin_location))\n\n self.lines = res","def getSegments(self):\n l = len(self.points)\n return [Segment(self.points[i % l], self.points[(i + 1) % l], \\\n color=self.side_color, width=self.side_width) for i in range(l)]","def center_of_charge(self):\n ret = [0.0, 0.0, 0.0]\n total_c = 0.0\n\n for at in range(self.natom()):\n c = self.charge(at)\n ret = add(ret, scale(self.xyz(at), c))\n total_c += c\n\n ret = scale(ret, 1.0 / total_c)\n return ret","def lines(self):\n for pair in pairs(self.points):\n yield Line(pair, shape=self)","def segments(self):\n return (self._subset((i,i+1)) for i in range(len(self)-1))","def angles(self):\n self._sort_measurements()\n return self._angles","def intersection_distances(self, altitude, start_distance, finish_distance):\n distances = []\n alt_index, alt_ratio = calculate_value_reference(self.distances,\n start_distance)\n start_alt = calculate_value(self.altitudes, alt_index, alt_ratio)\n\n finish_index, finish_ratio = calculate_value_reference(self.distances,\n finish_distance)\n while alt_index < finish_index:\n next_index = alt_index + 1\n next_ratio = 0.0\n next_alt = calculate_value(self.altitudes, next_index, next_ratio)\n\n # Does this section span the given altitude?\n if (next_alt != start_alt) and \\\n (min(start_alt, next_alt) < altitude < max(start_alt, next_alt)):\n delta = altitude - start_alt\n denom = next_alt - start_alt\n ratio = delta / denom\n distance = calculate_value(self.distances, alt_index, ratio)\n distances.append(distance)\n\n alt_index = next_index\n alt_ratio = next_ratio\n start_alt = next_alt\n\n if finish_ratio:\n finish_alt = calculate_value(self.altitudes, finish_index, finish_ratio)\n # Does the last section span the given altitude?\n if (start_alt != finish_alt) and \\\n (min(start_alt, finish_alt) < altitude < max(start_alt, finish_alt)):\n # Calculate the altitude at the end of the section to maximimse accuracy\n next_alt = calculate_value(self.altitudes, finish_index, 1.0)\n delta = altitude - start_alt\n denom = next_alt - start_alt\n ratio = delta / denom\n distance = calculate_value(self.distances, alt_index, ratio)\n distances.append(distance)\n\n return distances","def angles(self, num: int) -> Iterable[float]:\n if num < 2:\n raise ValueError(\"num >= 2\")\n start = self.dxf.start_angle % 360\n stop = self.dxf.end_angle % 360\n if stop <= start:\n stop += 360\n for angle in linspace(start, stop, num=num, endpoint=True):\n yield angle % 360","def cosines(amount, start, stop, truncated, sequence):\n\n for x in range(start, amount):\n y = abs(round(stop * math.cos(x)))\n if truncated and y >= stop:\n sequence.append(stop)\n elif y < start:\n sequence.append(start)\n else:\n sequence.append(y)\n return sequence","def get_distances_list(mid_points):\n n = len(mid_points)\n dist_list = np.zeros((n,n))\n\n for i in range(n):\n for j in range(i+1, n):\n dist_list[i][j] = compute_distance(mid_points[i], mid_points[j])\n \n return dist_list","def get_clock(self):\n return [np.sin(2*np.pi*(self.phase/self.cycle_len + self.theta_FL)/self.cycle_len),\n np.sin(2*np.pi*(self.phase/self.cycle_len + self.theta_FR)/self.cycle_len),\n np.sin(2*np.pi*(self.phase/self.cycle_len + self.theta_RL)/self.cycle_len),\n np.sin(2*np.pi*(self.phase/self.cycle_len + self.theta_RR)/self.cycle_len)\n ]","def scan(self):\n for angle in range(self.MIDPOINT-350, self.MIDPOINT+350, 50):\n self.servo(angle)\n self.scan_data[angle] = self.read_distance()","def get_distances(self, crds):\n self.all_dist = np.zeros((self.natom, self.natom))\n # Loop over upper triangle of atom pairs\n for iat in range(self.natom-1):\n # Get the atom indices\n at_inds = np.arange(len(crds))\n\n # Calc distances between atoms (only upper triangle though)\n at_msk = at_inds > iat\n all_ut_dist = crds[at_msk] - crds[iat]\n all_ut_dist = np.linalg.norm(all_ut_dist, axis=1)\n\n self.all_dist[iat, iat+1:] = all_ut_dist\n\n # Get lower triangle indices\n self.all_dist = self.all_dist + self.all_dist.T","def _getCirclePoints(self, center, from_point, to_angle, interval):\r\n\r\n # radius is needed for calculating the points\r\n radius = math.sqrt( math.pow((center[0] - from_point[0]),2) + math.pow(center[1]-from_point[1],2) )\r\n\r\n # get current angle from from_point. x,y: 1,0 = zero angle, 0,1 = -pi/2, -1,0 = pi, 0,1 as pi/2\r\n if (from_point[0] < center[0] and from_point[1] > center[1]): # angles in lower left quarter\r\n angle = math.pi - math.asin( (-(center[1] - from_point[1]) )/ radius )\r\n elif (from_point[0] < center[0] and from_point[1] <= center[1]): # angles in upper left\r\n angle = -math.pi - math.asin( (-(center[1] - from_point[1]) )/ radius )\r\n else:\r\n angle = math.asin( (-(center[1] - from_point[1]) )/ radius )\r\n\r\n # debug.brf(\"from angle deg: %s to_angle deg: %s\" % (math.degrees(angle), math.degrees(to_angle)))\r\n\r\n interval = math.radians(interval)\r\n\r\n points = [] # starting point\r\n points.append(from_point)\r\n\r\n # flag of direction the 'motion' goes to\r\n clockwise = False\r\n anticlockwise = False\r\n\r\n for i in range(60): # 60 points is the maximum\r\n\r\n if angle < to_angle: # if to_angle is bigger, length to clockwise direction is absolute value from to_angle substracted from current angle modulus the whole circle\r\n len_cw = math.fabs(angle - to_angle) % (2 * math.pi)\r\n len_acw = 2 * math.pi - math.fabs(len_cw)\r\n else: # same as above, but anticlockwise\r\n len_acw = math.fabs(angle - to_angle) % (2 * math.pi)\r\n len_cw = 2 * math.pi - math.fabs(len_acw)\r\n\r\n if len_cw > len_acw:\r\n # turn anticlockwise\r\n anticlockwise = True\r\n if clockwise == True: # if clockwise turns have been done, then no direction changing needed. break point drawing\r\n break\r\n\r\n angle = angle - interval\r\n\r\n else:\r\n # turn clockwise\r\n clockwise = True\r\n if anticlockwise == True: # if anticlockwise turns have been done, then no direction changing needed. break point drawing\r\n break\r\n\r\n angle = angle + interval\r\n\r\n # calculate x/y to current angle\r\n x = int(round(radius*math.cos(angle) + center[0]))\r\n y = int(round(radius*math.sin(angle) + center[1]))\r\n points.append( (x,y) )\r\n\r\n # debug.brf(\"angle %s (%s,%s) remainding: %s (deg: %s)\" % (str(math.degrees(angle)), x, y, str(angle - to_angle), str(math.degrees(angle-to_angle))))\r\n\r\n # append still the last wanted point to get accurate\r\n x = int(round(radius*math.cos(to_angle) + center[0]))\r\n y = int(round(radius*math.sin(to_angle) + center[1]))\r\n points.append( (x,y) )\r\n\r\n # debug.brf(\"points %s\" % points)\r\n return points","def getAngles(self):\n try:\n return self._angleList\n except AttributeError:\n pass\n forceConstant=self._raw_data[\"ANGLE_FORCE_CONSTANT\"]\n angleEquil=self._raw_data[\"ANGLE_EQUIL_VALUE\"]\n anglePointers = self._raw_data[\"ANGLES_INC_HYDROGEN\"] \\\n +self._raw_data[\"ANGLES_WITHOUT_HYDROGEN\"]\n self._angleList=[]\n forceConstConversionFactor = (units.kilocalorie_per_mole/(units.radian*units.radian)).conversion_factor_to(units.kilojoule_per_mole/(units.radian*units.radian))\n for ii in range(0,len(anglePointers),4):\n if int(anglePointers[ii])<0 or \\\n int(anglePointers[ii+1])<0 or \\\n int(anglePointers[ii+2])<0:\n raise Exception(\"Found negative angle atom pointers %s\"\n % ((anglePointers[ii],\n anglePointers[ii+1],\n anglePointers[ii+2]),))\n iType=int(anglePointers[ii+3])-1\n self._angleList.append((int(anglePointers[ii])//3,\n int(anglePointers[ii+1])//3,\n int(anglePointers[ii+2])//3,\n float(forceConstant[iType])*forceConstConversionFactor,\n float(angleEquil[iType])))\n return self._angleList","def find_ports(self, lines):\n ports = []\n left = None\n self.dists = []\n for line in lines:\n if left is None:\n if self.is_left(line):\n left = self.mid(line)\n else:\n if not self.is_left(line):\n ports.append(int(left[0] + self.mid(line)[0]) // 2)\n self.dists.append(left[0] - self.mid(line)[0])\n left = None\n # print(ports)\n return ports","def getSegments(self) -> List[int]:\n ...","def angles(self):\n penult = self._coordinates[-2]\n last = self._coordinates[-1]\n angles = []\n for c in self._coordinates:\n angle = (math.atan2(penult[0]-last[0], penult[1]-last[1]) -\n math.atan2(c[0]-last[0], c[1]-last[1]))\n angles.append(angle)\n penult, last = last, c\n return sorted(angles)","def distances(self):","def get_arms(self) -> (list, ob.TurnstileAxis):\n directions = [(0, 1), (1, 0), (0, -1), (-1, 0),\n (1, 1), (-1, 1), (1, -1), (-1, -1)]\n for dire in directions:\n x_neighbour = self.target.x_obj + dire[0]\n y_neighbour = self.target.y_obj + dire[1]\n neighbour = self.grid.obj_list[x_neighbour, y_neighbour]\n if isinstance(neighbour, ob.TurnstileAxis):\n axis = neighbour\n arms = []\n for dire in directions[:4]:\n x_potential_arm = axis.x_obj + dire[0]\n y_potential_arm = axis.y_obj + dire[1]\n potential_arm = self.grid.obj_list[\n x_potential_arm, y_potential_arm]\n if isinstance(potential_arm, ob.TurnstileBloc):\n arms.append(potential_arm)\n return arms, axis","def arc_tangents(amount, start, stop, truncated, sequence):\n ratio = (start + stop) / 2\n for x in range(start, amount):\n y = abs(round(ratio * math.atan(x)))\n if truncated and y >= stop:\n sequence.append(stop)\n elif y < start:\n sequence.append(start)\n else:\n sequence.append(y)\n return sequence","def __around_long_row(self):\n list_of_cord = []\n for i in range(-self.rad, self.rad + 1):\n for j in range(-self.rad, self.rad + 1):\n if (i == 0 and j == 0) or (i != 0 and j == 1):\n continue\n list_of_cord.append((i, j))\n return list_of_cord","def get(self, *args):\n return _libsbml.ListOfLineSegments_get(self, *args)","def iter_atoms(self):\n if self.segment is None:\n return iter(list())\n return self.segment.iter_atoms()","def iter_atoms(self):\n if self.segment is None:\n return iter(list())\n return self.segment.iter_atoms()","def segments(self):\n L = len(self.vertices)\n return itertools.chain((self._subset((i,i+1)) for i in range(len(self)-1)),\n (self._subset((L-1,0)),))","def getArcsTo(self):\n return [ arc for arc in sorted(self._arcsTo) ]","def getArcs(self):\n return [ arc for arc in sorted(self._arcs) ]","def get_lines( self ):\n return self._line_info","def get_lines( self ):\n return self._line_info","def angles(self):\n return self._angles","def lons(self):\n lons = []\n quads = self.getQuadrilaterals()\n groups = self._getGroupIndex()\n u_groups = np.unique(groups)\n ng = len(u_groups)\n for i in range(ng):\n q_ind = np.where(groups == u_groups[i])[0]\n nq = len(q_ind)\n top_lons = []\n bot_lons = []\n for j in range(nq):\n if j == 0:\n top0 = [quads[q_ind[j]][0].longitude]\n bot0 = [quads[q_ind[j]][3].longitude]\n top_lons = top_lons + top0\n bot_lons = bot_lons + bot0\n top_lons = top_lons + [quads[q_ind[j]][1].longitude]\n bot_lons = bot_lons + [quads[q_ind[j]][2].longitude]\n lons = lons + top_lons + bot_lons[::-1] + top0 + [np.nan]\n return np.array(lons)","def get_points_for_thick_line(start_x: float, start_y: float,\r\n end_x: float, end_y: float,\r\n line_width: float):\r\n vector_x = start_x - end_x\r\n vector_y = start_y - end_y\r\n perpendicular_x = vector_y\r\n perpendicular_y = -vector_x\r\n length = math.sqrt(vector_x * vector_x + vector_y * vector_y)\r\n if length == 0:\r\n normal_x = 1.0\r\n normal_y = 1.0\r\n else:\r\n normal_x = perpendicular_x / length\r\n normal_y = perpendicular_y / length\r\n r1_x = start_x + normal_x * line_width / 2\r\n r1_y = start_y + normal_y * line_width / 2\r\n r2_x = start_x - normal_x * line_width / 2\r\n r2_y = start_y - normal_y * line_width / 2\r\n r3_x = end_x + normal_x * line_width / 2\r\n r3_y = end_y + normal_y * line_width / 2\r\n r4_x = end_x - normal_x * line_width / 2\r\n r4_y = end_y - normal_y * line_width / 2\r\n points = (r1_x, r1_y), (r2_x, r2_y), (r4_x, r4_y), (r3_x, r3_y)\r\n return points","def get_coordinates(self):\r\n coordinates_list = []\r\n for i in range(self.__length):\r\n if self.__orientation == Direction.VERTICAL:\r\n temp = (self.__location[0] + i, self.__location[1])\r\n if self.__orientation == Direction.HORIZONTAL:\r\n temp = (self.__location[0], self.__location[1] + i)\r\n coordinates_list.append(temp)\r\n return coordinates_list","def get_arcs(self):\n arcs = []\n for arcs_list in self._inc.values():\n record = arcs_list.get_first_record()\n while record is not None:\n arc = record.element\n arcs.append(arc)\n record = record._next\n return arcs","def ActiveHlt2Lines(self) :\n return []","def __around_short_row(self):\n list_of_cord = []\n for i in range(-self.rad, self.rad + 1):\n for j in range(-self.rad, self.rad + 1):\n if (i == 0 and j == 0) or (i != 0 and j == -1):\n continue\n list_of_cord.append((i, j))\n return list_of_cord","def process_coordinates(void: 'ChargeSystem', steps: int) -> list:\n result = [[] for _ in void]\n\n for _ in range(steps):\n for ind, charge in enumerate(void):\n result[ind].append(list(charge.position))\n void.update()\n\n return result","def forward_kinematic_lines(self, q ):\n \n lines_pts = [] # list of array (n_pts x 3) for each lines\n \n ###########################\n # Your graphical code here\n ###########################\n \n # simple place holder\n for q_i in q:\n pts = np.zeros(( 1 , 3 )) # array of 1 pts for the line\n pts[0,0] = q_i # x cord of point 0 = q\n lines_pts.append( pts ) # list of all arrays of pts\n \n return lines_pts","def getStartSpots(self):\n spots = []\n if self.index == 0:\n startRow = 1\n endRow = 4\n if self.index == 1:\n startRow = 6\n endRow = 9\n for row in range(startRow, endRow):\n for col in range(1,9):\n spots += [(col, row)]\n return spots","def calculate_center(self):\n return [(self.startX + self.endX) / 2., (self.startY + self.endY) / 2.]","def get_landmarks(self):\n lm_x = [x for x, y in self.landmarks]\n lm_y = [y for x, y in self.landmarks]\n return [lm_x, lm_y]","def get_all_arcs(self):\n return [ (i, j) for i in self.constraints for j in self.constraints[i] ]","def line(center, length, rotation=0):\n unit = np.array([math.cos(rotation * 2 * PI / 360),\n math.sin(rotation * 2 * PI / 360)])\n end = center + unit * length / 2\n start = center - unit * length / 2\n return [\"PA{},{};\".format(*[int(coord) for coord in start]),\n \"PD{},{};\".format(*[int(coord) for coord in end]),\n \"PU;\"]","def iter_all_atoms(self):\n if self.segment is None:\n return iter(list())\n return self.segment.iter_all_atoms()","def iter_all_atoms(self):\n if self.segment is None:\n return iter(list())\n return self.segment.iter_all_atoms()","def get_distances(self):\n N = len(self.cells) # Number of cells\n distances = np.zeros([N, N]) # distances between cells\n positions = self.position_matrix() # positions of cells \n \n # get distances between cells (exploit symmetry between upper and lower triangular form)\n for i, position in enumerate(positions[:-1, :]): # Iterate matrix except the last one\n directions = positions[i+1:, :] - position # direction from i to j > i\n distances[i, i+1:] = np.linalg.norm(directions, axis=1) # length of directions\n \n return distances + distances.T # Add lower triangle of matrix to upper ","def get_line(start, end): \n # Setup initial conditions\n x1, y1 = start\n x2, y2 = end\n dx = x2 - x1\n dy = y2 - y1\n \n # Determine how steep the line is\n is_steep = abs(dy) > abs(dx)\n \n # Rotate line\n if is_steep:\n x1, y1 = y1, x1\n x2, y2 = y2, x2\n \n # Swap start and end points if necessary and store swap state\n swapped = False\n if x1 > x2:\n x1, x2 = x2, x1\n y1, y2 = y2, y1\n swapped = True\n \n # Recalculate differentials\n dx = x2 - x1\n dy = y2 - y1\n \n # Calculate error\n error = int(dx / 2.0)\n ystep = 1 if y1 < y2 else -1\n \n # Iterate over bounding box generating points between start and end\n y = y1\n points = []\n for x in range(x1, x2 + 1):\n coord = np.array((y, x)) if is_steep else np.array((x, y))\n points.append(coord)\n error -= abs(dy)\n if error < 0:\n y += ystep\n error += dx\n \n # Reverse the list if the coordinates were swapped\n if swapped:\n points.reverse()\n return np.array(points)","def angles(self):\n\n return self._angles","def get_annotated_lines(self):\n lines = [Line(idx + 1, x) for idx, x in enumerate(self.sourcelines)]\n\n try:\n lines[self.lineno - 1].current = True\n except IndexError:\n pass\n\n return lines","def _get_angles_csp2(m):\n sma = '[$([cX3](:*):*),$([cX2+](:*):*),$([CX3]=*),$([CX2+]=*)]' # corresponds to any C-sp2\n q = Chem.MolFromSmarts(sma)\n matches = m.GetSubstructMatches(q)\n iass3 = []\n for match in matches:\n j = match[0]\n ma = m.GetAtomWithIdx(j)\n neighbors = ma.GetNeighbors()\n nneib = len(neighbors)\n if nneib > 1:\n for i0 in range(nneib):\n for k0 in range(i0+1,nneib):\n i, k = [ neighbors[i0].GetIdx(), neighbors[k0].GetIdx() ];\n ias = [i,j,k]\n if (ias not in iass3) or (ias[::-1] not in iass3):\n iass3.append(ias)\n return iass3","def _rotate(self):\n new_points = list()\n for i in range(len(self._points)):\n x = self._points[i][0]\n y = self._points[i][1]\n new_x = self._center[0] + self._center[1] - y\n new_y = self._center[1] - self._center[0] + x\n new_points.append((new_x, new_y))\n return new_points","def ortho_line_cut(self):\n x_mid_left, y_mid_left = self.midpoint(0,1) # Computes the mid point of the LHS face of the edm cut\n x_mid_right, y_mid_right = self.midpoint(2,3) # Computes the mid point of the RHS face of the edm cut\n\n ave_grad = self.average_grad()\n m_horizontal = -1/ave_grad #90 degrees rotation of the vertical line average gradient\n\n horizontal_eq_c = y_mid_right - m_horizontal*x_mid_right # y offset of horizontal line\n vertical_eq_left_c = y_mid_left - ave_grad * x_mid_left # y offset of vertical line on left side\n\n x_intersect, y_intersect = self.intersect_point(m_horizontal, horizontal_eq_c, ave_grad,vertical_eq_left_c)\n\n\n coordleft = [x_intersect, y_intersect]\n coordright =[x_mid_right, y_mid_right]\n\n dist = self.distance(coordleft, coordright)\n\n return coordleft, coordright, dist","def find_out_difference_perpendiculars(lap, ref_lap):\n\n distances = []\n\n for i in lap.index:\n point = lap.loc[i]\n\n closest_index = find_closest_point(point, ref_lap)\n closest_point = ref_lap.loc[closest_index]\n\n neighbor_i = len(ref_lap) - 1 if closest_index == 0 else closest_index - 1\n neighbor1 = ref_lap.loc[neighbor_i]\n neighbor_i = 0 if len(ref_lap) == closest_index + 1 else closest_index + 1\n neighbor2 = ref_lap.loc[neighbor_i]\n\n v1 = create_vector(closest_point, point)\n v2 = create_vector(closest_point, neighbor1)\n v3 = create_vector(closest_point, neighbor2)\n\n angle1 = find_angle_between_vectors(v1, v2)\n angle2 = find_angle_between_vectors(v1, v3)\n\n degrees90 = math.pi / 2\n min_dist = -1\n if angle1 > degrees90 and angle2 > degrees90:\n min_dist = line_length(point.LAT, point.LON, closest_point.LAT, closest_point.LON)\n elif angle1 < degrees90 and angle2 < degrees90:\n dist1 = find_shortest_distance(point, closest_point, neighbor1)\n dist2 = find_shortest_distance(point, closest_point, neighbor2)\n min_dist = dist1 if dist1 <= dist2 else dist2\n elif angle1 <= degrees90:\n min_dist = find_shortest_distance(point, closest_point, neighbor1)\n elif angle2 <= degrees90:\n min_dist = find_shortest_distance(point, closest_point, neighbor2)\n\n if min_dist == -1:\n print('ERROR: Could not find distance')\n print(\"Indices: {} {}\\nAngles: {} {}\".format(i, closest_index, angle1, angle2))\n elif math.isnan(min_dist):\n print(\"NAN value!!!\\nIndices: {} {}\\nAngles: {} {}\".format(i, closest_index, angle1, angle2))\n elif min_dist < 0:\n print(\"Negative value!!!\\nIndices: {} {}\\nAngles: {} {}\".format(i, closest_index, angle1, angle2))\n else:\n min_dist = degrees2kilometers(min_dist) * 100000 # in centimeters\n distances.append(min_dist)\n\n return distances","def get_lines(cls, frame, radius=5, sys_context=None):\n\t\tcode_lines, start_line = cls.get_lines_with_start(frame, radius, sys_context)\n\t\treturn code_lines","def distances(self):\n self._sort_measurements()\n return self._distances","def exportCircles(self):\n # Remember to compute circumcircles if not done before\n # for t in self.triangles:\n # self.circles[t] = self.circumcenter(t)\n\n # Filter out triangles with any vertex in the extended BBox\n # Do sqrt of radius before of return\n return [(self.circles[(a, b, c)][0], sqrt(self.circles[(a, b, c)][1]))\n for (a, b, c) in self.triangles if a > 3 and b > 3 and c > 3]","def get_pairs(self, number: int, duration: int, line: int = -1) -> list:\n if line != -1 and len(self._pair_lines) != 0:\n pairs = self._pair_lines[line].get(number)\n if pairs is not None and pairs[0][\"time\"].duration() == duration:\n return pairs\n\n for line in self._pair_lines:\n pairs = line.get(number)\n if pairs is not None and pairs[0][\"time\"].duration() == duration:\n return pairs\n\n return []","def onePassRadon(self, angleAmount):\n rotated_image = self.rotateImage(angleAmount)\n (N, M) = rotated_image.shape\n radon_values = [0]*M\n for radon_line in range(M):\n linesum = 0\n for pixel in range(N):\n linesum += rotated_image[pixel][radon_line]\n radon_values[M - radon_line - 1] = linesum\n #print(radon_values)\n return radon_values","def find_line_through_point(center, theta, length):\n\n r = length\n cx, cy = center\n\n xo = int(r * math.sin(theta))\n yo = int(r * math.cos(theta))\n\n line_start = cx, cy\n line_end = cx + xo, cy + yo\n\n return line_start, line_end"],"string":"[\n \"def vary_distance_lines(self, n_lines,\\n start_theta=-180.0, end_theta=+180.0):\\n delta_theta = end_theta - start_theta\\n thetas = [start_theta + ((float(i) / (n_lines - 1)) * delta_theta)\\n for i in range(n_lines)]\\n thetas = list(map(np.radians, thetas))\\n\\n lines = []\\n for theta in thetas:\\n low = self.build_input(0.0, theta)\\n high = self.build_input(self.intersection_distance(theta), theta)\\n lines.append((low, high))\\n return lines\",\n \"def lines(self):\\n return lines(self.startEA, self.endEA)\",\n \"def marker_lines(self) -> list[Line]:\\n upper_point = (\\n self.leaf_center_px - self.leaf_width_px / 2 * self._analysis_ratio\\n )\\n lower_point = (\\n self.leaf_center_px + self.leaf_width_px / 2 * self._analysis_ratio\\n )\\n\\n lines = []\\n for mlc_position in self.position:\\n if self._orientation == Orientation.UP_DOWN:\\n line = Line((mlc_position, upper_point), (mlc_position, lower_point))\\n else:\\n line = Line((upper_point, mlc_position), (lower_point, mlc_position))\\n lines.append(line)\\n return lines\",\n \"def angles(self) -> list[npt.NDArray[np.float_]]:\\n result = []\\n a = cast(Segment, self.edges[-1])\\n for b in self.edges:\\n b = cast(Segment, b)\\n result.append(angle(a.vertices[1], a.vertices[0], b.vertices[1]))\\n a = b\\n\\n return result\",\n \"def get_lat_offsets(self):\\n\\n startlat = self.parameters['startlatitude']\\n stoplat = self.parameters['stoplatitude']\\n\\n #Given the start and stops,\\n startidx, startvalue = utils.getnearest(self.latitudes, startlat)\\n stopidx, stopvalue = utils.getnearest(self.latitudes, stoplat)\\n startidx -= 2\\n stopidx += 2\\n latslice = np.arange(startidx, stopidx + 1)\\n if utils.checkmonotonic(latslice):\\n latslice = latslice\\n else:\\n #TODO: Support pole crossing images\\n logger.error('Image is pole crossing, not currently supported.')\\n '''\\n print \\\"NOT MONOTONIC\\\"\\n #Handle wraps around the poles\\n latslice = np.arange(start_idx, stop_idx + 1)\\n nlats = self.startlookup.shape[1]\\n greatermask = np.where(latslice >= nlats)\\n latslice[greatermask] -= nlats\\n lessmask = np.where(latslice < 0)\\n latslice[lessmask] += self.startlookup.shape[1]\\n\\n self.latsort = np.argsort(latslice)\\n self.latslice = latslice[self.latsort]\\n self.latsort = np.argsort(self.latsort)\\n '''\\n latslice = None\\n logger.debug('Start latitude node is {}. Nearest lookup node is {}.'.format(startlat, startidx))\\n logger.debug('Stop latitude node is {}. Nearest lookup node is {}.'.format(stoplat, stopidx))\\n return latslice\",\n \"def direction_coordinates(self, gc_lines):\\n lins = [(_line[0][mid], _line[0][mid + 1], _line[1][mid], _line[1][mid + 1])\\n for _line, mid in zip(gc_lines, [len(_line[0]) // 2 for _line in gc_lines])\\n if len(_line[0]) > 2]\\n lens = [np.hypot(_line[0][0] - _line[0][-1], _line[0][0] - _line[0][-1]) * 110.\\n for _line in gc_lines\\n if len(_line[0]) > 2]\\n lins = [(x0 * np.cos(np.deg2rad(np.mean([y0, y1]))), x1 * np.cos(np.deg2rad(np.mean([y0, y1]))), y0, y1)\\n for x0, x1, y0, y1 in lins]\\n lins = [_x for _x, _l in zip(lins, lens) if _l > 10]\\n\\n direction = [(0.5 * (x0 + x1), 0.5 * (y0 + y1), x1 - x0, y1 - y0) for x0, x1, y0, y1 in lins]\\n direction = [(_u, _v, _x / np.hypot(_x, _y), _y / np.hypot(_x, _y))\\n for _u, _v, _x, _y in direction]\\n los = [rotate_point(point[2:], -self.dsbObsAngleAzimuth.value()) for point in direction]\\n\\n dist = 1.\\n tp_dir = (np.array(los).T * dist).T\\n\\n tps = [(x0, y0, x0 + tp_x, y0 + tp_y) for\\n ((x0, y0, _, _), (tp_x, tp_y)) in zip(direction, tp_dir)]\\n tps = [[(x0 / np.cos(np.deg2rad(y0)), y0), (x1 / np.cos(np.deg2rad(y0)), y1)] for (x0, y0, x1, y1) in tps]\\n return tps\",\n \"def __get_pts_lines(self, lines, arcs):\\n # store unique points\\n points_dict = {}\\n all_lines = []\\n for ind, line in enumerate(lines):\\n tup = self.__fix_tuple((line.start[0], line.start[1]))\\n start = self.__find_make_pt(tup, points_dict)\\n tup = self.__fix_tuple((line.end[0], line.end[1]))\\n end = self.__find_make_pt(tup, points_dict)\\n line = geometry.Line(start, end)\\n all_lines.append(line)\\n for ind, arc in enumerate(arcs):\\n # dxfgrabber arcs are stored ccw when looking at xy plane\\n # x horizontal\\n # y vertical\\n tup = self.__fix_tuple((arc.center[0], arc.center[1]))\\n center = self.__find_make_pt(tup, points_dict)\\n sign = -1\\n if self.__swapxy:\\n sign = 1\\n startangle = arc.start_angle*sign\\n endangle = arc.end_angle*sign\\n angle = endangle - startangle\\n if arc.end_angle < arc.start_angle:\\n angle = angle + 360*sign\\n \\\"\\\"\\\"\\n print('---------------------------------------')\\n print('| ARC')\\n print('center: %s' % center)\\n print('startangle: %f' % startangle)\\n print('endangle: %f' % endangle)\\n print('traversed_angle: %f' % angle)\\n \\\"\\\"\\\"\\n start_vect = geometry.Point(0, arc.radius)\\n if self.__swapxy == False:\\n start_vect = geometry.Point(arc.radius, 0)\\n start_vect.rot_ccw_deg(arc.start_angle*sign)\\n end_vect = geometry.Point(0, arc.radius)\\n if self.__swapxy == False:\\n end_vect = geometry.Point(arc.radius, 0)\\n end_vect.rot_ccw_deg(arc.end_angle*sign)\\n start = center + start_vect\\n start_tup = (start.x, start.y)\\n end = center + end_vect\\n end_tup = (end.x, end.y)\\n start = self.__find_make_pt(start_tup, points_dict)\\n end = self.__find_make_pt(end_tup, points_dict)\\n rvect = start - center\\n if abs(angle) <= 90:\\n arc = geometry.Arc(start, end, center)\\n all_lines.append(arc)\\n print('1 arc made')\\n continue\\n #print(' %s' % arc)\\n pieces = math.ceil(abs(angle)/90)\\n print('%i arcs being made' % pieces)\\n points = [start, end]\\n # 2 pieces need 3 points, we have start + end already --> 1 pt\\n inserts = pieces + 1 - 2\\n piece_ang = angle/pieces\\n #print('piece_ang = %f' % piece_ang)\\n while inserts > 0:\\n rvect.rot_ccw_deg(piece_ang)\\n point = center + rvect\\n tup = (point.x, point.y)\\n point = self.__find_make_pt(tup, points_dict)\\n points.insert(-1, point)\\n inserts = inserts - 1\\n for ind in range(len(points)-1):\\n #print(' %s' % arc)\\n arc = geometry.Arc(points[ind], points[ind+1], center)\\n all_lines.append(arc)\\n for line in all_lines:\\n line.save_to_points()\\n return [list(points_dict.values()), all_lines]\",\n \"def _gen_segments(self, markers, distances, threshold):\\n coordinates = list(itertools.combinations(range(len(markers)), 2))\\n self.logger.debug(\\n f\\\"{self.id}:Distances: {distances} [threshold = {threshold}]\\\")\\n indexes, = np.where(distances < threshold)\\n\\n segments = []\\n for idx in indexes:\\n i, j = coordinates[idx]\\n segments.append((markers[i], markers[j]))\\n\\n self.logger.debug(f\\\"{self.id}:Segments: {segments}\\\")\\n return segments\",\n \"def _calculate_distances(self):\\n all_dists = []\\n for ref in range(len(self.atoms)):\\n if self.atoms[ref].symbol in self.exclude:\\n continue\\n indices = list(range(ref+1, len(self.atoms)))\\n indices = self._filter_excluded(indices)\\n if len(indices) == 0:\\n continue\\n dists = self.atoms.get_distances(ref, indices, mic=True)\\n all_dists += list(dists)\\n \\n # Normalize by the mean distance\\n return np.array(all_dists)/np.mean(all_dists)\",\n \"def _get_lines(self) -> tuple[VGroup, VGroup]:\\n center = self.get_origin()\\n ratio_faded_lines = self.faded_line_ratio\\n offset = self.azimuth_offset\\n\\n if ratio_faded_lines == 0: # don't show faded lines\\n ratio_faded_lines = 1 # i.e. set ratio to 1\\n rstep = (1 / ratio_faded_lines) * self.x_axis.x_range[2]\\n astep = (1 / ratio_faded_lines) * (TAU * (1 / self.azimuth_step))\\n rlines1 = VGroup()\\n rlines2 = VGroup()\\n alines1 = VGroup()\\n alines2 = VGroup()\\n\\n rinput = np.arange(0, self.x_axis.x_range[1] + rstep, rstep)\\n ainput = np.arange(0, TAU, astep)\\n\\n unit_vector = self.x_axis.get_unit_vector()[0]\\n\\n for k, x in enumerate(rinput):\\n new_line = Circle(radius=x * unit_vector)\\n if k % ratio_faded_lines == 0:\\n alines1.add(new_line)\\n else:\\n alines2.add(new_line)\\n\\n line = Line(center, self.get_x_axis().get_end())\\n\\n for k, x in enumerate(ainput):\\n new_line = line.copy()\\n new_line.rotate(x + offset, about_point=center)\\n if k % ratio_faded_lines == 0:\\n rlines1.add(new_line)\\n else:\\n rlines2.add(new_line)\\n\\n lines1 = VGroup(*rlines1, *alines1)\\n lines2 = VGroup(*rlines2, *alines2)\\n return lines1, lines2\",\n \"def segment(self):\\n start = self.alignment.matching_function_startpoint(self.idx)\\n end = self.alignment.matching_function_endpoint(self.idx)\\n return [start, end]\",\n \"def find_lines(self):\\n return []\",\n \"def points(self):\\n angles_positions = [a.position[0] for a in self.angles]\\n x, y = 0, 0\\n points = [[x, y]]\\n for angle, length in zip(angles_positions, self.lengths):\\n x += length * math.sin(angle)\\n y += -length * math.cos(angle)\\n points.append([x, y])\\n return points\",\n \"def _gather_points(self):\\n # This is just a stub for now. We should really find the lines only\\n # inside the screen range here.\\n\\n x = self.index.get_data()\\n y = self.value.get_data()\\n rad= min(self.width/2.0,self.height/2.0)\\n sx = x*rad+ self.x + self.width/2.0\\n sy = y*rad+ self.y + self.height/2.0\\n\\n points = transpose(array((sx,sy)))\\n self._cached_data_pts = points\\n self._cache_valid = True\\n return\",\n \"def find_isolated_endpoints(lines):\\n \\n isolated_endpoints = []\\n count = len(lines)\\n print(\\\"Finding isolated end points 2/3\\\")\\n pb = pbar.ProgressBar(count)\\n for i, line in enumerate(lines):\\n pb += 1\\n other_lines = lines[:i] + lines[i+1:]\\n for q in [0,-1]:\\n endpoint = Point(line.coords[q])\\n if any(endpoint.touches(another_line) \\n for another_line in other_lines):\\n continue\\n else:\\n isolated_endpoints.append(endpoint)\\n del pb\\n return isolated_endpoints\",\n \"def _get_distances(self):\\n for molecule in self.values():\\n molecule.get_distances()\\n\\n # for atom in self.atoms:\\n # atom.get_distances()\",\n \"def generate_lines(self):\\n x = self.square[0]\\n y = self.square[1]\\n lines = [[]]\\n \\n lines.append( ( (x, y+a) for a in range(1,8) ) )\\n lines.append( ( (x+a, y) for a in range(1,8) ) )\\n lines.append( ( (x, y-a) for a in range(1,8) ) )\\n lines.append( ( (x-a, y) for a in range(1,8) ) )\\n \\n return lines\",\n \"def compute_start_end_points(linestrings):\\n starts = []\\n stops = []\\n for ls in linestrings:\\n pt = Point(ls.coords[0])\\n starts.append(round(CONUS[\\\"poly\\\"].exterior.project(pt), 2))\\n pt = Point(ls.coords[-1])\\n stops.append(round(CONUS[\\\"poly\\\"].exterior.project(pt), 2))\\n return starts, stops\",\n \"def get_station_lines(self):\\n station_lines = []\\n for wrapper in self.soup.find_all(\\\"div\\\", {\\\"class\\\": \\\"stop-wrapper\\\"}):\\n lines = wrapper.find(\\\"h3\\\").text.split(' ')[-1][1:-1]\\n station_lines.append(lines)\\n return np.array(station_lines).T\",\n \"def vary_theta_lines(self, n_lines):\\n max_rho = np.sqrt(\\n max(self.min_x**2, self.max_x**2) +\\n max(self.min_y**2, self.max_y**2))\\n\\n distances = [(float(i + 1) / n_lines) * max_rho\\n for i in range(n_lines)]\\n lines = []\\n for distance in distances:\\n for start, end in self.theta_breaks(distance):\\n low = self.build_input(distance, start)\\n high = self.build_input(distance, end)\\n lines.append((low, high))\\n return lines\",\n \"def getArcsFrom(self):\\n return [ arc for arc in sorted(self._arcsFrom) ]\",\n \"def detect_lines_from_geometry(\\n masked_derivative,\\n positions,\\n normals,\\n start_threshold,\\n continuation_threshold,\\n max_lines,\\n angle_weight,\\n force_dir,\\n):\\n\\n def to_absolute_threshold(filtered_image, threshold):\\n f = -filtered_image[filtered_image < 0]\\n mn = np.min(f)\\n mx = np.max(f)\\n\\n return -((mx - mn) * threshold + mn)\\n\\n thresh = to_absolute_threshold(masked_derivative, start_threshold)\\n proceed = (\\n thresh\\n if not continuation_threshold\\n else to_absolute_threshold(masked_derivative, continuation_threshold)\\n )\\n\\n # Generate lookup table which convert normal angle into table of points to be trialed\\n candidates = get_candidate_generator()\\n\\n lines = []\\n for flat_idx in np.argsort(masked_derivative.flatten()):\\n idx = np.unravel_index(flat_idx, masked_derivative.shape)\\n\\n if masked_derivative[idx[0], idx[1]] == KymoCode.seen:\\n continue\\n\\n if masked_derivative[idx[0], idx[1]] >= thresh or len(lines) >= max_lines:\\n break\\n\\n # Traverse the line. Note that traverse_line modifies the masked_derivative image by marking some as seen.\\n line = traverse_line(\\n idx, masked_derivative, positions, normals, proceed, candidates, angle_weight, force_dir\\n )\\n\\n if line:\\n lines.append(line)\\n\\n return lines\",\n \"def findLines(self, sequence): \\n size = self.size\\n lines_in_seq = {'Vertical': {}, 'Horizontal': {}, 'D-pos': {}, 'D-neg': {} }\\n\\n ###############\\n # Evaluate and append a line in a particular direction at a particular coordinate \\n def tallyLine(direction, coordinate, move):\\n tic = 0\\n tac = move[1]\\n current_line = lines_in_seq[direction].get(coordinate, 'Empty')\\n\\n if current_line is not None and current_line is 'Empty':\\n lines_in_seq[direction][coordinate] = [tac, move[0]]\\n\\n elif current_line is not None and current_line[tic] == tac:\\n lines_in_seq[direction][coordinate].append(move[0]) if move[0] not in lines_in_seq[direction][coordinate] else None\\n\\n else:\\n lines_in_seq[direction][coordinate] = None\\n \\n ###############\\n # Check if each point in the game sequence belongs to a line or not\\n for move in sequence:\\n x , y = self.getCoordinates(move[0])\\n\\n # Tally of horizontal and vertical lines\\n for direction in ['Horizontal', 'Vertical']:\\n coordinate = {'Vertical': x, 'Horizontal': y}[direction]\\n tallyLine(direction, coordinate, move)\\n \\n # Tally of the two possible diagonal lines\\n if x == y: \\n tallyLine('D-pos', 0, move)\\n if x + y == size - 1:\\n tallyLine('D-neg', 0, move)\\n\\n return lines_in_seq\",\n \"def list_all_lines(self):\\n lines = self.list_lines_gen(self.go_forward, start=self.get_oldest_index())\\n self.read_pos = self.write_pos\\n return lines\",\n \"def linelist(self):\\n line_list = Marker()\\n line_list.header = self._header\\n line_list.type = Marker.LINE_LIST\\n line_list.action = Marker.ADD\\n line_list.scale.x = 0.005\\n line_list.color = self.YELLOW\\n line_list.pose = deepcopy(self.POSE)\\n\\n line_list.points.extend((self._p1, self._p2))\\n line_list.points.extend((self._p2, self._p3))\\n line_list.points.extend((self._p3, self._p4))\\n line_list.points.extend((self._p4, self._p1))\\n line_list.points.extend((self._p5, self._p6))\\n line_list.points.extend((self._p6, self._p7))\\n line_list.points.extend((self._p7, self._p8))\\n line_list.points.extend((self._p8, self._p5))\\n line_list.points.extend((self._p1, self._p5))\\n line_list.points.extend((self._p2, self._p6))\\n line_list.points.extend((self._p3, self._p7))\\n line_list.points.extend((self._p4, self._p8))\\n\\n return line_list\",\n \"def center(self):\\n return [self.position[i]+self.radius for i in range(2)]\",\n \"def get_lines(self):\\n\\n\\t\\t# create a copy of original picture\\n\\t\\tcolor_image = np.copy(self.original)\\n\\n\\t\\t# remove colors channel\\n\\t\\tgrey_image = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)\\n\\n\\t\\t# apply threshold to distinguish lines better\\n\\t\\tret, thresh = cv2.threshold(grey_image, THRESHOLD, WHITE, BLACK)\\n\\n\\t\\t# define edges\\n\\t\\tedges = cv2.Canny(thresh, THRESHOLD_CANNY, WHITE)\\n\\n\\t\\t# get lines from all the edges\\n\\t\\tlines = cv2.HoughLines(edges, RHO, THETA, THRESHOLD)\\n\\n\\t\\t# lists to store coordinates of edges\\n\\t\\tX, Y = [], []\\n\\n\\t\\t# calculate coordinates\\n\\t\\tfor line in lines:\\n\\t\\t\\tfor rho,theta in line:\\n\\t\\t\\t\\ta = np.cos(theta)\\n\\t\\t\\t\\tb = np.sin(theta)\\n\\t\\t\\t\\tx0 = a*rho\\n\\t\\t\\t\\ty0 = b*rho\\n\\t\\t\\t\\tX.append(int(x0))\\n\\t\\t\\t\\tY.append(int(y0))\\n\\n\\t\\tX = self.reduce_lines(X)\\n\\t\\tY = self.reduce_lines(Y)\\n\\n\\t\\treturn X, Y\",\n \"def _get_spiral_points(self, start_distance, distance_step, num_legs=10):\\n start_loc = self.swarmie.get_odom_location().get_pose()\\n points = []\\n distance = start_distance\\n angle = math.pi / 2\\n\\n prev_point = Point()\\n prev_point.x = start_loc.x + distance * math.cos(start_loc.theta)\\n prev_point.y = start_loc.y + distance * math.sin(start_loc.theta)\\n points.append(prev_point)\\n\\n # todo: is this big enough, or too big?\\n for i in range(1, num_legs):\\n if i % 2 == 0:\\n distance += distance_step\\n\\n point = Point()\\n point.x = prev_point.x + distance * math.cos(start_loc.theta\\n + angle)\\n point.y = prev_point.y + distance * math.sin(start_loc.theta\\n + angle)\\n points.append(point)\\n prev_point = point\\n angle += math.pi / 2\\n\\n return points\",\n \"def get_file_lines(self, filepath, start, stop):\\n\\n if self._file_lines is None:\\n with open(os.path.join(self.repo_path, filepath)) as f:\\n self._file_lines = f.readlines()\\n\\n if stop is None:\\n lines = self._file_lines[start - 1:]\\n else:\\n lines = self._file_lines[start - 1:stop]\\n for i, line in enumerate(lines):\\n lines[i] = [start + i, True, line.rstrip()]\\n while lines and self.is_empty_line(lines[-1][-1]):\\n lines.pop()\\n\\n if not lines:\\n return []\\n\\n stop = lines[0][0]\\n start = max(1, stop - self.surround_by)\\n prefix_lines = []\\n for i, line in enumerate(self._file_lines[start - 1:stop - 1], start=start):\\n prefix_lines.append([i, False, line.rstrip()])\\n\\n start = lines[-1][0] + 1\\n stop = start + self.surround_by\\n suffix_lines = []\\n for i, line in enumerate(self._file_lines[start - 1:stop - 1], start=start):\\n suffix_lines.append([i, False, line.rstrip()])\\n\\n return prefix_lines + lines + suffix_lines\",\n \"def get_links(self, thetas):\\n\\n cum_theta = np.cumsum(thetas)\\n\\n results = np.zeros((self.arm_lengths.shape[0] + 1, 2))\\n\\n results[1:, 0] = np.cumsum(self.arm_lengths * np.cos(cum_theta))\\n results[1:, 1] = np.cumsum(self.arm_lengths * np.sin(cum_theta))\\n links = [Link(start, end) for start, end in zip(results[:-1], results[1:])]\\n\\n return links\",\n \"def get_internal_angles(self):\\n\\n angles = []\\n\\n for elx, elz in zip(self.grid['x'], self.grid['z']):\\n el_angles = []\\n xy = np.vstack((elx, elz))\\n for i in range(0, elx.size):\\n i1 = (i - 1) % elx.size\\n i2 = (i + 1) % elx.size\\n\\n a = (xy[:, i] - xy[:, i1])\\n b = (xy[:, i2] - xy[:, i])\\n # note that nodes are ordered counter-clockwise!\\n angle = np.pi - np.arctan2(\\n a[0] * b[1] - a[1] * b[0],\\n a[0] * b[0] + a[1] * b[1]\\n )\\n el_angles.append(angle * 180 / np.pi)\\n angles.append(el_angles)\\n return np.array(angles)\",\n \"def _getPath(self):\\r\\n (startRow, startCol) = self.startCell.position\\r\\n (endRow, endCol) = self.endCell.position\\r\\n if(startRow - endRow == 0):\\r\\n for col in range(min(endCol, startCol) + 1,\\r\\n max(endCol, startCol)):\\r\\n yield self.game.getCell(startRow, col)\\r\\n elif(startCol - endCol == 0):\\r\\n for row in range(min(startRow, endRow) + 1,\\r\\n max(startRow, endRow)):\\r\\n yield self.game.getCell(row, startCol)\\r\\n else:\\r\\n raise ValueError(\\\"Move is not straight line\\\")\",\n \"def inters_segment(self, s):\\r\\n x1 = s.start[0] - self.center[0]\\r\\n y1 = s.start[1] - self.center[1]\\r\\n x2 = s.end[0] - self.center[0]\\r\\n y2 = s.end[1] - self.center[1]\\r\\n dx = x2 - x1\\r\\n dy = y2 - y1\\r\\n dr = math.sqrt(dx * dx + dy * dy)\\r\\n D = x1 * y2 - x2 * y1\\r\\n dr2 = dr * dr\\r\\n d = self.radius * self.radius * dr2 - D * D \\r\\n \\r\\n if d < 0:\\r\\n return []\\r\\n else: \\r\\n if dy < 0:\\r\\n sgndy = -1\\r\\n else:\\r\\n sgndy = 1 \\r\\n \\r\\n Ddy = D * dy\\r\\n mDdx = -D * dx\\r\\n sgndydxsqrtd = sgndy * dx * math.sqrt(d)\\r\\n absdysqrtd = abs(dy) * math.sqrt(d) \\r\\n \\r\\n xa = float(Ddy + sgndydxsqrtd) / dr2 + self.center[0]\\r\\n ya = float(mDdx + absdysqrtd) / dr2 + self.center[1]\\r\\n \\r\\n xb = (Ddy - sgndydxsqrtd) / dr2 + self.center[0]\\r\\n yb = (mDdx - absdysqrtd) / dr2 + self.center[1]\\r\\n \\r\\n if (d == 0) or not s.contains_point(xb, yb):\\r\\n if s.contains_point(xa, ya):\\r\\n return [(int(xa), int(ya))]\\r\\n else:\\r\\n return []\\r\\n else:\\r\\n if s.contains_point(xa, ya):\\r\\n return [(int(xa), int(ya)), (int(xb), int(yb))]\\r\\n else:\\r\\n return [(int(xb), int(yb))]\",\n \"def getArcs(self):\\n return self.getArcsFrom()\",\n \"def getStarts(self) -> List[int]:\\n ...\",\n \"def findPathSegments(self, startTime, startPoint, startSpeed, startUnitVelocity, legalRotDirection):\\n pass\",\n \"def lines(self):\\n\\t\\tlines = []\\n\\t\\tfor t in self.trigrams:\\n\\t\\t\\tfor line in t.lines:\\n\\t\\t\\t\\tlines.append(line)\\n\\t\\treturn lines\",\n \"def mergeLines(lines):\\r\\n\\r\\n result = []\\r\\n\\r\\n for i in range(len(lines)):\\r\\n\\r\\n line = lines[i]\\r\\n\\r\\n rho = line[0][0] # Distance of line\\r\\n theta = line[0][1] # Angle of line\\r\\n\\r\\n if rho == 0 and theta == -100: continue # Line has been previously merged\\r\\n\\r\\n for j in range(i + 1, len(lines)):\\r\\n\\r\\n line2 = lines[j]\\r\\n\\r\\n rho2 = line2[0][0]\\r\\n theta2 = line2[0][1]\\r\\n\\r\\n # Difference in distance is less than 50 pixels\\r\\n # Difference in angle is less than 10 degrees\\r\\n if abs(rho2 - rho) < 50 and abs(theta2 - theta) < np.pi * 10 / 180:\\r\\n lines[j][0][0] = 0\\r\\n lines[j][0][1] = -100\\r\\n\\r\\n result.append(line)\\r\\n\\r\\n return result\",\n \"def build_steps(self) -> List[Coordinate]:\\n if self.orientation == Orientation.HORIZONTAL:\\n fill = self.start.y\\n else:\\n fill = self.start.x\\n\\n if self.end.x < self.start.x:\\n x_range = range(self.start.x, self.end.x - 1, -1)\\n else:\\n x_range = range(self.start.x, self.end.x + 1)\\n\\n if self.end.y < self.start.y:\\n y_range = range(self.start.y, self.end.y - 1, -1)\\n else:\\n y_range = range(self.start.y, self.end.y + 1)\\n\\n return list(zip_longest(x_range, y_range, fillvalue=fill))\",\n \"def trajectory(self):\\n traj = np.zeros((2, self.times.size))\\n for t, time in enumerate(self.times):\\n traj[:, t] = self.center_of_mass(time)\\n return traj\",\n \"def create_lines(self) -> None:\\n res = []\\n for connection in self.connections:\\n start_component = self.components[connection.start_entity]\\n end_component = self.components[connection.end_entity]\\n start_pin_location = (\\n start_component.location\\n + start_component.pin_locations[connection.start_pin]\\n )\\n end_pin_location = (\\n end_component.location + end_component.pin_locations[connection.end_pin]\\n )\\n\\n x_midpoint = (start_pin_location.x + end_pin_location.x) / 2\\n bend_start = Point(x_midpoint, start_pin_location.y)\\n bend_end = Point(x_midpoint, end_pin_location.y)\\n bends = [bend_start, bend_end]\\n res.append(Line(connection, start_pin_location, *bends, end_pin_location))\\n\\n self.lines = res\",\n \"def getSegments(self):\\n l = len(self.points)\\n return [Segment(self.points[i % l], self.points[(i + 1) % l], \\\\\\n color=self.side_color, width=self.side_width) for i in range(l)]\",\n \"def center_of_charge(self):\\n ret = [0.0, 0.0, 0.0]\\n total_c = 0.0\\n\\n for at in range(self.natom()):\\n c = self.charge(at)\\n ret = add(ret, scale(self.xyz(at), c))\\n total_c += c\\n\\n ret = scale(ret, 1.0 / total_c)\\n return ret\",\n \"def lines(self):\\n for pair in pairs(self.points):\\n yield Line(pair, shape=self)\",\n \"def segments(self):\\n return (self._subset((i,i+1)) for i in range(len(self)-1))\",\n \"def angles(self):\\n self._sort_measurements()\\n return self._angles\",\n \"def intersection_distances(self, altitude, start_distance, finish_distance):\\n distances = []\\n alt_index, alt_ratio = calculate_value_reference(self.distances,\\n start_distance)\\n start_alt = calculate_value(self.altitudes, alt_index, alt_ratio)\\n\\n finish_index, finish_ratio = calculate_value_reference(self.distances,\\n finish_distance)\\n while alt_index < finish_index:\\n next_index = alt_index + 1\\n next_ratio = 0.0\\n next_alt = calculate_value(self.altitudes, next_index, next_ratio)\\n\\n # Does this section span the given altitude?\\n if (next_alt != start_alt) and \\\\\\n (min(start_alt, next_alt) < altitude < max(start_alt, next_alt)):\\n delta = altitude - start_alt\\n denom = next_alt - start_alt\\n ratio = delta / denom\\n distance = calculate_value(self.distances, alt_index, ratio)\\n distances.append(distance)\\n\\n alt_index = next_index\\n alt_ratio = next_ratio\\n start_alt = next_alt\\n\\n if finish_ratio:\\n finish_alt = calculate_value(self.altitudes, finish_index, finish_ratio)\\n # Does the last section span the given altitude?\\n if (start_alt != finish_alt) and \\\\\\n (min(start_alt, finish_alt) < altitude < max(start_alt, finish_alt)):\\n # Calculate the altitude at the end of the section to maximimse accuracy\\n next_alt = calculate_value(self.altitudes, finish_index, 1.0)\\n delta = altitude - start_alt\\n denom = next_alt - start_alt\\n ratio = delta / denom\\n distance = calculate_value(self.distances, alt_index, ratio)\\n distances.append(distance)\\n\\n return distances\",\n \"def angles(self, num: int) -> Iterable[float]:\\n if num < 2:\\n raise ValueError(\\\"num >= 2\\\")\\n start = self.dxf.start_angle % 360\\n stop = self.dxf.end_angle % 360\\n if stop <= start:\\n stop += 360\\n for angle in linspace(start, stop, num=num, endpoint=True):\\n yield angle % 360\",\n \"def cosines(amount, start, stop, truncated, sequence):\\n\\n for x in range(start, amount):\\n y = abs(round(stop * math.cos(x)))\\n if truncated and y >= stop:\\n sequence.append(stop)\\n elif y < start:\\n sequence.append(start)\\n else:\\n sequence.append(y)\\n return sequence\",\n \"def get_distances_list(mid_points):\\n n = len(mid_points)\\n dist_list = np.zeros((n,n))\\n\\n for i in range(n):\\n for j in range(i+1, n):\\n dist_list[i][j] = compute_distance(mid_points[i], mid_points[j])\\n \\n return dist_list\",\n \"def get_clock(self):\\n return [np.sin(2*np.pi*(self.phase/self.cycle_len + self.theta_FL)/self.cycle_len),\\n np.sin(2*np.pi*(self.phase/self.cycle_len + self.theta_FR)/self.cycle_len),\\n np.sin(2*np.pi*(self.phase/self.cycle_len + self.theta_RL)/self.cycle_len),\\n np.sin(2*np.pi*(self.phase/self.cycle_len + self.theta_RR)/self.cycle_len)\\n ]\",\n \"def scan(self):\\n for angle in range(self.MIDPOINT-350, self.MIDPOINT+350, 50):\\n self.servo(angle)\\n self.scan_data[angle] = self.read_distance()\",\n \"def get_distances(self, crds):\\n self.all_dist = np.zeros((self.natom, self.natom))\\n # Loop over upper triangle of atom pairs\\n for iat in range(self.natom-1):\\n # Get the atom indices\\n at_inds = np.arange(len(crds))\\n\\n # Calc distances between atoms (only upper triangle though)\\n at_msk = at_inds > iat\\n all_ut_dist = crds[at_msk] - crds[iat]\\n all_ut_dist = np.linalg.norm(all_ut_dist, axis=1)\\n\\n self.all_dist[iat, iat+1:] = all_ut_dist\\n\\n # Get lower triangle indices\\n self.all_dist = self.all_dist + self.all_dist.T\",\n \"def _getCirclePoints(self, center, from_point, to_angle, interval):\\r\\n\\r\\n # radius is needed for calculating the points\\r\\n radius = math.sqrt( math.pow((center[0] - from_point[0]),2) + math.pow(center[1]-from_point[1],2) )\\r\\n\\r\\n # get current angle from from_point. x,y: 1,0 = zero angle, 0,1 = -pi/2, -1,0 = pi, 0,1 as pi/2\\r\\n if (from_point[0] < center[0] and from_point[1] > center[1]): # angles in lower left quarter\\r\\n angle = math.pi - math.asin( (-(center[1] - from_point[1]) )/ radius )\\r\\n elif (from_point[0] < center[0] and from_point[1] <= center[1]): # angles in upper left\\r\\n angle = -math.pi - math.asin( (-(center[1] - from_point[1]) )/ radius )\\r\\n else:\\r\\n angle = math.asin( (-(center[1] - from_point[1]) )/ radius )\\r\\n\\r\\n # debug.brf(\\\"from angle deg: %s to_angle deg: %s\\\" % (math.degrees(angle), math.degrees(to_angle)))\\r\\n\\r\\n interval = math.radians(interval)\\r\\n\\r\\n points = [] # starting point\\r\\n points.append(from_point)\\r\\n\\r\\n # flag of direction the 'motion' goes to\\r\\n clockwise = False\\r\\n anticlockwise = False\\r\\n\\r\\n for i in range(60): # 60 points is the maximum\\r\\n\\r\\n if angle < to_angle: # if to_angle is bigger, length to clockwise direction is absolute value from to_angle substracted from current angle modulus the whole circle\\r\\n len_cw = math.fabs(angle - to_angle) % (2 * math.pi)\\r\\n len_acw = 2 * math.pi - math.fabs(len_cw)\\r\\n else: # same as above, but anticlockwise\\r\\n len_acw = math.fabs(angle - to_angle) % (2 * math.pi)\\r\\n len_cw = 2 * math.pi - math.fabs(len_acw)\\r\\n\\r\\n if len_cw > len_acw:\\r\\n # turn anticlockwise\\r\\n anticlockwise = True\\r\\n if clockwise == True: # if clockwise turns have been done, then no direction changing needed. break point drawing\\r\\n break\\r\\n\\r\\n angle = angle - interval\\r\\n\\r\\n else:\\r\\n # turn clockwise\\r\\n clockwise = True\\r\\n if anticlockwise == True: # if anticlockwise turns have been done, then no direction changing needed. break point drawing\\r\\n break\\r\\n\\r\\n angle = angle + interval\\r\\n\\r\\n # calculate x/y to current angle\\r\\n x = int(round(radius*math.cos(angle) + center[0]))\\r\\n y = int(round(radius*math.sin(angle) + center[1]))\\r\\n points.append( (x,y) )\\r\\n\\r\\n # debug.brf(\\\"angle %s (%s,%s) remainding: %s (deg: %s)\\\" % (str(math.degrees(angle)), x, y, str(angle - to_angle), str(math.degrees(angle-to_angle))))\\r\\n\\r\\n # append still the last wanted point to get accurate\\r\\n x = int(round(radius*math.cos(to_angle) + center[0]))\\r\\n y = int(round(radius*math.sin(to_angle) + center[1]))\\r\\n points.append( (x,y) )\\r\\n\\r\\n # debug.brf(\\\"points %s\\\" % points)\\r\\n return points\",\n \"def getAngles(self):\\n try:\\n return self._angleList\\n except AttributeError:\\n pass\\n forceConstant=self._raw_data[\\\"ANGLE_FORCE_CONSTANT\\\"]\\n angleEquil=self._raw_data[\\\"ANGLE_EQUIL_VALUE\\\"]\\n anglePointers = self._raw_data[\\\"ANGLES_INC_HYDROGEN\\\"] \\\\\\n +self._raw_data[\\\"ANGLES_WITHOUT_HYDROGEN\\\"]\\n self._angleList=[]\\n forceConstConversionFactor = (units.kilocalorie_per_mole/(units.radian*units.radian)).conversion_factor_to(units.kilojoule_per_mole/(units.radian*units.radian))\\n for ii in range(0,len(anglePointers),4):\\n if int(anglePointers[ii])<0 or \\\\\\n int(anglePointers[ii+1])<0 or \\\\\\n int(anglePointers[ii+2])<0:\\n raise Exception(\\\"Found negative angle atom pointers %s\\\"\\n % ((anglePointers[ii],\\n anglePointers[ii+1],\\n anglePointers[ii+2]),))\\n iType=int(anglePointers[ii+3])-1\\n self._angleList.append((int(anglePointers[ii])//3,\\n int(anglePointers[ii+1])//3,\\n int(anglePointers[ii+2])//3,\\n float(forceConstant[iType])*forceConstConversionFactor,\\n float(angleEquil[iType])))\\n return self._angleList\",\n \"def find_ports(self, lines):\\n ports = []\\n left = None\\n self.dists = []\\n for line in lines:\\n if left is None:\\n if self.is_left(line):\\n left = self.mid(line)\\n else:\\n if not self.is_left(line):\\n ports.append(int(left[0] + self.mid(line)[0]) // 2)\\n self.dists.append(left[0] - self.mid(line)[0])\\n left = None\\n # print(ports)\\n return ports\",\n \"def getSegments(self) -> List[int]:\\n ...\",\n \"def angles(self):\\n penult = self._coordinates[-2]\\n last = self._coordinates[-1]\\n angles = []\\n for c in self._coordinates:\\n angle = (math.atan2(penult[0]-last[0], penult[1]-last[1]) -\\n math.atan2(c[0]-last[0], c[1]-last[1]))\\n angles.append(angle)\\n penult, last = last, c\\n return sorted(angles)\",\n \"def distances(self):\",\n \"def get_arms(self) -> (list, ob.TurnstileAxis):\\n directions = [(0, 1), (1, 0), (0, -1), (-1, 0),\\n (1, 1), (-1, 1), (1, -1), (-1, -1)]\\n for dire in directions:\\n x_neighbour = self.target.x_obj + dire[0]\\n y_neighbour = self.target.y_obj + dire[1]\\n neighbour = self.grid.obj_list[x_neighbour, y_neighbour]\\n if isinstance(neighbour, ob.TurnstileAxis):\\n axis = neighbour\\n arms = []\\n for dire in directions[:4]:\\n x_potential_arm = axis.x_obj + dire[0]\\n y_potential_arm = axis.y_obj + dire[1]\\n potential_arm = self.grid.obj_list[\\n x_potential_arm, y_potential_arm]\\n if isinstance(potential_arm, ob.TurnstileBloc):\\n arms.append(potential_arm)\\n return arms, axis\",\n \"def arc_tangents(amount, start, stop, truncated, sequence):\\n ratio = (start + stop) / 2\\n for x in range(start, amount):\\n y = abs(round(ratio * math.atan(x)))\\n if truncated and y >= stop:\\n sequence.append(stop)\\n elif y < start:\\n sequence.append(start)\\n else:\\n sequence.append(y)\\n return sequence\",\n \"def __around_long_row(self):\\n list_of_cord = []\\n for i in range(-self.rad, self.rad + 1):\\n for j in range(-self.rad, self.rad + 1):\\n if (i == 0 and j == 0) or (i != 0 and j == 1):\\n continue\\n list_of_cord.append((i, j))\\n return list_of_cord\",\n \"def get(self, *args):\\n return _libsbml.ListOfLineSegments_get(self, *args)\",\n \"def iter_atoms(self):\\n if self.segment is None:\\n return iter(list())\\n return self.segment.iter_atoms()\",\n \"def iter_atoms(self):\\n if self.segment is None:\\n return iter(list())\\n return self.segment.iter_atoms()\",\n \"def segments(self):\\n L = len(self.vertices)\\n return itertools.chain((self._subset((i,i+1)) for i in range(len(self)-1)),\\n (self._subset((L-1,0)),))\",\n \"def getArcsTo(self):\\n return [ arc for arc in sorted(self._arcsTo) ]\",\n \"def getArcs(self):\\n return [ arc for arc in sorted(self._arcs) ]\",\n \"def get_lines( self ):\\n return self._line_info\",\n \"def get_lines( self ):\\n return self._line_info\",\n \"def angles(self):\\n return self._angles\",\n \"def lons(self):\\n lons = []\\n quads = self.getQuadrilaterals()\\n groups = self._getGroupIndex()\\n u_groups = np.unique(groups)\\n ng = len(u_groups)\\n for i in range(ng):\\n q_ind = np.where(groups == u_groups[i])[0]\\n nq = len(q_ind)\\n top_lons = []\\n bot_lons = []\\n for j in range(nq):\\n if j == 0:\\n top0 = [quads[q_ind[j]][0].longitude]\\n bot0 = [quads[q_ind[j]][3].longitude]\\n top_lons = top_lons + top0\\n bot_lons = bot_lons + bot0\\n top_lons = top_lons + [quads[q_ind[j]][1].longitude]\\n bot_lons = bot_lons + [quads[q_ind[j]][2].longitude]\\n lons = lons + top_lons + bot_lons[::-1] + top0 + [np.nan]\\n return np.array(lons)\",\n \"def get_points_for_thick_line(start_x: float, start_y: float,\\r\\n end_x: float, end_y: float,\\r\\n line_width: float):\\r\\n vector_x = start_x - end_x\\r\\n vector_y = start_y - end_y\\r\\n perpendicular_x = vector_y\\r\\n perpendicular_y = -vector_x\\r\\n length = math.sqrt(vector_x * vector_x + vector_y * vector_y)\\r\\n if length == 0:\\r\\n normal_x = 1.0\\r\\n normal_y = 1.0\\r\\n else:\\r\\n normal_x = perpendicular_x / length\\r\\n normal_y = perpendicular_y / length\\r\\n r1_x = start_x + normal_x * line_width / 2\\r\\n r1_y = start_y + normal_y * line_width / 2\\r\\n r2_x = start_x - normal_x * line_width / 2\\r\\n r2_y = start_y - normal_y * line_width / 2\\r\\n r3_x = end_x + normal_x * line_width / 2\\r\\n r3_y = end_y + normal_y * line_width / 2\\r\\n r4_x = end_x - normal_x * line_width / 2\\r\\n r4_y = end_y - normal_y * line_width / 2\\r\\n points = (r1_x, r1_y), (r2_x, r2_y), (r4_x, r4_y), (r3_x, r3_y)\\r\\n return points\",\n \"def get_coordinates(self):\\r\\n coordinates_list = []\\r\\n for i in range(self.__length):\\r\\n if self.__orientation == Direction.VERTICAL:\\r\\n temp = (self.__location[0] + i, self.__location[1])\\r\\n if self.__orientation == Direction.HORIZONTAL:\\r\\n temp = (self.__location[0], self.__location[1] + i)\\r\\n coordinates_list.append(temp)\\r\\n return coordinates_list\",\n \"def get_arcs(self):\\n arcs = []\\n for arcs_list in self._inc.values():\\n record = arcs_list.get_first_record()\\n while record is not None:\\n arc = record.element\\n arcs.append(arc)\\n record = record._next\\n return arcs\",\n \"def ActiveHlt2Lines(self) :\\n return []\",\n \"def __around_short_row(self):\\n list_of_cord = []\\n for i in range(-self.rad, self.rad + 1):\\n for j in range(-self.rad, self.rad + 1):\\n if (i == 0 and j == 0) or (i != 0 and j == -1):\\n continue\\n list_of_cord.append((i, j))\\n return list_of_cord\",\n \"def process_coordinates(void: 'ChargeSystem', steps: int) -> list:\\n result = [[] for _ in void]\\n\\n for _ in range(steps):\\n for ind, charge in enumerate(void):\\n result[ind].append(list(charge.position))\\n void.update()\\n\\n return result\",\n \"def forward_kinematic_lines(self, q ):\\n \\n lines_pts = [] # list of array (n_pts x 3) for each lines\\n \\n ###########################\\n # Your graphical code here\\n ###########################\\n \\n # simple place holder\\n for q_i in q:\\n pts = np.zeros(( 1 , 3 )) # array of 1 pts for the line\\n pts[0,0] = q_i # x cord of point 0 = q\\n lines_pts.append( pts ) # list of all arrays of pts\\n \\n return lines_pts\",\n \"def getStartSpots(self):\\n spots = []\\n if self.index == 0:\\n startRow = 1\\n endRow = 4\\n if self.index == 1:\\n startRow = 6\\n endRow = 9\\n for row in range(startRow, endRow):\\n for col in range(1,9):\\n spots += [(col, row)]\\n return spots\",\n \"def calculate_center(self):\\n return [(self.startX + self.endX) / 2., (self.startY + self.endY) / 2.]\",\n \"def get_landmarks(self):\\n lm_x = [x for x, y in self.landmarks]\\n lm_y = [y for x, y in self.landmarks]\\n return [lm_x, lm_y]\",\n \"def get_all_arcs(self):\\n return [ (i, j) for i in self.constraints for j in self.constraints[i] ]\",\n \"def line(center, length, rotation=0):\\n unit = np.array([math.cos(rotation * 2 * PI / 360),\\n math.sin(rotation * 2 * PI / 360)])\\n end = center + unit * length / 2\\n start = center - unit * length / 2\\n return [\\\"PA{},{};\\\".format(*[int(coord) for coord in start]),\\n \\\"PD{},{};\\\".format(*[int(coord) for coord in end]),\\n \\\"PU;\\\"]\",\n \"def iter_all_atoms(self):\\n if self.segment is None:\\n return iter(list())\\n return self.segment.iter_all_atoms()\",\n \"def iter_all_atoms(self):\\n if self.segment is None:\\n return iter(list())\\n return self.segment.iter_all_atoms()\",\n \"def get_distances(self):\\n N = len(self.cells) # Number of cells\\n distances = np.zeros([N, N]) # distances between cells\\n positions = self.position_matrix() # positions of cells \\n \\n # get distances between cells (exploit symmetry between upper and lower triangular form)\\n for i, position in enumerate(positions[:-1, :]): # Iterate matrix except the last one\\n directions = positions[i+1:, :] - position # direction from i to j > i\\n distances[i, i+1:] = np.linalg.norm(directions, axis=1) # length of directions\\n \\n return distances + distances.T # Add lower triangle of matrix to upper \",\n \"def get_line(start, end): \\n # Setup initial conditions\\n x1, y1 = start\\n x2, y2 = end\\n dx = x2 - x1\\n dy = y2 - y1\\n \\n # Determine how steep the line is\\n is_steep = abs(dy) > abs(dx)\\n \\n # Rotate line\\n if is_steep:\\n x1, y1 = y1, x1\\n x2, y2 = y2, x2\\n \\n # Swap start and end points if necessary and store swap state\\n swapped = False\\n if x1 > x2:\\n x1, x2 = x2, x1\\n y1, y2 = y2, y1\\n swapped = True\\n \\n # Recalculate differentials\\n dx = x2 - x1\\n dy = y2 - y1\\n \\n # Calculate error\\n error = int(dx / 2.0)\\n ystep = 1 if y1 < y2 else -1\\n \\n # Iterate over bounding box generating points between start and end\\n y = y1\\n points = []\\n for x in range(x1, x2 + 1):\\n coord = np.array((y, x)) if is_steep else np.array((x, y))\\n points.append(coord)\\n error -= abs(dy)\\n if error < 0:\\n y += ystep\\n error += dx\\n \\n # Reverse the list if the coordinates were swapped\\n if swapped:\\n points.reverse()\\n return np.array(points)\",\n \"def angles(self):\\n\\n return self._angles\",\n \"def get_annotated_lines(self):\\n lines = [Line(idx + 1, x) for idx, x in enumerate(self.sourcelines)]\\n\\n try:\\n lines[self.lineno - 1].current = True\\n except IndexError:\\n pass\\n\\n return lines\",\n \"def _get_angles_csp2(m):\\n sma = '[$([cX3](:*):*),$([cX2+](:*):*),$([CX3]=*),$([CX2+]=*)]' # corresponds to any C-sp2\\n q = Chem.MolFromSmarts(sma)\\n matches = m.GetSubstructMatches(q)\\n iass3 = []\\n for match in matches:\\n j = match[0]\\n ma = m.GetAtomWithIdx(j)\\n neighbors = ma.GetNeighbors()\\n nneib = len(neighbors)\\n if nneib > 1:\\n for i0 in range(nneib):\\n for k0 in range(i0+1,nneib):\\n i, k = [ neighbors[i0].GetIdx(), neighbors[k0].GetIdx() ];\\n ias = [i,j,k]\\n if (ias not in iass3) or (ias[::-1] not in iass3):\\n iass3.append(ias)\\n return iass3\",\n \"def _rotate(self):\\n new_points = list()\\n for i in range(len(self._points)):\\n x = self._points[i][0]\\n y = self._points[i][1]\\n new_x = self._center[0] + self._center[1] - y\\n new_y = self._center[1] - self._center[0] + x\\n new_points.append((new_x, new_y))\\n return new_points\",\n \"def ortho_line_cut(self):\\n x_mid_left, y_mid_left = self.midpoint(0,1) # Computes the mid point of the LHS face of the edm cut\\n x_mid_right, y_mid_right = self.midpoint(2,3) # Computes the mid point of the RHS face of the edm cut\\n\\n ave_grad = self.average_grad()\\n m_horizontal = -1/ave_grad #90 degrees rotation of the vertical line average gradient\\n\\n horizontal_eq_c = y_mid_right - m_horizontal*x_mid_right # y offset of horizontal line\\n vertical_eq_left_c = y_mid_left - ave_grad * x_mid_left # y offset of vertical line on left side\\n\\n x_intersect, y_intersect = self.intersect_point(m_horizontal, horizontal_eq_c, ave_grad,vertical_eq_left_c)\\n\\n\\n coordleft = [x_intersect, y_intersect]\\n coordright =[x_mid_right, y_mid_right]\\n\\n dist = self.distance(coordleft, coordright)\\n\\n return coordleft, coordright, dist\",\n \"def find_out_difference_perpendiculars(lap, ref_lap):\\n\\n distances = []\\n\\n for i in lap.index:\\n point = lap.loc[i]\\n\\n closest_index = find_closest_point(point, ref_lap)\\n closest_point = ref_lap.loc[closest_index]\\n\\n neighbor_i = len(ref_lap) - 1 if closest_index == 0 else closest_index - 1\\n neighbor1 = ref_lap.loc[neighbor_i]\\n neighbor_i = 0 if len(ref_lap) == closest_index + 1 else closest_index + 1\\n neighbor2 = ref_lap.loc[neighbor_i]\\n\\n v1 = create_vector(closest_point, point)\\n v2 = create_vector(closest_point, neighbor1)\\n v3 = create_vector(closest_point, neighbor2)\\n\\n angle1 = find_angle_between_vectors(v1, v2)\\n angle2 = find_angle_between_vectors(v1, v3)\\n\\n degrees90 = math.pi / 2\\n min_dist = -1\\n if angle1 > degrees90 and angle2 > degrees90:\\n min_dist = line_length(point.LAT, point.LON, closest_point.LAT, closest_point.LON)\\n elif angle1 < degrees90 and angle2 < degrees90:\\n dist1 = find_shortest_distance(point, closest_point, neighbor1)\\n dist2 = find_shortest_distance(point, closest_point, neighbor2)\\n min_dist = dist1 if dist1 <= dist2 else dist2\\n elif angle1 <= degrees90:\\n min_dist = find_shortest_distance(point, closest_point, neighbor1)\\n elif angle2 <= degrees90:\\n min_dist = find_shortest_distance(point, closest_point, neighbor2)\\n\\n if min_dist == -1:\\n print('ERROR: Could not find distance')\\n print(\\\"Indices: {} {}\\\\nAngles: {} {}\\\".format(i, closest_index, angle1, angle2))\\n elif math.isnan(min_dist):\\n print(\\\"NAN value!!!\\\\nIndices: {} {}\\\\nAngles: {} {}\\\".format(i, closest_index, angle1, angle2))\\n elif min_dist < 0:\\n print(\\\"Negative value!!!\\\\nIndices: {} {}\\\\nAngles: {} {}\\\".format(i, closest_index, angle1, angle2))\\n else:\\n min_dist = degrees2kilometers(min_dist) * 100000 # in centimeters\\n distances.append(min_dist)\\n\\n return distances\",\n \"def get_lines(cls, frame, radius=5, sys_context=None):\\n\\t\\tcode_lines, start_line = cls.get_lines_with_start(frame, radius, sys_context)\\n\\t\\treturn code_lines\",\n \"def distances(self):\\n self._sort_measurements()\\n return self._distances\",\n \"def exportCircles(self):\\n # Remember to compute circumcircles if not done before\\n # for t in self.triangles:\\n # self.circles[t] = self.circumcenter(t)\\n\\n # Filter out triangles with any vertex in the extended BBox\\n # Do sqrt of radius before of return\\n return [(self.circles[(a, b, c)][0], sqrt(self.circles[(a, b, c)][1]))\\n for (a, b, c) in self.triangles if a > 3 and b > 3 and c > 3]\",\n \"def get_pairs(self, number: int, duration: int, line: int = -1) -> list:\\n if line != -1 and len(self._pair_lines) != 0:\\n pairs = self._pair_lines[line].get(number)\\n if pairs is not None and pairs[0][\\\"time\\\"].duration() == duration:\\n return pairs\\n\\n for line in self._pair_lines:\\n pairs = line.get(number)\\n if pairs is not None and pairs[0][\\\"time\\\"].duration() == duration:\\n return pairs\\n\\n return []\",\n \"def onePassRadon(self, angleAmount):\\n rotated_image = self.rotateImage(angleAmount)\\n (N, M) = rotated_image.shape\\n radon_values = [0]*M\\n for radon_line in range(M):\\n linesum = 0\\n for pixel in range(N):\\n linesum += rotated_image[pixel][radon_line]\\n radon_values[M - radon_line - 1] = linesum\\n #print(radon_values)\\n return radon_values\",\n \"def find_line_through_point(center, theta, length):\\n\\n r = length\\n cx, cy = center\\n\\n xo = int(r * math.sin(theta))\\n yo = int(r * math.cos(theta))\\n\\n line_start = cx, cy\\n line_end = cx + xo, cy + yo\\n\\n return line_start, line_end\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6329989","0.6249319","0.62350285","0.5960659","0.582981","0.5782247","0.57550585","0.5716322","0.5686487","0.56017864","0.5553188","0.5502591","0.5473933","0.54706573","0.5465637","0.54402316","0.54249376","0.5414995","0.5412383","0.54076225","0.5404326","0.5385753","0.5365593","0.5359811","0.5350391","0.534553","0.53316456","0.5329858","0.5306679","0.53057206","0.5301155","0.529359","0.528732","0.52661145","0.5263381","0.5263378","0.5260938","0.52606606","0.525761","0.5255939","0.5247329","0.5237171","0.5235728","0.52312773","0.5224873","0.52244496","0.52167684","0.5216656","0.5202208","0.5200166","0.5196263","0.51958966","0.5189989","0.5186243","0.5177061","0.51671046","0.5160465","0.5151898","0.51505786","0.5147919","0.51456475","0.51326334","0.51325583","0.51222414","0.51222414","0.5120219","0.5111729","0.510978","0.50982314","0.50982314","0.5094069","0.5082651","0.5081982","0.50811493","0.5080331","0.5073082","0.5065809","0.50657976","0.5065454","0.5064172","0.5063787","0.50514853","0.5043248","0.50418407","0.5040987","0.5040987","0.50208","0.5020137","0.5016433","0.5014866","0.5011779","0.50101596","0.50025654","0.4999291","0.49940565","0.49922276","0.4991318","0.49904722","0.49892405","0.49861676"],"string":"[\n \"0.6329989\",\n \"0.6249319\",\n \"0.62350285\",\n \"0.5960659\",\n \"0.582981\",\n \"0.5782247\",\n \"0.57550585\",\n \"0.5716322\",\n \"0.5686487\",\n \"0.56017864\",\n \"0.5553188\",\n \"0.5502591\",\n \"0.5473933\",\n \"0.54706573\",\n \"0.5465637\",\n \"0.54402316\",\n \"0.54249376\",\n \"0.5414995\",\n \"0.5412383\",\n \"0.54076225\",\n \"0.5404326\",\n \"0.5385753\",\n \"0.5365593\",\n \"0.5359811\",\n \"0.5350391\",\n \"0.534553\",\n \"0.53316456\",\n \"0.5329858\",\n \"0.5306679\",\n \"0.53057206\",\n \"0.5301155\",\n \"0.529359\",\n \"0.528732\",\n \"0.52661145\",\n \"0.5263381\",\n \"0.5263378\",\n \"0.5260938\",\n \"0.52606606\",\n \"0.525761\",\n \"0.5255939\",\n \"0.5247329\",\n \"0.5237171\",\n \"0.5235728\",\n \"0.52312773\",\n \"0.5224873\",\n \"0.52244496\",\n \"0.52167684\",\n \"0.5216656\",\n \"0.5202208\",\n \"0.5200166\",\n \"0.5196263\",\n \"0.51958966\",\n \"0.5189989\",\n \"0.5186243\",\n \"0.5177061\",\n \"0.51671046\",\n \"0.5160465\",\n \"0.5151898\",\n \"0.51505786\",\n \"0.5147919\",\n \"0.51456475\",\n \"0.51326334\",\n \"0.51325583\",\n \"0.51222414\",\n \"0.51222414\",\n \"0.5120219\",\n \"0.5111729\",\n \"0.510978\",\n \"0.50982314\",\n \"0.50982314\",\n \"0.5094069\",\n \"0.5082651\",\n \"0.5081982\",\n \"0.50811493\",\n \"0.5080331\",\n \"0.5073082\",\n \"0.5065809\",\n \"0.50657976\",\n \"0.5065454\",\n \"0.5064172\",\n \"0.5063787\",\n \"0.50514853\",\n \"0.5043248\",\n \"0.50418407\",\n \"0.5040987\",\n \"0.5040987\",\n \"0.50208\",\n \"0.5020137\",\n \"0.5016433\",\n \"0.5014866\",\n \"0.5011779\",\n \"0.50101596\",\n \"0.50025654\",\n \"0.4999291\",\n \"0.49940565\",\n \"0.49922276\",\n \"0.4991318\",\n \"0.49904722\",\n \"0.49892405\",\n \"0.49861676\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.56110704"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":76687,"cells":{"query":{"kind":"string","value":"Perform a simple key validation. In case key is invalid raises exception."},"document":{"kind":"string","value":"def validate_key(key):\r\n try:\r\n secret.Secret(key)\r\n except secret.Secret.InvalidSecret as e:\r\n raise KeyIsInvalid(e.message)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def isValidKey(key):\n return True","def validate_key_throw(*args):\n validation_result = validate_key(*args)\n if not validation_result:\n raise ValueError(str(validation_result))\n return validation_result","def _check_key(self, key):\n raise NotImplementedError","def validate_key(self, key: keyType) -> bool:\n if isinstance(key, (dict,bool)):\n raise Exception\n if key is None:\n raise Exception\n # Numerical key object has no len(),\n # so explicitly specify which types are not allowed to use empty value as keys\n if isinstance(key, (str, tuple, set, list)) and (len(key) == 0):\n raise Exception\n return True","def _check_key(key): # type: (str) -> None\n if not key:\n raise ValueError('Key must not be empty.')\n if '.' in key:\n raise ValueError('Key must not contain dots.')","def validate(self, key, val):\n return True","def validate(self, key, val):\n return True","def keyIsValid(key):\n\n isValid = 1\n \n try:\n temp = getParam(key)\n\n except ValueError:\n isValid = 0\n warning(\" WARNING: %s not set\" % (key))\n\n return isValid","def test_valid_key(self):\n f = lws.valid_data_key\n assert f('string', int, r'string') is False\n assert f('string', str, r'test') is False\n assert f(123, int, '123') is False\n assert f(123.00, float, '123') is False\n assert f('123', str, r'[0-9]*') is True","def test_is_valid_label_key_invalid_input():\n # test length violations\n assert not is_valid_label_key(key=None) # Too short\n assert not is_valid_label_key(key=\"\") # Too short\n assert not is_valid_label_key(key=f\"{'p' * 254}/n\") # prefix too long\n assert not is_valid_label_key(key=\"/n\") # prefix too short\n assert not is_valid_label_key(key=\"p/\") # name too short\n assert not is_valid_label_key(key=\"a\" * 254) # name too long\n assert not is_valid_label_key(key=f\"d/{'b'*64}\") # name too long\n # test first character violations (not alphanum)\n assert not is_valid_label_key(key=\"-a\")\n assert not is_valid_label_key(key=\".b\")\n assert not is_valid_label_key(key=\" c\")\n # test last character violations (not alphanum)\n assert not is_valid_label_key(key=\"a-\")\n assert not is_valid_label_key(key=\"b.\")\n assert not is_valid_label_key(key=\"c \")\n assert not is_valid_label_key(key=\"sw33T#\")\n # test middle characters violations\n assert not is_valid_label_key(key=\"a$$a\")\n assert not is_valid_label_key(key=\"b b\")","def test_is_valid_annotation_key_invalid_input():\n # test length violations\n assert not is_valid_annotation_key(key=None) # Too short\n assert not is_valid_annotation_key(key=\"\") # Too short\n assert not is_valid_annotation_key(key=f\"{'p' * 254}/n\") # prefix too long\n assert not is_valid_annotation_key(key=\"/n\") # prefix too short\n assert not is_valid_annotation_key(key=\"p/\") # name too short\n assert not is_valid_annotation_key(key=\"a\" * 254) # name too long\n assert not is_valid_annotation_key(key=f\"d/{'b'*64}\") # name too long\n # test first character violations (not alphanum)\n assert not is_valid_annotation_key(key=\"-a\")\n assert not is_valid_annotation_key(key=\".b\")\n assert not is_valid_annotation_key(key=\" c\")\n # test last character violations (not alphanum)\n assert not is_valid_annotation_key(key=\"a-\")\n assert not is_valid_annotation_key(key=\"b.\")\n assert not is_valid_annotation_key(key=\"c \")\n assert not is_valid_annotation_key(key=\"sw33T#\")\n # test middle characters violations\n assert not is_valid_annotation_key(key=\"a$$a\")\n assert not is_valid_annotation_key(key=\"b b\")","def __check_key_validity(self, key):\n if not isinstance(key, tuple):\n raise TypeError(\"key must be a tuple\")\n if len(key) != 2:\n raise ValueError(\"key must be of length two\")\n if not (isinstance(key[0], int) and isinstance(key[1], int)):\n raise TypeError(\"elements of key must be integers\")\n if not ((0 <= key[0] < self.m) and (0 <= key[1] < self.n)):\n raise exc.OutOfBoundsError(\"key is out of bounds\")","def test_invalid_keys(self):\n\n\t\ttry:\n\t\t\tyield self.conn.set(\"this has spaces\", 1)\n\t\texcept ValueError:\n\t\t\tpass\n\t\telse:\n\t\t\tself.fail(\"key with spaces did not raise ValueError\")\n\n\t\ttry:\n\t\t\tyield self.conn.set(\"\\x10control\\x02characters\\x11\", 1)\n\t\texcept ValueError:\n\t\t\tpass\n\t\telse:\n\t\t\tself.fail(\"key with control characters did not raise ValueError\")\n\n\t\ttry:\n\t\t\tyield self.conn.set(\"a\" * (SERVER_MAX_KEY_LENGTH + 1), 1)\n\t\texcept ValueError:\n\t\t\tpass\n\t\telse:\n\t\t\tself.fail(\"long key did not raise ValueError\")\n\n\t\ttry:\n\t\t\tyield self.conn.set(u\"unicode\\u4f1a\", 1)\n\t\texcept TypeError:\n\t\t\tpass\n\t\telse:\n\t\t\tself.fail(\"unicode key did not raise ValueError\")","def test_getKey_nokey(self):\n filename = os.path.join(os.getcwd(), 'sekrit')\n key = crypto.getKey(filename)\n self.failUnlessIsInstance(key, basestring,\n \"key isn't a string! type=%r\" % type(key))","def test_is_valid_label_key_valid_input():\n # test valid label keys\n assert is_valid_label_key(key=\"l0l\")\n assert is_valid_label_key(key=\"l0L\")\n assert is_valid_label_key(key=\"L-l\")\n assert is_valid_label_key(key=\"L.L\")\n assert is_valid_label_key(key=\"4-you\")\n assert is_valid_label_key(key=\"you.2\")\n assert is_valid_label_key(key=\"p/n\")\n assert is_valid_label_key(key=\"prefix/you.2\")\n assert is_valid_label_key(key=\"how.sad/to-see\")\n assert is_valid_label_key(key=f\"{'d'*253}/{'n'*63}\")","def is_key_valid(self,key):\n if not key or any(map(lambda s: s in key,space_chars))\\\n or any(map(lambda s: s in key,bad_chars)):\n return False \n return True","def test_encrypt_key_invalid(self):\n with pytest.raises(EncryptionError):\n encrypt('message', key=b'0' * 31)","def _is_valid_key(self, key):\r\n\r\n # Check the length\r\n if len(key) > 250:\r\n return False\r\n\r\n # Check that there are no spaces or control characters\r\n for char in key:\r\n if ord(char) < 33 or ord(char) == 127:\r\n return False\r\n\r\n return True","def _is_valid_key(self, key):\n\t\t\n\t\t# If the key is not a string\n\t\tif not isinstance(key, str):\n\t\t\treturn False\n\t\telse:\n\t\t\tkey = str.upper(key)\n\t\t\n\t\t# If the given key does not match the standard notation XY\n\t\tif len(key) != 2:\n\t\t\treturn False\n\t\t\n\t\t# If the key is out of the board\n\t\tif key[0] not in self.columns or key[1] not in self.rows:\n\t\t\treturn False\n\t\t\n\t\t# Otherwise the key is valid\n\t\treturn True","def _validate_and_split_key(self, key):\n if self._len_keys == 1:\n return self._validate_and_split_len_one(key)\n else:\n return self._validate_and_split_len(key)","def _validate_key(self, key):\n if isinstance(key, str):\n key = unicode(key, 'utf-8')\n elif not isinstance(key, unicode):\n raise TypeError(\n \"`key` must be `str` or `unicode`, not `{}`\".format(\n key.__class__.__name__)\n )\n return key","def test_key_none(self):\n try:\n AlphaVantage()\n self.fail(msg='A None api key must raise an error')\n except ValueError:\n self.assertTrue(True)","def test_key_none(self):\n try:\n AlphaVantage()\n self.fail(msg='A None api key must raise an error')\n except ValueError:\n self.assertTrue(True)","def _raiseIfWebsafeKeyNotValid(websafeKey, kind):\n # Check that websafeKey is not None\n if not websafeKey:\n raise endpoints.BadRequestException(\n \"Websafe key not provided for '%s'\" % kind)\n # Try to decode the websafe key into a real key\n try:\n key = ndb.Key(urlsafe=websafeKey)\n except:\n raise endpoints.BadRequestException(\n \"Websafe key provided for '%s' could not be decoded: %s\" %\n (kind, websafeKey))\n # Ensure that the key is of the desired kind\n if key.kind() != kind:\n raise endpoints.BadRequestException(\n \"Websafe key is not of the '%s' kind: %s\" % (kind, websafeKey))\n # If all is well, return the key\n return key","def test_handle_key_error():\n\n @handle_key_error\n def get_item(key):\n data = {\"A\": 1, \"B\": 2}\n return data[key]\n\n value = get_item(\"A\")\n assert value == 1\n\n with pytest.raises(InvalidParameter) as exc:\n get_item(\"C\")\n\n assert \"C\" in str(exc.value)","def test_validate_api_key(app, seed_data, key, result):\n user_id, api_key = seed_data\n if key == 'use-valid-key':\n key = api_key\n with app.app_context():\n assert auth.validate_api_key(user_id, key) == result","def is_valid_key(key, crypt_method):\n logger.info(f\"key: {key}, crypt_method: {crypt_method}\")\n if crypt_method == 'C':\n while type(key) is not int or key not in range(0, 95):\n try:\n key = Check.is_integer(key)[1]\n if key not in range(0, 95):\n raise ValueError\n except (TypeError, ValueError):\n print(\"You must enter an integer between 1 and 95!\")\n key = input(\"Enter an encryption key\\n>> \")\n elif crypt_method in ('M', 'P'):\n pass\n else:\n return False\n return True, key","def test_allow_extra_keys(self):\n from natcap.invest import validation\n\n args = {'a': 'a', 'b': 'b'}\n spec = {\n 'a': {\n 'type': 'freestyle_string',\n 'name': 'a',\n 'about': 'a freestyle string',\n 'required': True\n }\n }\n message = 'DEBUG:natcap.invest.validation:Provided key b does not exist in MODEL_SPEC'\n\n with self.assertLogs('natcap.invest.validation', level='DEBUG') as cm:\n validation.validate(args, spec)\n self.assertTrue(message in cm.output)","def test_basic_singleton_key_error(self):\n\n schema = {\n ('root', str): {\n ('sample node', str, 'sample'): ('node', str, r'[a-z]*')\n }\n }\n data = {'root': {'not sample': 'node'}}\n\n ERRORS = lws.return_errors()\n expected_schema = {\n ('root', 'root'): [('sample node', ERRORS['key'])]\n }\n expected_data = {\n ('root', 'root'): [('not sample', ERRORS['key'])]\n }\n\n assert dict(lws.validate_schema(schema, data)) == expected_schema\n assert dict(lws.validate_data(schema, data)) == expected_data","def test_getObjectByKey_raises_KeyError(self):\n try:\n self.tile_bucket.getObjectByKey('foo_key')\n except KeyError:\n return\n assert(False)","def is_valid(key):\n return key[0:2] == \"MR\" and key[2:].isdigit() and len(key) in [9, 10]","def creation_validation(**_):\n\n for property_key in constants.KEYPAIR_REQUIRED_PROPERTIES:\n utils.validate_node_property(property_key, ctx.node.properties)\n\n key_file = _get_path_to_key_file()\n key_file_in_filesystem = _search_for_key_file(key_file)\n\n if ctx.node.properties['use_external_resource']:\n if not key_file_in_filesystem:\n raise NonRecoverableError(\n 'External resource, but the key file does not exist locally.')\n try:\n _get_key_pair_by_id(ctx.node.properties['resource_id'])\n except NonRecoverableError as e:\n raise NonRecoverableError(\n 'External resource, '\n 'but the key pair does not exist in the account: '\n '{0}'.format(str(e)))\n else:\n if key_file_in_filesystem:\n raise NonRecoverableError(\n 'Not external resource, '\n 'but the key file exists locally.')\n try:\n _get_key_pair_by_id(ctx.node.properties['resource_id'])\n except NonRecoverableError:\n pass\n else:\n raise NonRecoverableError(\n 'Not external resource, '\n 'but the key pair exists in the account.')","def valid_key(self): \n self.so.ValidKey.restype = c_bool\n result = self.so.ValidKey()\n return result","def validate_handshake_public_key(cls, public_key: bytes) -> None:\n ...","def validate_keystore_key(args):\n expected_name = 'keyRings/%s/cryptoKeys/%s'\n expected_name %= (args.kms_keyring, args.kms_key)\n describe_output = ''\n try:\n describe_output = subprocess.check_output(\n ['gcloud', 'kms', 'keys', 'describe', args.kms_key,\n '--project', args.kms_project,\n '--location', args.kms_location,\n '--keyring', args.kms_keyring,\n '--format', 'value(name)'])\n except subprocess.CalledProcessError:\n pass\n if expected_name in describe_output:\n return\n # Print warning and exit if output did not include the key.\n warning = 'KMS key \"%s\" not found in keyring=%s project=%s location=%s'\n warning %= (args.kms_key,\n args.kms_keyring,\n args.kms_project,\n args.kms_location)\n Print.YL(warning)\n sys.exit(1)","def key(self, value=None):\n if self.crypt_method == 'C':\n key_type = \"number\"\n else:\n key_type = \"string\"\n\n input_message = f\"Please enter a {key_type} as a \" \\\n f\"{self.crypt_type}ion key\\n>> \"\n if value is None:\n key = input(input_message)\n else:\n key = value\n\n is_valid_key, key = Check.is_valid_key(key, self.crypt_method)\n if is_valid_key:\n self._key = key\n else:\n raise ValueError(f\"Key{key} is invalid\")","def test_invalid_key(self):\n with pytest.raises(yaenv.EnvError) as err:\n _ = yaenv.core.EnvVar('221b=\"starts with number\"')\n assert 'Invalid key' in str(err.value)\n with pytest.raises(yaenv.EnvError) as err:\n _ = yaenv.core.EnvVar('_=\"not assignable\"')\n assert 'Invalid key' in str(err.value)\n with pytest.raises(yaenv.EnvError) as err:\n _ = yaenv.core.EnvVar('o-o=\"invalid character\"')\n assert 'Invalid key' in str(err.value)","def test_invalid_chars_ssck(self):\r\n valid_base = SlashSeparatedCourseKey(u'org.dept-1%2', u'course.sub-2%3', u'run.faster-4%5')\r\n for key in SlashSeparatedCourseKey.KEY_FIELDS:\r\n with self.assertRaises(InvalidKeyError):\r\n # this ends up calling the constructor where the legality check should occur\r\n valid_base.replace(**{key: u'funny thing'})","def test_is_valid_annotation_key_valid_input():\n # test valid label keys\n assert is_valid_annotation_key(key=\"l0l\")\n assert is_valid_annotation_key(key=\"l0L\")\n assert is_valid_annotation_key(key=\"L-l\")\n assert is_valid_annotation_key(key=\"L.L\")\n assert is_valid_annotation_key(key=\"4-you\")\n assert is_valid_annotation_key(key=\"you.2\")\n assert is_valid_annotation_key(key=\"p/n\")\n assert is_valid_annotation_key(key=\"prefix/you.2\")\n assert is_valid_annotation_key(key=\"how.sad/to-see\")\n assert is_valid_annotation_key(key=f\"{'d'*253}/{'n'*63}\")","def validate(self):\n if not self.key or not self.certificates:\n raise ValueError(\"Key or certificate missing in Keypair\")","def check_auth_publickey(self, username, key):\n return AUTH_FAILED","def test_getKey_keyexists(self):\n filename = self.mktemp()\n with open(filename, 'wb') as fh:\n fh.write(SEKRIT_KEY)\n fh.flush()\n\n key = crypto.getKey(filename)\n self.failUnlessIsInstance(key, basestring,\n \"key isn't a string! type=%r\" % type(key))\n self.assertEqual(SEKRIT_KEY, key,\n \"\"\"The example key and the one read from file differ!\n key (in hex): %s\n SEKRIT_KEY (in hex): %s\"\"\"\n % (key.encode('hex'), SEKRIT_KEY.encode('hex')))","def validate_instruction_keys(instruction: TransactionInstruction, expected: int) -> None:\n if len(instruction.keys) < expected:\n raise ValueError(f\"invalid instruction: found {len(instruction.keys)} keys, expected at least {expected}\")","def test_rekey_key_format(self):\n old_key = b'0' * 32\n encrypted = encrypt('message', key=old_key)\n\n with pytest.raises(EncryptionError):\n rekey(encrypted, old_key=old_key, new_key=b'1' * 31)","def testKeyInfoTooShort(self):\n key = 5\n self.assertRaises(ValueError, dataToToken, key, data='x', keyInfo='xx')","def _check_transform_key(key: Hashable) -> None:\n _test_hashable = hash(key) # The only 'real' way to make sure is hashable\n # if not isinstance(key, Hashable):\n # raise TypeError((type(key), \"transformation lookup key is not hashable\"))","def test_generate_key(self): \n k = Key().generate()\n self.assertRegex(k, \"[a-zA-Z0-9+\\/]+={0,2}\")","def validate(self, key: keyType, value: valueType) -> bool:\n return self.validate_key(key) and self.validate_value(value)","def test_create_key():\n\n assert symmetric.create_key() != \"\"","def remote_verifyKey(self, key, protocol):\r\n if self._authenticated.called:\r\n return Failure(InvalidKey('Only one guess is possible.'))\r\n\r\n if isinstance(protocol, Failure):\r\n self._authenticated.errback(protocol)\r\n else:\r\n if self._key != key:\r\n e = Failure(InvalidKey('Wrong key supplied.'))\r\n self._authenticated.errback(e)\r\n return e\r\n\r\n self._authenticated.callback(protocol)","def check_keys(self):","def is_key(v) -> bool:\n try:\n _validate(v, prefixes=[b\"edsk\", b\"edpk\", b\"spsk\", b\"p2sk\", b\"sppk\", b\"p2pk\"])\n except (ValueError, TypeError):\n return False\n return True","def test_neg_exists_key_invalid_data(self, key, ex_code, ex_msg):\n with pytest.raises(e.ParamError):\n key, _ = self.as_connection.exists(key)","def test_map_missing_key_encountered():\n with pytest.raises(KeyError):\n Map().read_key(10, b\"\")","def _validateKey(self, key, cls = None):\n\n key_class_types = [self._BaseKey__class, self._LocalKey__class,\n self._MsgKey__class, ErrorMsgManager]\n\n if cls:\n if inspect.isclass(cls) and cls in key_class_types:\n classes = [cls]\n else:\n return None\n else:\n classes = key_class_types\n return any([isinstance(key, cls) for cls in classes])","def test_rekey_non_encrypted(self):\n with pytest.raises(EncryptionError):\n rekey('message', old_key=b'0' * 32, new_key=b'1' * 32)","def test_encryption_public_key_not_given(self) -> None:\n\n given = \"Hello, World!\"\n\n encryptor = DataEncryption()\n\n self.assertRaises(ValueError, lambda: encryptor.encrypt_data(given))","def test_dweet_for_with_an_invalid_key(self):\n try:\n dweepy.dweet_for(self.my_thing_id, test_data, key='badkey')\n except dweepy.DweepyError as e:\n self.assertEqual(e.args[0], 'the key you provided doesn\\'t work with this thing')\n else:\n self.fail(\"shouldn't ever get called\")","def key_error_message(self,key):\n if not key:\n return 'key is blank.'\n elif any(map(lambda s: s in key,space_chars)):\n return '\"{}\" contains whitespace.'.format(key)\n elif any(map(lambda s: s in key,bad_chars)):\n return '\"{}\" contains special characters.'.format(key)","def test_invalid_chars_location(self):\r\n course_key = SlashSeparatedCourseKey(u'org.dept-1%2', u'course.sub-2%3', u'run.faster-4%5')\r\n valid_base = course_key.make_usage_key('tomato-again%9', 'block-head:sub-4%9')\r\n for key in SlashSeparatedCourseKey.KEY_FIELDS:\r\n with self.assertRaises(InvalidKeyError):\r\n # this ends up calling the constructor where the legality check should occur\r\n valid_base.replace(**{key: u'funny thing'})","def set_key(self, key, value) -> True:\n\n if key in self.key_functs:\n # Validate the value\n real_val = self.key_functs[key](value)\n \n if real_val is not None:\n self.key_values[key] = real_val \n self.key_satified[key] = True\n return True\n\n # Invalid key\n return False","def checkKey(self):\n # TO DO for checking API authentication\n if self.apikey is None:\n return False\n else:\n return True","def test_keyerror(self):\n try:\n self.db['foo']\n except KeyError, e:\n assert \"no key 'foo' in database <SequenceFileDB\" in str(e), str(e)","def test_validation(self):\r\n\r\n with self.assertRaises(ValidationError):\r\n Dictionary().validate([1,2,3])\r\n\r\n with self.assertRaises(ValidationError):\r\n Dictionary().validate('stringy')\r\n\r\n with self.assertRaises(ValidationError):\r\n Dictionary().validate(1)","def __missing__(self, key):\n global MISSING\n MISSING = key # For debugging - save name of missing key\n return INVALID","def check_empty_key(self, key):\n if key is None or key == \"\" or key == self.empty_api_key:\n print(\"ERROR, A KEY IS EMPTY - CHECK YOUR FILE\")\n return False\n return True","def _check_key_type(cls, key: Any) -> K:\n if not isinstance(key, cls.keytype):\n raise KeyError(\n f\"{cls!r} accepts only keys of type {cls.keytype!r}, \"\n f\"got {type(key)!r}\"\n )\n return cast(K, key)","def handle_key(self, k):\n\t\treturn False","def test_decrypt_key_invalid(self):\n encrypted = encrypt('message', key=b'0' * 32)\n\n with pytest.raises(EncryptionError):\n decrypt(encrypted, key=b'0' * 31)","def test_validate_with_invalid_key_format_type(self):\n key_format_type = \"invalid\"\n kwargs = {'key_format_type': key_format_type}\n\n self.assertRaisesRegex(\n TypeError, \"invalid key format type\", Digest, **kwargs)","def test_throws_base_price_missing_key(self):\n with pytest.raises(marshmallow.ValidationError):\n BasePrice.Schema().loads(json.dumps(base_price_missing_key))","def test_missingKey(self):\n self.assertIsNone(self.users.key(\"mystery domain\", \"mystery user\"))","def test_PerfectModel_verify_metric_keyerrors(\n perfectModelEnsemble_initialized_control, metric\n):\n with pytest.raises(KeyError) as excinfo:\n perfectModelEnsemble_initialized_control.verify(\n comparison=\"e2c\",\n metric=metric,\n dim=[],\n )\n assert \"Specify metric from\" in str(excinfo.value)","def test_incorrect_prediction_key(self):\n self._config['Prediction key'] = 'wrong_key'\n with self.assertRaisesRegex(ValueError, 'Invalid prediction key'):\n self._gen.generate(\n example=self._example,\n model=self._model,\n dataset=self._dataset,\n config=self._config)","def test_prediction_key_required(self):\n self._config['Prediction key'] = ''\n with self.assertRaisesRegex(ValueError,\n 'Please provide the prediction key'):\n self._gen.generate(\n example=self._example,\n model=self._model,\n dataset=self._dataset,\n config=self._config)","def _is_valid(key):\n is_valid = False\n for valid_key in VALID_KEYS:\n if valid_key in key:\n is_valid = True\n for invalid_key in INVALID_KEYS:\n if invalid_key in key:\n is_valid = False\n return is_valid","def test_validation(self):\n\n with self.assertRaises(ValidationError):\n Dictionary().validate([1,2,3])\n\n with self.assertRaises(ValidationError):\n Dictionary().validate('stringy')\n\n with self.assertRaises(ValidationError):\n Dictionary().validate(1)","def test_keys_failure(self):\n storage = Storage()\n storage._keys_dict = {'1': 'one',\n 'abc': '1'}\n self.assertRaises(StoragePatternError, storage.keys, 'ab[cd')","def testBadKeyToToken(self):\n key = 5\n self.assertRaises(ValueError, dataToToken, key, data='hey')","def validate_key_code(self, code):\n\n key = self.connect().query(KeyCode)\\\n .filter(KeyCode.code == code)\\\n .first()\n\n if key and (key.user and key.enabled):\n return True\n return False","def test_api_key (key):\n\tdb = getattr(g,'db', None)\n\n\tif isinstance(key, unicode):\n\t\tkey = key.encode('utf-8')\n\n\tqry = \"SELECT apikey FROM api_keys WHERE apikey=%s;\"\n\twith db as cur:\n\t\treturn 0 < cur.execute(qry, (key,))","def validate_authkey(value):\n if not len(value) == 32:\n raise ValidationError(\n 'Value must be a string containing 32 alphanumeric characters')","def setup_key_encrypt(self):\r\n\t\tself.max_key = math.floor(len(self.message) / 2)\r\n\t\twhile True:\r\n\t\t\tkey = input(f\"Please enter a key value less than or equal to {self.max_key}. --> \")\r\n\t\t\ttry:\r\n\t\t\t\tself.key = int(key)\r\n\t\t\texcept ValueError:\r\n\t\t\t\tprint(\"Key needs to be a number.\")\r\n\t\t\t\tcontinue\r\n\t\t\tif self.key > self.max_key: \t\t\t\r\n\t\t\t\tprint(f\"{key} is too big of a number.\")\t\r\n\t\t\telif self.key == 0:\r\n\t\t\t\tprint(\"0 cannot be a key\")\t\t\t\r\n\t\t\telse:\t\t\t\r\n\t\t\t\tbreak","def key_validation(dictionary):\n assert dictionary is not None\n invalid = {}\n for key in dictionary:\n value = dictionary[key]\n if is_empty(value):\n continue\n if key in valid_check:\n if not valid_check[key](value):\n invalid[invalids[key][\"error\"]] = key + \" is invalid\"\n return invalid","def test_get_invalid_key(test_file):\n md = OSXMetaData(test_file.name)\n with pytest.raises(KeyError):\n md[\"invalid_key\"]","def __init__(self, key: bytes):\n\n if len(key) != 32:\n raise ValueError('Key must be 32 bytes long')\n self.key = key","def api_key_check():\n req_path = request.path\n method_type = request.method\n app.logger.info(\">>> path = {}, method = {}\".format(req_path, method_type))\n\n if not app_props.api_key_check:\n app.logger.debug('>>> api key check closed')\n return None\n\n if req_path in app_props.api_key_white_list:\n app.logger.info('>>> {} in white list, pass'.format(req_path))\n return None\n headers = request.headers\n api_key_from_req = headers.get('x-api-key')\n if not api_key_from_req:\n app.logger.debug('>>> enter api-key error')\n return resp_json(BaseResp.err('no x-api-key header'))\n\n key_obj = Key.query.filter_by(api_key=api_key_from_req).first()\n if key_obj:\n app.logger.debug('>>> consumer_id = {}, secret_key = {}'.format(key_obj.consumer_id, key_obj.secret_key))\n g.consumer_id = key_obj.consumer_id\n g.secret_key = key_obj.secret_key\n return None\n else:\n return resp_json(BaseResp.err('Err api key'))","def test_validate_yubikey(self):\n from_key = self.yk_rnd.from_key(self.yk_public_id, self.yk_key)\n self.assertTrue(pyhsm.yubikey.validate_yubikey_with_aead( \\\n self.hsm, from_key, self.aead.data, self.kh_validate))","def clean_key_name(self):\n key = self.cleaned_data['key_name']\n # Ensure key starts with prefix\n if not key.startswith(self.get_key_prefix()):\n raise forms.ValidationError('Key does not have required prefix.')\n # Ensure key exists\n if not self.get_upload_key():\n raise forms.ValidationError('Key does not exist.')\n return key","def check_len( string_key ) : \r\n\r\n if len( string_key ) != 4 :\r\n\r\n raise Eggog( \"'%s': EGI wants the key to be exactly four characters!\" % (string_key, ) ) \r\n \r\n else :\r\n \r\n return True","def test_validate_non_included_keys():\n field = PartialDictField(included_keys=['a'], value_field=CharField(max_length=5),\n required=False)\n data = {'b': '123456'}\n try:\n field.run_validators(data)\n except ValidationError:\n assert False, 'Got a ValidationError for a non-included key'","def check_type( string_key ) : \r\n\r\n if type( string_key ) != type( '' ) : \r\n\r\n # raise self.__class__( \"'%s': EGI wants the key to be four _characters_ (not %s) !\" % (type(string_key), ) ) \r\n raise Eggog( \"'%s': EGI wants the key to be four _characters_ (not %s) !\" % (type(string_key), ) ) \r\n \r\n else :\r\n \r\n return True","def rsa_is_valid_publickey(key):\r\n # must be a dict\r\n if type(key) is not dict:\r\n return False\r\n\r\n # missing the right keys\r\n if 'e' not in key or 'n' not in key:\r\n return False\r\n\r\n # has extra data in the key\r\n if len(key) != 2:\r\n return False\r\n\r\n for item in ['e', 'n']:\r\n # must have integer or long types for the key components...\r\n if type(key[item]) is not int and type(key[item]) is not long:\r\n return False\r\n\r\n if key['e'] < key['n']:\r\n # Seems valid...\r\n return True\r\n else:\r\n return False","async def test_invalid_key(hass: HomeAssistant, invalid_key_api: Mock) -> None:\n result = await hass.config_entries.flow.async_init(\n DOMAIN, context={\"source\": SOURCE_USER}\n )\n assert result.get(\"type\") == data_entry_flow.FlowResultType.FORM\n assert result.get(\"step_id\") == \"user\"\n\n # Test filling in API key\n result = await hass.config_entries.flow.async_init(\n DOMAIN,\n context={\"source\": SOURCE_USER},\n data={CONF_API_TOKEN: \"psk_123456789\"},\n )\n assert result.get(\"type\") == data_entry_flow.FlowResultType.FORM\n # Goes back to the user step\n assert result.get(\"step_id\") == \"user\"\n assert result.get(\"errors\") == {\"api_token\": \"invalid_api_token\"}","def testKeyInfoTooLong(self):\n key = 5\n self.assertRaises(ValueError, dataToToken, key, data='hey',\n keyInfo='xxxxx')","def verify_hack_key(self):\r\n\t\tself.percent_english = Dict_Control(self.my_code).check_key()\r\n\t\t#If more than half the words are english, the key will pass. \r\n\t\tif self.percent_english > 50:\r\n\t\t\tself.hack_plausible = True","def error(self, key, **kwargs):\n try:\n msg = self.error_messages[key]\n except KeyError:\n class_name = self.__class__.__name__\n raise AssertionError('Error with key={} is not found for class={}'.format(key, class_name))\n message_string = msg.format(**kwargs)\n raise ValidationError(message_string, code=key)","def test_decrypt_key_incorrect(self):\n right_key = b'0' * 32\n wrong_key = b'1' * 32\n\n encrypted = encrypt('message', key=right_key)\n\n with pytest.raises(EncryptionError):\n decrypt(encrypted, key=wrong_key)","def handle_key(self, key):\n pass","def check_key(self):\n\n if self.type == \"RSA\" and self.size < 1024:\n raise HostkeyError(\"RSA keys must at least be 1024 bits.\")\n elif self.type == \"DSA\" and self.size != 1024:\n raise HostkeyError(\"DSA keys can only be 1024 bits.\")\n elif self.type == \"ECDSA\" and self.size not in [256, 384, 521]: # yes, that is *really* 521 bits, not a typo!\n raise HostkeyError(\"ECDSA key must be either 256, 384 or 521 bits (yes, 521 not 512!)\")\n elif self.type ==\"ED25519\" and self.size != 128:\n raise HostkeyError(\"ED25519 keys have a fixed size, which cannot be altered.\") # can't really happen, size is ignored for ED25519\n\n # if privkey is already there check size\n self.key_exists = False\n self.key_current_size = 0\n if os.path.exists(self.fullpath):\n self.key_exists = True\n if self.type == \"ED25519\":\n self.curve = \"EC25519\"\n self.key_current_size = 128 # somewhat erbitrary, attack complexity on ED25519 is larger that brute forcing a 128bit key\n self.key_exists = True\n elif self.type == \"RSA1\":\n self.key_exists = True\n self.key_current_size = 1024\n else:\n try:\n with open(self.fullpath, \"rb\") as key_file:\n self.privkey = crypto_serialization.load_pem_private_key(key_file.read(), password=None, backend=crypto_default_backend())\n except IOError:\n raise HostkeyError(get_exception())\n\n if self.type == \"DSA\" or self.type == \"RSA\":\n self.key_current_size = self.privkey.key_size\n elif self.type == \"ED25519\":\n self.key_current_size = 128\n elif self.type == \"ECDSA\":\n self.pubkey = self.privkey.public_key()\n if self.pubkey.curve.name == \"secp256r1\":\n self.key_current_size = 256\n elif self.pubkey.curve.name == \"secp384r1\":\n self.key_current_size = 384\n elif self.pubkey.curve.name == \"secp521r1\":\n self.key_current_size = 521\n else:\n self.curve = self.pubkey.curve.name"],"string":"[\n \"def isValidKey(key):\\n return True\",\n \"def validate_key_throw(*args):\\n validation_result = validate_key(*args)\\n if not validation_result:\\n raise ValueError(str(validation_result))\\n return validation_result\",\n \"def _check_key(self, key):\\n raise NotImplementedError\",\n \"def validate_key(self, key: keyType) -> bool:\\n if isinstance(key, (dict,bool)):\\n raise Exception\\n if key is None:\\n raise Exception\\n # Numerical key object has no len(),\\n # so explicitly specify which types are not allowed to use empty value as keys\\n if isinstance(key, (str, tuple, set, list)) and (len(key) == 0):\\n raise Exception\\n return True\",\n \"def _check_key(key): # type: (str) -> None\\n if not key:\\n raise ValueError('Key must not be empty.')\\n if '.' in key:\\n raise ValueError('Key must not contain dots.')\",\n \"def validate(self, key, val):\\n return True\",\n \"def validate(self, key, val):\\n return True\",\n \"def keyIsValid(key):\\n\\n isValid = 1\\n \\n try:\\n temp = getParam(key)\\n\\n except ValueError:\\n isValid = 0\\n warning(\\\" WARNING: %s not set\\\" % (key))\\n\\n return isValid\",\n \"def test_valid_key(self):\\n f = lws.valid_data_key\\n assert f('string', int, r'string') is False\\n assert f('string', str, r'test') is False\\n assert f(123, int, '123') is False\\n assert f(123.00, float, '123') is False\\n assert f('123', str, r'[0-9]*') is True\",\n \"def test_is_valid_label_key_invalid_input():\\n # test length violations\\n assert not is_valid_label_key(key=None) # Too short\\n assert not is_valid_label_key(key=\\\"\\\") # Too short\\n assert not is_valid_label_key(key=f\\\"{'p' * 254}/n\\\") # prefix too long\\n assert not is_valid_label_key(key=\\\"/n\\\") # prefix too short\\n assert not is_valid_label_key(key=\\\"p/\\\") # name too short\\n assert not is_valid_label_key(key=\\\"a\\\" * 254) # name too long\\n assert not is_valid_label_key(key=f\\\"d/{'b'*64}\\\") # name too long\\n # test first character violations (not alphanum)\\n assert not is_valid_label_key(key=\\\"-a\\\")\\n assert not is_valid_label_key(key=\\\".b\\\")\\n assert not is_valid_label_key(key=\\\" c\\\")\\n # test last character violations (not alphanum)\\n assert not is_valid_label_key(key=\\\"a-\\\")\\n assert not is_valid_label_key(key=\\\"b.\\\")\\n assert not is_valid_label_key(key=\\\"c \\\")\\n assert not is_valid_label_key(key=\\\"sw33T#\\\")\\n # test middle characters violations\\n assert not is_valid_label_key(key=\\\"a$$a\\\")\\n assert not is_valid_label_key(key=\\\"b b\\\")\",\n \"def test_is_valid_annotation_key_invalid_input():\\n # test length violations\\n assert not is_valid_annotation_key(key=None) # Too short\\n assert not is_valid_annotation_key(key=\\\"\\\") # Too short\\n assert not is_valid_annotation_key(key=f\\\"{'p' * 254}/n\\\") # prefix too long\\n assert not is_valid_annotation_key(key=\\\"/n\\\") # prefix too short\\n assert not is_valid_annotation_key(key=\\\"p/\\\") # name too short\\n assert not is_valid_annotation_key(key=\\\"a\\\" * 254) # name too long\\n assert not is_valid_annotation_key(key=f\\\"d/{'b'*64}\\\") # name too long\\n # test first character violations (not alphanum)\\n assert not is_valid_annotation_key(key=\\\"-a\\\")\\n assert not is_valid_annotation_key(key=\\\".b\\\")\\n assert not is_valid_annotation_key(key=\\\" c\\\")\\n # test last character violations (not alphanum)\\n assert not is_valid_annotation_key(key=\\\"a-\\\")\\n assert not is_valid_annotation_key(key=\\\"b.\\\")\\n assert not is_valid_annotation_key(key=\\\"c \\\")\\n assert not is_valid_annotation_key(key=\\\"sw33T#\\\")\\n # test middle characters violations\\n assert not is_valid_annotation_key(key=\\\"a$$a\\\")\\n assert not is_valid_annotation_key(key=\\\"b b\\\")\",\n \"def __check_key_validity(self, key):\\n if not isinstance(key, tuple):\\n raise TypeError(\\\"key must be a tuple\\\")\\n if len(key) != 2:\\n raise ValueError(\\\"key must be of length two\\\")\\n if not (isinstance(key[0], int) and isinstance(key[1], int)):\\n raise TypeError(\\\"elements of key must be integers\\\")\\n if not ((0 <= key[0] < self.m) and (0 <= key[1] < self.n)):\\n raise exc.OutOfBoundsError(\\\"key is out of bounds\\\")\",\n \"def test_invalid_keys(self):\\n\\n\\t\\ttry:\\n\\t\\t\\tyield self.conn.set(\\\"this has spaces\\\", 1)\\n\\t\\texcept ValueError:\\n\\t\\t\\tpass\\n\\t\\telse:\\n\\t\\t\\tself.fail(\\\"key with spaces did not raise ValueError\\\")\\n\\n\\t\\ttry:\\n\\t\\t\\tyield self.conn.set(\\\"\\\\x10control\\\\x02characters\\\\x11\\\", 1)\\n\\t\\texcept ValueError:\\n\\t\\t\\tpass\\n\\t\\telse:\\n\\t\\t\\tself.fail(\\\"key with control characters did not raise ValueError\\\")\\n\\n\\t\\ttry:\\n\\t\\t\\tyield self.conn.set(\\\"a\\\" * (SERVER_MAX_KEY_LENGTH + 1), 1)\\n\\t\\texcept ValueError:\\n\\t\\t\\tpass\\n\\t\\telse:\\n\\t\\t\\tself.fail(\\\"long key did not raise ValueError\\\")\\n\\n\\t\\ttry:\\n\\t\\t\\tyield self.conn.set(u\\\"unicode\\\\u4f1a\\\", 1)\\n\\t\\texcept TypeError:\\n\\t\\t\\tpass\\n\\t\\telse:\\n\\t\\t\\tself.fail(\\\"unicode key did not raise ValueError\\\")\",\n \"def test_getKey_nokey(self):\\n filename = os.path.join(os.getcwd(), 'sekrit')\\n key = crypto.getKey(filename)\\n self.failUnlessIsInstance(key, basestring,\\n \\\"key isn't a string! type=%r\\\" % type(key))\",\n \"def test_is_valid_label_key_valid_input():\\n # test valid label keys\\n assert is_valid_label_key(key=\\\"l0l\\\")\\n assert is_valid_label_key(key=\\\"l0L\\\")\\n assert is_valid_label_key(key=\\\"L-l\\\")\\n assert is_valid_label_key(key=\\\"L.L\\\")\\n assert is_valid_label_key(key=\\\"4-you\\\")\\n assert is_valid_label_key(key=\\\"you.2\\\")\\n assert is_valid_label_key(key=\\\"p/n\\\")\\n assert is_valid_label_key(key=\\\"prefix/you.2\\\")\\n assert is_valid_label_key(key=\\\"how.sad/to-see\\\")\\n assert is_valid_label_key(key=f\\\"{'d'*253}/{'n'*63}\\\")\",\n \"def is_key_valid(self,key):\\n if not key or any(map(lambda s: s in key,space_chars))\\\\\\n or any(map(lambda s: s in key,bad_chars)):\\n return False \\n return True\",\n \"def test_encrypt_key_invalid(self):\\n with pytest.raises(EncryptionError):\\n encrypt('message', key=b'0' * 31)\",\n \"def _is_valid_key(self, key):\\r\\n\\r\\n # Check the length\\r\\n if len(key) > 250:\\r\\n return False\\r\\n\\r\\n # Check that there are no spaces or control characters\\r\\n for char in key:\\r\\n if ord(char) < 33 or ord(char) == 127:\\r\\n return False\\r\\n\\r\\n return True\",\n \"def _is_valid_key(self, key):\\n\\t\\t\\n\\t\\t# If the key is not a string\\n\\t\\tif not isinstance(key, str):\\n\\t\\t\\treturn False\\n\\t\\telse:\\n\\t\\t\\tkey = str.upper(key)\\n\\t\\t\\n\\t\\t# If the given key does not match the standard notation XY\\n\\t\\tif len(key) != 2:\\n\\t\\t\\treturn False\\n\\t\\t\\n\\t\\t# If the key is out of the board\\n\\t\\tif key[0] not in self.columns or key[1] not in self.rows:\\n\\t\\t\\treturn False\\n\\t\\t\\n\\t\\t# Otherwise the key is valid\\n\\t\\treturn True\",\n \"def _validate_and_split_key(self, key):\\n if self._len_keys == 1:\\n return self._validate_and_split_len_one(key)\\n else:\\n return self._validate_and_split_len(key)\",\n \"def _validate_key(self, key):\\n if isinstance(key, str):\\n key = unicode(key, 'utf-8')\\n elif not isinstance(key, unicode):\\n raise TypeError(\\n \\\"`key` must be `str` or `unicode`, not `{}`\\\".format(\\n key.__class__.__name__)\\n )\\n return key\",\n \"def test_key_none(self):\\n try:\\n AlphaVantage()\\n self.fail(msg='A None api key must raise an error')\\n except ValueError:\\n self.assertTrue(True)\",\n \"def test_key_none(self):\\n try:\\n AlphaVantage()\\n self.fail(msg='A None api key must raise an error')\\n except ValueError:\\n self.assertTrue(True)\",\n \"def _raiseIfWebsafeKeyNotValid(websafeKey, kind):\\n # Check that websafeKey is not None\\n if not websafeKey:\\n raise endpoints.BadRequestException(\\n \\\"Websafe key not provided for '%s'\\\" % kind)\\n # Try to decode the websafe key into a real key\\n try:\\n key = ndb.Key(urlsafe=websafeKey)\\n except:\\n raise endpoints.BadRequestException(\\n \\\"Websafe key provided for '%s' could not be decoded: %s\\\" %\\n (kind, websafeKey))\\n # Ensure that the key is of the desired kind\\n if key.kind() != kind:\\n raise endpoints.BadRequestException(\\n \\\"Websafe key is not of the '%s' kind: %s\\\" % (kind, websafeKey))\\n # If all is well, return the key\\n return key\",\n \"def test_handle_key_error():\\n\\n @handle_key_error\\n def get_item(key):\\n data = {\\\"A\\\": 1, \\\"B\\\": 2}\\n return data[key]\\n\\n value = get_item(\\\"A\\\")\\n assert value == 1\\n\\n with pytest.raises(InvalidParameter) as exc:\\n get_item(\\\"C\\\")\\n\\n assert \\\"C\\\" in str(exc.value)\",\n \"def test_validate_api_key(app, seed_data, key, result):\\n user_id, api_key = seed_data\\n if key == 'use-valid-key':\\n key = api_key\\n with app.app_context():\\n assert auth.validate_api_key(user_id, key) == result\",\n \"def is_valid_key(key, crypt_method):\\n logger.info(f\\\"key: {key}, crypt_method: {crypt_method}\\\")\\n if crypt_method == 'C':\\n while type(key) is not int or key not in range(0, 95):\\n try:\\n key = Check.is_integer(key)[1]\\n if key not in range(0, 95):\\n raise ValueError\\n except (TypeError, ValueError):\\n print(\\\"You must enter an integer between 1 and 95!\\\")\\n key = input(\\\"Enter an encryption key\\\\n>> \\\")\\n elif crypt_method in ('M', 'P'):\\n pass\\n else:\\n return False\\n return True, key\",\n \"def test_allow_extra_keys(self):\\n from natcap.invest import validation\\n\\n args = {'a': 'a', 'b': 'b'}\\n spec = {\\n 'a': {\\n 'type': 'freestyle_string',\\n 'name': 'a',\\n 'about': 'a freestyle string',\\n 'required': True\\n }\\n }\\n message = 'DEBUG:natcap.invest.validation:Provided key b does not exist in MODEL_SPEC'\\n\\n with self.assertLogs('natcap.invest.validation', level='DEBUG') as cm:\\n validation.validate(args, spec)\\n self.assertTrue(message in cm.output)\",\n \"def test_basic_singleton_key_error(self):\\n\\n schema = {\\n ('root', str): {\\n ('sample node', str, 'sample'): ('node', str, r'[a-z]*')\\n }\\n }\\n data = {'root': {'not sample': 'node'}}\\n\\n ERRORS = lws.return_errors()\\n expected_schema = {\\n ('root', 'root'): [('sample node', ERRORS['key'])]\\n }\\n expected_data = {\\n ('root', 'root'): [('not sample', ERRORS['key'])]\\n }\\n\\n assert dict(lws.validate_schema(schema, data)) == expected_schema\\n assert dict(lws.validate_data(schema, data)) == expected_data\",\n \"def test_getObjectByKey_raises_KeyError(self):\\n try:\\n self.tile_bucket.getObjectByKey('foo_key')\\n except KeyError:\\n return\\n assert(False)\",\n \"def is_valid(key):\\n return key[0:2] == \\\"MR\\\" and key[2:].isdigit() and len(key) in [9, 10]\",\n \"def creation_validation(**_):\\n\\n for property_key in constants.KEYPAIR_REQUIRED_PROPERTIES:\\n utils.validate_node_property(property_key, ctx.node.properties)\\n\\n key_file = _get_path_to_key_file()\\n key_file_in_filesystem = _search_for_key_file(key_file)\\n\\n if ctx.node.properties['use_external_resource']:\\n if not key_file_in_filesystem:\\n raise NonRecoverableError(\\n 'External resource, but the key file does not exist locally.')\\n try:\\n _get_key_pair_by_id(ctx.node.properties['resource_id'])\\n except NonRecoverableError as e:\\n raise NonRecoverableError(\\n 'External resource, '\\n 'but the key pair does not exist in the account: '\\n '{0}'.format(str(e)))\\n else:\\n if key_file_in_filesystem:\\n raise NonRecoverableError(\\n 'Not external resource, '\\n 'but the key file exists locally.')\\n try:\\n _get_key_pair_by_id(ctx.node.properties['resource_id'])\\n except NonRecoverableError:\\n pass\\n else:\\n raise NonRecoverableError(\\n 'Not external resource, '\\n 'but the key pair exists in the account.')\",\n \"def valid_key(self): \\n self.so.ValidKey.restype = c_bool\\n result = self.so.ValidKey()\\n return result\",\n \"def validate_handshake_public_key(cls, public_key: bytes) -> None:\\n ...\",\n \"def validate_keystore_key(args):\\n expected_name = 'keyRings/%s/cryptoKeys/%s'\\n expected_name %= (args.kms_keyring, args.kms_key)\\n describe_output = ''\\n try:\\n describe_output = subprocess.check_output(\\n ['gcloud', 'kms', 'keys', 'describe', args.kms_key,\\n '--project', args.kms_project,\\n '--location', args.kms_location,\\n '--keyring', args.kms_keyring,\\n '--format', 'value(name)'])\\n except subprocess.CalledProcessError:\\n pass\\n if expected_name in describe_output:\\n return\\n # Print warning and exit if output did not include the key.\\n warning = 'KMS key \\\"%s\\\" not found in keyring=%s project=%s location=%s'\\n warning %= (args.kms_key,\\n args.kms_keyring,\\n args.kms_project,\\n args.kms_location)\\n Print.YL(warning)\\n sys.exit(1)\",\n \"def key(self, value=None):\\n if self.crypt_method == 'C':\\n key_type = \\\"number\\\"\\n else:\\n key_type = \\\"string\\\"\\n\\n input_message = f\\\"Please enter a {key_type} as a \\\" \\\\\\n f\\\"{self.crypt_type}ion key\\\\n>> \\\"\\n if value is None:\\n key = input(input_message)\\n else:\\n key = value\\n\\n is_valid_key, key = Check.is_valid_key(key, self.crypt_method)\\n if is_valid_key:\\n self._key = key\\n else:\\n raise ValueError(f\\\"Key{key} is invalid\\\")\",\n \"def test_invalid_key(self):\\n with pytest.raises(yaenv.EnvError) as err:\\n _ = yaenv.core.EnvVar('221b=\\\"starts with number\\\"')\\n assert 'Invalid key' in str(err.value)\\n with pytest.raises(yaenv.EnvError) as err:\\n _ = yaenv.core.EnvVar('_=\\\"not assignable\\\"')\\n assert 'Invalid key' in str(err.value)\\n with pytest.raises(yaenv.EnvError) as err:\\n _ = yaenv.core.EnvVar('o-o=\\\"invalid character\\\"')\\n assert 'Invalid key' in str(err.value)\",\n \"def test_invalid_chars_ssck(self):\\r\\n valid_base = SlashSeparatedCourseKey(u'org.dept-1%2', u'course.sub-2%3', u'run.faster-4%5')\\r\\n for key in SlashSeparatedCourseKey.KEY_FIELDS:\\r\\n with self.assertRaises(InvalidKeyError):\\r\\n # this ends up calling the constructor where the legality check should occur\\r\\n valid_base.replace(**{key: u'funny thing'})\",\n \"def test_is_valid_annotation_key_valid_input():\\n # test valid label keys\\n assert is_valid_annotation_key(key=\\\"l0l\\\")\\n assert is_valid_annotation_key(key=\\\"l0L\\\")\\n assert is_valid_annotation_key(key=\\\"L-l\\\")\\n assert is_valid_annotation_key(key=\\\"L.L\\\")\\n assert is_valid_annotation_key(key=\\\"4-you\\\")\\n assert is_valid_annotation_key(key=\\\"you.2\\\")\\n assert is_valid_annotation_key(key=\\\"p/n\\\")\\n assert is_valid_annotation_key(key=\\\"prefix/you.2\\\")\\n assert is_valid_annotation_key(key=\\\"how.sad/to-see\\\")\\n assert is_valid_annotation_key(key=f\\\"{'d'*253}/{'n'*63}\\\")\",\n \"def validate(self):\\n if not self.key or not self.certificates:\\n raise ValueError(\\\"Key or certificate missing in Keypair\\\")\",\n \"def check_auth_publickey(self, username, key):\\n return AUTH_FAILED\",\n \"def test_getKey_keyexists(self):\\n filename = self.mktemp()\\n with open(filename, 'wb') as fh:\\n fh.write(SEKRIT_KEY)\\n fh.flush()\\n\\n key = crypto.getKey(filename)\\n self.failUnlessIsInstance(key, basestring,\\n \\\"key isn't a string! type=%r\\\" % type(key))\\n self.assertEqual(SEKRIT_KEY, key,\\n \\\"\\\"\\\"The example key and the one read from file differ!\\n key (in hex): %s\\n SEKRIT_KEY (in hex): %s\\\"\\\"\\\"\\n % (key.encode('hex'), SEKRIT_KEY.encode('hex')))\",\n \"def validate_instruction_keys(instruction: TransactionInstruction, expected: int) -> None:\\n if len(instruction.keys) < expected:\\n raise ValueError(f\\\"invalid instruction: found {len(instruction.keys)} keys, expected at least {expected}\\\")\",\n \"def test_rekey_key_format(self):\\n old_key = b'0' * 32\\n encrypted = encrypt('message', key=old_key)\\n\\n with pytest.raises(EncryptionError):\\n rekey(encrypted, old_key=old_key, new_key=b'1' * 31)\",\n \"def testKeyInfoTooShort(self):\\n key = 5\\n self.assertRaises(ValueError, dataToToken, key, data='x', keyInfo='xx')\",\n \"def _check_transform_key(key: Hashable) -> None:\\n _test_hashable = hash(key) # The only 'real' way to make sure is hashable\\n # if not isinstance(key, Hashable):\\n # raise TypeError((type(key), \\\"transformation lookup key is not hashable\\\"))\",\n \"def test_generate_key(self): \\n k = Key().generate()\\n self.assertRegex(k, \\\"[a-zA-Z0-9+\\\\/]+={0,2}\\\")\",\n \"def validate(self, key: keyType, value: valueType) -> bool:\\n return self.validate_key(key) and self.validate_value(value)\",\n \"def test_create_key():\\n\\n assert symmetric.create_key() != \\\"\\\"\",\n \"def remote_verifyKey(self, key, protocol):\\r\\n if self._authenticated.called:\\r\\n return Failure(InvalidKey('Only one guess is possible.'))\\r\\n\\r\\n if isinstance(protocol, Failure):\\r\\n self._authenticated.errback(protocol)\\r\\n else:\\r\\n if self._key != key:\\r\\n e = Failure(InvalidKey('Wrong key supplied.'))\\r\\n self._authenticated.errback(e)\\r\\n return e\\r\\n\\r\\n self._authenticated.callback(protocol)\",\n \"def check_keys(self):\",\n \"def is_key(v) -> bool:\\n try:\\n _validate(v, prefixes=[b\\\"edsk\\\", b\\\"edpk\\\", b\\\"spsk\\\", b\\\"p2sk\\\", b\\\"sppk\\\", b\\\"p2pk\\\"])\\n except (ValueError, TypeError):\\n return False\\n return True\",\n \"def test_neg_exists_key_invalid_data(self, key, ex_code, ex_msg):\\n with pytest.raises(e.ParamError):\\n key, _ = self.as_connection.exists(key)\",\n \"def test_map_missing_key_encountered():\\n with pytest.raises(KeyError):\\n Map().read_key(10, b\\\"\\\")\",\n \"def _validateKey(self, key, cls = None):\\n\\n key_class_types = [self._BaseKey__class, self._LocalKey__class,\\n self._MsgKey__class, ErrorMsgManager]\\n\\n if cls:\\n if inspect.isclass(cls) and cls in key_class_types:\\n classes = [cls]\\n else:\\n return None\\n else:\\n classes = key_class_types\\n return any([isinstance(key, cls) for cls in classes])\",\n \"def test_rekey_non_encrypted(self):\\n with pytest.raises(EncryptionError):\\n rekey('message', old_key=b'0' * 32, new_key=b'1' * 32)\",\n \"def test_encryption_public_key_not_given(self) -> None:\\n\\n given = \\\"Hello, World!\\\"\\n\\n encryptor = DataEncryption()\\n\\n self.assertRaises(ValueError, lambda: encryptor.encrypt_data(given))\",\n \"def test_dweet_for_with_an_invalid_key(self):\\n try:\\n dweepy.dweet_for(self.my_thing_id, test_data, key='badkey')\\n except dweepy.DweepyError as e:\\n self.assertEqual(e.args[0], 'the key you provided doesn\\\\'t work with this thing')\\n else:\\n self.fail(\\\"shouldn't ever get called\\\")\",\n \"def key_error_message(self,key):\\n if not key:\\n return 'key is blank.'\\n elif any(map(lambda s: s in key,space_chars)):\\n return '\\\"{}\\\" contains whitespace.'.format(key)\\n elif any(map(lambda s: s in key,bad_chars)):\\n return '\\\"{}\\\" contains special characters.'.format(key)\",\n \"def test_invalid_chars_location(self):\\r\\n course_key = SlashSeparatedCourseKey(u'org.dept-1%2', u'course.sub-2%3', u'run.faster-4%5')\\r\\n valid_base = course_key.make_usage_key('tomato-again%9', 'block-head:sub-4%9')\\r\\n for key in SlashSeparatedCourseKey.KEY_FIELDS:\\r\\n with self.assertRaises(InvalidKeyError):\\r\\n # this ends up calling the constructor where the legality check should occur\\r\\n valid_base.replace(**{key: u'funny thing'})\",\n \"def set_key(self, key, value) -> True:\\n\\n if key in self.key_functs:\\n # Validate the value\\n real_val = self.key_functs[key](value)\\n \\n if real_val is not None:\\n self.key_values[key] = real_val \\n self.key_satified[key] = True\\n return True\\n\\n # Invalid key\\n return False\",\n \"def checkKey(self):\\n # TO DO for checking API authentication\\n if self.apikey is None:\\n return False\\n else:\\n return True\",\n \"def test_keyerror(self):\\n try:\\n self.db['foo']\\n except KeyError, e:\\n assert \\\"no key 'foo' in database <SequenceFileDB\\\" in str(e), str(e)\",\n \"def test_validation(self):\\r\\n\\r\\n with self.assertRaises(ValidationError):\\r\\n Dictionary().validate([1,2,3])\\r\\n\\r\\n with self.assertRaises(ValidationError):\\r\\n Dictionary().validate('stringy')\\r\\n\\r\\n with self.assertRaises(ValidationError):\\r\\n Dictionary().validate(1)\",\n \"def __missing__(self, key):\\n global MISSING\\n MISSING = key # For debugging - save name of missing key\\n return INVALID\",\n \"def check_empty_key(self, key):\\n if key is None or key == \\\"\\\" or key == self.empty_api_key:\\n print(\\\"ERROR, A KEY IS EMPTY - CHECK YOUR FILE\\\")\\n return False\\n return True\",\n \"def _check_key_type(cls, key: Any) -> K:\\n if not isinstance(key, cls.keytype):\\n raise KeyError(\\n f\\\"{cls!r} accepts only keys of type {cls.keytype!r}, \\\"\\n f\\\"got {type(key)!r}\\\"\\n )\\n return cast(K, key)\",\n \"def handle_key(self, k):\\n\\t\\treturn False\",\n \"def test_decrypt_key_invalid(self):\\n encrypted = encrypt('message', key=b'0' * 32)\\n\\n with pytest.raises(EncryptionError):\\n decrypt(encrypted, key=b'0' * 31)\",\n \"def test_validate_with_invalid_key_format_type(self):\\n key_format_type = \\\"invalid\\\"\\n kwargs = {'key_format_type': key_format_type}\\n\\n self.assertRaisesRegex(\\n TypeError, \\\"invalid key format type\\\", Digest, **kwargs)\",\n \"def test_throws_base_price_missing_key(self):\\n with pytest.raises(marshmallow.ValidationError):\\n BasePrice.Schema().loads(json.dumps(base_price_missing_key))\",\n \"def test_missingKey(self):\\n self.assertIsNone(self.users.key(\\\"mystery domain\\\", \\\"mystery user\\\"))\",\n \"def test_PerfectModel_verify_metric_keyerrors(\\n perfectModelEnsemble_initialized_control, metric\\n):\\n with pytest.raises(KeyError) as excinfo:\\n perfectModelEnsemble_initialized_control.verify(\\n comparison=\\\"e2c\\\",\\n metric=metric,\\n dim=[],\\n )\\n assert \\\"Specify metric from\\\" in str(excinfo.value)\",\n \"def test_incorrect_prediction_key(self):\\n self._config['Prediction key'] = 'wrong_key'\\n with self.assertRaisesRegex(ValueError, 'Invalid prediction key'):\\n self._gen.generate(\\n example=self._example,\\n model=self._model,\\n dataset=self._dataset,\\n config=self._config)\",\n \"def test_prediction_key_required(self):\\n self._config['Prediction key'] = ''\\n with self.assertRaisesRegex(ValueError,\\n 'Please provide the prediction key'):\\n self._gen.generate(\\n example=self._example,\\n model=self._model,\\n dataset=self._dataset,\\n config=self._config)\",\n \"def _is_valid(key):\\n is_valid = False\\n for valid_key in VALID_KEYS:\\n if valid_key in key:\\n is_valid = True\\n for invalid_key in INVALID_KEYS:\\n if invalid_key in key:\\n is_valid = False\\n return is_valid\",\n \"def test_validation(self):\\n\\n with self.assertRaises(ValidationError):\\n Dictionary().validate([1,2,3])\\n\\n with self.assertRaises(ValidationError):\\n Dictionary().validate('stringy')\\n\\n with self.assertRaises(ValidationError):\\n Dictionary().validate(1)\",\n \"def test_keys_failure(self):\\n storage = Storage()\\n storage._keys_dict = {'1': 'one',\\n 'abc': '1'}\\n self.assertRaises(StoragePatternError, storage.keys, 'ab[cd')\",\n \"def testBadKeyToToken(self):\\n key = 5\\n self.assertRaises(ValueError, dataToToken, key, data='hey')\",\n \"def validate_key_code(self, code):\\n\\n key = self.connect().query(KeyCode)\\\\\\n .filter(KeyCode.code == code)\\\\\\n .first()\\n\\n if key and (key.user and key.enabled):\\n return True\\n return False\",\n \"def test_api_key (key):\\n\\tdb = getattr(g,'db', None)\\n\\n\\tif isinstance(key, unicode):\\n\\t\\tkey = key.encode('utf-8')\\n\\n\\tqry = \\\"SELECT apikey FROM api_keys WHERE apikey=%s;\\\"\\n\\twith db as cur:\\n\\t\\treturn 0 < cur.execute(qry, (key,))\",\n \"def validate_authkey(value):\\n if not len(value) == 32:\\n raise ValidationError(\\n 'Value must be a string containing 32 alphanumeric characters')\",\n \"def setup_key_encrypt(self):\\r\\n\\t\\tself.max_key = math.floor(len(self.message) / 2)\\r\\n\\t\\twhile True:\\r\\n\\t\\t\\tkey = input(f\\\"Please enter a key value less than or equal to {self.max_key}. --> \\\")\\r\\n\\t\\t\\ttry:\\r\\n\\t\\t\\t\\tself.key = int(key)\\r\\n\\t\\t\\texcept ValueError:\\r\\n\\t\\t\\t\\tprint(\\\"Key needs to be a number.\\\")\\r\\n\\t\\t\\t\\tcontinue\\r\\n\\t\\t\\tif self.key > self.max_key: \\t\\t\\t\\r\\n\\t\\t\\t\\tprint(f\\\"{key} is too big of a number.\\\")\\t\\r\\n\\t\\t\\telif self.key == 0:\\r\\n\\t\\t\\t\\tprint(\\\"0 cannot be a key\\\")\\t\\t\\t\\r\\n\\t\\t\\telse:\\t\\t\\t\\r\\n\\t\\t\\t\\tbreak\",\n \"def key_validation(dictionary):\\n assert dictionary is not None\\n invalid = {}\\n for key in dictionary:\\n value = dictionary[key]\\n if is_empty(value):\\n continue\\n if key in valid_check:\\n if not valid_check[key](value):\\n invalid[invalids[key][\\\"error\\\"]] = key + \\\" is invalid\\\"\\n return invalid\",\n \"def test_get_invalid_key(test_file):\\n md = OSXMetaData(test_file.name)\\n with pytest.raises(KeyError):\\n md[\\\"invalid_key\\\"]\",\n \"def __init__(self, key: bytes):\\n\\n if len(key) != 32:\\n raise ValueError('Key must be 32 bytes long')\\n self.key = key\",\n \"def api_key_check():\\n req_path = request.path\\n method_type = request.method\\n app.logger.info(\\\">>> path = {}, method = {}\\\".format(req_path, method_type))\\n\\n if not app_props.api_key_check:\\n app.logger.debug('>>> api key check closed')\\n return None\\n\\n if req_path in app_props.api_key_white_list:\\n app.logger.info('>>> {} in white list, pass'.format(req_path))\\n return None\\n headers = request.headers\\n api_key_from_req = headers.get('x-api-key')\\n if not api_key_from_req:\\n app.logger.debug('>>> enter api-key error')\\n return resp_json(BaseResp.err('no x-api-key header'))\\n\\n key_obj = Key.query.filter_by(api_key=api_key_from_req).first()\\n if key_obj:\\n app.logger.debug('>>> consumer_id = {}, secret_key = {}'.format(key_obj.consumer_id, key_obj.secret_key))\\n g.consumer_id = key_obj.consumer_id\\n g.secret_key = key_obj.secret_key\\n return None\\n else:\\n return resp_json(BaseResp.err('Err api key'))\",\n \"def test_validate_yubikey(self):\\n from_key = self.yk_rnd.from_key(self.yk_public_id, self.yk_key)\\n self.assertTrue(pyhsm.yubikey.validate_yubikey_with_aead( \\\\\\n self.hsm, from_key, self.aead.data, self.kh_validate))\",\n \"def clean_key_name(self):\\n key = self.cleaned_data['key_name']\\n # Ensure key starts with prefix\\n if not key.startswith(self.get_key_prefix()):\\n raise forms.ValidationError('Key does not have required prefix.')\\n # Ensure key exists\\n if not self.get_upload_key():\\n raise forms.ValidationError('Key does not exist.')\\n return key\",\n \"def check_len( string_key ) : \\r\\n\\r\\n if len( string_key ) != 4 :\\r\\n\\r\\n raise Eggog( \\\"'%s': EGI wants the key to be exactly four characters!\\\" % (string_key, ) ) \\r\\n \\r\\n else :\\r\\n \\r\\n return True\",\n \"def test_validate_non_included_keys():\\n field = PartialDictField(included_keys=['a'], value_field=CharField(max_length=5),\\n required=False)\\n data = {'b': '123456'}\\n try:\\n field.run_validators(data)\\n except ValidationError:\\n assert False, 'Got a ValidationError for a non-included key'\",\n \"def check_type( string_key ) : \\r\\n\\r\\n if type( string_key ) != type( '' ) : \\r\\n\\r\\n # raise self.__class__( \\\"'%s': EGI wants the key to be four _characters_ (not %s) !\\\" % (type(string_key), ) ) \\r\\n raise Eggog( \\\"'%s': EGI wants the key to be four _characters_ (not %s) !\\\" % (type(string_key), ) ) \\r\\n \\r\\n else :\\r\\n \\r\\n return True\",\n \"def rsa_is_valid_publickey(key):\\r\\n # must be a dict\\r\\n if type(key) is not dict:\\r\\n return False\\r\\n\\r\\n # missing the right keys\\r\\n if 'e' not in key or 'n' not in key:\\r\\n return False\\r\\n\\r\\n # has extra data in the key\\r\\n if len(key) != 2:\\r\\n return False\\r\\n\\r\\n for item in ['e', 'n']:\\r\\n # must have integer or long types for the key components...\\r\\n if type(key[item]) is not int and type(key[item]) is not long:\\r\\n return False\\r\\n\\r\\n if key['e'] < key['n']:\\r\\n # Seems valid...\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"async def test_invalid_key(hass: HomeAssistant, invalid_key_api: Mock) -> None:\\n result = await hass.config_entries.flow.async_init(\\n DOMAIN, context={\\\"source\\\": SOURCE_USER}\\n )\\n assert result.get(\\\"type\\\") == data_entry_flow.FlowResultType.FORM\\n assert result.get(\\\"step_id\\\") == \\\"user\\\"\\n\\n # Test filling in API key\\n result = await hass.config_entries.flow.async_init(\\n DOMAIN,\\n context={\\\"source\\\": SOURCE_USER},\\n data={CONF_API_TOKEN: \\\"psk_123456789\\\"},\\n )\\n assert result.get(\\\"type\\\") == data_entry_flow.FlowResultType.FORM\\n # Goes back to the user step\\n assert result.get(\\\"step_id\\\") == \\\"user\\\"\\n assert result.get(\\\"errors\\\") == {\\\"api_token\\\": \\\"invalid_api_token\\\"}\",\n \"def testKeyInfoTooLong(self):\\n key = 5\\n self.assertRaises(ValueError, dataToToken, key, data='hey',\\n keyInfo='xxxxx')\",\n \"def verify_hack_key(self):\\r\\n\\t\\tself.percent_english = Dict_Control(self.my_code).check_key()\\r\\n\\t\\t#If more than half the words are english, the key will pass. \\r\\n\\t\\tif self.percent_english > 50:\\r\\n\\t\\t\\tself.hack_plausible = True\",\n \"def error(self, key, **kwargs):\\n try:\\n msg = self.error_messages[key]\\n except KeyError:\\n class_name = self.__class__.__name__\\n raise AssertionError('Error with key={} is not found for class={}'.format(key, class_name))\\n message_string = msg.format(**kwargs)\\n raise ValidationError(message_string, code=key)\",\n \"def test_decrypt_key_incorrect(self):\\n right_key = b'0' * 32\\n wrong_key = b'1' * 32\\n\\n encrypted = encrypt('message', key=right_key)\\n\\n with pytest.raises(EncryptionError):\\n decrypt(encrypted, key=wrong_key)\",\n \"def handle_key(self, key):\\n pass\",\n \"def check_key(self):\\n\\n if self.type == \\\"RSA\\\" and self.size < 1024:\\n raise HostkeyError(\\\"RSA keys must at least be 1024 bits.\\\")\\n elif self.type == \\\"DSA\\\" and self.size != 1024:\\n raise HostkeyError(\\\"DSA keys can only be 1024 bits.\\\")\\n elif self.type == \\\"ECDSA\\\" and self.size not in [256, 384, 521]: # yes, that is *really* 521 bits, not a typo!\\n raise HostkeyError(\\\"ECDSA key must be either 256, 384 or 521 bits (yes, 521 not 512!)\\\")\\n elif self.type ==\\\"ED25519\\\" and self.size != 128:\\n raise HostkeyError(\\\"ED25519 keys have a fixed size, which cannot be altered.\\\") # can't really happen, size is ignored for ED25519\\n\\n # if privkey is already there check size\\n self.key_exists = False\\n self.key_current_size = 0\\n if os.path.exists(self.fullpath):\\n self.key_exists = True\\n if self.type == \\\"ED25519\\\":\\n self.curve = \\\"EC25519\\\"\\n self.key_current_size = 128 # somewhat erbitrary, attack complexity on ED25519 is larger that brute forcing a 128bit key\\n self.key_exists = True\\n elif self.type == \\\"RSA1\\\":\\n self.key_exists = True\\n self.key_current_size = 1024\\n else:\\n try:\\n with open(self.fullpath, \\\"rb\\\") as key_file:\\n self.privkey = crypto_serialization.load_pem_private_key(key_file.read(), password=None, backend=crypto_default_backend())\\n except IOError:\\n raise HostkeyError(get_exception())\\n\\n if self.type == \\\"DSA\\\" or self.type == \\\"RSA\\\":\\n self.key_current_size = self.privkey.key_size\\n elif self.type == \\\"ED25519\\\":\\n self.key_current_size = 128\\n elif self.type == \\\"ECDSA\\\":\\n self.pubkey = self.privkey.public_key()\\n if self.pubkey.curve.name == \\\"secp256r1\\\":\\n self.key_current_size = 256\\n elif self.pubkey.curve.name == \\\"secp384r1\\\":\\n self.key_current_size = 384\\n elif self.pubkey.curve.name == \\\"secp521r1\\\":\\n self.key_current_size = 521\\n else:\\n self.curve = self.pubkey.curve.name\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7987643","0.7463676","0.73541176","0.71353656","0.69899154","0.69897455","0.69897455","0.69758594","0.6815677","0.6798602","0.6623998","0.65970945","0.65419054","0.6538826","0.6535715","0.65314674","0.64951634","0.64704937","0.64693004","0.6450726","0.64469653","0.6437141","0.6437141","0.6408927","0.63918597","0.63843757","0.6357905","0.63139117","0.6299255","0.6295412","0.62867707","0.62763786","0.62584853","0.62481326","0.62453175","0.623417","0.62250173","0.62149704","0.6185859","0.61766326","0.61381114","0.6131444","0.6124167","0.60863644","0.60766315","0.60725886","0.60692376","0.60598475","0.60470587","0.6045539","0.6044998","0.6017218","0.6017208","0.60026443","0.5978719","0.59750855","0.59633225","0.5961625","0.5956172","0.59511346","0.5946124","0.5938223","0.59374964","0.5928059","0.5927448","0.59201396","0.591673","0.5893573","0.58925605","0.5891931","0.589023","0.58874804","0.5887456","0.5887432","0.58791834","0.5878884","0.5878242","0.5875496","0.58717585","0.58679914","0.5861912","0.5856411","0.5854826","0.5852841","0.5850632","0.5840055","0.5832215","0.5796281","0.5790783","0.5789592","0.57681376","0.5762687","0.57616866","0.5731121","0.5724301","0.571813","0.5715062","0.571464","0.57114655","0.5710245"],"string":"[\n \"0.7987643\",\n \"0.7463676\",\n \"0.73541176\",\n \"0.71353656\",\n \"0.69899154\",\n \"0.69897455\",\n \"0.69897455\",\n \"0.69758594\",\n \"0.6815677\",\n \"0.6798602\",\n \"0.6623998\",\n \"0.65970945\",\n \"0.65419054\",\n \"0.6538826\",\n \"0.6535715\",\n \"0.65314674\",\n \"0.64951634\",\n \"0.64704937\",\n \"0.64693004\",\n \"0.6450726\",\n \"0.64469653\",\n \"0.6437141\",\n \"0.6437141\",\n \"0.6408927\",\n \"0.63918597\",\n \"0.63843757\",\n \"0.6357905\",\n \"0.63139117\",\n \"0.6299255\",\n \"0.6295412\",\n \"0.62867707\",\n \"0.62763786\",\n \"0.62584853\",\n \"0.62481326\",\n \"0.62453175\",\n \"0.623417\",\n \"0.62250173\",\n \"0.62149704\",\n \"0.6185859\",\n \"0.61766326\",\n \"0.61381114\",\n \"0.6131444\",\n \"0.6124167\",\n \"0.60863644\",\n \"0.60766315\",\n \"0.60725886\",\n \"0.60692376\",\n \"0.60598475\",\n \"0.60470587\",\n \"0.6045539\",\n \"0.6044998\",\n \"0.6017218\",\n \"0.6017208\",\n \"0.60026443\",\n \"0.5978719\",\n \"0.59750855\",\n \"0.59633225\",\n \"0.5961625\",\n \"0.5956172\",\n \"0.59511346\",\n \"0.5946124\",\n \"0.5938223\",\n \"0.59374964\",\n \"0.5928059\",\n \"0.5927448\",\n \"0.59201396\",\n \"0.591673\",\n \"0.5893573\",\n \"0.58925605\",\n \"0.5891931\",\n \"0.589023\",\n \"0.58874804\",\n \"0.5887456\",\n \"0.5887432\",\n \"0.58791834\",\n \"0.5878884\",\n \"0.5878242\",\n \"0.5875496\",\n \"0.58717585\",\n \"0.58679914\",\n \"0.5861912\",\n \"0.5856411\",\n \"0.5854826\",\n \"0.5852841\",\n \"0.5850632\",\n \"0.5840055\",\n \"0.5832215\",\n \"0.5796281\",\n \"0.5790783\",\n \"0.5789592\",\n \"0.57681376\",\n \"0.5762687\",\n \"0.57616866\",\n \"0.5731121\",\n \"0.5724301\",\n \"0.571813\",\n \"0.5715062\",\n \"0.571464\",\n \"0.57114655\",\n \"0.5710245\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7446105"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":76688,"cells":{"query":{"kind":"string","value":"Allows to set key only once"},"document":{"kind":"string","value":"def key(self, key_val):\r\n\r\n if self.key_exists():\r\n raise EncryptedField.KeyAlreadyExists()\r\n\r\n validate_key(key_val)\r\n\r\n if self.model_class not in self._keys:\r\n EncryptedField._keys[self.model_class] = {}\r\n\r\n EncryptedField._keys[self.model_class][id(self)] = key_val"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def key(self, key):\n return self.__key.set(key)","def set_key(self, key):\n self.key = key","def _newKey(self, key):\n pass","def set_key(self, key):\n\t\tif key in self.control_map:\n\t\t\tindex = self.control_map.index(key)\n\t\t\tcurrent_key = self.control_map[self.option_index]\n\t\t\tself.control_map[index] = current_key\n\t\t\tself.control_map[self.option_index] = key\n\t\t\treturn\n\t\tself.control_map[self.option_index] = key","def key(self, key):\n self._key = key","def key(self, key):\n self._key = key","def set(self, key, value):","def set(self, key, value):","def setdefault(self, key):\n pass","def key(self, key):\n\n self._key = key","def key(self, key):\n\n self._key = key","def update_key(self):\n self.__prev_key = self.__new_key","def _single_setitem(self, key, item):\n self._dict[key] = item","def set(self, key: t.Hashable, value: t.Any) -> None:","def __getitem__(self, key):\n assert key in self._isKeyUsed.keys()\n self._isKeyUsed[key] = True\n return super().__getitem__(key)","def __setitem__(self, key, value):\n if key not in self.ordered_keys:\n self.ordered_keys.append(key)\n super().__setitem__(key, value)","def setnoempty(self, key, value):\r\n if value:\r\n self[key] = value","def _key_generated(self, key, index):\n self.keys[self.get_address(key)] = key\n self.last_generated_index = index","def _set_key(self, key):\n\n # select 56 bits from the 64-bit key\n key = self._permutate(self.__pc1, self._string_to_bitlist(key))\n self.L = key[:28]\n self.R = key[28:]\n for i in range(0, 16):\n for j in range(0, self.__left_rotations[i]):\n self.L.append(self.L[0])\n del self.L[0]\n self.R.append(self.R[0])\n del self.R[0]\n # select 48 bits from 56 bits\n self.Kn[i] = self._permutate(self.__pc2, self.L + self.R)","def setdefault_key_value(self):\n raise NotImplementedError","def unique_key(self, unique_key):\n\n self._unique_key = unique_key","def _newKey(self, key):\n self._testKeySubNsAdd()\n self._getKeyList().append(key)","def __init__(self):\n self._key = ''","def setKey(self, time, attributeIndex, hash, value, view) -> None:\n ...","def _update_key(cls, spec):\n if cls.KEY is not None:\n cls._set_key(spec, spec[\"keys\"].popleft())\n elif cls.REF is not None:\n spec[\"ref\"] = cls.REF","def __init__(self, key):\n self.key = key","def setitem_key_value(self):\n raise NotImplementedError","def handle_key(self, key):\n pass","def set_key(attr):\n cmds.setKeyframe(attr)","def set_key(self, key, value) -> True:\n\n if key in self.key_functs:\n # Validate the value\n real_val = self.key_functs[key](value)\n \n if real_val is not None:\n self.key_values[key] = real_val \n self.key_satified[key] = True\n return True\n\n # Invalid key\n return False","def load_key(self, key):\n self.key = key","def _set_key(cls, spec, value):\n spec[cls.KEY] = value\n if cls.REF:\n spec[\"ref\"] = value","def __setitem__(self, key, value):\n if key in self.define:\n warnings.warn('Key {} is being overwritten to {}. It had a value of {}. Hope you know what you are doing.'.format(key, value, self.define[key]))\n self.define[key] = value","def set(self, key, value):\n raise NotImplementedError","def set_one(self, name_group, key, value):\n self.set(name_group, key, value)\n for item in self.get_all_childname(name_group):\n if item != key:\n self.set(name_group, item, False)","def _update_append_key(self):\n self.append_key += 1","def _set(self, key, value):\n self._data[key] = value\n return self._data[key]","def __setitem__(self, key, value):\n pass","def __setitem__(self, key, value):\n pass","def __setitem__(self, key, value):\n pass","def setKeyDictionary(self, dictionary, key, value):\n if key not in dictionary:\n dictionary[key] = value\n elif len(dictionary[key]) == 0:\n dictionary[key] = {value}\n else:\n dictionary[key] = dictionary[key] | {value}","def setKey(self, key):\n if hasattr(key, '__class__') and issubclass(key.__class__, ObjectKey):\n key = key.getKey()\n\n self.__key = key","def key():","def __setitem__(self, key, value) -> None:\n # Allows value modification only in __init__.\n caller_method = inspect.getouterframes(inspect.currentframe(), 2)[1][3]\n if caller_method != \"__init__\":\n raise AttributeError\n\n self.__stash[key] = value","def setdefault(self, key, failobj=None):\n super(ReadOnlyDict, self).setdefault(key, failobj) # pragma: no cover","def __setitem__(self, key, val):\n self.set[key] = val","def key():\n pass","def key():\n pass","def touchKBucket(self, key):","def put(self, key, val):\n pass","def handle_key(self, k):\n\t\treturn False","def set(self, key):\n if key == 0:\n self._servo.d_key(.1)\n elif key == 1:\n self._servo.ctrl_d(.1)\n elif key == 2:\n self._servo.ctrl_u(.1)\n elif key == 3:\n self._servo.ctrl_enter(.1)\n elif key == 4:\n self._servo.enter_key(.1)\n elif key == 5:\n self._servo.refresh_key(.1)\n elif key == 6:\n self._servo.ctrl_refresh_key(.1)\n elif key == 7:\n self._servo.sysrq_x(.1)\n else:\n raise kbError(\"Unknown key enum: %s\", key)","def __setitem__(self, key, value):","def __init__(self, key=None):\n self.key = key","def do_key(self, cmd):\n self.params.set('key', cmd, 'global')","def def_key(x):\n return x","def __setitem__(key, value):","def set_force(self, key, value):\n return self.__setitem__(key, value, force=True)","def __setitem__(self, key, value):\n self.set(key, value)","def __setitem__(self, key, value):\n self.set(key, value)","def __setitem__(self, key, value):\n self.set(key, value)","def test_set_key():\n\n assert symmetric.set_key(\"test\") == \"test\"","def keyEquivalent( self ):\n\t\treturn None","def _put(self, key: str, value):\n pass","def set_key(self, key, toggle):\n\t\tif toggle:\n\t\t\tkey_string = VALID_KEYS[key]\n\t\t\tself.screen.set_key(key_string)\n\t\t\tcontrols = SelectControls(self.screen.player)\n\t\t\tself.screen.player.current_level.screen_manager.set_controls(controls)\n\t\t\tself.screen.setting_key = False","def __setitem__(self, key, value):\n dict.__setitem__(self, key, value)\n\n self.changed()","def _single_setitem(self, key, item):\n getattr(self._cpp_obj, self._setter)(key, item)","def __setitem__(self,key,value):\n if key in self.deleted: self.deleted.remove(key)\n if key not in self.changed: self.changed.append(key)\n self.data[key] = value","def update_key(self, key):\n self._api_key = key","def useKeyID(self, keyID):\n\n if keyID not in self._data[\"keys\"]:\n raise ConfigException(\"keyID does not exist: %s\" % keyID)\n self._keyID = keyID","def set_key_id(self, key_id=''):\n self.key_id = key_id","def set_any(self, key, value):\n self._context[key] = value","def set_handler(key):\n def wrapper(func):\n func.set_key = key\n return func\n\n return wrapper","def store(self, key, value):\n pass","def switch_on_key(self, key):\n if key in self.switched_off_keys:\n self._switched_off_keys.pop(self._switched_off_keys.index(key))\n self._config[key] = self._config.pop(\"# \"+key)","def set(self, key, value):\r\n self.set_many({key: value})","def __setitem__(self, key, val):\n dict.__setitem__(self, key, val)","def __setitem__(self, key: Any, value: Any) -> NoReturn:\n raise RuntimeError(\"magicgui.Container does not support item setting.\")","def set(self, key, value):\n self.remove(key)\n self.add(key, value)","def __setitem__(self, key, val):\n for k,v in list(self.__dict__.items()):\n if k == key:\n self.__dict__[key] = val\n return \n print((\"Item %s could not be updated...\" %key))","def key_id(self, key_id):\n\n self._key_id = key_id","def __setitem__(self, key, value):\n self.put(key, value)","def __setitem__(self, key, value):\n self.put(key, value)","def test_one_key(self):\n self.now = time.time()\n with patch('time.time', self.fake_time):\n self.storage.set('1', 'one', moe=self.now + 1)\n self.now += 2\n self.gc.expire_random()\n self.assertRaises(StorageKeyError, self.storage.get, '1')","def __setitem__(self, key, value):\n while self is not None:\n if key in self._dict:\n self._dict[key] = value\n return\n else:\n self = self.parent\n raise KeyError(\"%s was not declared\" % key)","def _update_prepend_key(self):\n self.prepend_key -= 1","def __setitem__(self, key, value):\n self.settings.set(key, value)","def __init__(__self__, *,\n key_data: pulumi.Input[str]):\n pulumi.set(__self__, \"key_data\", key_data)","def begin_set_key(self):\n\t\tself.setting_key = True\n\t\tself.player.current_level.screen_manager.set_controls(ControlSettingControls(self))","def _put_to_back(self, key, value):\n\n pass","def dal_set(key, obj):\n global store\n return store.set(urllib.quote(key), obj)","def setdefault(self, key, failobj=None):\n with self.__plock:\n if key not in self._keys:\n self._keys.append(key)\n return UserDict.setdefault(self, key, failobj)","def _set_unique_keys(self, keys):\n # make this final once set\n if self._unique_keys:\n raise AlreadySetError()\n\n self._unique_keys = self._prepare_keys(keys)","def __setitem__(self, key, value):\n super(ReadOnlyDict, self).__setitem__(key, value)","def __setitem__(self,key,value):\n self._register[key] = value\n self._register.sync()","def __setitem__(self, key: str, value: Any) -> None:\n self._store[key.lower()] = (key, value)","def onkey(self, fun, key):\n if fun is None:\n if key in self._keys:\n self._keys.remove(key)\n elif key not in self._keys:\n self._keys.append(key)\n self._onkeyrelease(fun, key)","def add_key(mu_key):\n params['key'] = mu_key","def set(self, key, value):\n if key in self.keys:\n self.remove(key)\n self.keys[key] = None\n self.buffered[key] = value\n self.cache[key] = value\n if self.autocommit:\n commit()","def setnx(self, key, value):\n return self.set(key, value, nx=True)","def setKey(self, key, value):\n\t\tself.keyMap[key] = value\n\n\t\tif key == \"help\" :\n\t\t\tif value == 1 :\n\t\t\t\tself.helpText.setText( \\\n\t\t\t\t\t\"arrows to move or turn\\n\" + \\\n\t\t\t\t\t\"shift-arrows to change view\\n\" + \\\n\t\t\t\t\t\"z/Z to zoom in/out, r to reset\\n\" + \\\n\t\t\t\t\t\",/. to slide left/right\")\n\t\t\telse :\n\t\t\t\tself.helpText.setText(\"h for help\")\n\n\t\tif value == 1 : return\n\n\t\t# special cases for releasing keys with modifiers\n\t\tif key == \"zoom-in\" :\n\t\t\tself.keyMap[\"zoom-out\"] = 0\n\t\tif key == \"left\" or key == \"right\" :\n\t\t\tself.keyMap[\"cam-left\"] = 0\n\t\t\tself.keyMap[\"cam-right\"] = 0\n\t\tif key == \"forward\" or key == \"backward\" :\n\t\t\tself.keyMap[\"cam-up\"] = 0\n\t\t\tself.keyMap[\"cam-down\"] = 0"],"string":"[\n \"def key(self, key):\\n return self.__key.set(key)\",\n \"def set_key(self, key):\\n self.key = key\",\n \"def _newKey(self, key):\\n pass\",\n \"def set_key(self, key):\\n\\t\\tif key in self.control_map:\\n\\t\\t\\tindex = self.control_map.index(key)\\n\\t\\t\\tcurrent_key = self.control_map[self.option_index]\\n\\t\\t\\tself.control_map[index] = current_key\\n\\t\\t\\tself.control_map[self.option_index] = key\\n\\t\\t\\treturn\\n\\t\\tself.control_map[self.option_index] = key\",\n \"def key(self, key):\\n self._key = key\",\n \"def key(self, key):\\n self._key = key\",\n \"def set(self, key, value):\",\n \"def set(self, key, value):\",\n \"def setdefault(self, key):\\n pass\",\n \"def key(self, key):\\n\\n self._key = key\",\n \"def key(self, key):\\n\\n self._key = key\",\n \"def update_key(self):\\n self.__prev_key = self.__new_key\",\n \"def _single_setitem(self, key, item):\\n self._dict[key] = item\",\n \"def set(self, key: t.Hashable, value: t.Any) -> None:\",\n \"def __getitem__(self, key):\\n assert key in self._isKeyUsed.keys()\\n self._isKeyUsed[key] = True\\n return super().__getitem__(key)\",\n \"def __setitem__(self, key, value):\\n if key not in self.ordered_keys:\\n self.ordered_keys.append(key)\\n super().__setitem__(key, value)\",\n \"def setnoempty(self, key, value):\\r\\n if value:\\r\\n self[key] = value\",\n \"def _key_generated(self, key, index):\\n self.keys[self.get_address(key)] = key\\n self.last_generated_index = index\",\n \"def _set_key(self, key):\\n\\n # select 56 bits from the 64-bit key\\n key = self._permutate(self.__pc1, self._string_to_bitlist(key))\\n self.L = key[:28]\\n self.R = key[28:]\\n for i in range(0, 16):\\n for j in range(0, self.__left_rotations[i]):\\n self.L.append(self.L[0])\\n del self.L[0]\\n self.R.append(self.R[0])\\n del self.R[0]\\n # select 48 bits from 56 bits\\n self.Kn[i] = self._permutate(self.__pc2, self.L + self.R)\",\n \"def setdefault_key_value(self):\\n raise NotImplementedError\",\n \"def unique_key(self, unique_key):\\n\\n self._unique_key = unique_key\",\n \"def _newKey(self, key):\\n self._testKeySubNsAdd()\\n self._getKeyList().append(key)\",\n \"def __init__(self):\\n self._key = ''\",\n \"def setKey(self, time, attributeIndex, hash, value, view) -> None:\\n ...\",\n \"def _update_key(cls, spec):\\n if cls.KEY is not None:\\n cls._set_key(spec, spec[\\\"keys\\\"].popleft())\\n elif cls.REF is not None:\\n spec[\\\"ref\\\"] = cls.REF\",\n \"def __init__(self, key):\\n self.key = key\",\n \"def setitem_key_value(self):\\n raise NotImplementedError\",\n \"def handle_key(self, key):\\n pass\",\n \"def set_key(attr):\\n cmds.setKeyframe(attr)\",\n \"def set_key(self, key, value) -> True:\\n\\n if key in self.key_functs:\\n # Validate the value\\n real_val = self.key_functs[key](value)\\n \\n if real_val is not None:\\n self.key_values[key] = real_val \\n self.key_satified[key] = True\\n return True\\n\\n # Invalid key\\n return False\",\n \"def load_key(self, key):\\n self.key = key\",\n \"def _set_key(cls, spec, value):\\n spec[cls.KEY] = value\\n if cls.REF:\\n spec[\\\"ref\\\"] = value\",\n \"def __setitem__(self, key, value):\\n if key in self.define:\\n warnings.warn('Key {} is being overwritten to {}. It had a value of {}. Hope you know what you are doing.'.format(key, value, self.define[key]))\\n self.define[key] = value\",\n \"def set(self, key, value):\\n raise NotImplementedError\",\n \"def set_one(self, name_group, key, value):\\n self.set(name_group, key, value)\\n for item in self.get_all_childname(name_group):\\n if item != key:\\n self.set(name_group, item, False)\",\n \"def _update_append_key(self):\\n self.append_key += 1\",\n \"def _set(self, key, value):\\n self._data[key] = value\\n return self._data[key]\",\n \"def __setitem__(self, key, value):\\n pass\",\n \"def __setitem__(self, key, value):\\n pass\",\n \"def __setitem__(self, key, value):\\n pass\",\n \"def setKeyDictionary(self, dictionary, key, value):\\n if key not in dictionary:\\n dictionary[key] = value\\n elif len(dictionary[key]) == 0:\\n dictionary[key] = {value}\\n else:\\n dictionary[key] = dictionary[key] | {value}\",\n \"def setKey(self, key):\\n if hasattr(key, '__class__') and issubclass(key.__class__, ObjectKey):\\n key = key.getKey()\\n\\n self.__key = key\",\n \"def key():\",\n \"def __setitem__(self, key, value) -> None:\\n # Allows value modification only in __init__.\\n caller_method = inspect.getouterframes(inspect.currentframe(), 2)[1][3]\\n if caller_method != \\\"__init__\\\":\\n raise AttributeError\\n\\n self.__stash[key] = value\",\n \"def setdefault(self, key, failobj=None):\\n super(ReadOnlyDict, self).setdefault(key, failobj) # pragma: no cover\",\n \"def __setitem__(self, key, val):\\n self.set[key] = val\",\n \"def key():\\n pass\",\n \"def key():\\n pass\",\n \"def touchKBucket(self, key):\",\n \"def put(self, key, val):\\n pass\",\n \"def handle_key(self, k):\\n\\t\\treturn False\",\n \"def set(self, key):\\n if key == 0:\\n self._servo.d_key(.1)\\n elif key == 1:\\n self._servo.ctrl_d(.1)\\n elif key == 2:\\n self._servo.ctrl_u(.1)\\n elif key == 3:\\n self._servo.ctrl_enter(.1)\\n elif key == 4:\\n self._servo.enter_key(.1)\\n elif key == 5:\\n self._servo.refresh_key(.1)\\n elif key == 6:\\n self._servo.ctrl_refresh_key(.1)\\n elif key == 7:\\n self._servo.sysrq_x(.1)\\n else:\\n raise kbError(\\\"Unknown key enum: %s\\\", key)\",\n \"def __setitem__(self, key, value):\",\n \"def __init__(self, key=None):\\n self.key = key\",\n \"def do_key(self, cmd):\\n self.params.set('key', cmd, 'global')\",\n \"def def_key(x):\\n return x\",\n \"def __setitem__(key, value):\",\n \"def set_force(self, key, value):\\n return self.__setitem__(key, value, force=True)\",\n \"def __setitem__(self, key, value):\\n self.set(key, value)\",\n \"def __setitem__(self, key, value):\\n self.set(key, value)\",\n \"def __setitem__(self, key, value):\\n self.set(key, value)\",\n \"def test_set_key():\\n\\n assert symmetric.set_key(\\\"test\\\") == \\\"test\\\"\",\n \"def keyEquivalent( self ):\\n\\t\\treturn None\",\n \"def _put(self, key: str, value):\\n pass\",\n \"def set_key(self, key, toggle):\\n\\t\\tif toggle:\\n\\t\\t\\tkey_string = VALID_KEYS[key]\\n\\t\\t\\tself.screen.set_key(key_string)\\n\\t\\t\\tcontrols = SelectControls(self.screen.player)\\n\\t\\t\\tself.screen.player.current_level.screen_manager.set_controls(controls)\\n\\t\\t\\tself.screen.setting_key = False\",\n \"def __setitem__(self, key, value):\\n dict.__setitem__(self, key, value)\\n\\n self.changed()\",\n \"def _single_setitem(self, key, item):\\n getattr(self._cpp_obj, self._setter)(key, item)\",\n \"def __setitem__(self,key,value):\\n if key in self.deleted: self.deleted.remove(key)\\n if key not in self.changed: self.changed.append(key)\\n self.data[key] = value\",\n \"def update_key(self, key):\\n self._api_key = key\",\n \"def useKeyID(self, keyID):\\n\\n if keyID not in self._data[\\\"keys\\\"]:\\n raise ConfigException(\\\"keyID does not exist: %s\\\" % keyID)\\n self._keyID = keyID\",\n \"def set_key_id(self, key_id=''):\\n self.key_id = key_id\",\n \"def set_any(self, key, value):\\n self._context[key] = value\",\n \"def set_handler(key):\\n def wrapper(func):\\n func.set_key = key\\n return func\\n\\n return wrapper\",\n \"def store(self, key, value):\\n pass\",\n \"def switch_on_key(self, key):\\n if key in self.switched_off_keys:\\n self._switched_off_keys.pop(self._switched_off_keys.index(key))\\n self._config[key] = self._config.pop(\\\"# \\\"+key)\",\n \"def set(self, key, value):\\r\\n self.set_many({key: value})\",\n \"def __setitem__(self, key, val):\\n dict.__setitem__(self, key, val)\",\n \"def __setitem__(self, key: Any, value: Any) -> NoReturn:\\n raise RuntimeError(\\\"magicgui.Container does not support item setting.\\\")\",\n \"def set(self, key, value):\\n self.remove(key)\\n self.add(key, value)\",\n \"def __setitem__(self, key, val):\\n for k,v in list(self.__dict__.items()):\\n if k == key:\\n self.__dict__[key] = val\\n return \\n print((\\\"Item %s could not be updated...\\\" %key))\",\n \"def key_id(self, key_id):\\n\\n self._key_id = key_id\",\n \"def __setitem__(self, key, value):\\n self.put(key, value)\",\n \"def __setitem__(self, key, value):\\n self.put(key, value)\",\n \"def test_one_key(self):\\n self.now = time.time()\\n with patch('time.time', self.fake_time):\\n self.storage.set('1', 'one', moe=self.now + 1)\\n self.now += 2\\n self.gc.expire_random()\\n self.assertRaises(StorageKeyError, self.storage.get, '1')\",\n \"def __setitem__(self, key, value):\\n while self is not None:\\n if key in self._dict:\\n self._dict[key] = value\\n return\\n else:\\n self = self.parent\\n raise KeyError(\\\"%s was not declared\\\" % key)\",\n \"def _update_prepend_key(self):\\n self.prepend_key -= 1\",\n \"def __setitem__(self, key, value):\\n self.settings.set(key, value)\",\n \"def __init__(__self__, *,\\n key_data: pulumi.Input[str]):\\n pulumi.set(__self__, \\\"key_data\\\", key_data)\",\n \"def begin_set_key(self):\\n\\t\\tself.setting_key = True\\n\\t\\tself.player.current_level.screen_manager.set_controls(ControlSettingControls(self))\",\n \"def _put_to_back(self, key, value):\\n\\n pass\",\n \"def dal_set(key, obj):\\n global store\\n return store.set(urllib.quote(key), obj)\",\n \"def setdefault(self, key, failobj=None):\\n with self.__plock:\\n if key not in self._keys:\\n self._keys.append(key)\\n return UserDict.setdefault(self, key, failobj)\",\n \"def _set_unique_keys(self, keys):\\n # make this final once set\\n if self._unique_keys:\\n raise AlreadySetError()\\n\\n self._unique_keys = self._prepare_keys(keys)\",\n \"def __setitem__(self, key, value):\\n super(ReadOnlyDict, self).__setitem__(key, value)\",\n \"def __setitem__(self,key,value):\\n self._register[key] = value\\n self._register.sync()\",\n \"def __setitem__(self, key: str, value: Any) -> None:\\n self._store[key.lower()] = (key, value)\",\n \"def onkey(self, fun, key):\\n if fun is None:\\n if key in self._keys:\\n self._keys.remove(key)\\n elif key not in self._keys:\\n self._keys.append(key)\\n self._onkeyrelease(fun, key)\",\n \"def add_key(mu_key):\\n params['key'] = mu_key\",\n \"def set(self, key, value):\\n if key in self.keys:\\n self.remove(key)\\n self.keys[key] = None\\n self.buffered[key] = value\\n self.cache[key] = value\\n if self.autocommit:\\n commit()\",\n \"def setnx(self, key, value):\\n return self.set(key, value, nx=True)\",\n \"def setKey(self, key, value):\\n\\t\\tself.keyMap[key] = value\\n\\n\\t\\tif key == \\\"help\\\" :\\n\\t\\t\\tif value == 1 :\\n\\t\\t\\t\\tself.helpText.setText( \\\\\\n\\t\\t\\t\\t\\t\\\"arrows to move or turn\\\\n\\\" + \\\\\\n\\t\\t\\t\\t\\t\\\"shift-arrows to change view\\\\n\\\" + \\\\\\n\\t\\t\\t\\t\\t\\\"z/Z to zoom in/out, r to reset\\\\n\\\" + \\\\\\n\\t\\t\\t\\t\\t\\\",/. to slide left/right\\\")\\n\\t\\t\\telse :\\n\\t\\t\\t\\tself.helpText.setText(\\\"h for help\\\")\\n\\n\\t\\tif value == 1 : return\\n\\n\\t\\t# special cases for releasing keys with modifiers\\n\\t\\tif key == \\\"zoom-in\\\" :\\n\\t\\t\\tself.keyMap[\\\"zoom-out\\\"] = 0\\n\\t\\tif key == \\\"left\\\" or key == \\\"right\\\" :\\n\\t\\t\\tself.keyMap[\\\"cam-left\\\"] = 0\\n\\t\\t\\tself.keyMap[\\\"cam-right\\\"] = 0\\n\\t\\tif key == \\\"forward\\\" or key == \\\"backward\\\" :\\n\\t\\t\\tself.keyMap[\\\"cam-up\\\"] = 0\\n\\t\\t\\tself.keyMap[\\\"cam-down\\\"] = 0\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7539311","0.74440265","0.74026906","0.7250793","0.723817","0.723817","0.70751673","0.70751673","0.7062565","0.7028014","0.7028014","0.6877834","0.67624867","0.67489475","0.66592693","0.6637476","0.6608595","0.6566029","0.65246844","0.6480407","0.6471509","0.6467654","0.6447527","0.64440703","0.64303035","0.6404","0.63936603","0.6386707","0.6366428","0.6362809","0.63528115","0.6349479","0.63460845","0.6327138","0.63269675","0.63060486","0.6273541","0.62672794","0.62672794","0.62672794","0.62646335","0.62621164","0.6259979","0.62348324","0.6232339","0.6226555","0.6225992","0.6225992","0.62135255","0.62119246","0.6205036","0.6204802","0.6200768","0.6195507","0.61832696","0.61802214","0.61796594","0.6166172","0.61537397","0.61537397","0.61537397","0.61445594","0.6132818","0.61201817","0.6117371","0.6096125","0.60859776","0.6084462","0.60831577","0.60809726","0.60796773","0.60746276","0.6070749","0.6068788","0.6064993","0.6059749","0.60595834","0.605416","0.60480666","0.603907","0.6026136","0.60255486","0.60255486","0.60243404","0.6022492","0.6022447","0.6021109","0.6020134","0.60183704","0.6012629","0.60115284","0.6010866","0.6009832","0.6001183","0.5994436","0.5994284","0.5985511","0.5984172","0.59830165","0.5948563","0.5946895"],"string":"[\n \"0.7539311\",\n \"0.74440265\",\n \"0.74026906\",\n \"0.7250793\",\n \"0.723817\",\n \"0.723817\",\n \"0.70751673\",\n \"0.70751673\",\n \"0.7062565\",\n \"0.7028014\",\n \"0.7028014\",\n \"0.6877834\",\n \"0.67624867\",\n \"0.67489475\",\n \"0.66592693\",\n \"0.6637476\",\n \"0.6608595\",\n \"0.6566029\",\n \"0.65246844\",\n \"0.6480407\",\n \"0.6471509\",\n \"0.6467654\",\n \"0.6447527\",\n \"0.64440703\",\n \"0.64303035\",\n \"0.6404\",\n \"0.63936603\",\n \"0.6386707\",\n \"0.6366428\",\n \"0.6362809\",\n \"0.63528115\",\n \"0.6349479\",\n \"0.63460845\",\n \"0.6327138\",\n \"0.63269675\",\n \"0.63060486\",\n \"0.6273541\",\n \"0.62672794\",\n \"0.62672794\",\n \"0.62672794\",\n \"0.62646335\",\n \"0.62621164\",\n \"0.6259979\",\n \"0.62348324\",\n \"0.6232339\",\n \"0.6226555\",\n \"0.6225992\",\n \"0.6225992\",\n \"0.62135255\",\n \"0.62119246\",\n \"0.6205036\",\n \"0.6204802\",\n \"0.6200768\",\n \"0.6195507\",\n \"0.61832696\",\n \"0.61802214\",\n \"0.61796594\",\n \"0.6166172\",\n \"0.61537397\",\n \"0.61537397\",\n \"0.61537397\",\n \"0.61445594\",\n \"0.6132818\",\n \"0.61201817\",\n \"0.6117371\",\n \"0.6096125\",\n \"0.60859776\",\n \"0.6084462\",\n \"0.60831577\",\n \"0.60809726\",\n \"0.60796773\",\n \"0.60746276\",\n \"0.6070749\",\n \"0.6068788\",\n \"0.6064993\",\n \"0.6059749\",\n \"0.60595834\",\n \"0.605416\",\n \"0.60480666\",\n \"0.603907\",\n \"0.6026136\",\n \"0.60255486\",\n \"0.60255486\",\n \"0.60243404\",\n \"0.6022492\",\n \"0.6022447\",\n \"0.6021109\",\n \"0.6020134\",\n \"0.60183704\",\n \"0.6012629\",\n \"0.60115284\",\n \"0.6010866\",\n \"0.6009832\",\n \"0.6001183\",\n \"0.5994436\",\n \"0.5994284\",\n \"0.5985511\",\n \"0.5984172\",\n \"0.59830165\",\n \"0.5948563\",\n \"0.5946895\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76689,"cells":{"query":{"kind":"string","value":"create a qos policy bandwidth limit rules client"},"document":{"kind":"string","value":"def get_client(client_mgr,\n set_property=False,\n with_name=\"qos_dscp_marking_rules_client\"):\n manager = getattr(client_mgr, 'manager', client_mgr)\n net_client = getattr(manager, 'networks_client')\n try:\n _params = manager.default_params_with_timeout_values.copy()\n except Exception:\n _params = {}\n client = DscpMarkingRulesClient(net_client.auth_provider,\n net_client.service,\n net_client.region,\n net_client.endpoint_type,\n **_params)\n if set_property:\n setattr(manager, with_name, client)\n return client"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def bandwidth_limit_rule_create(request, policy_id, **kwargs):\n body = {'bandwidth_limit_rule': kwargs}\n if 'tenant_id' not in kwargs:\n kwargs['tenant_id'] = request.user.project_id\n body = {'bandwidth_limit_rule': kwargs}\n rule = 'bandwidth_limit_rule'\n bandwidth_limit_rule = neutronclient(request)\\\n .create_bandwidth_limit_rule(policy_id, body).get(rule)\n return BandwidthLimitRule(bandwidth_limit_rule)","def create_qos_bandwidth_limit_rule(\n self,\n policy_name_or_id,\n max_kbps,\n **kwargs,\n ):\n if not self._has_neutron_extension('qos'):\n raise exc.OpenStackCloudUnavailableExtension(\n 'QoS extension is not available on target cloud'\n )\n\n policy = self.network.find_qos_policy(policy_name_or_id)\n if not policy:\n raise exc.OpenStackCloudResourceNotFound(\n \"QoS policy {name_or_id} not Found.\".format(\n name_or_id=policy_name_or_id\n )\n )\n\n if kwargs.get(\"direction\") is not None:\n if not self._has_neutron_extension('qos-bw-limit-direction'):\n kwargs.pop(\"direction\")\n self.log.debug(\n \"'qos-bw-limit-direction' extension is not available on \"\n \"target cloud\"\n )\n\n kwargs['max_kbps'] = max_kbps\n\n return self.network.create_qos_bandwidth_limit_rule(policy, **kwargs)","def CreateRateLimitOptions(client, args):\n\n messages = client.messages\n rate_limit_options = messages.SecurityPolicyRuleRateLimitOptions()\n is_updated = False\n\n if (args.IsSpecified('rate_limit_threshold_count') or\n args.IsSpecified('rate_limit_threshold_interval_sec')):\n rate_limit_threshold = (\n messages.SecurityPolicyRuleRateLimitOptionsThreshold())\n if args.IsSpecified('rate_limit_threshold_count'):\n rate_limit_threshold.count = args.rate_limit_threshold_count\n if args.IsSpecified('rate_limit_threshold_interval_sec'):\n rate_limit_threshold.intervalSec = args.rate_limit_threshold_interval_sec\n rate_limit_options.rateLimitThreshold = rate_limit_threshold\n is_updated = True\n\n if args.IsSpecified('conform_action'):\n rate_limit_options.conformAction = _ConvertConformAction(\n args.conform_action)\n is_updated = True\n if args.IsSpecified('exceed_action'):\n rate_limit_options.exceedAction = _ConvertExceedAction(args.exceed_action)\n is_updated = True\n if args.IsSpecified('enforce_on_key'):\n rate_limit_options.enforceOnKey = (\n messages.SecurityPolicyRuleRateLimitOptions.EnforceOnKeyValueValuesEnum(\n _ConvertEnforceOnKey(args.enforce_on_key)))\n is_updated = True\n if args.IsSpecified('enforce_on_key_name'):\n rate_limit_options.enforceOnKeyName = args.enforce_on_key_name\n is_updated = True\n\n if (args.IsSpecified('ban_threshold_count') or\n args.IsSpecified('ban_threshold_interval_sec')):\n ban_threshold = messages.SecurityPolicyRuleRateLimitOptionsThreshold()\n if args.IsSpecified('ban_threshold_count'):\n ban_threshold.count = args.ban_threshold_count\n if args.IsSpecified('ban_threshold_interval_sec'):\n ban_threshold.intervalSec = args.ban_threshold_interval_sec\n rate_limit_options.banThreshold = ban_threshold\n is_updated = True\n\n if args.IsSpecified('ban_duration_sec'):\n rate_limit_options.banDurationSec = args.ban_duration_sec\n is_updated = True\n\n return rate_limit_options if is_updated else None","def minimum_bandwidth_rule_create(request, policy_id, **kwargs):\n if 'tenant_id' not in kwargs:\n kwargs['tenant_id'] = request.user.project_id\n body = {'minimum_bandwidth_rule': kwargs}\n rule = 'minimum_bandwidth_rule'\n minimum_bandwidth_rule = neutronclient(request)\\\n .create_minimum_bandwidth_rule(policy_id, body).get(rule)\n return MinimumBandwidthRule(minimum_bandwidth_rule)","def create_qos_minimum_bandwidth_rule(\n self,\n policy_name_or_id,\n min_kbps,\n **kwargs,\n ):\n if not self._has_neutron_extension('qos'):\n raise exc.OpenStackCloudUnavailableExtension(\n 'QoS extension is not available on target cloud'\n )\n\n policy = self.network.find_qos_policy(policy_name_or_id)\n if not policy:\n raise exc.OpenStackCloudResourceNotFound(\n \"QoS policy {name_or_id} not Found.\".format(\n name_or_id=policy_name_or_id\n )\n )\n\n kwargs['min_kbps'] = min_kbps\n\n return self.network.create_qos_minimum_bandwidth_rule(policy, **kwargs)","def policy_create(request, **kwargs):\n body = {'policy': kwargs}\n policy = neutronclient(request).create_qos_policy(body=body).get('policy')\n return QoSPolicy(policy)","def create_qos(self, specs):\n return self._unify_qos(self._impl.create_qos(specs))","async def qos(\n self,\n prefetch_size: int = 0,\n prefetch_count: int = 0,\n connection_global: bool = False,\n ):\n await self.channel.basic_qos(\n prefetch_size=prefetch_size,\n prefetch_count=prefetch_count,\n connection_global=connection_global,\n )","def create_qos(self, specs):\n aname = \"cinder_v%s.create_qos\" % self.version\n name = self.generate_random_name()\n\n with atomic.ActionTimer(self, aname):\n return self._get_client().qos_specs.create(name, specs)","def VolumeSetQos(min_iops,\n max_iops,\n burst_iops,\n#pylint: disable=unused-argument\n volume_names,\n volume_ids,\n volume_prefix,\n volume_regex,\n volume_count,\n source_account,\n source_account_id,\n test,\n mvip,\n username,\n password):\n#pylint: enable=unused-argument\n\n options = copy.deepcopy(locals())\n for key in [\"min_iops\", \"max_iops\", \"burst_iops\"]:\n options.pop(key, None)\n\n return VolumeModify(property_name=\"qos\",\n property_value={\n \"minIOPS\" : min_iops,\n \"maxIOPS\" : max_iops,\n \"burstIOPS\" : burst_iops\n },\n **options)","def get_port_qos_rxrate(self, iface, qos):\n pytest.skip(\"Method is not supported by Iperf TG\")","def test_qos_specs(self):\n qos = {'maxIOPS': 1000, 'maxBWS': 2048}\n snapshot = fake_snapshot.fake_snapshot_obj(\n self.ctx, **{'volume': self.volume,\n 'provider_id': self.snapshot_id,\n 'volume_size': 8})\n extraspecs = {}\n self.driver._get_volumetype_qos = mock.MagicMock()\n self.driver._get_volumetype_qos.return_value = qos\n self.driver._get_volumetype_extraspecs = mock.MagicMock()\n self.driver._get_volumetype_extraspecs.return_value = extraspecs\n\n props = self.driver.initialize_connection_snapshot(\n snapshot,\n self.connector)\n\n self.assertEqual(1000, int(props['data']['iopsLimit']))\n self.assertEqual(2048, int(props['data']['bandwidthLimit']))","def set_qos(self, on_ok):\n self._channel.basic_qos(\n prefetch_count=self._prefetch_count, callback=on_ok)","def _create_qos_specs(self, name, values=None):\n if values:\n specs = dict(name=name, qos_specs=values)\n else:\n specs = {'name': name,\n 'consumer': 'back-end',\n 'specs': {\n 'key1': 'value1',\n 'key2': 'value2'}}\n return db.qos_specs_create(self.ctxt, specs)['id']","def create_qos(self, **kwargs):\n post_body = json.dumps({'qos_specs': kwargs})\n resp, body = self.post('qos-specs', post_body)\n body = json.loads(body)\n self.validate_response(schema.show_qos, resp, body)\n return rest_client.ResponseBody(resp, body)","def test_b_negative_add_qos(self):\n qoss = {\"qos_120\": 120, \"qos_-5\": -5}\n for qos_name, qos_value in qoss.iteritems():\n testflow.step(\n \"Create CPU QoS %s on datacenter %s with parameters: %s\",\n qos_name, conf.DC_NAME[0], qos_value\n )\n assert not ll_datacenters.add_qos_to_datacenter(\n datacenter=conf.DC_NAME[0],\n qos_name=qos_name,\n qos_type=conf.QOS_TYPE_CPU,\n cpu_limit=qos_value\n )","def bdev_set_qos_limit(\n client,\n name,\n rw_ios_per_sec=None,\n rw_mbytes_per_sec=None,\n r_mbytes_per_sec=None,\n w_mbytes_per_sec=None):\n params = {}\n params['name'] = name\n if rw_ios_per_sec is not None:\n params['rw_ios_per_sec'] = rw_ios_per_sec\n if rw_mbytes_per_sec is not None:\n params['rw_mbytes_per_sec'] = rw_mbytes_per_sec\n if r_mbytes_per_sec is not None:\n params['r_mbytes_per_sec'] = r_mbytes_per_sec\n if w_mbytes_per_sec is not None:\n params['w_mbytes_per_sec'] = w_mbytes_per_sec\n return client.call('bdev_set_qos_limit', params)","def qos_settings_control_modify(self, enforcement=None, ratebucket_rebalance=None):\n return self.request( \"qos-settings-control-modify\", {\n 'enforcement': [ enforcement, 'enforcement', [ bool, 'None' ], False ],\n 'ratebucket_rebalance': [ ratebucket_rebalance, 'ratebucket-rebalance', [ bool, 'None' ], False ],\n }, {\n } )","def change_bandwidth(self,edge_notice):\n #print(\"==============带宽控制=============\",edge_notice)\n ports_details = edge_notice[\"port_details\"]\n\n total_bd = edge_notice[\"bandwidth\"][\"mobilenet\"]\n self.reset_bandwidth()\n print(\"########\",total_bd)\n #print(\"model_details+++++++++\",model_details)\n #print(\"port_details+++++++++\", ports_details)\n class_id = 20\n try:\n for model_name in [\"mobilenet\"]:\n ports_list = ports_details[model_name]\n for dport in ports_list:\n # 1. create a class under the root\n create_class = \"tc class add dev eth0 parent 1: classid 1:\"+str(class_id)+\" htb rate \"+str(total_bd)+\"mbit ceil \"+str(total_bd)+\"mbit\"\n create_branch = \"tc qdisc add dev eth0 parent 1:\"+str(class_id)+\" handle \"+str(class_id)+\": sfq perturb 10\"\n create_filter = \"tc filter add dev eth0 protocol ip parent 1: prio 1 u32 match ip dport \"+str(dport)+\" 0xffff flowid 1:\"+str(class_id)\n #print(create_class)\n class_id = class_id+1\n self.__excecute__(create_class)\n self.__excecute__(create_branch)\n self.__excecute__(create_filter)\n except Exception as e:\n print(\" bandwidth limitation errors\",e)\n #print(\"==========finsih========\")","def limit():\n bwc = BandwidthConfigurator()\n bwc.limit()","def test_create_qos_queue(self):\r\n resource = 'qos_queue'\r\n cmd = qos.CreateQoSQueue(\r\n test_cli20.MyApp(sys.stdout), None)\r\n myid = 'myid'\r\n name = 'my_queue'\r\n default = False\r\n args = ['--default', default, name]\r\n position_names = ['name', 'default']\r\n position_values = [name, default]\r\n self._test_create_resource(resource, cmd, name, myid, args,\r\n position_names, position_values)","def minimum_packet_rate_rule_create(request, policy_id, **kwargs):\n body = {'minimum_packet_rate_rule': kwargs}\n if 'tenant_id' not in kwargs:\n kwargs['tenant_id'] = request.user.project_id\n body = {'minimum_packet_rate_rule': kwargs}\n rule = 'minimum_packet_rate_rule'\n minimum_packet_rate_rule = neutronclient(request)\\\n .create_minimum_packet_rate_rule(policy_id, body).get(rule)\n return MinimumPacketRateRule(minimum_packet_rate_rule)","def setBw(self, newapif, mode): \n self.newapif = newapif\n bandwidth = wifiParameters.set_bw(mode)\n os.system(\"tc qdisc add dev %s root tbf rate %smbit latency 2ms burst 15k\" % (self.newapif, bandwidth))","def get_qos_bandwidth_limit_rule(self, policy_name_or_id, rule_id):\n if not self._has_neutron_extension('qos'):\n raise exc.OpenStackCloudUnavailableExtension(\n 'QoS extension is not available on target cloud'\n )\n\n policy = self.network.find_qos_policy(policy_name_or_id)\n if not policy:\n raise exc.OpenStackCloudResourceNotFound(\n \"QoS policy {name_or_id} not Found.\".format(\n name_or_id=policy_name_or_id\n )\n )\n\n return self.network.get_qos_bandwidth_limit_rule(rule_id, policy)","def CreateRateLimitOptions(\n client, args, support_fairshare, support_multiple_rate_limit_keys\n):\n\n messages = client.messages\n rate_limit_options = messages.SecurityPolicyRuleRateLimitOptions()\n is_updated = False\n\n if (args.IsSpecified('rate_limit_threshold_count') or\n args.IsSpecified('rate_limit_threshold_interval_sec')):\n rate_limit_threshold = (\n messages.SecurityPolicyRuleRateLimitOptionsThreshold())\n if args.IsSpecified('rate_limit_threshold_count'):\n rate_limit_threshold.count = args.rate_limit_threshold_count\n if args.IsSpecified('rate_limit_threshold_interval_sec'):\n rate_limit_threshold.intervalSec = args.rate_limit_threshold_interval_sec\n rate_limit_options.rateLimitThreshold = rate_limit_threshold\n is_updated = True\n\n if args.IsSpecified('conform_action'):\n rate_limit_options.conformAction = _ConvertConformAction(\n args.conform_action)\n is_updated = True\n if args.IsSpecified('exceed_action'):\n rate_limit_options.exceedAction = _ConvertExceedAction(args.exceed_action)\n is_updated = True\n if (args.IsSpecified('exceed_redirect_type') or\n args.IsSpecified('exceed_redirect_target')):\n exceed_redirect_options = messages.SecurityPolicyRuleRedirectOptions()\n if args.IsSpecified('exceed_redirect_type'):\n exceed_redirect_options.type = (\n messages.SecurityPolicyRuleRedirectOptions.TypeValueValuesEnum(\n _ConvertRedirectType(args.exceed_redirect_type)))\n if args.IsSpecified('exceed_redirect_target'):\n exceed_redirect_options.target = args.exceed_redirect_target\n rate_limit_options.exceedRedirectOptions = exceed_redirect_options\n is_updated = True\n if args.IsSpecified('enforce_on_key'):\n rate_limit_options.enforceOnKey = (\n messages.SecurityPolicyRuleRateLimitOptions.EnforceOnKeyValueValuesEnum(\n ConvertEnforceOnKey(args.enforce_on_key)))\n is_updated = True\n if args.IsSpecified('enforce_on_key_name'):\n rate_limit_options.enforceOnKeyName = args.enforce_on_key_name\n is_updated = True\n\n if support_multiple_rate_limit_keys and args.IsSpecified(\n 'enforce_on_key_configs'\n ):\n enforce_on_key_configs = []\n for k, v in args.enforce_on_key_configs.items():\n enforce_on_key_configs.append(\n messages.SecurityPolicyRuleRateLimitOptionsEnforceOnKeyConfig(\n enforceOnKeyType=messages.SecurityPolicyRuleRateLimitOptionsEnforceOnKeyConfig.EnforceOnKeyTypeValueValuesEnum(\n ConvertEnforceOnKey(k)\n ),\n enforceOnKeyName=v if v else None,\n )\n )\n rate_limit_options.enforceOnKeyConfigs = enforce_on_key_configs\n is_updated = True\n\n if (args.IsSpecified('ban_threshold_count') or\n args.IsSpecified('ban_threshold_interval_sec')):\n ban_threshold = messages.SecurityPolicyRuleRateLimitOptionsThreshold()\n if args.IsSpecified('ban_threshold_count'):\n ban_threshold.count = args.ban_threshold_count\n if args.IsSpecified('ban_threshold_interval_sec'):\n ban_threshold.intervalSec = args.ban_threshold_interval_sec\n rate_limit_options.banThreshold = ban_threshold\n is_updated = True\n\n if args.IsSpecified('ban_duration_sec'):\n rate_limit_options.banDurationSec = args.ban_duration_sec\n is_updated = True\n\n if support_fairshare and (\n args.IsSpecified('exceed_action_rpc_status_code') or\n args.IsSpecified('exceed_action_rpc_status_message')):\n exceed_action_rpc_status = (\n messages.SecurityPolicyRuleRateLimitOptionsRpcStatus()\n )\n if args.IsSpecified('exceed_action_rpc_status_code'):\n exceed_action_rpc_status.code = args.exceed_action_rpc_status_code\n if args.IsSpecified('exceed_action_rpc_status_message'):\n exceed_action_rpc_status.message = args.exceed_action_rpc_status_message\n rate_limit_options.exceedActionRpcStatus = exceed_action_rpc_status\n is_updated = True\n\n return rate_limit_options if is_updated else None","def __init__(__self__,\n resource_name: str,\n args: InterRegionTrafficQosPolicyArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def create_qos_policy(self, **kwargs):\n if not self._has_neutron_extension('qos'):\n raise exc.OpenStackCloudUnavailableExtension(\n 'QoS extension is not available on target cloud'\n )\n\n default = kwargs.pop(\"default\", None)\n if default is not None:\n if self._has_neutron_extension('qos-default'):\n kwargs['is_default'] = default\n else:\n self.log.debug(\n \"'qos-default' extension is not available on \"\n \"target cloud\"\n )\n\n return self.network.create_qos_policy(**kwargs)","def __init__(self, is_active=None, limit_bandwidth=None, limit_bandwidth_unit=None, limit_date_range_from=None, limit_date_range_to=None, limit_num_clients=None, limit_num_downloads=None, limit_package_query=None, limit_path_query=None, metadata=None, scheduled_reset_at=None, scheduled_reset_period=None, token=None):\n\n self._is_active = None\n self._limit_bandwidth = None\n self._limit_bandwidth_unit = None\n self._limit_date_range_from = None\n self._limit_date_range_to = None\n self._limit_num_clients = None\n self._limit_num_downloads = None\n self._limit_package_query = None\n self._limit_path_query = None\n self._metadata = None\n self._scheduled_reset_at = None\n self._scheduled_reset_period = None\n self._token = None\n\n if is_active is not None:\n self.is_active = is_active\n if limit_bandwidth is not None:\n self.limit_bandwidth = limit_bandwidth\n if limit_bandwidth_unit is not None:\n self.limit_bandwidth_unit = limit_bandwidth_unit\n if limit_date_range_from is not None:\n self.limit_date_range_from = limit_date_range_from\n if limit_date_range_to is not None:\n self.limit_date_range_to = limit_date_range_to\n if limit_num_clients is not None:\n self.limit_num_clients = limit_num_clients\n if limit_num_downloads is not None:\n self.limit_num_downloads = limit_num_downloads\n if limit_package_query is not None:\n self.limit_package_query = limit_package_query\n if limit_path_query is not None:\n self.limit_path_query = limit_path_query\n if metadata is not None:\n self.metadata = metadata\n if scheduled_reset_at is not None:\n self.scheduled_reset_at = scheduled_reset_at\n if scheduled_reset_period is not None:\n self.scheduled_reset_period = scheduled_reset_period\n if token is not None:\n self.token = token","def search_qos_bandwidth_limit_rules(\n self,\n policy_name_or_id,\n rule_id=None,\n filters=None,\n ):\n rules = self.list_qos_bandwidth_limit_rules(policy_name_or_id, filters)\n return _utils._filter_list(rules, rule_id, filters)","def test_400_enable_qos(self):\n if self._get_openstack_release() >= self.trusty_mitaka:\n unit = self.n_ovs_sentry\n set_default = {'enable-qos': 'False'}\n set_alternate = {'enable-qos': 'True'}\n self.d.configure('neutron-api', set_alternate)\n self._wait_and_check(sleep=60)\n qos_plugin = 'qos'\n config = u._get_config(\n self.neutron_api_sentry, '/etc/neutron/neutron.conf')\n service_plugins = config.get(\n 'DEFAULT',\n 'service_plugins').split(',')\n if qos_plugin not in service_plugins:\n message = \"{} not in service_plugins\".format(qos_plugin)\n amulet.raise_status(amulet.FAIL, msg=message)\n\n config = u._get_config(\n unit,\n '/etc/neutron/plugins/ml2/openvswitch_agent.ini')\n extensions = config.get('agent', 'extensions').split(',')\n if qos_plugin not in extensions:\n message = \"qos not in extensions\"\n amulet.raise_status(amulet.FAIL, msg=message)\n\n u.log.debug('Setting QoS back to {}'.format(\n set_default['enable-qos']))\n self.d.configure('neutron-api', set_default)\n self._wait_and_check()\n u.log.debug('OK')","def _get_qos(self):\n return self.__qos","def _get_qos(self):\n return self.__qos","def _get_qos(self):\n return self.__qos","def set_bandwidth(ops, kernel):\n \n if kernel == 'epanechnikov':\n bw_constant = 2.2\n else:\n bw_constant = 1\n\n n = np.shape(ops[0].traj)[0]\n bandwidth = bw_constant*n**(-1/6)\n\n print('Selected bandwidth: ' + str(bandwidth)+ '\\n')\n\n return bandwidth","def pre_qos_queue_create(self, resource_dict):\n pass","def create_policy_request():\n return {\n 'public_key':\n r'BBLewg4VqLR38b38daE7Fj\\/uhr543uGrEpyoPFgmFZK6EZ9g2XdK\\/i65RrSJ6sJ96aXD3DJHY3Me2GJQO9\\/ifjE=',\n 'label':\n 'Integration Test Policy',\n 'operations': [{\n 'sensor_id': 10,\n 'action': 'SHARE',\n }, {\n 'sensor_id': 53,\n 'action': 'BIN',\n 'bins': [30.0, 60.0, 90.0]\n }, {\n 'sensor_id': 55,\n 'action': 'MOVING_AVG',\n 'interval': 300\n }]\n }","def resource_type(self):\n return 'qos'","def create_qos_queue(self, body=None):\r\n return self.post(self.qos_queues_path, body=body)","def snmpqosqos_error_create_policy_failedrate(self) :\n\t\ttry :\n\t\t\treturn self._snmpqosqos_error_create_policy_failedrate\n\t\texcept Exception as e:\n\t\t\traise e","def qos(self):\n if self == SubscribeResult.qos0:\n rv = 0\n elif self == SubscribeResult.qos1:\n rv = 1\n elif self == SubscribeResult.qos2:\n rv = 2\n else:\n raise TypeError()\n\n return rv","def create_capacity_limiter(total_tokens: float) -> abc.CapacityLimiter:\n return get_asynclib().CapacityLimiter(total_tokens)","def qos_settings_control_get(self, desired_attributes=None):\n return self.request( \"qos-settings-control-get\", {\n 'desired_attributes': [ desired_attributes, 'desired-attributes', [ QosSettingsControlInfo, 'None' ], False ],\n }, {\n 'attributes': [ QosSettingsControlInfo, False ],\n } )","def get_data_qos(args):\n diff_data(args, \"qos\")","def list_qos_bandwidth_limit_rules(self, policy_name_or_id, filters=None):\n if not self._has_neutron_extension('qos'):\n raise exc.OpenStackCloudUnavailableExtension(\n 'QoS extension is not available on target cloud'\n )\n\n policy = self.network.find_qos_policy(policy_name_or_id)\n if not policy:\n raise exc.OpenStackCloudResourceNotFound(\n \"QoS policy {name_or_id} not Found.\".format(\n name_or_id=policy_name_or_id\n )\n )\n\n # Translate None from search interface to empty {} for kwargs below\n if not filters:\n filters = {}\n\n return list(\n self.network.qos_bandwidth_limit_rules(\n qos_policy=policy, **filters\n )\n )","def list_qos_minimum_bandwidth_rules(\n self, policy_name_or_id, filters=None\n ):\n if not self._has_neutron_extension('qos'):\n raise exc.OpenStackCloudUnavailableExtension(\n 'QoS extension is not available on target cloud'\n )\n\n policy = self.network.find_qos_policy(policy_name_or_id)\n if not policy:\n raise exc.OpenStackCloudResourceNotFound(\n \"QoS policy {name_or_id} not Found.\".format(\n name_or_id=policy_name_or_id\n )\n )\n\n # Translate None from search interface to empty {} for kwargs below\n if not filters:\n filters = {}\n\n return list(\n self.network.qos_minimum_bandwidth_rules(policy, **filters)\n )","def search_qos_minimum_bandwidth_rules(\n self,\n policy_name_or_id,\n rule_id=None,\n filters=None,\n ):\n rules = self.list_qos_minimum_bandwidth_rules(\n policy_name_or_id, filters\n )\n return _utils._filter_list(rules, rule_id, filters)","def test_create_dispatch_policy(self):\n pass","def pick_policy(Qs, rates, num_of_servers):\n new_rates = copy.deepcopy(rates)\n Q = np.random.choice(Qs)\n srv = np.random.randint(1, num_of_servers + 1)\n state = pick_a_state(Q)\n scale = np.random.uniform(0, 2)\n new_rates[srv][state] *= scale\n return new_rates, srv","def post_qos_queue_create(self, resource_dict):\n pass","def notify(self, new_maxrate, new_maxexemptrate, usedbytes, usedi2bytes):\n # Prepare message parameters from the template\n message = \"\"\n params = {'slice': self.name, 'hostname': socket.gethostname(),\n 'since': time.asctime(time.gmtime(self.time)) + \" GMT\",\n 'until': time.asctime(time.gmtime(self.time + period)) + \" GMT\",\n 'date': time.asctime(time.gmtime()) + \" GMT\",\n 'period': format_period(period)}\n\n if new_maxrate != (self.MaxRate * 1000):\n # Format template parameters for low bandwidth message\n params['class'] = \"low bandwidth\"\n params['bytes'] = format_bytes(usedbytes - self.bytes)\n params['limit'] = format_bytes(self.MaxKByte * 1024)\n params['new_maxrate'] = bwlimit.format_tc_rate(new_maxrate)\n\n # Cap low bandwidth burst rate\n message += template % params\n logger.log(\"bwmon: ** %(slice)s %(class)s capped at %(new_maxrate)s/s \" % params)\n\n if new_maxexemptrate != (self.Maxi2Rate * 1000):\n # Format template parameters for high bandwidth message\n params['class'] = \"high bandwidth\"\n params['bytes'] = format_bytes(usedi2bytes - self.i2bytes)\n params['limit'] = format_bytes(self.Maxi2KByte * 1024)\n params['new_maxrate'] = bwlimit.format_tc_rate(new_maxexemptrate)\n\n message += template % params\n logger.log(\"bwmon: ** %(slice)s %(class)s capped at %(new_maxrate)s/s \" % params)\n\n # Notify slice\n if self.emailed == False:\n subject = \"pl_mom capped bandwidth of slice %(slice)s on %(hostname)s\" % params\n if DEBUG:\n logger.log(\"bwmon: \"+ subject)\n logger.log(\"bwmon: \"+ message + (footer % params))\n else:\n self.emailed = True\n logger.log(\"bwmon: Emailing %s\" % self.name)\n slicemail(self.name, subject, message + (footer % params))","def get_qos_minimum_bandwidth_rule(self, policy_name_or_id, rule_id):\n if not self._has_neutron_extension('qos'):\n raise exc.OpenStackCloudUnavailableExtension(\n 'QoS extension is not available on target cloud'\n )\n\n policy = self.network.find_qos_policy(policy_name_or_id)\n if not policy:\n raise exc.OpenStackCloudResourceNotFound(\n \"QoS policy {name_or_id} not Found.\".format(\n name_or_id=policy_name_or_id\n )\n )\n\n return self.network.get_qos_minimum_bandwidth_rule(rule_id, policy)","def qos_stats(val):\n\n global qos_object, qos_interface\n\n if request.form.get('endPoll'):\n qos_object.pop(request.form.get('endPoll'))\n return 'Object Removed'\n\n if not request.form.get('openPage'):\n\n if request.form.get('action') == 'qos':\n qos_stats = qos_object.get(request.form.get('interface')).get_interface_queues_out()\n return jsonify({'data': qos_stats})\n\n if not qos_object:\n qos_object[request.form.get('openPage')] = GetBandwidth.CalcBandwidth(device, netconf_port, username, password, request.form.get('openPage'))\n qos_interface = request.form.get('openPage')\n return render_template('qos_stats.html', interface=qos_interface, device=device)\n elif request.form.get('openPage') is None:\n return render_template('qos_stats.html', interface=qos_interface, device=device)\n else:\n if qos_object.get(request.form.get('openPage')) is None:\n # (host=self.host, port=self.netconf_port, username=self.username, password=self.password,\n new_object = GetBandwidth.CalcBandwidth(device, netconf_port, username, password, request.form.get('openPage'))\n qos_object[request.form.get('openPage')] = new_object\n\n qos_interface = request.form.get('openPage')\n return render_template('qos_stats.html', interface=qos_interface, device=device)","def update_qos_bandwidth_limit_rule(\n self, policy_name_or_id, rule_id, **kwargs\n ):\n if not self._has_neutron_extension('qos'):\n raise exc.OpenStackCloudUnavailableExtension(\n 'QoS extension is not available on target cloud'\n )\n\n policy = self.network.find_qos_policy(\n policy_name_or_id, ignore_missing=True\n )\n if not policy:\n raise exc.OpenStackCloudResourceNotFound(\n \"QoS policy {name_or_id} not Found.\".format(\n name_or_id=policy_name_or_id\n )\n )\n\n if kwargs.get(\"direction\") is not None:\n if not self._has_neutron_extension('qos-bw-limit-direction'):\n kwargs.pop(\"direction\")\n self.log.debug(\n \"'qos-bw-limit-direction' extension is not available on \"\n \"target cloud\"\n )\n\n if not kwargs:\n self.log.debug(\"No QoS bandwidth limit rule data to update\")\n return\n\n curr_rule = self.network.get_qos_bandwidth_limit_rule(\n qos_rule=rule_id, qos_policy=policy\n )\n if not curr_rule:\n raise exc.OpenStackCloudException(\n \"QoS bandwidth_limit_rule {rule_id} not found in policy \"\n \"{policy_id}\".format(rule_id=rule_id, policy_id=policy['id'])\n )\n\n return self.network.update_qos_bandwidth_limit_rule(\n qos_rule=curr_rule, qos_policy=policy, **kwargs\n )","def pre_qos_forwarding_class_create(self, resource_dict):\n pass","def qos(self) -> int:\n return self._qos","def set_qos(self, qos_id, set_specs_args):\n aname = \"cinder_v%s.set_qos\" % self.version\n with atomic.ActionTimer(self, aname):\n return self._get_client().qos_specs.set_keys(qos_id,\n set_specs_args)","def new_capacity_rule(mod, prj, prd):\n return 0","def dynamic_vnic_conn_policy_create(handle, name, descr=None, dynamic_eth=\"54\",\n adaptor_profile_name=None,\n protection=\"protected\",\n parent_dn=\"org-root\", **kwargs):\n\n from ucscsdk.mometa.vnic.VnicDynamicConPolicy import VnicDynamicConPolicy\n\n obj = handle.query_dn(parent_dn)\n if not obj:\n raise UcscOperationError(\"dynamic_vnic_conn_policy_create\",\n \"Org %s does not exist\" % parent_dn)\n\n mo = VnicDynamicConPolicy(parent_mo_or_dn=obj,\n name=name,\n descr=descr,\n dynamic_eth=dynamic_eth,\n protection=protection,\n adaptor_profile_name=adaptor_profile_name)\n\n mo.set_prop_multiple(**kwargs)\n\n handle.add_mo(mo, modify_present=True)\n handle.commit()\n return mo","def test_create_qos_queue_all_values(self):\r\n resource = 'qos_queue'\r\n cmd = qos.CreateQoSQueue(\r\n test_cli20.MyApp(sys.stdout), None)\r\n myid = 'myid'\r\n name = 'my_queue'\r\n default = False\r\n min = '10'\r\n max = '40'\r\n qos_marking = 'untrusted'\r\n dscp = '0'\r\n args = ['--default', default, '--min', min, '--max', max,\r\n '--qos-marking', qos_marking, '--dscp', dscp, name]\r\n position_names = ['name', 'default', 'min', 'max', 'qos_marking',\r\n 'dscp']\r\n position_values = [name, default, min, max, qos_marking, dscp]\r\n self._test_create_resource(resource, cmd, name, myid, args,\r\n position_names, position_values)","def __init__(__self__,\n resource_name: str,\n args: RateLimitingArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def update_qos_minimum_bandwidth_rule(\n self, policy_name_or_id, rule_id, **kwargs\n ):\n if not self._has_neutron_extension('qos'):\n raise exc.OpenStackCloudUnavailableExtension(\n 'QoS extension is not available on target cloud'\n )\n\n policy = self.network.find_qos_policy(policy_name_or_id)\n if not policy:\n raise exc.OpenStackCloudResourceNotFound(\n \"QoS policy {name_or_id} not Found.\".format(\n name_or_id=policy_name_or_id\n )\n )\n\n if not kwargs:\n self.log.debug(\"No QoS minimum bandwidth rule data to update\")\n return\n\n curr_rule = self.network.get_qos_minimum_bandwidth_rule(\n rule_id, policy\n )\n if not curr_rule:\n raise exc.OpenStackCloudException(\n \"QoS minimum_bandwidth_rule {rule_id} not found in policy \"\n \"{policy_id}\".format(rule_id=rule_id, policy_id=policy['id'])\n )\n\n return self.network.update_qos_minimum_bandwidth_rule(\n curr_rule, policy, **kwargs\n )","def make_Bandwidth(typicalBandwidth=2500, maximumBandwidth=10000):\n\n return {'maximumBandwidth': maximumBandwidth,\n 'typicalBandwidth': typicalBandwidth\n }","def qos_policy_group_create(self, policy_group, vserver, return_record=None, max_throughput=None):\n return self.request( \"qos-policy-group-create\", {\n 'policy_group': [ policy_group, 'policy-group', [ basestring, 'None' ], False ],\n 'vserver': [ vserver, 'vserver', [ basestring, 'vserver-name' ], False ],\n 'return_record': [ return_record, 'return-record', [ bool, 'None' ], False ],\n 'max_throughput': [ max_throughput, 'max-throughput', [ basestring, 'qos-tput' ], False ],\n }, {\n 'result': [ QosPolicyGroupInfo, False ],\n } )","def test_waf_policy_basic(self, resource_group):\n # multi-line comment below\n subscription = self.current_subscription()\n blockpolicy = self.create_random_name(prefix='cli', length=24)\n ruleName = self.create_random_name(prefix='cli', length=24)\n cmd = 'az network front-door waf-policy create -g {resource_group} -n {blockpolicy} --mode prevention'.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(result['name'], blockpolicy)\n self.assertEqual(result['policySettings']['mode'], \"Prevention\")\n self.assertEqual(result['policySettings']['requestBodyCheck'], \"Enabled\")\n self.assertIn('customRules', result)\n self.assertIn('managedRules', result)\n self.assertIn('id', result)\n self.assertEqual(result['sku']['name'], \"Classic_AzureFrontDoor\")\n\n standardskupolicy = self.create_random_name(prefix='cli', length=24)\n cmd = 'az network front-door waf-policy create -g {resource_group} -n {standardskupolicy} --mode prevention --sku Standard_AzureFrontDoor'.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(result['name'], standardskupolicy)\n self.assertEqual(result['policySettings']['mode'], \"Prevention\")\n self.assertEqual(result['policySettings']['requestBodyCheck'], \"Enabled\")\n self.assertIn('customRules', result)\n self.assertIn('managedRules', result)\n self.assertIn('id', result)\n self.assertEqual(result['sku']['name'], \"Standard_AzureFrontDoor\")\n\n detectionredirectpolicy = self.create_random_name(prefix='cli', length=24)\n cmd = 'az network front-door waf-policy create -g {resource_group} -n {detectionredirectpolicy} --mode Detection --redirect-url http://www.microsoft.com --sku Premium_AzureFrontDoor'.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(result['name'], detectionredirectpolicy)\n self.assertEqual(result['policySettings']['mode'], \"Detection\")\n self.assertEqual(result['policySettings']['redirectUrl'], \"http://www.microsoft.com\")\n self.assertEqual(result['policySettings']['requestBodyCheck'], \"Enabled\")\n self.assertIn('customRules', result)\n self.assertIn('managedRules', result)\n self.assertIn('id', result)\n self.assertEqual(result['sku']['name'], \"Premium_AzureFrontDoor\")\n\n detectioncbcpolicy = self.create_random_name(prefix='cli', length=24)\n cmd = 'az network front-door waf-policy create -g {resource_group} -n {detectioncbcpolicy} --mode Detection --redirect-url http://www.microsoft.com --custom-block-response-status-code 406 --sku Classic_AzureFrontDoor'.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(result['name'], detectioncbcpolicy)\n self.assertEqual(result['policySettings']['mode'], \"Detection\")\n self.assertEqual(result['policySettings']['redirectUrl'], \"http://www.microsoft.com\")\n self.assertEqual(result['policySettings']['customBlockResponseStatusCode'], 406)\n self.assertEqual(result['policySettings']['requestBodyCheck'], \"Enabled\")\n self.assertIn('customRules', result)\n self.assertIn('managedRules', result)\n self.assertIn('id', result)\n self.assertEqual(result['sku']['name'], \"Classic_AzureFrontDoor\")\n\n detectioncbbpolicy = self.create_random_name(prefix='cli', length=24)\n cmd = 'az network front-door waf-policy create -g {resource_group} -n {detectioncbbpolicy} --mode Detection --redirect-url http://www.microsoft.com --custom-block-response-status-code 406 --custom-block-response-body YiBvZHk='.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(result['name'], detectioncbbpolicy)\n self.assertEqual(result['policySettings']['mode'], \"Detection\")\n self.assertEqual(result['policySettings']['enabledState'], \"Enabled\")\n self.assertEqual(result['policySettings']['redirectUrl'], \"http://www.microsoft.com\")\n self.assertEqual(result['policySettings']['customBlockResponseStatusCode'], 406)\n self.assertEqual(result['policySettings']['customBlockResponseBody'], \"YiBvZHk=\")\n self.assertEqual(result['policySettings']['requestBodyCheck'], \"Enabled\")\n self.assertIn('customRules', result)\n self.assertIn('managedRules', result)\n self.assertIn('id', result)\n self.assertEqual(result['sku']['name'], \"Classic_AzureFrontDoor\")\n\n detectiondisabledpolicy = self.create_random_name(prefix='cli', length=24)\n cmd = 'az network front-door waf-policy create -g {resource_group} -n {detectiondisabledpolicy} --mode Detection --disabled'.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(result['name'], detectiondisabledpolicy)\n self.assertEqual(result['policySettings']['mode'], \"Detection\")\n self.assertEqual(result['policySettings']['enabledState'], \"Disabled\")\n self.assertEqual(result['policySettings']['redirectUrl'], None)\n self.assertEqual(result['policySettings']['customBlockResponseStatusCode'], None)\n self.assertEqual(result['policySettings']['customBlockResponseBody'], None)\n self.assertEqual(result['policySettings']['requestBodyCheck'], \"Enabled\")\n self.assertIn('customRules', result)\n self.assertIn('managedRules', result)\n self.assertIn('id', result)\n self.assertEqual(result['sku']['name'], \"Classic_AzureFrontDoor\")\n\n cmd = 'az network front-door waf-policy update -g {resource_group} -n {detectiondisabledpolicy} --mode Detection'.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertIn('customRules', result)\n self.assertIn('managedRules', result)\n self.assertIn('id', result)\n self.assertEqual(result['policySettings']['enabledState'], \"Enabled\")\n self.assertEqual(result['policySettings']['requestBodyCheck'], \"Enabled\")\n self.assertEqual(result['sku']['name'], \"Classic_AzureFrontDoor\")\n\n cmd = 'az network front-door waf-policy update -g {resource_group} -n {blockpolicy} --tags test=best'.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(result['name'], blockpolicy)\n self.assertEqual(result['policySettings']['mode'], \"Prevention\")\n # TODO uncomment once API support for updating tags is fixed :-O\n # self.assertEqual(result['tags'], { 'test': 'best' })\n self.assertIn('customRules', result)\n self.assertIn('managedRules', result)\n self.assertIn('id', result)\n self.assertEqual(result['sku']['name'], \"Classic_AzureFrontDoor\")\n\n cmd = 'az network front-door waf-policy update -g {resource_group} -n {blockpolicy} --mode detection --sku Classic_AzureFrontDoor'.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(result['name'], blockpolicy)\n self.assertEqual(result['policySettings']['mode'], \"Detection\")\n self.assertEqual(result['policySettings']['requestBodyCheck'], \"Enabled\")\n self.assertEqual(result['sku']['name'], \"Classic_AzureFrontDoor\")\n\n cmd = 'az network front-door waf-policy update -g {resource_group} -n {blockpolicy} --mode prevention --redirect-url http://www.microsoft.com'.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(result['name'], blockpolicy)\n self.assertEqual(result['policySettings']['mode'], \"Prevention\")\n self.assertEqual(result['policySettings']['redirectUrl'], 'http://www.microsoft.com')\n self.assertEqual(result['sku']['name'], \"Classic_AzureFrontDoor\")\n\n cmd = 'az network front-door waf-policy update -g {resource_group} -n {blockpolicy} --custom-block-response-status-code 406'.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(result['name'], blockpolicy)\n self.assertEqual(result['policySettings']['mode'], \"Prevention\")\n self.assertEqual(result['policySettings']['customBlockResponseStatusCode'], 406)\n self.assertEqual(result['policySettings']['requestBodyCheck'], \"Enabled\")\n self.assertEqual(result['sku']['name'], \"Classic_AzureFrontDoor\")\n\n cmd = 'az network front-door waf-policy update -g {resource_group} -n {blockpolicy} --custom-block-response-status-code 405 --custom-block-response-body YiBvZHk='.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(result['name'], blockpolicy)\n self.assertEqual(result['policySettings']['mode'], \"Prevention\")\n self.assertEqual(result['policySettings']['customBlockResponseStatusCode'], 405)\n self.assertEqual(result['policySettings']['customBlockResponseBody'], \"YiBvZHk=\")\n self.assertEqual(result['policySettings']['requestBodyCheck'], \"Enabled\")\n self.assertEqual(result['sku']['name'], \"Classic_AzureFrontDoor\")\n\n cmd = 'az network front-door waf-policy update -g {resource_group} -n {blockpolicy} --disabled'.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(result['name'], blockpolicy)\n self.assertEqual(result['policySettings']['enabledState'], \"Disabled\")\n self.assertEqual(result['policySettings']['requestBodyCheck'], \"Enabled\")\n self.assertEqual(result['sku']['name'], \"Classic_AzureFrontDoor\")\n\n cmd = 'az network front-door waf-policy show -g {resource_group} -n {blockpolicy}'.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(result['name'], blockpolicy)\n # spot check\n self.assertEqual(result['policySettings']['enabledState'], \"Disabled\")\n self.assertEqual(result['policySettings']['customBlockResponseStatusCode'], 405)\n self.assertEqual(result['policySettings']['requestBodyCheck'], \"Enabled\")\n self.assertEqual(result['sku']['name'], \"Classic_AzureFrontDoor\")\n\n cmd = 'az network front-door waf-policy list -g {resource_group}'.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(len(result), 6)\n blockPolicyObject = [policy for policy in result if policy['name'] == blockpolicy][0]\n self.assertEqual(blockPolicyObject['name'], blockpolicy)\n\n cmd = 'az network front-door waf-policy delete -g {resource_group} -n {blockpolicy}'.format(**locals())\n result = self.cmd(cmd)\n\n cmd = 'az network front-door waf-policy list -g {resource_group}'.format(**locals())\n result = self.cmd(cmd).get_output_in_json()\n self.assertEqual(len(result), 5)\n self.assertEqual(len([policy for policy in result if policy['name'] == blockpolicy]), 0)","def policy(agent):","def create(window_type=WindowType.DEFAULT, rate=10):\n pass","def set_qos(self, qos, set_specs_args):\n self._impl.set_qos(qos.id, set_specs_args)\n return self._unify_qos(qos)","def snmpqosqos_packets_filteredrate(self) :\n\t\ttry :\n\t\t\treturn self._snmpqosqos_packets_filteredrate\n\t\texcept Exception as e:\n\t\t\traise e","def qos_policy_group_modify(self, policy_group, max_throughput=None):\n return self.request( \"qos-policy-group-modify\", {\n 'policy_group': [ policy_group, 'policy-group', [ basestring, 'None' ], False ],\n 'max_throughput': [ max_throughput, 'max-throughput', [ basestring, 'qos-tput' ], False ],\n }, {\n } )","def bandwidth_limit_rule_update(request, policy_id, rule_id, **kwargs):\n body = {'bandwidth_limit_rule': kwargs}\n ruleType = 'bandwidth_limit_rule'\n bandwidthlimit_update = neutronclient(request)\\\n .update_bandwidth_limit_rule(rule_id, policy_id, body)\\\n .get(ruleType)\n return BandwidthLimitRule(bandwidthlimit_update)","def bandwidth_limit_rule_delete(request, policy_id, rule_id):\n neutronclient(request).delete_bandwidth_limit_rule(rule_id, policy_id)","def apply_policy(self, policy):\n tenant_name = policy['tenant_name']\n fw_id = policy['fw_id']\n fw_name = policy['fw_name']\n LOG.debug(\"asa_apply_policy: tenant=%s fw_id=%s fw_name=%s\",\n tenant_name, fw_id, fw_name)\n cmds = [\"conf t\", \"changeto context \" + tenant_name]\n\n rule_dict = policy['rules']\n for rule_id in rule_dict:\n rule = rule_dict[rule_id]\n protocol = rule['protocol']\n name = rule['name']\n enabled = rule['enabled']\n dst_port = rule['destination_port']\n src_port = rule['source_port']\n\n if (rule['source_ip_address'] is not None):\n src_ip = IPNetwork(rule['source_ip_address'])\n else:\n src_ip = IPNetwork('0.0.0.0/0')\n\n if (rule['destination_ip_address'] is not None):\n dst_ip = IPNetwork(rule['destination_ip_address'])\n else:\n dst_ip = IPNetwork('0.0.0.0/0')\n\n if rule['action'] == 'allow':\n action = 'permit'\n else:\n action = 'deny'\n\n LOG.debug(\"rule[%s]: name=%s enabled=%s prot=%s dport=%s sport=%s \\\n dip=%s %s sip=%s %s action=%s\",\n rule_id, name, enabled, protocol, dst_port, src_port,\n dst_ip.network, dst_ip.netmask,\n src_ip.network, src_ip.netmask, action)\n\n acl = \"access-list \"\n acl = (acl + tenant_name + \" extended \" + action + \" \" +\n protocol + \" \")\n if (rule['source_ip_address'] is None):\n acl = acl + \"any \"\n else:\n acl = acl + str(src_ip.network) + \" \" + (\n str(src_ip.netmask) + \" \")\n if (src_port is not None):\n if (':' in src_port):\n range = src_port.replace(':', ' ')\n acl = acl + \"range \" + range + \" \"\n else:\n acl = acl + \"eq \" + src_port + \" \"\n if (rule['destination_ip_address'] is None):\n acl = acl + \"any \"\n else:\n acl = acl + str(dst_ip.network) + \" \" + \\\n str(dst_ip.netmask) + \" \"\n if (dst_port is not None):\n if (':' in dst_port):\n range = dst_port.replace(':', ' ')\n acl = acl + \"range \" + range + \" \"\n else:\n acl = acl + \"eq \" + dst_port + \" \"\n if (enabled is False):\n acl = acl + 'inactive'\n\n # remove the old ace for this rule\n if (rule_id in self.rule_tbl):\n cmds.append('no ' + self.rule_tbl[rule_id])\n\n self.rule_tbl[rule_id] = acl\n if tenant_name in self.tenant_rule:\n if rule_id not in self.tenant_rule[tenant_name]['rule_lst']:\n self.tenant_rule[tenant_name]['rule_lst'].append(rule_id)\n cmds.append(acl)\n cmds.append(\"access-group \" + tenant_name + \" global\")\n cmds.append(\"write memory\")\n\n LOG.debug(cmds)\n data = {\"commands\": cmds}\n return self.rest_send_cli(data)","def addRSTQoS(self, tmpFD):\n if self.qosPolicy == 'privatens':\n self._calculateQoS(tmpFD)\n elif self.qosPolicy == 'hostlevel':\n self._calculateQoS(tmpFD, True)\n elif self.qosPolicy == 'default-not-set':\n self.logger.info('QoS Policy not set in configuration. set one of privatens or hostlevel.')\n else:\n raise ConfigException(f'QoS Policy {self.qosPolicy} is not supported.')","def __init__(self, name=None, queue_mode=None, description=None, redrive_policy=None, max_consume_count=None, retention_hours=None):\n \n \n\n self._name = None\n self._queue_mode = None\n self._description = None\n self._redrive_policy = None\n self._max_consume_count = None\n self._retention_hours = None\n self.discriminator = None\n\n self.name = name\n if queue_mode is not None:\n self.queue_mode = queue_mode\n if description is not None:\n self.description = description\n if redrive_policy is not None:\n self.redrive_policy = redrive_policy\n if max_consume_count is not None:\n self.max_consume_count = max_consume_count\n if retention_hours is not None:\n self.retention_hours = retention_hours","def snmpqosqos_flow_memrate(self) :\n\t\ttry :\n\t\t\treturn self._snmpqosqos_flow_memrate\n\t\texcept Exception as e:\n\t\t\traise e","def snmpqosqos_real_bytesrate(self) :\n\t\ttry :\n\t\t\treturn self._snmpqosqos_real_bytesrate\n\t\texcept Exception as e:\n\t\t\traise e","def dynamic_control_limits():\n try:\n content = request.get_json()\n data = content[\"data\"]\n for elm in data:\n data[elm] = [data[elm]]\n data = pd.DataFrame.from_dict(content[\"data\"])\n fname = inspect.stack()[0][3]\n param = content[\"param\"][fname]\n result = pd.DataFrame()\n remove_outliers_train = param[\"remove_outliers_train\"]\n remove_outliers_run = param[\"remove_outliers_run\"]\n training_length = param[\"training_length\"]\n runtime_window = param[\"runtime_window\"]\n param_db = dict()\n param_db[\"IdMachine\"] = data[\"IdMachine\"].unique()[0]\n param_db[\"IdSig\"] = data[\"IdSig\"].unique()[0]\n # define parameter in order to ge control limits from database\n # get control limits if already computed\n df_param = db_manager.get_param(param_db)\n # get data stored in the database\n df_data = db_manager.get_data(param_db)\n # merge old data and new data\n df_data = pd.concat([df_data, data], ignore_index=True, sort=False)\n # get identifier os plit if present\n # check if control limits already computed\n\n if len(df_param) > 0:\n # check if there is enough data for application of control limits\n # print(\"len(df_data)=\"+str(len(df_data))+\" runtime_window=\"+str(runtime_window) )\n if len(df_data) >= runtime_window:\n # sort data by timestamp\n df_data.sort_values(by=[\"TimeStamp\"], inplace=True, ascending=True)\n df_data.reset_index(drop=True, inplace=True)\n result = ts.run_cl_analysis(\n ts, df_data, df_param, param\n )\n # add in the result the sendout parameters, so we don't need to publish these values\n # db_manager.delete_data(param_db)\n else:\n # check if there is enough data for computation of control limits\n # print(\"len(df_data)=\"+str(len(df_data))+\" training_length=\"+str(training_length) )\n if len(df_data) >= training_length:\n # sort data by timestamp\n df_data.sort_values(by=[\"TimeStamp\"], inplace=True, ascending=True)\n df_data.reset_index(drop=True, inplace=True)\n result = ts.calculate_cl(df_data, remove_outliers_train)\n # store control limits\n db_manager.store_param(result, param_db)\n # delete old data\n # db_manager.delete_data(param_db)\n # we want to publish these values out from sibyl\n result[\"content\"] = \"cl\"\n else:\n db_manager.store_data(data, param_db)\n if len(result) > 0:\n result[\"IdMachine\"] = param_db[\"IdMachine\"]\n result[\"IdSig\"] = param_db[\"IdSig\"]\n result[\"TimeStamp\"] = df_data.tail(1)[\"TimeStamp\"].values[0]\n # result[\"Source\"] = df_data[\"Source\"].unique()[0]\n data_to_send = {}\n data_to_send[\"IdSig\"] = param_db[\"IdSig\"]\n data_to_send[\"IdMachine\"] = param_db[\"IdMachine\"]\n data_to_send[\"TimeStamp\"] = df_data.tail(1)[\"TimeStamp\"].values[0]\n data_to_send[\"Value\"] = content[\"data\"][\"Value\"][0]\n Kind = result[\"Kind\"]\n Value = result[\"Value\"]\n data_to_send[\"dynamic_control_limits\"] = {}\n for k, v in zip(Kind, Value):\n data_to_send[\"dynamic_control_limits\"][k] = v\n return jsonify(data_to_send)\n except Exception as e:\n return jsonify({\"error\": str(e)})","def snmpqosqos_flow_recyclesrate(self) :\n\t\ttry :\n\t\t\treturn self._snmpqosqos_flow_recyclesrate\n\t\texcept Exception as e:\n\t\t\traise e","def test_change_default_throttling_settings_http_with_overwrite_throttled_rate_above_account_quota():","def createNetworkSwitchQosRule(self, networkId: str, vlan: int, **kwargs):\n\n kwargs.update(locals())\n\n if 'protocol' in kwargs:\n options = ['ANY', 'TCP', 'UDP']\n assert kwargs['protocol'] in options, f'''\"protocol\" cannot be \"{kwargs['protocol']}\", & must be set to one of: {options}'''\n\n metadata = {\n 'tags': ['switch', 'configure', 'qosRules'],\n 'operation': 'createNetworkSwitchQosRule',\n }\n resource = f'/networks/{networkId}/switch/qosRules'\n\n body_params = ['vlan', 'protocol', 'srcPort', 'srcPortRange', 'dstPort', 'dstPortRange', 'dscp']\n payload = {k: v for (k, v) in kwargs.items() if k in body_params}\n\n return self._session.post(metadata, resource, payload)","def addVlanQoS(self, tmpFD):\n self._getvlanlistqos()\n\n for l2req in self.activeL2:\n interface = l2req['destport']\n name = f\"{interface}-{l2req['vlan']}\"\n if not l2req['params']:\n self.logger.info('This specific vlan request did not provided any QOS. \\\n Ignoring QOS Rules for it')\n continue\n outrate, outtype = self.convertToRate(l2req['params'])\n if self.params[interface]['intf_max'] - outrate <= 0:\n raise OverSubscribeException(\"Node is oversubscribed. \\\n Will not modify present QoS Rules.\")\n self.params[interface]['intf_max'] -= outrate\n tmpFD.write(f\"\"\"\n# SENSE VLAN {l2req['vlan']} {l2req['destport']} {outrate}{outtype}\ninterface vlan.{l2req['vlan']} {name}-IN input rate {outrate}{outtype}\ninterface vlan.{l2req['vlan']} {name}-OUT output rate {outrate}{outtype}\\n\"\"\")","def test_different_types_rss_queue_rule(self):\n self.pmdout.start_testpmd(\"%s\" % self.cores, \"--rxq=8 --txq=8 --port-topology=chained\")\n self.dut.send_expect(\"set fwd rxonly\", \"testpmd> \", 120)\n self.dut.send_expect(\"set verbose 1\", \"testpmd> \", 120)\n self.dut.send_expect(\"start\", \"testpmd> \", 120)\n time.sleep(2)\n\n # Create a rss queue rule\n self.dut.send_expect(\n \"flow create 0 ingress pattern end actions rss types udp ipv4-tcp ipv6-sctp ipv4-other end queues 1 4 7 end / end\", \"created\")\n # send the packets and verify the results\n if (self.nic in [\"fortville_eagle\", \"fortville_spirit\",\n \"fortville_spirit_single\", \"fortpark_TLV\", \"fortville_25g\"]):\n rss_queue = [\"1\", \"4\", \"7\"]\n self.send_and_check(self.pkt2, rss_queue)\n self.send_and_check(self.pkt3, rss_queue)\n self.send_and_check(self.pkt4, rss_queue)\n self.send_and_check(self.pkt5, rss_queue)\n self.send_and_check(self.pkt6, rss_queue)\n rss_queue = [\"0\"]\n self.send_and_check(self.pkt1, rss_queue)\n self.send_and_check(self.pkt7, rss_queue)\n self.send_and_check(self.pkt8, rss_queue)\n elif(self.nic in [\"cavium_a063\"]):\n rss_queue = [\"1\", \"4\", \"7\"]\n self.send_and_check(self.pkt2, rss_queue)\n self.send_and_check(self.pkt3, rss_queue)\n self.send_and_check(self.pkt6, rss_queue)\n else:\n rss_queue = [\"1\", \"4\", \"7\"]\n self.send_and_check(self.pkt2, rss_queue)\n self.send_and_check(self.pkt3, rss_queue)\n self.send_and_check(self.pkt6, rss_queue)\n rss_queue = [\"0\"]\n self.send_and_check(self.pkt1, rss_queue)\n self.send_and_check(self.pkt4, rss_queue)\n self.send_and_check(self.pkt5, rss_queue)\n self.send_and_check(self.pkt7, rss_queue)\n self.send_and_check(self.pkt8, rss_queue)\n\n # There can't be more than one RSS queue rule existing.\n self.dut.send_expect(\n \"flow create 0 ingress pattern end actions rss types ipv6-other end queues 3 end / end\", \"error\")","def new_capacity_rule(mod, g, p):\n return 0","def set_bw(self, mode):\n self.bandwidth = 0\n if (mode=='a'):\n self.bandwidth = 54\n elif(mode=='b'):\n self.bandwidth = 11\n elif(mode=='g'):\n self.bandwidth = 54 \n elif(mode=='n'):\n self.bandwidth = 600\n elif(mode=='ac'):\n self.bandwidth = 6777 \n \n return self.bandwidth","def get_qos(self):\n return self.qos","def _create_objective(self, meta, m):\n ## cashflow eval\n rule = partial(self._cashflow_rule, meta)\n m.obj = pyo.Objective(rule=rule, sense=pyo.maximize)","def test_rate_limit_handling(self):\n self.create_org(status=CONNECTED)\n response = self.app.post('/adapter/qbo/test/update')\n\n # 500 should not be returned by the adapter\n self.assertEqual(response.status_code, 429)","def minimum_bandwidth_rule_delete(request, policy_id, rule_id):\n\n neutronclient(request).delete_minimum_bandwidth_rule(rule_id, policy_id)","def delete_qos_bandwidth_limit_rule(self, policy_name_or_id, rule_id):\n if not self._has_neutron_extension('qos'):\n raise exc.OpenStackCloudUnavailableExtension(\n 'QoS extension is not available on target cloud'\n )\n\n policy = self.network.find_qos_policy(policy_name_or_id)\n if not policy:\n raise exc.OpenStackCloudResourceNotFound(\n \"QoS policy {name_or_id} not Found.\".format(\n name_or_id=policy_name_or_id\n )\n )\n\n try:\n self.network.delete_qos_bandwidth_limit_rule(\n rule_id, policy, ignore_missing=False\n )\n except exceptions.ResourceNotFound:\n self.log.debug(\n \"QoS bandwidth limit rule {rule_id} not found in policy \"\n \"{policy_id}. Ignoring.\".format(\n rule_id=rule_id, policy_id=policy['id']\n )\n )\n return False\n\n return True","def __new__(cls, rate_limit_group):\n self = object.__new__(cls)\n self.rate_limit_group = rate_limit_group\n self.acquired = False\n return self","def __new__(cls, rate_limit_groups):\n self = object.__new__(cls)\n self.rate_limit_groups = rate_limit_groups\n self.acquired = False\n return self","def __init__(self, id, policy, policy_args, board_size, logq):\n self.id = id\n self.chain = []\n self.len = 0\n self.was_initialized = False\n self.policy_class = policy\n self.round = 0\n\n self.sq = mp.Queue()\n self.aq = mp.Queue()\n self.mq = mp.Queue()\n self.policy = policy(policy_args, board_size, self.sq, self.aq, self.mq, logq, id)\n self.policy.daemon = True\n self.policy.start()","def test_change_default_throttling_settings_http_with_overwrite_throttled_burst_above_account_quota():","def snmpqosqos_sch_links_createdrate(self) :\n\t\ttry :\n\t\t\treturn self._snmpqosqos_sch_links_createdrate\n\t\texcept Exception as e:\n\t\t\traise e","def test_change_default_throttling_settings_http_with_overwrite_throttled_burst_above_50():","def test_limit_cpu(self):\n url = '/api/apps'\n body = {'cluster': 'autotest'}\n response = self.client.post(url, json.dumps(body), content_type='application/json')\n self.assertEqual(response.status_code, 201)\n app_id = response.data['id']\n url = '/api/apps/{app_id}/limits'.format(**locals())\n # check default limit\n response = self.client.get(url, content_type='application/json')\n self.assertEqual(response.status_code, 200)\n self.assertIn('cpu', response.data)\n self.assertEqual(json.loads(response.data['cpu']), {})\n # regression test for https://github.com/deis/deis/issues/1563\n self.assertNotIn('\"', response.data['cpu'])\n # set an initial limit\n body = {'cpu': json.dumps({'web': '1024'})}\n response = self.client.post(url, json.dumps(body), content_type='application/json')\n self.assertEqual(response.status_code, 201)\n self.assertIn('x-deis-release', response._headers)\n limit1 = response.data\n # check memory limits\n response = self.client.get(url, content_type='application/json')\n self.assertEqual(response.status_code, 200)\n self.assertIn('cpu', response.data)\n cpu = json.loads(response.data['cpu'])\n self.assertIn('web', cpu)\n self.assertEqual(cpu['web'], '1024')\n # set an additional value\n body = {'cpu': json.dumps({'worker': '512'})}\n response = self.client.post(url, json.dumps(body), content_type='application/json')\n self.assertEqual(response.status_code, 201)\n limit2 = response.data\n self.assertNotEqual(limit1['uuid'], limit2['uuid'])\n cpu = json.loads(response.data['cpu'])\n self.assertIn('worker', cpu)\n self.assertEqual(cpu['worker'], '512')\n self.assertIn('web', cpu)\n self.assertEqual(cpu['web'], '1024')\n # read the limit again\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n limit3 = response.data\n self.assertEqual(limit2, limit3)\n cpu = json.loads(response.data['cpu'])\n self.assertIn('worker', cpu)\n self.assertEqual(cpu['worker'], '512')\n self.assertIn('web', cpu)\n self.assertEqual(cpu['web'], '1024')\n # unset a value\n body = {'memory': json.dumps({'worker': None})}\n response = self.client.post(url, json.dumps(body), content_type='application/json')\n self.assertEqual(response.status_code, 201)\n limit4 = response.data\n self.assertNotEqual(limit3['uuid'], limit4['uuid'])\n self.assertNotIn('worker', json.dumps(response.data['memory']))\n # disallow put/patch/delete\n self.assertEqual(self.client.put(url).status_code, 405)\n self.assertEqual(self.client.patch(url).status_code, 405)\n self.assertEqual(self.client.delete(url).status_code, 405)\n return limit4","def test_create_hyperflex_ucsm_config_policy(self):\n pass","def make_decision_with_policy(self, policy_type, *args):\n if policy_type == 1: # ADP\n assert len(args) == 2, 'args should be exactly 2'\n cur_K = -self.K_im_traj[-1]\n distance_2_tan, radian_at_tan = args\n self.dis_sum += distance_2_tan\n pwm_l_new, pwm_r_new = policy.adp(distance_2_tan, radian_at_tan, self.dis_sum, cur_K)\n elif policy_type == 2: # pure pursuit\n l_d, sin_alpha = args\n amp = 150\n pwm_l_new, pwm_r_new = policy.pure_pursuit(l_d, sin_alpha, amp)\n elif policy_type == 3: # Car following with ADP\n assert len(args) == 3, 'args should be exactly 3'\n cur_K = -self.K_im_traj[-1]\n distance_2_tan, radian_at_tan, estimated_dis = args\n self.dis_sum += distance_2_tan\n if self.is_recording and self.counter % 100 == 0:\n np.save('./.out/record', self.record)\n pwm_l_new, pwm_r_new = policy.car_following_with_adp(distance_2_tan, radian_at_tan, self.dis_sum, cur_K, estimated_dis, self.record)\n print(self.counter)\n self.counter += 1\n elif policy_type == 4:\n K = 0.5\n dis2car, = args\n pwm_l_new, pwm_r_new = policy.car_following(dis2car, K)\n elif policy_type == 5:\n d_arc, d_curve, theta = args\n pwm_l_new, pwm_r_new = policy.adp_coupled_car_following(d_arc, d_curve, theta, self.z, self.K_coupled)\n else:\n pwm_l_new, pwm_r_new = 0, 0\n print('Policy Not Found')\n self.motor.motor_set_new_speed(pwm_l_new, pwm_r_new)","def snmpqosqos_actions_createdrate(self) :\n\t\ttry :\n\t\t\treturn self._snmpqosqos_actions_createdrate\n\t\texcept Exception as e:\n\t\t\traise e","def test_change_default_throttling_settings_http_with_overwrite_throttled_rate_above_50():","def add_qos(self, qos):\n \n qos_id = qos[\"ovsdb:qos-entries\"][0][\"qos-id\"]\n self.qos_dict[qos_id] = qos"],"string":"[\n \"def bandwidth_limit_rule_create(request, policy_id, **kwargs):\\n body = {'bandwidth_limit_rule': kwargs}\\n if 'tenant_id' not in kwargs:\\n kwargs['tenant_id'] = request.user.project_id\\n body = {'bandwidth_limit_rule': kwargs}\\n rule = 'bandwidth_limit_rule'\\n bandwidth_limit_rule = neutronclient(request)\\\\\\n .create_bandwidth_limit_rule(policy_id, body).get(rule)\\n return BandwidthLimitRule(bandwidth_limit_rule)\",\n \"def create_qos_bandwidth_limit_rule(\\n self,\\n policy_name_or_id,\\n max_kbps,\\n **kwargs,\\n ):\\n if not self._has_neutron_extension('qos'):\\n raise exc.OpenStackCloudUnavailableExtension(\\n 'QoS extension is not available on target cloud'\\n )\\n\\n policy = self.network.find_qos_policy(policy_name_or_id)\\n if not policy:\\n raise exc.OpenStackCloudResourceNotFound(\\n \\\"QoS policy {name_or_id} not Found.\\\".format(\\n name_or_id=policy_name_or_id\\n )\\n )\\n\\n if kwargs.get(\\\"direction\\\") is not None:\\n if not self._has_neutron_extension('qos-bw-limit-direction'):\\n kwargs.pop(\\\"direction\\\")\\n self.log.debug(\\n \\\"'qos-bw-limit-direction' extension is not available on \\\"\\n \\\"target cloud\\\"\\n )\\n\\n kwargs['max_kbps'] = max_kbps\\n\\n return self.network.create_qos_bandwidth_limit_rule(policy, **kwargs)\",\n \"def CreateRateLimitOptions(client, args):\\n\\n messages = client.messages\\n rate_limit_options = messages.SecurityPolicyRuleRateLimitOptions()\\n is_updated = False\\n\\n if (args.IsSpecified('rate_limit_threshold_count') or\\n args.IsSpecified('rate_limit_threshold_interval_sec')):\\n rate_limit_threshold = (\\n messages.SecurityPolicyRuleRateLimitOptionsThreshold())\\n if args.IsSpecified('rate_limit_threshold_count'):\\n rate_limit_threshold.count = args.rate_limit_threshold_count\\n if args.IsSpecified('rate_limit_threshold_interval_sec'):\\n rate_limit_threshold.intervalSec = args.rate_limit_threshold_interval_sec\\n rate_limit_options.rateLimitThreshold = rate_limit_threshold\\n is_updated = True\\n\\n if args.IsSpecified('conform_action'):\\n rate_limit_options.conformAction = _ConvertConformAction(\\n args.conform_action)\\n is_updated = True\\n if args.IsSpecified('exceed_action'):\\n rate_limit_options.exceedAction = _ConvertExceedAction(args.exceed_action)\\n is_updated = True\\n if args.IsSpecified('enforce_on_key'):\\n rate_limit_options.enforceOnKey = (\\n messages.SecurityPolicyRuleRateLimitOptions.EnforceOnKeyValueValuesEnum(\\n _ConvertEnforceOnKey(args.enforce_on_key)))\\n is_updated = True\\n if args.IsSpecified('enforce_on_key_name'):\\n rate_limit_options.enforceOnKeyName = args.enforce_on_key_name\\n is_updated = True\\n\\n if (args.IsSpecified('ban_threshold_count') or\\n args.IsSpecified('ban_threshold_interval_sec')):\\n ban_threshold = messages.SecurityPolicyRuleRateLimitOptionsThreshold()\\n if args.IsSpecified('ban_threshold_count'):\\n ban_threshold.count = args.ban_threshold_count\\n if args.IsSpecified('ban_threshold_interval_sec'):\\n ban_threshold.intervalSec = args.ban_threshold_interval_sec\\n rate_limit_options.banThreshold = ban_threshold\\n is_updated = True\\n\\n if args.IsSpecified('ban_duration_sec'):\\n rate_limit_options.banDurationSec = args.ban_duration_sec\\n is_updated = True\\n\\n return rate_limit_options if is_updated else None\",\n \"def minimum_bandwidth_rule_create(request, policy_id, **kwargs):\\n if 'tenant_id' not in kwargs:\\n kwargs['tenant_id'] = request.user.project_id\\n body = {'minimum_bandwidth_rule': kwargs}\\n rule = 'minimum_bandwidth_rule'\\n minimum_bandwidth_rule = neutronclient(request)\\\\\\n .create_minimum_bandwidth_rule(policy_id, body).get(rule)\\n return MinimumBandwidthRule(minimum_bandwidth_rule)\",\n \"def create_qos_minimum_bandwidth_rule(\\n self,\\n policy_name_or_id,\\n min_kbps,\\n **kwargs,\\n ):\\n if not self._has_neutron_extension('qos'):\\n raise exc.OpenStackCloudUnavailableExtension(\\n 'QoS extension is not available on target cloud'\\n )\\n\\n policy = self.network.find_qos_policy(policy_name_or_id)\\n if not policy:\\n raise exc.OpenStackCloudResourceNotFound(\\n \\\"QoS policy {name_or_id} not Found.\\\".format(\\n name_or_id=policy_name_or_id\\n )\\n )\\n\\n kwargs['min_kbps'] = min_kbps\\n\\n return self.network.create_qos_minimum_bandwidth_rule(policy, **kwargs)\",\n \"def policy_create(request, **kwargs):\\n body = {'policy': kwargs}\\n policy = neutronclient(request).create_qos_policy(body=body).get('policy')\\n return QoSPolicy(policy)\",\n \"def create_qos(self, specs):\\n return self._unify_qos(self._impl.create_qos(specs))\",\n \"async def qos(\\n self,\\n prefetch_size: int = 0,\\n prefetch_count: int = 0,\\n connection_global: bool = False,\\n ):\\n await self.channel.basic_qos(\\n prefetch_size=prefetch_size,\\n prefetch_count=prefetch_count,\\n connection_global=connection_global,\\n )\",\n \"def create_qos(self, specs):\\n aname = \\\"cinder_v%s.create_qos\\\" % self.version\\n name = self.generate_random_name()\\n\\n with atomic.ActionTimer(self, aname):\\n return self._get_client().qos_specs.create(name, specs)\",\n \"def VolumeSetQos(min_iops,\\n max_iops,\\n burst_iops,\\n#pylint: disable=unused-argument\\n volume_names,\\n volume_ids,\\n volume_prefix,\\n volume_regex,\\n volume_count,\\n source_account,\\n source_account_id,\\n test,\\n mvip,\\n username,\\n password):\\n#pylint: enable=unused-argument\\n\\n options = copy.deepcopy(locals())\\n for key in [\\\"min_iops\\\", \\\"max_iops\\\", \\\"burst_iops\\\"]:\\n options.pop(key, None)\\n\\n return VolumeModify(property_name=\\\"qos\\\",\\n property_value={\\n \\\"minIOPS\\\" : min_iops,\\n \\\"maxIOPS\\\" : max_iops,\\n \\\"burstIOPS\\\" : burst_iops\\n },\\n **options)\",\n \"def get_port_qos_rxrate(self, iface, qos):\\n pytest.skip(\\\"Method is not supported by Iperf TG\\\")\",\n \"def test_qos_specs(self):\\n qos = {'maxIOPS': 1000, 'maxBWS': 2048}\\n snapshot = fake_snapshot.fake_snapshot_obj(\\n self.ctx, **{'volume': self.volume,\\n 'provider_id': self.snapshot_id,\\n 'volume_size': 8})\\n extraspecs = {}\\n self.driver._get_volumetype_qos = mock.MagicMock()\\n self.driver._get_volumetype_qos.return_value = qos\\n self.driver._get_volumetype_extraspecs = mock.MagicMock()\\n self.driver._get_volumetype_extraspecs.return_value = extraspecs\\n\\n props = self.driver.initialize_connection_snapshot(\\n snapshot,\\n self.connector)\\n\\n self.assertEqual(1000, int(props['data']['iopsLimit']))\\n self.assertEqual(2048, int(props['data']['bandwidthLimit']))\",\n \"def set_qos(self, on_ok):\\n self._channel.basic_qos(\\n prefetch_count=self._prefetch_count, callback=on_ok)\",\n \"def _create_qos_specs(self, name, values=None):\\n if values:\\n specs = dict(name=name, qos_specs=values)\\n else:\\n specs = {'name': name,\\n 'consumer': 'back-end',\\n 'specs': {\\n 'key1': 'value1',\\n 'key2': 'value2'}}\\n return db.qos_specs_create(self.ctxt, specs)['id']\",\n \"def create_qos(self, **kwargs):\\n post_body = json.dumps({'qos_specs': kwargs})\\n resp, body = self.post('qos-specs', post_body)\\n body = json.loads(body)\\n self.validate_response(schema.show_qos, resp, body)\\n return rest_client.ResponseBody(resp, body)\",\n \"def test_b_negative_add_qos(self):\\n qoss = {\\\"qos_120\\\": 120, \\\"qos_-5\\\": -5}\\n for qos_name, qos_value in qoss.iteritems():\\n testflow.step(\\n \\\"Create CPU QoS %s on datacenter %s with parameters: %s\\\",\\n qos_name, conf.DC_NAME[0], qos_value\\n )\\n assert not ll_datacenters.add_qos_to_datacenter(\\n datacenter=conf.DC_NAME[0],\\n qos_name=qos_name,\\n qos_type=conf.QOS_TYPE_CPU,\\n cpu_limit=qos_value\\n )\",\n \"def bdev_set_qos_limit(\\n client,\\n name,\\n rw_ios_per_sec=None,\\n rw_mbytes_per_sec=None,\\n r_mbytes_per_sec=None,\\n w_mbytes_per_sec=None):\\n params = {}\\n params['name'] = name\\n if rw_ios_per_sec is not None:\\n params['rw_ios_per_sec'] = rw_ios_per_sec\\n if rw_mbytes_per_sec is not None:\\n params['rw_mbytes_per_sec'] = rw_mbytes_per_sec\\n if r_mbytes_per_sec is not None:\\n params['r_mbytes_per_sec'] = r_mbytes_per_sec\\n if w_mbytes_per_sec is not None:\\n params['w_mbytes_per_sec'] = w_mbytes_per_sec\\n return client.call('bdev_set_qos_limit', params)\",\n \"def qos_settings_control_modify(self, enforcement=None, ratebucket_rebalance=None):\\n return self.request( \\\"qos-settings-control-modify\\\", {\\n 'enforcement': [ enforcement, 'enforcement', [ bool, 'None' ], False ],\\n 'ratebucket_rebalance': [ ratebucket_rebalance, 'ratebucket-rebalance', [ bool, 'None' ], False ],\\n }, {\\n } )\",\n \"def change_bandwidth(self,edge_notice):\\n #print(\\\"==============带宽控制=============\\\",edge_notice)\\n ports_details = edge_notice[\\\"port_details\\\"]\\n\\n total_bd = edge_notice[\\\"bandwidth\\\"][\\\"mobilenet\\\"]\\n self.reset_bandwidth()\\n print(\\\"########\\\",total_bd)\\n #print(\\\"model_details+++++++++\\\",model_details)\\n #print(\\\"port_details+++++++++\\\", ports_details)\\n class_id = 20\\n try:\\n for model_name in [\\\"mobilenet\\\"]:\\n ports_list = ports_details[model_name]\\n for dport in ports_list:\\n # 1. create a class under the root\\n create_class = \\\"tc class add dev eth0 parent 1: classid 1:\\\"+str(class_id)+\\\" htb rate \\\"+str(total_bd)+\\\"mbit ceil \\\"+str(total_bd)+\\\"mbit\\\"\\n create_branch = \\\"tc qdisc add dev eth0 parent 1:\\\"+str(class_id)+\\\" handle \\\"+str(class_id)+\\\": sfq perturb 10\\\"\\n create_filter = \\\"tc filter add dev eth0 protocol ip parent 1: prio 1 u32 match ip dport \\\"+str(dport)+\\\" 0xffff flowid 1:\\\"+str(class_id)\\n #print(create_class)\\n class_id = class_id+1\\n self.__excecute__(create_class)\\n self.__excecute__(create_branch)\\n self.__excecute__(create_filter)\\n except Exception as e:\\n print(\\\" bandwidth limitation errors\\\",e)\\n #print(\\\"==========finsih========\\\")\",\n \"def limit():\\n bwc = BandwidthConfigurator()\\n bwc.limit()\",\n \"def test_create_qos_queue(self):\\r\\n resource = 'qos_queue'\\r\\n cmd = qos.CreateQoSQueue(\\r\\n test_cli20.MyApp(sys.stdout), None)\\r\\n myid = 'myid'\\r\\n name = 'my_queue'\\r\\n default = False\\r\\n args = ['--default', default, name]\\r\\n position_names = ['name', 'default']\\r\\n position_values = [name, default]\\r\\n self._test_create_resource(resource, cmd, name, myid, args,\\r\\n position_names, position_values)\",\n \"def minimum_packet_rate_rule_create(request, policy_id, **kwargs):\\n body = {'minimum_packet_rate_rule': kwargs}\\n if 'tenant_id' not in kwargs:\\n kwargs['tenant_id'] = request.user.project_id\\n body = {'minimum_packet_rate_rule': kwargs}\\n rule = 'minimum_packet_rate_rule'\\n minimum_packet_rate_rule = neutronclient(request)\\\\\\n .create_minimum_packet_rate_rule(policy_id, body).get(rule)\\n return MinimumPacketRateRule(minimum_packet_rate_rule)\",\n \"def setBw(self, newapif, mode): \\n self.newapif = newapif\\n bandwidth = wifiParameters.set_bw(mode)\\n os.system(\\\"tc qdisc add dev %s root tbf rate %smbit latency 2ms burst 15k\\\" % (self.newapif, bandwidth))\",\n \"def get_qos_bandwidth_limit_rule(self, policy_name_or_id, rule_id):\\n if not self._has_neutron_extension('qos'):\\n raise exc.OpenStackCloudUnavailableExtension(\\n 'QoS extension is not available on target cloud'\\n )\\n\\n policy = self.network.find_qos_policy(policy_name_or_id)\\n if not policy:\\n raise exc.OpenStackCloudResourceNotFound(\\n \\\"QoS policy {name_or_id} not Found.\\\".format(\\n name_or_id=policy_name_or_id\\n )\\n )\\n\\n return self.network.get_qos_bandwidth_limit_rule(rule_id, policy)\",\n \"def CreateRateLimitOptions(\\n client, args, support_fairshare, support_multiple_rate_limit_keys\\n):\\n\\n messages = client.messages\\n rate_limit_options = messages.SecurityPolicyRuleRateLimitOptions()\\n is_updated = False\\n\\n if (args.IsSpecified('rate_limit_threshold_count') or\\n args.IsSpecified('rate_limit_threshold_interval_sec')):\\n rate_limit_threshold = (\\n messages.SecurityPolicyRuleRateLimitOptionsThreshold())\\n if args.IsSpecified('rate_limit_threshold_count'):\\n rate_limit_threshold.count = args.rate_limit_threshold_count\\n if args.IsSpecified('rate_limit_threshold_interval_sec'):\\n rate_limit_threshold.intervalSec = args.rate_limit_threshold_interval_sec\\n rate_limit_options.rateLimitThreshold = rate_limit_threshold\\n is_updated = True\\n\\n if args.IsSpecified('conform_action'):\\n rate_limit_options.conformAction = _ConvertConformAction(\\n args.conform_action)\\n is_updated = True\\n if args.IsSpecified('exceed_action'):\\n rate_limit_options.exceedAction = _ConvertExceedAction(args.exceed_action)\\n is_updated = True\\n if (args.IsSpecified('exceed_redirect_type') or\\n args.IsSpecified('exceed_redirect_target')):\\n exceed_redirect_options = messages.SecurityPolicyRuleRedirectOptions()\\n if args.IsSpecified('exceed_redirect_type'):\\n exceed_redirect_options.type = (\\n messages.SecurityPolicyRuleRedirectOptions.TypeValueValuesEnum(\\n _ConvertRedirectType(args.exceed_redirect_type)))\\n if args.IsSpecified('exceed_redirect_target'):\\n exceed_redirect_options.target = args.exceed_redirect_target\\n rate_limit_options.exceedRedirectOptions = exceed_redirect_options\\n is_updated = True\\n if args.IsSpecified('enforce_on_key'):\\n rate_limit_options.enforceOnKey = (\\n messages.SecurityPolicyRuleRateLimitOptions.EnforceOnKeyValueValuesEnum(\\n ConvertEnforceOnKey(args.enforce_on_key)))\\n is_updated = True\\n if args.IsSpecified('enforce_on_key_name'):\\n rate_limit_options.enforceOnKeyName = args.enforce_on_key_name\\n is_updated = True\\n\\n if support_multiple_rate_limit_keys and args.IsSpecified(\\n 'enforce_on_key_configs'\\n ):\\n enforce_on_key_configs = []\\n for k, v in args.enforce_on_key_configs.items():\\n enforce_on_key_configs.append(\\n messages.SecurityPolicyRuleRateLimitOptionsEnforceOnKeyConfig(\\n enforceOnKeyType=messages.SecurityPolicyRuleRateLimitOptionsEnforceOnKeyConfig.EnforceOnKeyTypeValueValuesEnum(\\n ConvertEnforceOnKey(k)\\n ),\\n enforceOnKeyName=v if v else None,\\n )\\n )\\n rate_limit_options.enforceOnKeyConfigs = enforce_on_key_configs\\n is_updated = True\\n\\n if (args.IsSpecified('ban_threshold_count') or\\n args.IsSpecified('ban_threshold_interval_sec')):\\n ban_threshold = messages.SecurityPolicyRuleRateLimitOptionsThreshold()\\n if args.IsSpecified('ban_threshold_count'):\\n ban_threshold.count = args.ban_threshold_count\\n if args.IsSpecified('ban_threshold_interval_sec'):\\n ban_threshold.intervalSec = args.ban_threshold_interval_sec\\n rate_limit_options.banThreshold = ban_threshold\\n is_updated = True\\n\\n if args.IsSpecified('ban_duration_sec'):\\n rate_limit_options.banDurationSec = args.ban_duration_sec\\n is_updated = True\\n\\n if support_fairshare and (\\n args.IsSpecified('exceed_action_rpc_status_code') or\\n args.IsSpecified('exceed_action_rpc_status_message')):\\n exceed_action_rpc_status = (\\n messages.SecurityPolicyRuleRateLimitOptionsRpcStatus()\\n )\\n if args.IsSpecified('exceed_action_rpc_status_code'):\\n exceed_action_rpc_status.code = args.exceed_action_rpc_status_code\\n if args.IsSpecified('exceed_action_rpc_status_message'):\\n exceed_action_rpc_status.message = args.exceed_action_rpc_status_message\\n rate_limit_options.exceedActionRpcStatus = exceed_action_rpc_status\\n is_updated = True\\n\\n return rate_limit_options if is_updated else None\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: InterRegionTrafficQosPolicyArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def create_qos_policy(self, **kwargs):\\n if not self._has_neutron_extension('qos'):\\n raise exc.OpenStackCloudUnavailableExtension(\\n 'QoS extension is not available on target cloud'\\n )\\n\\n default = kwargs.pop(\\\"default\\\", None)\\n if default is not None:\\n if self._has_neutron_extension('qos-default'):\\n kwargs['is_default'] = default\\n else:\\n self.log.debug(\\n \\\"'qos-default' extension is not available on \\\"\\n \\\"target cloud\\\"\\n )\\n\\n return self.network.create_qos_policy(**kwargs)\",\n \"def __init__(self, is_active=None, limit_bandwidth=None, limit_bandwidth_unit=None, limit_date_range_from=None, limit_date_range_to=None, limit_num_clients=None, limit_num_downloads=None, limit_package_query=None, limit_path_query=None, metadata=None, scheduled_reset_at=None, scheduled_reset_period=None, token=None):\\n\\n self._is_active = None\\n self._limit_bandwidth = None\\n self._limit_bandwidth_unit = None\\n self._limit_date_range_from = None\\n self._limit_date_range_to = None\\n self._limit_num_clients = None\\n self._limit_num_downloads = None\\n self._limit_package_query = None\\n self._limit_path_query = None\\n self._metadata = None\\n self._scheduled_reset_at = None\\n self._scheduled_reset_period = None\\n self._token = None\\n\\n if is_active is not None:\\n self.is_active = is_active\\n if limit_bandwidth is not None:\\n self.limit_bandwidth = limit_bandwidth\\n if limit_bandwidth_unit is not None:\\n self.limit_bandwidth_unit = limit_bandwidth_unit\\n if limit_date_range_from is not None:\\n self.limit_date_range_from = limit_date_range_from\\n if limit_date_range_to is not None:\\n self.limit_date_range_to = limit_date_range_to\\n if limit_num_clients is not None:\\n self.limit_num_clients = limit_num_clients\\n if limit_num_downloads is not None:\\n self.limit_num_downloads = limit_num_downloads\\n if limit_package_query is not None:\\n self.limit_package_query = limit_package_query\\n if limit_path_query is not None:\\n self.limit_path_query = limit_path_query\\n if metadata is not None:\\n self.metadata = metadata\\n if scheduled_reset_at is not None:\\n self.scheduled_reset_at = scheduled_reset_at\\n if scheduled_reset_period is not None:\\n self.scheduled_reset_period = scheduled_reset_period\\n if token is not None:\\n self.token = token\",\n \"def search_qos_bandwidth_limit_rules(\\n self,\\n policy_name_or_id,\\n rule_id=None,\\n filters=None,\\n ):\\n rules = self.list_qos_bandwidth_limit_rules(policy_name_or_id, filters)\\n return _utils._filter_list(rules, rule_id, filters)\",\n \"def test_400_enable_qos(self):\\n if self._get_openstack_release() >= self.trusty_mitaka:\\n unit = self.n_ovs_sentry\\n set_default = {'enable-qos': 'False'}\\n set_alternate = {'enable-qos': 'True'}\\n self.d.configure('neutron-api', set_alternate)\\n self._wait_and_check(sleep=60)\\n qos_plugin = 'qos'\\n config = u._get_config(\\n self.neutron_api_sentry, '/etc/neutron/neutron.conf')\\n service_plugins = config.get(\\n 'DEFAULT',\\n 'service_plugins').split(',')\\n if qos_plugin not in service_plugins:\\n message = \\\"{} not in service_plugins\\\".format(qos_plugin)\\n amulet.raise_status(amulet.FAIL, msg=message)\\n\\n config = u._get_config(\\n unit,\\n '/etc/neutron/plugins/ml2/openvswitch_agent.ini')\\n extensions = config.get('agent', 'extensions').split(',')\\n if qos_plugin not in extensions:\\n message = \\\"qos not in extensions\\\"\\n amulet.raise_status(amulet.FAIL, msg=message)\\n\\n u.log.debug('Setting QoS back to {}'.format(\\n set_default['enable-qos']))\\n self.d.configure('neutron-api', set_default)\\n self._wait_and_check()\\n u.log.debug('OK')\",\n \"def _get_qos(self):\\n return self.__qos\",\n \"def _get_qos(self):\\n return self.__qos\",\n \"def _get_qos(self):\\n return self.__qos\",\n \"def set_bandwidth(ops, kernel):\\n \\n if kernel == 'epanechnikov':\\n bw_constant = 2.2\\n else:\\n bw_constant = 1\\n\\n n = np.shape(ops[0].traj)[0]\\n bandwidth = bw_constant*n**(-1/6)\\n\\n print('Selected bandwidth: ' + str(bandwidth)+ '\\\\n')\\n\\n return bandwidth\",\n \"def pre_qos_queue_create(self, resource_dict):\\n pass\",\n \"def create_policy_request():\\n return {\\n 'public_key':\\n r'BBLewg4VqLR38b38daE7Fj\\\\/uhr543uGrEpyoPFgmFZK6EZ9g2XdK\\\\/i65RrSJ6sJ96aXD3DJHY3Me2GJQO9\\\\/ifjE=',\\n 'label':\\n 'Integration Test Policy',\\n 'operations': [{\\n 'sensor_id': 10,\\n 'action': 'SHARE',\\n }, {\\n 'sensor_id': 53,\\n 'action': 'BIN',\\n 'bins': [30.0, 60.0, 90.0]\\n }, {\\n 'sensor_id': 55,\\n 'action': 'MOVING_AVG',\\n 'interval': 300\\n }]\\n }\",\n \"def resource_type(self):\\n return 'qos'\",\n \"def create_qos_queue(self, body=None):\\r\\n return self.post(self.qos_queues_path, body=body)\",\n \"def snmpqosqos_error_create_policy_failedrate(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._snmpqosqos_error_create_policy_failedrate\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def qos(self):\\n if self == SubscribeResult.qos0:\\n rv = 0\\n elif self == SubscribeResult.qos1:\\n rv = 1\\n elif self == SubscribeResult.qos2:\\n rv = 2\\n else:\\n raise TypeError()\\n\\n return rv\",\n \"def create_capacity_limiter(total_tokens: float) -> abc.CapacityLimiter:\\n return get_asynclib().CapacityLimiter(total_tokens)\",\n \"def qos_settings_control_get(self, desired_attributes=None):\\n return self.request( \\\"qos-settings-control-get\\\", {\\n 'desired_attributes': [ desired_attributes, 'desired-attributes', [ QosSettingsControlInfo, 'None' ], False ],\\n }, {\\n 'attributes': [ QosSettingsControlInfo, False ],\\n } )\",\n \"def get_data_qos(args):\\n diff_data(args, \\\"qos\\\")\",\n \"def list_qos_bandwidth_limit_rules(self, policy_name_or_id, filters=None):\\n if not self._has_neutron_extension('qos'):\\n raise exc.OpenStackCloudUnavailableExtension(\\n 'QoS extension is not available on target cloud'\\n )\\n\\n policy = self.network.find_qos_policy(policy_name_or_id)\\n if not policy:\\n raise exc.OpenStackCloudResourceNotFound(\\n \\\"QoS policy {name_or_id} not Found.\\\".format(\\n name_or_id=policy_name_or_id\\n )\\n )\\n\\n # Translate None from search interface to empty {} for kwargs below\\n if not filters:\\n filters = {}\\n\\n return list(\\n self.network.qos_bandwidth_limit_rules(\\n qos_policy=policy, **filters\\n )\\n )\",\n \"def list_qos_minimum_bandwidth_rules(\\n self, policy_name_or_id, filters=None\\n ):\\n if not self._has_neutron_extension('qos'):\\n raise exc.OpenStackCloudUnavailableExtension(\\n 'QoS extension is not available on target cloud'\\n )\\n\\n policy = self.network.find_qos_policy(policy_name_or_id)\\n if not policy:\\n raise exc.OpenStackCloudResourceNotFound(\\n \\\"QoS policy {name_or_id} not Found.\\\".format(\\n name_or_id=policy_name_or_id\\n )\\n )\\n\\n # Translate None from search interface to empty {} for kwargs below\\n if not filters:\\n filters = {}\\n\\n return list(\\n self.network.qos_minimum_bandwidth_rules(policy, **filters)\\n )\",\n \"def search_qos_minimum_bandwidth_rules(\\n self,\\n policy_name_or_id,\\n rule_id=None,\\n filters=None,\\n ):\\n rules = self.list_qos_minimum_bandwidth_rules(\\n policy_name_or_id, filters\\n )\\n return _utils._filter_list(rules, rule_id, filters)\",\n \"def test_create_dispatch_policy(self):\\n pass\",\n \"def pick_policy(Qs, rates, num_of_servers):\\n new_rates = copy.deepcopy(rates)\\n Q = np.random.choice(Qs)\\n srv = np.random.randint(1, num_of_servers + 1)\\n state = pick_a_state(Q)\\n scale = np.random.uniform(0, 2)\\n new_rates[srv][state] *= scale\\n return new_rates, srv\",\n \"def post_qos_queue_create(self, resource_dict):\\n pass\",\n \"def notify(self, new_maxrate, new_maxexemptrate, usedbytes, usedi2bytes):\\n # Prepare message parameters from the template\\n message = \\\"\\\"\\n params = {'slice': self.name, 'hostname': socket.gethostname(),\\n 'since': time.asctime(time.gmtime(self.time)) + \\\" GMT\\\",\\n 'until': time.asctime(time.gmtime(self.time + period)) + \\\" GMT\\\",\\n 'date': time.asctime(time.gmtime()) + \\\" GMT\\\",\\n 'period': format_period(period)}\\n\\n if new_maxrate != (self.MaxRate * 1000):\\n # Format template parameters for low bandwidth message\\n params['class'] = \\\"low bandwidth\\\"\\n params['bytes'] = format_bytes(usedbytes - self.bytes)\\n params['limit'] = format_bytes(self.MaxKByte * 1024)\\n params['new_maxrate'] = bwlimit.format_tc_rate(new_maxrate)\\n\\n # Cap low bandwidth burst rate\\n message += template % params\\n logger.log(\\\"bwmon: ** %(slice)s %(class)s capped at %(new_maxrate)s/s \\\" % params)\\n\\n if new_maxexemptrate != (self.Maxi2Rate * 1000):\\n # Format template parameters for high bandwidth message\\n params['class'] = \\\"high bandwidth\\\"\\n params['bytes'] = format_bytes(usedi2bytes - self.i2bytes)\\n params['limit'] = format_bytes(self.Maxi2KByte * 1024)\\n params['new_maxrate'] = bwlimit.format_tc_rate(new_maxexemptrate)\\n\\n message += template % params\\n logger.log(\\\"bwmon: ** %(slice)s %(class)s capped at %(new_maxrate)s/s \\\" % params)\\n\\n # Notify slice\\n if self.emailed == False:\\n subject = \\\"pl_mom capped bandwidth of slice %(slice)s on %(hostname)s\\\" % params\\n if DEBUG:\\n logger.log(\\\"bwmon: \\\"+ subject)\\n logger.log(\\\"bwmon: \\\"+ message + (footer % params))\\n else:\\n self.emailed = True\\n logger.log(\\\"bwmon: Emailing %s\\\" % self.name)\\n slicemail(self.name, subject, message + (footer % params))\",\n \"def get_qos_minimum_bandwidth_rule(self, policy_name_or_id, rule_id):\\n if not self._has_neutron_extension('qos'):\\n raise exc.OpenStackCloudUnavailableExtension(\\n 'QoS extension is not available on target cloud'\\n )\\n\\n policy = self.network.find_qos_policy(policy_name_or_id)\\n if not policy:\\n raise exc.OpenStackCloudResourceNotFound(\\n \\\"QoS policy {name_or_id} not Found.\\\".format(\\n name_or_id=policy_name_or_id\\n )\\n )\\n\\n return self.network.get_qos_minimum_bandwidth_rule(rule_id, policy)\",\n \"def qos_stats(val):\\n\\n global qos_object, qos_interface\\n\\n if request.form.get('endPoll'):\\n qos_object.pop(request.form.get('endPoll'))\\n return 'Object Removed'\\n\\n if not request.form.get('openPage'):\\n\\n if request.form.get('action') == 'qos':\\n qos_stats = qos_object.get(request.form.get('interface')).get_interface_queues_out()\\n return jsonify({'data': qos_stats})\\n\\n if not qos_object:\\n qos_object[request.form.get('openPage')] = GetBandwidth.CalcBandwidth(device, netconf_port, username, password, request.form.get('openPage'))\\n qos_interface = request.form.get('openPage')\\n return render_template('qos_stats.html', interface=qos_interface, device=device)\\n elif request.form.get('openPage') is None:\\n return render_template('qos_stats.html', interface=qos_interface, device=device)\\n else:\\n if qos_object.get(request.form.get('openPage')) is None:\\n # (host=self.host, port=self.netconf_port, username=self.username, password=self.password,\\n new_object = GetBandwidth.CalcBandwidth(device, netconf_port, username, password, request.form.get('openPage'))\\n qos_object[request.form.get('openPage')] = new_object\\n\\n qos_interface = request.form.get('openPage')\\n return render_template('qos_stats.html', interface=qos_interface, device=device)\",\n \"def update_qos_bandwidth_limit_rule(\\n self, policy_name_or_id, rule_id, **kwargs\\n ):\\n if not self._has_neutron_extension('qos'):\\n raise exc.OpenStackCloudUnavailableExtension(\\n 'QoS extension is not available on target cloud'\\n )\\n\\n policy = self.network.find_qos_policy(\\n policy_name_or_id, ignore_missing=True\\n )\\n if not policy:\\n raise exc.OpenStackCloudResourceNotFound(\\n \\\"QoS policy {name_or_id} not Found.\\\".format(\\n name_or_id=policy_name_or_id\\n )\\n )\\n\\n if kwargs.get(\\\"direction\\\") is not None:\\n if not self._has_neutron_extension('qos-bw-limit-direction'):\\n kwargs.pop(\\\"direction\\\")\\n self.log.debug(\\n \\\"'qos-bw-limit-direction' extension is not available on \\\"\\n \\\"target cloud\\\"\\n )\\n\\n if not kwargs:\\n self.log.debug(\\\"No QoS bandwidth limit rule data to update\\\")\\n return\\n\\n curr_rule = self.network.get_qos_bandwidth_limit_rule(\\n qos_rule=rule_id, qos_policy=policy\\n )\\n if not curr_rule:\\n raise exc.OpenStackCloudException(\\n \\\"QoS bandwidth_limit_rule {rule_id} not found in policy \\\"\\n \\\"{policy_id}\\\".format(rule_id=rule_id, policy_id=policy['id'])\\n )\\n\\n return self.network.update_qos_bandwidth_limit_rule(\\n qos_rule=curr_rule, qos_policy=policy, **kwargs\\n )\",\n \"def pre_qos_forwarding_class_create(self, resource_dict):\\n pass\",\n \"def qos(self) -> int:\\n return self._qos\",\n \"def set_qos(self, qos_id, set_specs_args):\\n aname = \\\"cinder_v%s.set_qos\\\" % self.version\\n with atomic.ActionTimer(self, aname):\\n return self._get_client().qos_specs.set_keys(qos_id,\\n set_specs_args)\",\n \"def new_capacity_rule(mod, prj, prd):\\n return 0\",\n \"def dynamic_vnic_conn_policy_create(handle, name, descr=None, dynamic_eth=\\\"54\\\",\\n adaptor_profile_name=None,\\n protection=\\\"protected\\\",\\n parent_dn=\\\"org-root\\\", **kwargs):\\n\\n from ucscsdk.mometa.vnic.VnicDynamicConPolicy import VnicDynamicConPolicy\\n\\n obj = handle.query_dn(parent_dn)\\n if not obj:\\n raise UcscOperationError(\\\"dynamic_vnic_conn_policy_create\\\",\\n \\\"Org %s does not exist\\\" % parent_dn)\\n\\n mo = VnicDynamicConPolicy(parent_mo_or_dn=obj,\\n name=name,\\n descr=descr,\\n dynamic_eth=dynamic_eth,\\n protection=protection,\\n adaptor_profile_name=adaptor_profile_name)\\n\\n mo.set_prop_multiple(**kwargs)\\n\\n handle.add_mo(mo, modify_present=True)\\n handle.commit()\\n return mo\",\n \"def test_create_qos_queue_all_values(self):\\r\\n resource = 'qos_queue'\\r\\n cmd = qos.CreateQoSQueue(\\r\\n test_cli20.MyApp(sys.stdout), None)\\r\\n myid = 'myid'\\r\\n name = 'my_queue'\\r\\n default = False\\r\\n min = '10'\\r\\n max = '40'\\r\\n qos_marking = 'untrusted'\\r\\n dscp = '0'\\r\\n args = ['--default', default, '--min', min, '--max', max,\\r\\n '--qos-marking', qos_marking, '--dscp', dscp, name]\\r\\n position_names = ['name', 'default', 'min', 'max', 'qos_marking',\\r\\n 'dscp']\\r\\n position_values = [name, default, min, max, qos_marking, dscp]\\r\\n self._test_create_resource(resource, cmd, name, myid, args,\\r\\n position_names, position_values)\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: RateLimitingArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def update_qos_minimum_bandwidth_rule(\\n self, policy_name_or_id, rule_id, **kwargs\\n ):\\n if not self._has_neutron_extension('qos'):\\n raise exc.OpenStackCloudUnavailableExtension(\\n 'QoS extension is not available on target cloud'\\n )\\n\\n policy = self.network.find_qos_policy(policy_name_or_id)\\n if not policy:\\n raise exc.OpenStackCloudResourceNotFound(\\n \\\"QoS policy {name_or_id} not Found.\\\".format(\\n name_or_id=policy_name_or_id\\n )\\n )\\n\\n if not kwargs:\\n self.log.debug(\\\"No QoS minimum bandwidth rule data to update\\\")\\n return\\n\\n curr_rule = self.network.get_qos_minimum_bandwidth_rule(\\n rule_id, policy\\n )\\n if not curr_rule:\\n raise exc.OpenStackCloudException(\\n \\\"QoS minimum_bandwidth_rule {rule_id} not found in policy \\\"\\n \\\"{policy_id}\\\".format(rule_id=rule_id, policy_id=policy['id'])\\n )\\n\\n return self.network.update_qos_minimum_bandwidth_rule(\\n curr_rule, policy, **kwargs\\n )\",\n \"def make_Bandwidth(typicalBandwidth=2500, maximumBandwidth=10000):\\n\\n return {'maximumBandwidth': maximumBandwidth,\\n 'typicalBandwidth': typicalBandwidth\\n }\",\n \"def qos_policy_group_create(self, policy_group, vserver, return_record=None, max_throughput=None):\\n return self.request( \\\"qos-policy-group-create\\\", {\\n 'policy_group': [ policy_group, 'policy-group', [ basestring, 'None' ], False ],\\n 'vserver': [ vserver, 'vserver', [ basestring, 'vserver-name' ], False ],\\n 'return_record': [ return_record, 'return-record', [ bool, 'None' ], False ],\\n 'max_throughput': [ max_throughput, 'max-throughput', [ basestring, 'qos-tput' ], False ],\\n }, {\\n 'result': [ QosPolicyGroupInfo, False ],\\n } )\",\n \"def test_waf_policy_basic(self, resource_group):\\n # multi-line comment below\\n subscription = self.current_subscription()\\n blockpolicy = self.create_random_name(prefix='cli', length=24)\\n ruleName = self.create_random_name(prefix='cli', length=24)\\n cmd = 'az network front-door waf-policy create -g {resource_group} -n {blockpolicy} --mode prevention'.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(result['name'], blockpolicy)\\n self.assertEqual(result['policySettings']['mode'], \\\"Prevention\\\")\\n self.assertEqual(result['policySettings']['requestBodyCheck'], \\\"Enabled\\\")\\n self.assertIn('customRules', result)\\n self.assertIn('managedRules', result)\\n self.assertIn('id', result)\\n self.assertEqual(result['sku']['name'], \\\"Classic_AzureFrontDoor\\\")\\n\\n standardskupolicy = self.create_random_name(prefix='cli', length=24)\\n cmd = 'az network front-door waf-policy create -g {resource_group} -n {standardskupolicy} --mode prevention --sku Standard_AzureFrontDoor'.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(result['name'], standardskupolicy)\\n self.assertEqual(result['policySettings']['mode'], \\\"Prevention\\\")\\n self.assertEqual(result['policySettings']['requestBodyCheck'], \\\"Enabled\\\")\\n self.assertIn('customRules', result)\\n self.assertIn('managedRules', result)\\n self.assertIn('id', result)\\n self.assertEqual(result['sku']['name'], \\\"Standard_AzureFrontDoor\\\")\\n\\n detectionredirectpolicy = self.create_random_name(prefix='cli', length=24)\\n cmd = 'az network front-door waf-policy create -g {resource_group} -n {detectionredirectpolicy} --mode Detection --redirect-url http://www.microsoft.com --sku Premium_AzureFrontDoor'.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(result['name'], detectionredirectpolicy)\\n self.assertEqual(result['policySettings']['mode'], \\\"Detection\\\")\\n self.assertEqual(result['policySettings']['redirectUrl'], \\\"http://www.microsoft.com\\\")\\n self.assertEqual(result['policySettings']['requestBodyCheck'], \\\"Enabled\\\")\\n self.assertIn('customRules', result)\\n self.assertIn('managedRules', result)\\n self.assertIn('id', result)\\n self.assertEqual(result['sku']['name'], \\\"Premium_AzureFrontDoor\\\")\\n\\n detectioncbcpolicy = self.create_random_name(prefix='cli', length=24)\\n cmd = 'az network front-door waf-policy create -g {resource_group} -n {detectioncbcpolicy} --mode Detection --redirect-url http://www.microsoft.com --custom-block-response-status-code 406 --sku Classic_AzureFrontDoor'.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(result['name'], detectioncbcpolicy)\\n self.assertEqual(result['policySettings']['mode'], \\\"Detection\\\")\\n self.assertEqual(result['policySettings']['redirectUrl'], \\\"http://www.microsoft.com\\\")\\n self.assertEqual(result['policySettings']['customBlockResponseStatusCode'], 406)\\n self.assertEqual(result['policySettings']['requestBodyCheck'], \\\"Enabled\\\")\\n self.assertIn('customRules', result)\\n self.assertIn('managedRules', result)\\n self.assertIn('id', result)\\n self.assertEqual(result['sku']['name'], \\\"Classic_AzureFrontDoor\\\")\\n\\n detectioncbbpolicy = self.create_random_name(prefix='cli', length=24)\\n cmd = 'az network front-door waf-policy create -g {resource_group} -n {detectioncbbpolicy} --mode Detection --redirect-url http://www.microsoft.com --custom-block-response-status-code 406 --custom-block-response-body YiBvZHk='.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(result['name'], detectioncbbpolicy)\\n self.assertEqual(result['policySettings']['mode'], \\\"Detection\\\")\\n self.assertEqual(result['policySettings']['enabledState'], \\\"Enabled\\\")\\n self.assertEqual(result['policySettings']['redirectUrl'], \\\"http://www.microsoft.com\\\")\\n self.assertEqual(result['policySettings']['customBlockResponseStatusCode'], 406)\\n self.assertEqual(result['policySettings']['customBlockResponseBody'], \\\"YiBvZHk=\\\")\\n self.assertEqual(result['policySettings']['requestBodyCheck'], \\\"Enabled\\\")\\n self.assertIn('customRules', result)\\n self.assertIn('managedRules', result)\\n self.assertIn('id', result)\\n self.assertEqual(result['sku']['name'], \\\"Classic_AzureFrontDoor\\\")\\n\\n detectiondisabledpolicy = self.create_random_name(prefix='cli', length=24)\\n cmd = 'az network front-door waf-policy create -g {resource_group} -n {detectiondisabledpolicy} --mode Detection --disabled'.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(result['name'], detectiondisabledpolicy)\\n self.assertEqual(result['policySettings']['mode'], \\\"Detection\\\")\\n self.assertEqual(result['policySettings']['enabledState'], \\\"Disabled\\\")\\n self.assertEqual(result['policySettings']['redirectUrl'], None)\\n self.assertEqual(result['policySettings']['customBlockResponseStatusCode'], None)\\n self.assertEqual(result['policySettings']['customBlockResponseBody'], None)\\n self.assertEqual(result['policySettings']['requestBodyCheck'], \\\"Enabled\\\")\\n self.assertIn('customRules', result)\\n self.assertIn('managedRules', result)\\n self.assertIn('id', result)\\n self.assertEqual(result['sku']['name'], \\\"Classic_AzureFrontDoor\\\")\\n\\n cmd = 'az network front-door waf-policy update -g {resource_group} -n {detectiondisabledpolicy} --mode Detection'.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertIn('customRules', result)\\n self.assertIn('managedRules', result)\\n self.assertIn('id', result)\\n self.assertEqual(result['policySettings']['enabledState'], \\\"Enabled\\\")\\n self.assertEqual(result['policySettings']['requestBodyCheck'], \\\"Enabled\\\")\\n self.assertEqual(result['sku']['name'], \\\"Classic_AzureFrontDoor\\\")\\n\\n cmd = 'az network front-door waf-policy update -g {resource_group} -n {blockpolicy} --tags test=best'.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(result['name'], blockpolicy)\\n self.assertEqual(result['policySettings']['mode'], \\\"Prevention\\\")\\n # TODO uncomment once API support for updating tags is fixed :-O\\n # self.assertEqual(result['tags'], { 'test': 'best' })\\n self.assertIn('customRules', result)\\n self.assertIn('managedRules', result)\\n self.assertIn('id', result)\\n self.assertEqual(result['sku']['name'], \\\"Classic_AzureFrontDoor\\\")\\n\\n cmd = 'az network front-door waf-policy update -g {resource_group} -n {blockpolicy} --mode detection --sku Classic_AzureFrontDoor'.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(result['name'], blockpolicy)\\n self.assertEqual(result['policySettings']['mode'], \\\"Detection\\\")\\n self.assertEqual(result['policySettings']['requestBodyCheck'], \\\"Enabled\\\")\\n self.assertEqual(result['sku']['name'], \\\"Classic_AzureFrontDoor\\\")\\n\\n cmd = 'az network front-door waf-policy update -g {resource_group} -n {blockpolicy} --mode prevention --redirect-url http://www.microsoft.com'.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(result['name'], blockpolicy)\\n self.assertEqual(result['policySettings']['mode'], \\\"Prevention\\\")\\n self.assertEqual(result['policySettings']['redirectUrl'], 'http://www.microsoft.com')\\n self.assertEqual(result['sku']['name'], \\\"Classic_AzureFrontDoor\\\")\\n\\n cmd = 'az network front-door waf-policy update -g {resource_group} -n {blockpolicy} --custom-block-response-status-code 406'.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(result['name'], blockpolicy)\\n self.assertEqual(result['policySettings']['mode'], \\\"Prevention\\\")\\n self.assertEqual(result['policySettings']['customBlockResponseStatusCode'], 406)\\n self.assertEqual(result['policySettings']['requestBodyCheck'], \\\"Enabled\\\")\\n self.assertEqual(result['sku']['name'], \\\"Classic_AzureFrontDoor\\\")\\n\\n cmd = 'az network front-door waf-policy update -g {resource_group} -n {blockpolicy} --custom-block-response-status-code 405 --custom-block-response-body YiBvZHk='.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(result['name'], blockpolicy)\\n self.assertEqual(result['policySettings']['mode'], \\\"Prevention\\\")\\n self.assertEqual(result['policySettings']['customBlockResponseStatusCode'], 405)\\n self.assertEqual(result['policySettings']['customBlockResponseBody'], \\\"YiBvZHk=\\\")\\n self.assertEqual(result['policySettings']['requestBodyCheck'], \\\"Enabled\\\")\\n self.assertEqual(result['sku']['name'], \\\"Classic_AzureFrontDoor\\\")\\n\\n cmd = 'az network front-door waf-policy update -g {resource_group} -n {blockpolicy} --disabled'.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(result['name'], blockpolicy)\\n self.assertEqual(result['policySettings']['enabledState'], \\\"Disabled\\\")\\n self.assertEqual(result['policySettings']['requestBodyCheck'], \\\"Enabled\\\")\\n self.assertEqual(result['sku']['name'], \\\"Classic_AzureFrontDoor\\\")\\n\\n cmd = 'az network front-door waf-policy show -g {resource_group} -n {blockpolicy}'.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(result['name'], blockpolicy)\\n # spot check\\n self.assertEqual(result['policySettings']['enabledState'], \\\"Disabled\\\")\\n self.assertEqual(result['policySettings']['customBlockResponseStatusCode'], 405)\\n self.assertEqual(result['policySettings']['requestBodyCheck'], \\\"Enabled\\\")\\n self.assertEqual(result['sku']['name'], \\\"Classic_AzureFrontDoor\\\")\\n\\n cmd = 'az network front-door waf-policy list -g {resource_group}'.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(len(result), 6)\\n blockPolicyObject = [policy for policy in result if policy['name'] == blockpolicy][0]\\n self.assertEqual(blockPolicyObject['name'], blockpolicy)\\n\\n cmd = 'az network front-door waf-policy delete -g {resource_group} -n {blockpolicy}'.format(**locals())\\n result = self.cmd(cmd)\\n\\n cmd = 'az network front-door waf-policy list -g {resource_group}'.format(**locals())\\n result = self.cmd(cmd).get_output_in_json()\\n self.assertEqual(len(result), 5)\\n self.assertEqual(len([policy for policy in result if policy['name'] == blockpolicy]), 0)\",\n \"def policy(agent):\",\n \"def create(window_type=WindowType.DEFAULT, rate=10):\\n pass\",\n \"def set_qos(self, qos, set_specs_args):\\n self._impl.set_qos(qos.id, set_specs_args)\\n return self._unify_qos(qos)\",\n \"def snmpqosqos_packets_filteredrate(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._snmpqosqos_packets_filteredrate\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def qos_policy_group_modify(self, policy_group, max_throughput=None):\\n return self.request( \\\"qos-policy-group-modify\\\", {\\n 'policy_group': [ policy_group, 'policy-group', [ basestring, 'None' ], False ],\\n 'max_throughput': [ max_throughput, 'max-throughput', [ basestring, 'qos-tput' ], False ],\\n }, {\\n } )\",\n \"def bandwidth_limit_rule_update(request, policy_id, rule_id, **kwargs):\\n body = {'bandwidth_limit_rule': kwargs}\\n ruleType = 'bandwidth_limit_rule'\\n bandwidthlimit_update = neutronclient(request)\\\\\\n .update_bandwidth_limit_rule(rule_id, policy_id, body)\\\\\\n .get(ruleType)\\n return BandwidthLimitRule(bandwidthlimit_update)\",\n \"def bandwidth_limit_rule_delete(request, policy_id, rule_id):\\n neutronclient(request).delete_bandwidth_limit_rule(rule_id, policy_id)\",\n \"def apply_policy(self, policy):\\n tenant_name = policy['tenant_name']\\n fw_id = policy['fw_id']\\n fw_name = policy['fw_name']\\n LOG.debug(\\\"asa_apply_policy: tenant=%s fw_id=%s fw_name=%s\\\",\\n tenant_name, fw_id, fw_name)\\n cmds = [\\\"conf t\\\", \\\"changeto context \\\" + tenant_name]\\n\\n rule_dict = policy['rules']\\n for rule_id in rule_dict:\\n rule = rule_dict[rule_id]\\n protocol = rule['protocol']\\n name = rule['name']\\n enabled = rule['enabled']\\n dst_port = rule['destination_port']\\n src_port = rule['source_port']\\n\\n if (rule['source_ip_address'] is not None):\\n src_ip = IPNetwork(rule['source_ip_address'])\\n else:\\n src_ip = IPNetwork('0.0.0.0/0')\\n\\n if (rule['destination_ip_address'] is not None):\\n dst_ip = IPNetwork(rule['destination_ip_address'])\\n else:\\n dst_ip = IPNetwork('0.0.0.0/0')\\n\\n if rule['action'] == 'allow':\\n action = 'permit'\\n else:\\n action = 'deny'\\n\\n LOG.debug(\\\"rule[%s]: name=%s enabled=%s prot=%s dport=%s sport=%s \\\\\\n dip=%s %s sip=%s %s action=%s\\\",\\n rule_id, name, enabled, protocol, dst_port, src_port,\\n dst_ip.network, dst_ip.netmask,\\n src_ip.network, src_ip.netmask, action)\\n\\n acl = \\\"access-list \\\"\\n acl = (acl + tenant_name + \\\" extended \\\" + action + \\\" \\\" +\\n protocol + \\\" \\\")\\n if (rule['source_ip_address'] is None):\\n acl = acl + \\\"any \\\"\\n else:\\n acl = acl + str(src_ip.network) + \\\" \\\" + (\\n str(src_ip.netmask) + \\\" \\\")\\n if (src_port is not None):\\n if (':' in src_port):\\n range = src_port.replace(':', ' ')\\n acl = acl + \\\"range \\\" + range + \\\" \\\"\\n else:\\n acl = acl + \\\"eq \\\" + src_port + \\\" \\\"\\n if (rule['destination_ip_address'] is None):\\n acl = acl + \\\"any \\\"\\n else:\\n acl = acl + str(dst_ip.network) + \\\" \\\" + \\\\\\n str(dst_ip.netmask) + \\\" \\\"\\n if (dst_port is not None):\\n if (':' in dst_port):\\n range = dst_port.replace(':', ' ')\\n acl = acl + \\\"range \\\" + range + \\\" \\\"\\n else:\\n acl = acl + \\\"eq \\\" + dst_port + \\\" \\\"\\n if (enabled is False):\\n acl = acl + 'inactive'\\n\\n # remove the old ace for this rule\\n if (rule_id in self.rule_tbl):\\n cmds.append('no ' + self.rule_tbl[rule_id])\\n\\n self.rule_tbl[rule_id] = acl\\n if tenant_name in self.tenant_rule:\\n if rule_id not in self.tenant_rule[tenant_name]['rule_lst']:\\n self.tenant_rule[tenant_name]['rule_lst'].append(rule_id)\\n cmds.append(acl)\\n cmds.append(\\\"access-group \\\" + tenant_name + \\\" global\\\")\\n cmds.append(\\\"write memory\\\")\\n\\n LOG.debug(cmds)\\n data = {\\\"commands\\\": cmds}\\n return self.rest_send_cli(data)\",\n \"def addRSTQoS(self, tmpFD):\\n if self.qosPolicy == 'privatens':\\n self._calculateQoS(tmpFD)\\n elif self.qosPolicy == 'hostlevel':\\n self._calculateQoS(tmpFD, True)\\n elif self.qosPolicy == 'default-not-set':\\n self.logger.info('QoS Policy not set in configuration. set one of privatens or hostlevel.')\\n else:\\n raise ConfigException(f'QoS Policy {self.qosPolicy} is not supported.')\",\n \"def __init__(self, name=None, queue_mode=None, description=None, redrive_policy=None, max_consume_count=None, retention_hours=None):\\n \\n \\n\\n self._name = None\\n self._queue_mode = None\\n self._description = None\\n self._redrive_policy = None\\n self._max_consume_count = None\\n self._retention_hours = None\\n self.discriminator = None\\n\\n self.name = name\\n if queue_mode is not None:\\n self.queue_mode = queue_mode\\n if description is not None:\\n self.description = description\\n if redrive_policy is not None:\\n self.redrive_policy = redrive_policy\\n if max_consume_count is not None:\\n self.max_consume_count = max_consume_count\\n if retention_hours is not None:\\n self.retention_hours = retention_hours\",\n \"def snmpqosqos_flow_memrate(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._snmpqosqos_flow_memrate\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def snmpqosqos_real_bytesrate(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._snmpqosqos_real_bytesrate\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def dynamic_control_limits():\\n try:\\n content = request.get_json()\\n data = content[\\\"data\\\"]\\n for elm in data:\\n data[elm] = [data[elm]]\\n data = pd.DataFrame.from_dict(content[\\\"data\\\"])\\n fname = inspect.stack()[0][3]\\n param = content[\\\"param\\\"][fname]\\n result = pd.DataFrame()\\n remove_outliers_train = param[\\\"remove_outliers_train\\\"]\\n remove_outliers_run = param[\\\"remove_outliers_run\\\"]\\n training_length = param[\\\"training_length\\\"]\\n runtime_window = param[\\\"runtime_window\\\"]\\n param_db = dict()\\n param_db[\\\"IdMachine\\\"] = data[\\\"IdMachine\\\"].unique()[0]\\n param_db[\\\"IdSig\\\"] = data[\\\"IdSig\\\"].unique()[0]\\n # define parameter in order to ge control limits from database\\n # get control limits if already computed\\n df_param = db_manager.get_param(param_db)\\n # get data stored in the database\\n df_data = db_manager.get_data(param_db)\\n # merge old data and new data\\n df_data = pd.concat([df_data, data], ignore_index=True, sort=False)\\n # get identifier os plit if present\\n # check if control limits already computed\\n\\n if len(df_param) > 0:\\n # check if there is enough data for application of control limits\\n # print(\\\"len(df_data)=\\\"+str(len(df_data))+\\\" runtime_window=\\\"+str(runtime_window) )\\n if len(df_data) >= runtime_window:\\n # sort data by timestamp\\n df_data.sort_values(by=[\\\"TimeStamp\\\"], inplace=True, ascending=True)\\n df_data.reset_index(drop=True, inplace=True)\\n result = ts.run_cl_analysis(\\n ts, df_data, df_param, param\\n )\\n # add in the result the sendout parameters, so we don't need to publish these values\\n # db_manager.delete_data(param_db)\\n else:\\n # check if there is enough data for computation of control limits\\n # print(\\\"len(df_data)=\\\"+str(len(df_data))+\\\" training_length=\\\"+str(training_length) )\\n if len(df_data) >= training_length:\\n # sort data by timestamp\\n df_data.sort_values(by=[\\\"TimeStamp\\\"], inplace=True, ascending=True)\\n df_data.reset_index(drop=True, inplace=True)\\n result = ts.calculate_cl(df_data, remove_outliers_train)\\n # store control limits\\n db_manager.store_param(result, param_db)\\n # delete old data\\n # db_manager.delete_data(param_db)\\n # we want to publish these values out from sibyl\\n result[\\\"content\\\"] = \\\"cl\\\"\\n else:\\n db_manager.store_data(data, param_db)\\n if len(result) > 0:\\n result[\\\"IdMachine\\\"] = param_db[\\\"IdMachine\\\"]\\n result[\\\"IdSig\\\"] = param_db[\\\"IdSig\\\"]\\n result[\\\"TimeStamp\\\"] = df_data.tail(1)[\\\"TimeStamp\\\"].values[0]\\n # result[\\\"Source\\\"] = df_data[\\\"Source\\\"].unique()[0]\\n data_to_send = {}\\n data_to_send[\\\"IdSig\\\"] = param_db[\\\"IdSig\\\"]\\n data_to_send[\\\"IdMachine\\\"] = param_db[\\\"IdMachine\\\"]\\n data_to_send[\\\"TimeStamp\\\"] = df_data.tail(1)[\\\"TimeStamp\\\"].values[0]\\n data_to_send[\\\"Value\\\"] = content[\\\"data\\\"][\\\"Value\\\"][0]\\n Kind = result[\\\"Kind\\\"]\\n Value = result[\\\"Value\\\"]\\n data_to_send[\\\"dynamic_control_limits\\\"] = {}\\n for k, v in zip(Kind, Value):\\n data_to_send[\\\"dynamic_control_limits\\\"][k] = v\\n return jsonify(data_to_send)\\n except Exception as e:\\n return jsonify({\\\"error\\\": str(e)})\",\n \"def snmpqosqos_flow_recyclesrate(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._snmpqosqos_flow_recyclesrate\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def test_change_default_throttling_settings_http_with_overwrite_throttled_rate_above_account_quota():\",\n \"def createNetworkSwitchQosRule(self, networkId: str, vlan: int, **kwargs):\\n\\n kwargs.update(locals())\\n\\n if 'protocol' in kwargs:\\n options = ['ANY', 'TCP', 'UDP']\\n assert kwargs['protocol'] in options, f'''\\\"protocol\\\" cannot be \\\"{kwargs['protocol']}\\\", & must be set to one of: {options}'''\\n\\n metadata = {\\n 'tags': ['switch', 'configure', 'qosRules'],\\n 'operation': 'createNetworkSwitchQosRule',\\n }\\n resource = f'/networks/{networkId}/switch/qosRules'\\n\\n body_params = ['vlan', 'protocol', 'srcPort', 'srcPortRange', 'dstPort', 'dstPortRange', 'dscp']\\n payload = {k: v for (k, v) in kwargs.items() if k in body_params}\\n\\n return self._session.post(metadata, resource, payload)\",\n \"def addVlanQoS(self, tmpFD):\\n self._getvlanlistqos()\\n\\n for l2req in self.activeL2:\\n interface = l2req['destport']\\n name = f\\\"{interface}-{l2req['vlan']}\\\"\\n if not l2req['params']:\\n self.logger.info('This specific vlan request did not provided any QOS. \\\\\\n Ignoring QOS Rules for it')\\n continue\\n outrate, outtype = self.convertToRate(l2req['params'])\\n if self.params[interface]['intf_max'] - outrate <= 0:\\n raise OverSubscribeException(\\\"Node is oversubscribed. \\\\\\n Will not modify present QoS Rules.\\\")\\n self.params[interface]['intf_max'] -= outrate\\n tmpFD.write(f\\\"\\\"\\\"\\n# SENSE VLAN {l2req['vlan']} {l2req['destport']} {outrate}{outtype}\\ninterface vlan.{l2req['vlan']} {name}-IN input rate {outrate}{outtype}\\ninterface vlan.{l2req['vlan']} {name}-OUT output rate {outrate}{outtype}\\\\n\\\"\\\"\\\")\",\n \"def test_different_types_rss_queue_rule(self):\\n self.pmdout.start_testpmd(\\\"%s\\\" % self.cores, \\\"--rxq=8 --txq=8 --port-topology=chained\\\")\\n self.dut.send_expect(\\\"set fwd rxonly\\\", \\\"testpmd> \\\", 120)\\n self.dut.send_expect(\\\"set verbose 1\\\", \\\"testpmd> \\\", 120)\\n self.dut.send_expect(\\\"start\\\", \\\"testpmd> \\\", 120)\\n time.sleep(2)\\n\\n # Create a rss queue rule\\n self.dut.send_expect(\\n \\\"flow create 0 ingress pattern end actions rss types udp ipv4-tcp ipv6-sctp ipv4-other end queues 1 4 7 end / end\\\", \\\"created\\\")\\n # send the packets and verify the results\\n if (self.nic in [\\\"fortville_eagle\\\", \\\"fortville_spirit\\\",\\n \\\"fortville_spirit_single\\\", \\\"fortpark_TLV\\\", \\\"fortville_25g\\\"]):\\n rss_queue = [\\\"1\\\", \\\"4\\\", \\\"7\\\"]\\n self.send_and_check(self.pkt2, rss_queue)\\n self.send_and_check(self.pkt3, rss_queue)\\n self.send_and_check(self.pkt4, rss_queue)\\n self.send_and_check(self.pkt5, rss_queue)\\n self.send_and_check(self.pkt6, rss_queue)\\n rss_queue = [\\\"0\\\"]\\n self.send_and_check(self.pkt1, rss_queue)\\n self.send_and_check(self.pkt7, rss_queue)\\n self.send_and_check(self.pkt8, rss_queue)\\n elif(self.nic in [\\\"cavium_a063\\\"]):\\n rss_queue = [\\\"1\\\", \\\"4\\\", \\\"7\\\"]\\n self.send_and_check(self.pkt2, rss_queue)\\n self.send_and_check(self.pkt3, rss_queue)\\n self.send_and_check(self.pkt6, rss_queue)\\n else:\\n rss_queue = [\\\"1\\\", \\\"4\\\", \\\"7\\\"]\\n self.send_and_check(self.pkt2, rss_queue)\\n self.send_and_check(self.pkt3, rss_queue)\\n self.send_and_check(self.pkt6, rss_queue)\\n rss_queue = [\\\"0\\\"]\\n self.send_and_check(self.pkt1, rss_queue)\\n self.send_and_check(self.pkt4, rss_queue)\\n self.send_and_check(self.pkt5, rss_queue)\\n self.send_and_check(self.pkt7, rss_queue)\\n self.send_and_check(self.pkt8, rss_queue)\\n\\n # There can't be more than one RSS queue rule existing.\\n self.dut.send_expect(\\n \\\"flow create 0 ingress pattern end actions rss types ipv6-other end queues 3 end / end\\\", \\\"error\\\")\",\n \"def new_capacity_rule(mod, g, p):\\n return 0\",\n \"def set_bw(self, mode):\\n self.bandwidth = 0\\n if (mode=='a'):\\n self.bandwidth = 54\\n elif(mode=='b'):\\n self.bandwidth = 11\\n elif(mode=='g'):\\n self.bandwidth = 54 \\n elif(mode=='n'):\\n self.bandwidth = 600\\n elif(mode=='ac'):\\n self.bandwidth = 6777 \\n \\n return self.bandwidth\",\n \"def get_qos(self):\\n return self.qos\",\n \"def _create_objective(self, meta, m):\\n ## cashflow eval\\n rule = partial(self._cashflow_rule, meta)\\n m.obj = pyo.Objective(rule=rule, sense=pyo.maximize)\",\n \"def test_rate_limit_handling(self):\\n self.create_org(status=CONNECTED)\\n response = self.app.post('/adapter/qbo/test/update')\\n\\n # 500 should not be returned by the adapter\\n self.assertEqual(response.status_code, 429)\",\n \"def minimum_bandwidth_rule_delete(request, policy_id, rule_id):\\n\\n neutronclient(request).delete_minimum_bandwidth_rule(rule_id, policy_id)\",\n \"def delete_qos_bandwidth_limit_rule(self, policy_name_or_id, rule_id):\\n if not self._has_neutron_extension('qos'):\\n raise exc.OpenStackCloudUnavailableExtension(\\n 'QoS extension is not available on target cloud'\\n )\\n\\n policy = self.network.find_qos_policy(policy_name_or_id)\\n if not policy:\\n raise exc.OpenStackCloudResourceNotFound(\\n \\\"QoS policy {name_or_id} not Found.\\\".format(\\n name_or_id=policy_name_or_id\\n )\\n )\\n\\n try:\\n self.network.delete_qos_bandwidth_limit_rule(\\n rule_id, policy, ignore_missing=False\\n )\\n except exceptions.ResourceNotFound:\\n self.log.debug(\\n \\\"QoS bandwidth limit rule {rule_id} not found in policy \\\"\\n \\\"{policy_id}. Ignoring.\\\".format(\\n rule_id=rule_id, policy_id=policy['id']\\n )\\n )\\n return False\\n\\n return True\",\n \"def __new__(cls, rate_limit_group):\\n self = object.__new__(cls)\\n self.rate_limit_group = rate_limit_group\\n self.acquired = False\\n return self\",\n \"def __new__(cls, rate_limit_groups):\\n self = object.__new__(cls)\\n self.rate_limit_groups = rate_limit_groups\\n self.acquired = False\\n return self\",\n \"def __init__(self, id, policy, policy_args, board_size, logq):\\n self.id = id\\n self.chain = []\\n self.len = 0\\n self.was_initialized = False\\n self.policy_class = policy\\n self.round = 0\\n\\n self.sq = mp.Queue()\\n self.aq = mp.Queue()\\n self.mq = mp.Queue()\\n self.policy = policy(policy_args, board_size, self.sq, self.aq, self.mq, logq, id)\\n self.policy.daemon = True\\n self.policy.start()\",\n \"def test_change_default_throttling_settings_http_with_overwrite_throttled_burst_above_account_quota():\",\n \"def snmpqosqos_sch_links_createdrate(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._snmpqosqos_sch_links_createdrate\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def test_change_default_throttling_settings_http_with_overwrite_throttled_burst_above_50():\",\n \"def test_limit_cpu(self):\\n url = '/api/apps'\\n body = {'cluster': 'autotest'}\\n response = self.client.post(url, json.dumps(body), content_type='application/json')\\n self.assertEqual(response.status_code, 201)\\n app_id = response.data['id']\\n url = '/api/apps/{app_id}/limits'.format(**locals())\\n # check default limit\\n response = self.client.get(url, content_type='application/json')\\n self.assertEqual(response.status_code, 200)\\n self.assertIn('cpu', response.data)\\n self.assertEqual(json.loads(response.data['cpu']), {})\\n # regression test for https://github.com/deis/deis/issues/1563\\n self.assertNotIn('\\\"', response.data['cpu'])\\n # set an initial limit\\n body = {'cpu': json.dumps({'web': '1024'})}\\n response = self.client.post(url, json.dumps(body), content_type='application/json')\\n self.assertEqual(response.status_code, 201)\\n self.assertIn('x-deis-release', response._headers)\\n limit1 = response.data\\n # check memory limits\\n response = self.client.get(url, content_type='application/json')\\n self.assertEqual(response.status_code, 200)\\n self.assertIn('cpu', response.data)\\n cpu = json.loads(response.data['cpu'])\\n self.assertIn('web', cpu)\\n self.assertEqual(cpu['web'], '1024')\\n # set an additional value\\n body = {'cpu': json.dumps({'worker': '512'})}\\n response = self.client.post(url, json.dumps(body), content_type='application/json')\\n self.assertEqual(response.status_code, 201)\\n limit2 = response.data\\n self.assertNotEqual(limit1['uuid'], limit2['uuid'])\\n cpu = json.loads(response.data['cpu'])\\n self.assertIn('worker', cpu)\\n self.assertEqual(cpu['worker'], '512')\\n self.assertIn('web', cpu)\\n self.assertEqual(cpu['web'], '1024')\\n # read the limit again\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 200)\\n limit3 = response.data\\n self.assertEqual(limit2, limit3)\\n cpu = json.loads(response.data['cpu'])\\n self.assertIn('worker', cpu)\\n self.assertEqual(cpu['worker'], '512')\\n self.assertIn('web', cpu)\\n self.assertEqual(cpu['web'], '1024')\\n # unset a value\\n body = {'memory': json.dumps({'worker': None})}\\n response = self.client.post(url, json.dumps(body), content_type='application/json')\\n self.assertEqual(response.status_code, 201)\\n limit4 = response.data\\n self.assertNotEqual(limit3['uuid'], limit4['uuid'])\\n self.assertNotIn('worker', json.dumps(response.data['memory']))\\n # disallow put/patch/delete\\n self.assertEqual(self.client.put(url).status_code, 405)\\n self.assertEqual(self.client.patch(url).status_code, 405)\\n self.assertEqual(self.client.delete(url).status_code, 405)\\n return limit4\",\n \"def test_create_hyperflex_ucsm_config_policy(self):\\n pass\",\n \"def make_decision_with_policy(self, policy_type, *args):\\n if policy_type == 1: # ADP\\n assert len(args) == 2, 'args should be exactly 2'\\n cur_K = -self.K_im_traj[-1]\\n distance_2_tan, radian_at_tan = args\\n self.dis_sum += distance_2_tan\\n pwm_l_new, pwm_r_new = policy.adp(distance_2_tan, radian_at_tan, self.dis_sum, cur_K)\\n elif policy_type == 2: # pure pursuit\\n l_d, sin_alpha = args\\n amp = 150\\n pwm_l_new, pwm_r_new = policy.pure_pursuit(l_d, sin_alpha, amp)\\n elif policy_type == 3: # Car following with ADP\\n assert len(args) == 3, 'args should be exactly 3'\\n cur_K = -self.K_im_traj[-1]\\n distance_2_tan, radian_at_tan, estimated_dis = args\\n self.dis_sum += distance_2_tan\\n if self.is_recording and self.counter % 100 == 0:\\n np.save('./.out/record', self.record)\\n pwm_l_new, pwm_r_new = policy.car_following_with_adp(distance_2_tan, radian_at_tan, self.dis_sum, cur_K, estimated_dis, self.record)\\n print(self.counter)\\n self.counter += 1\\n elif policy_type == 4:\\n K = 0.5\\n dis2car, = args\\n pwm_l_new, pwm_r_new = policy.car_following(dis2car, K)\\n elif policy_type == 5:\\n d_arc, d_curve, theta = args\\n pwm_l_new, pwm_r_new = policy.adp_coupled_car_following(d_arc, d_curve, theta, self.z, self.K_coupled)\\n else:\\n pwm_l_new, pwm_r_new = 0, 0\\n print('Policy Not Found')\\n self.motor.motor_set_new_speed(pwm_l_new, pwm_r_new)\",\n \"def snmpqosqos_actions_createdrate(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._snmpqosqos_actions_createdrate\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def test_change_default_throttling_settings_http_with_overwrite_throttled_rate_above_50():\",\n \"def add_qos(self, qos):\\n \\n qos_id = qos[\\\"ovsdb:qos-entries\\\"][0][\\\"qos-id\\\"]\\n self.qos_dict[qos_id] = qos\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6386853","0.63850254","0.6247085","0.6161535","0.6106285","0.60777605","0.5950055","0.5935536","0.590857","0.5905467","0.5810064","0.5765103","0.56232476","0.5616136","0.558902","0.5584673","0.5562813","0.55526304","0.5522369","0.5482483","0.54449725","0.5426305","0.5415012","0.54041153","0.54005045","0.53920966","0.5387137","0.5357318","0.53528064","0.5338634","0.53074545","0.53074545","0.53074545","0.53030366","0.52886534","0.526579","0.5248758","0.524439","0.52208817","0.5210373","0.5206044","0.51781654","0.51747537","0.51572824","0.51298565","0.5110108","0.51097083","0.50941336","0.50884783","0.5076859","0.507347","0.5070187","0.5066521","0.50653124","0.50619346","0.50448364","0.50363815","0.501709","0.5005639","0.49866816","0.49859062","0.49771884","0.49751937","0.4972975","0.4970447","0.4959667","0.4959426","0.49494994","0.49431455","0.49395198","0.4933229","0.49282718","0.4924752","0.49183792","0.48970404","0.48930287","0.48870447","0.48807454","0.48789054","0.48784238","0.48746246","0.4864703","0.48596734","0.48497155","0.4841606","0.48278677","0.4827782","0.48258063","0.4825126","0.48198447","0.48198","0.4819599","0.48183927","0.48177752","0.48136026","0.48115957","0.48068136","0.48055527","0.48016798","0.47934335","0.4792151"],"string":"[\n \"0.6386853\",\n \"0.63850254\",\n \"0.6247085\",\n \"0.6161535\",\n \"0.6106285\",\n \"0.60777605\",\n \"0.5950055\",\n \"0.5935536\",\n \"0.590857\",\n \"0.5905467\",\n \"0.5810064\",\n \"0.5765103\",\n \"0.56232476\",\n \"0.5616136\",\n \"0.558902\",\n \"0.5584673\",\n \"0.5562813\",\n \"0.55526304\",\n \"0.5522369\",\n \"0.5482483\",\n \"0.54449725\",\n \"0.5426305\",\n \"0.5415012\",\n \"0.54041153\",\n \"0.54005045\",\n \"0.53920966\",\n \"0.5387137\",\n \"0.5357318\",\n \"0.53528064\",\n \"0.5338634\",\n \"0.53074545\",\n \"0.53074545\",\n \"0.53074545\",\n \"0.53030366\",\n \"0.52886534\",\n \"0.526579\",\n \"0.5248758\",\n \"0.524439\",\n \"0.52208817\",\n \"0.5210373\",\n \"0.5206044\",\n \"0.51781654\",\n \"0.51747537\",\n \"0.51572824\",\n \"0.51298565\",\n \"0.5110108\",\n \"0.51097083\",\n \"0.50941336\",\n \"0.50884783\",\n \"0.5076859\",\n \"0.507347\",\n \"0.5070187\",\n \"0.5066521\",\n \"0.50653124\",\n \"0.50619346\",\n \"0.50448364\",\n \"0.50363815\",\n 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ACCO, adding it to globalMap"},"document":{"kind":"string","value":"def makeMapping(globalMap):\n \n from memops.xml.Implementation import bool2str, str2bool\n\n # Set up top level dictionaries\n loadMaps = globalMap.get('loadMaps')\n mapsByGuid = globalMap.get('mapsByGuid')\n\n abstractTypes = globalMap.get('ACCO').get('abstractTypes')\n exolinks = globalMap.get('ACCO').get('exolinks')\n\n # Class AccessControlStore\n currentMap = {}\n abstractTypes['AccessControlStore'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:18:10_00001'] = currentMap\n loadMaps['ACCO.AccessControlStore'] = currentMap\n currentMap['tag'] = 'ACCO.AccessControlStore'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:18:10_00001'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'accessControlStores'\n currentMap['isTop'] = True\n currentMap['objkey'] = 'name'\n currentMap['class'] = memops.api.AccessControl.AccessControlStore\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute AccessControlStore.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute AccessControlStore.createdBy\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute AccessControlStore.guid\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\n\n # Attribute AccessControlStore.isModifiable\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute AccessControlStore.lastUnlockedBy\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute AccessControlStore.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00006'] = currentMap\n loadMaps['ACCO.AccessControlStore.name'] = currentMap\n currentMap['tag'] = 'ACCO.AccessControlStore.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00006'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Role AccessControlStore.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role AccessControlStore.accessObjects\n currentMap = {}\n contentMap['accessObjects'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00013'] = currentMap\n loadMaps['ACCO.AccessControlStore.accessObjects'] = currentMap\n currentMap['tag'] = 'ACCO.AccessControlStore.accessObjects'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00013'\n currentMap['name'] = 'accessObjects'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('ACCO').get('abstractTypes')\n\n # Role AccessControlStore.userGroups\n currentMap = {}\n contentMap['userGroups'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00003'] = currentMap\n loadMaps['ACCO.AccessControlStore.userGroups'] = currentMap\n currentMap['tag'] = 'ACCO.AccessControlStore.userGroups'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00003'\n currentMap['name'] = 'userGroups'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('ACCO').get('abstractTypes')\n\n # Role AccessControlStore.users\n currentMap = {}\n contentMap['users'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00001'] = currentMap\n loadMaps['ACCO.AccessControlStore.users'] = currentMap\n currentMap['tag'] = 'ACCO.AccessControlStore.users'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:38_00001'\n currentMap['name'] = 'users'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('ACCO').get('abstractTypes')\n # End of AccessControlStore\n\n currentMap = abstractTypes.get('AccessControlStore')\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy']\n currentMap['headerAttrs'] = aList\n aList = ['name']\n currentMap['simpleAttrs'] = aList\n aList = ['users', 'userGroups', 'accessObjects', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['accessObjects', 'userGroups', 'users']\n currentMap['children'] = aList\n\n # Class AccessObject\n currentMap = {}\n abstractTypes['AccessObject'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00014'] = currentMap\n loadMaps['ACCO.AccessObject'] = currentMap\n currentMap['tag'] = 'ACCO.AccessObject'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00014'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'accessObjects'\n currentMap['objkey'] = 'name'\n currentMap['class'] = memops.api.AccessControl.AccessObject\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute AccessObject.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute AccessObject.description\n currentMap = {}\n contentMap['description'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00005'] = currentMap\n loadMaps['ACCO.AccessObject.description'] = currentMap\n currentMap['tag'] = 'ACCO.AccessObject.description'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00005'\n currentMap['name'] = 'description'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['proc'] = 'direct'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00035')\n\n # Attribute AccessObject.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00004'] = currentMap\n loadMaps['ACCO.AccessObject.name'] = currentMap\n currentMap['tag'] = 'ACCO.AccessObject.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00004'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Role AccessObject.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role AccessObject.permissions\n currentMap = {}\n contentMap['permissions'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00001'] = currentMap\n loadMaps['ACCO.AccessObject.permissions'] = currentMap\n currentMap['tag'] = 'ACCO.AccessObject.permissions'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:27_00001'\n currentMap['name'] = 'permissions'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['content'] = globalMap.get('ACCO').get('abstractTypes')\n # End of AccessObject\n\n currentMap = abstractTypes.get('AccessObject')\n aList = ['description', 'name']\n currentMap['simpleAttrs'] = aList\n aList = ['permissions', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['permissions']\n currentMap['children'] = aList\n\n # Class Permission\n currentMap = {}\n abstractTypes['Permission'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00018'] = currentMap\n loadMaps['ACCO.Permission'] = currentMap\n currentMap['tag'] = 'ACCO.Permission'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00018'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'permissions'\n currentMap['class'] = memops.api.AccessControl.Permission\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute Permission.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute Permission.opType\n currentMap = {}\n contentMap['opType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00021'] = currentMap\n loadMaps['ACCO.Permission.opType'] = currentMap\n currentMap['tag'] = 'ACCO.Permission.opType'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00021'\n currentMap['name'] = 'opType'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 'any'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute Permission.permission\n currentMap = {}\n contentMap['permission'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00023'] = currentMap\n loadMaps['ACCO.Permission.permission'] = currentMap\n currentMap['tag'] = 'ACCO.Permission.permission'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00023'\n currentMap['name'] = 'permission'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = True\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute Permission.permissionClass\n currentMap = {}\n contentMap['permissionClass'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00020'] = currentMap\n loadMaps['ACCO.Permission.permissionClass'] = currentMap\n currentMap['tag'] = 'ACCO.Permission.permissionClass'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00020'\n currentMap['name'] = 'permissionClass'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 'any'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute Permission.roleName\n currentMap = {}\n contentMap['roleName'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00022'] = currentMap\n loadMaps['ACCO.Permission.roleName'] = currentMap\n currentMap['tag'] = 'ACCO.Permission.roleName'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00022'\n currentMap['name'] = 'roleName'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 'any'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Role Permission.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role Permission.userGroup\n currentMap = {}\n contentMap['userGroup'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00016'] = currentMap\n loadMaps['ACCO.Permission.userGroup'] = currentMap\n currentMap['tag'] = 'ACCO.Permission.userGroup'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00016'\n currentMap['name'] = 'userGroup'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['copyOverride'] = True\n # End of Permission\n\n currentMap = abstractTypes.get('Permission')\n aList = ['opType', 'permission', 'permissionClass', 'roleName']\n currentMap['headerAttrs'] = aList\n aList = ['userGroup']\n currentMap['optLinks'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class User\n currentMap = {}\n abstractTypes['User'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00017'] = currentMap\n loadMaps['ACCO.User'] = currentMap\n currentMap['tag'] = 'ACCO.User'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00017'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'users'\n currentMap['objkey'] = 'name'\n currentMap['class'] = memops.api.AccessControl.User\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute User.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute User.isSuperuser\n currentMap = {}\n contentMap['isSuperuser'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-05-06-13:30:17_00060'] = currentMap\n loadMaps['ACCO.User.isSuperuser'] = currentMap\n currentMap['tag'] = 'ACCO.User.isSuperuser'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-05-06-13:30:17_00060'\n currentMap['name'] = 'isSuperuser'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = False\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute User.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00019'] = currentMap\n loadMaps['ACCO.User.name'] = currentMap\n currentMap['tag'] = 'ACCO.User.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00019'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Attribute User.passwordHashed\n currentMap = {}\n contentMap['passwordHashed'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2009-08-19-17:31:11_00005'] = currentMap\n loadMaps['ACCO.User.passwordHashed'] = currentMap\n currentMap['tag'] = 'ACCO.User.passwordHashed'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2009-08-19-17:31:11_00005'\n currentMap['name'] = 'passwordHashed'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['proc'] = 'direct'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00035')\n\n # Role User.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role User.ledGroups\n currentMap = {}\n contentMap['ledGroups'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00014'] = currentMap\n loadMaps['ACCO.User.ledGroups'] = currentMap\n currentMap['tag'] = 'ACCO.User.ledGroups'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00014'\n currentMap['name'] = 'ledGroups'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n\n # Role User.userGroups\n currentMap = {}\n contentMap['userGroups'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00012'] = currentMap\n loadMaps['ACCO.User.userGroups'] = currentMap\n currentMap['tag'] = 'ACCO.User.userGroups'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00012'\n currentMap['name'] = 'userGroups'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n # End of User\n\n currentMap = abstractTypes.get('User')\n aList = ['isSuperuser']\n currentMap['headerAttrs'] = aList\n aList = ['name', 'passwordHashed', 'ledGroups', 'userGroups']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class UserGroup\n currentMap = {}\n abstractTypes['UserGroup'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00016'] = currentMap\n loadMaps['ACCO.UserGroup'] = currentMap\n currentMap['tag'] = 'ACCO.UserGroup'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00016'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'userGroups'\n currentMap['objkey'] = 'name'\n currentMap['class'] = memops.api.AccessControl.UserGroup\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute UserGroup.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute UserGroup.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00018'] = currentMap\n loadMaps['ACCO.UserGroup.name'] = currentMap\n currentMap['tag'] = 'ACCO.UserGroup.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00018'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Role UserGroup.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role UserGroup.leaders\n currentMap = {}\n contentMap['leaders'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00015'] = currentMap\n loadMaps['ACCO.UserGroup.leaders'] = currentMap\n currentMap['tag'] = 'ACCO.UserGroup.leaders'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00015'\n currentMap['name'] = 'leaders'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n\n # Role UserGroup.members\n currentMap = {}\n contentMap['members'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00013'] = currentMap\n loadMaps['ACCO.UserGroup.members'] = currentMap\n currentMap['tag'] = 'ACCO.UserGroup.members'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00013'\n currentMap['name'] = 'members'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = False\n\n # Role UserGroup.permissions\n currentMap = {}\n contentMap['permissions'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00017'] = currentMap\n loadMaps['ACCO.UserGroup.permissions'] = currentMap\n currentMap['tag'] = 'ACCO.UserGroup.permissions'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-14:16:23_00017'\n currentMap['name'] = 'permissions'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = False\n # End of UserGroup\n\n currentMap = abstractTypes.get('UserGroup')\n aList = ['name', 'leaders', 'members', 'permissions']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Out-of-package link to AccessControlStore\n currentMap = {}\n exolinks['AccessControlStore'] = currentMap\n loadMaps['ACCO.exo-AccessControlStore'] = currentMap\n currentMap['tag'] = 'ACCO.exo-AccessControlStore'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:18:10_00001'\n currentMap['name'] = 'AccessControlStore'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = memops.api.AccessControl.AccessControlStore\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n\n # Out-of-package link to AccessObject\n currentMap = {}\n exolinks['AccessObject'] = currentMap\n loadMaps['ACCO.exo-AccessObject'] = currentMap\n currentMap['tag'] = 'ACCO.exo-AccessObject'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00014'\n currentMap['name'] = 'AccessObject'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = memops.api.AccessControl.AccessObject\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to Permission\n currentMap = {}\n exolinks['Permission'] = currentMap\n loadMaps['ACCO.exo-Permission'] = currentMap\n currentMap['tag'] = 'ACCO.exo-Permission'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00018'\n currentMap['name'] = 'Permission'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = memops.api.AccessControl.Permission\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n aList.append(globalMap.get('ACCO').get('exolinks'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\n\n # Out-of-package link to User\n currentMap = {}\n exolinks['User'] = currentMap\n loadMaps['ACCO.exo-User'] = currentMap\n currentMap['tag'] = 'ACCO.exo-User'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00017'\n currentMap['name'] = 'User'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = memops.api.AccessControl.User\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to UserGroup\n currentMap = {}\n exolinks['UserGroup'] = currentMap\n loadMaps['ACCO.exo-UserGroup'] = currentMap\n currentMap['tag'] = 'ACCO.exo-UserGroup'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00016'\n currentMap['name'] = 'UserGroup'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = memops.api.AccessControl.UserGroup\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def makeMapping(globalMap):\n \n from memops.xml.Implementation import bool2str, str2bool\n\n # Set up top level dictionaries\n loadMaps = globalMap.get('loadMaps')\n mapsByGuid = globalMap.get('mapsByGuid')\n\n abstractTypes = globalMap.get('ANAP').get('abstractTypes')\n exolinks = globalMap.get('ANAP').get('exolinks')\n\n # DataType GraphicsHandlerType\n currentMap = {}\n abstractTypes['GraphicsHandlerType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-03-11:26:03_00001'] = currentMap\n loadMaps['ANAP.GraphicsHandlerType'] = currentMap\n currentMap['tag'] = 'ANAP.GraphicsHandlerType'\n currentMap['type'] = 'simple'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-03-11:26:03_00001'\n currentMap['toStr'] = 'text'\n currentMap['cnvrt'] = 'text'\n\n # Class AnalysisProfile\n currentMap = {}\n abstractTypes['AnalysisProfile'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00004'] = currentMap\n loadMaps['ANAP.AnalysisProfile'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00004'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'analysisProfiles'\n currentMap['isTop'] = True\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.AnalysisProfile\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute AnalysisProfile.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute AnalysisProfile.bgColor\n currentMap = {}\n contentMap['bgColor'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00031'] = currentMap\n loadMaps['ANAP.AnalysisProfile.bgColor'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.bgColor'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00031'\n currentMap['name'] = 'bgColor'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['default'] = '#FFFFFF'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\n\n # Attribute AnalysisProfile.createdBy\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute AnalysisProfile.fgColor\n currentMap = {}\n contentMap['fgColor'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00032'] = currentMap\n loadMaps['ANAP.AnalysisProfile.fgColor'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.fgColor'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00032'\n currentMap['name'] = 'fgColor'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['default'] = '#000000'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\n\n # Attribute AnalysisProfile.font\n currentMap = {}\n contentMap['font'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00030'] = currentMap\n loadMaps['ANAP.AnalysisProfile.font'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.font'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00030'\n currentMap['name'] = 'font'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Attribute AnalysisProfile.graphicsHandler\n currentMap = {}\n contentMap['graphicsHandler'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00040'] = currentMap\n loadMaps['ANAP.AnalysisProfile.graphicsHandler'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.graphicsHandler'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00040'\n currentMap['name'] = 'graphicsHandler'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 'Tk'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-10-03-11:26:03_00001')\n\n # Attribute AnalysisProfile.guid\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\n\n # Attribute AnalysisProfile.isModifiable\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute AnalysisProfile.lastUnlockedBy\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute AnalysisProfile.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00029'] = currentMap\n loadMaps['ANAP.AnalysisProfile.name'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00029'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute AnalysisProfile.panView\n currentMap = {}\n contentMap['panView'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00036'] = currentMap\n loadMaps['ANAP.AnalysisProfile.panView'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.panView'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00036'\n currentMap['name'] = 'panView'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = True\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute AnalysisProfile.sendBugReports\n currentMap = {}\n contentMap['sendBugReports'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00004'] = currentMap\n loadMaps['ANAP.AnalysisProfile.sendBugReports'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.sendBugReports'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00004'\n currentMap['name'] = 'sendBugReports'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 'maybe'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2010-11-17-16:21:33_00001')\n\n # Attribute AnalysisProfile.transientDialogs\n currentMap = {}\n contentMap['transientDialogs'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00037'] = currentMap\n loadMaps['ANAP.AnalysisProfile.transientDialogs'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.transientDialogs'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00037'\n currentMap['name'] = 'transientDialogs'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = True\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute AnalysisProfile.transientWindows\n currentMap = {}\n contentMap['transientWindows'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00038'] = currentMap\n loadMaps['ANAP.AnalysisProfile.transientWindows'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.transientWindows'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00038'\n currentMap['name'] = 'transientWindows'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = False\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute AnalysisProfile.twoCharShortcuts\n currentMap = {}\n contentMap['twoCharShortcuts'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00039'] = currentMap\n loadMaps['ANAP.AnalysisProfile.twoCharShortcuts'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.twoCharShortcuts'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00039'\n currentMap['name'] = 'twoCharShortcuts'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = False\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute AnalysisProfile.useCrosshair\n currentMap = {}\n contentMap['useCrosshair'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00034'] = currentMap\n loadMaps['ANAP.AnalysisProfile.useCrosshair'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.useCrosshair'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00034'\n currentMap['name'] = 'useCrosshair'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = True\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute AnalysisProfile.useGlobalShortcuts\n currentMap = {}\n contentMap['useGlobalShortcuts'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00035'] = currentMap\n loadMaps['ANAP.AnalysisProfile.useGlobalShortcuts'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.useGlobalShortcuts'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00035'\n currentMap['name'] = 'useGlobalShortcuts'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['default'] = False\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute AnalysisProfile.userEmail\n currentMap = {}\n contentMap['userEmail'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00003'] = currentMap\n loadMaps['ANAP.AnalysisProfile.userEmail'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.userEmail'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00003'\n currentMap['name'] = 'userEmail'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\n\n # Attribute AnalysisProfile.userName\n currentMap = {}\n contentMap['userName'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00001'] = currentMap\n loadMaps['ANAP.AnalysisProfile.userName'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.userName'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00001'\n currentMap['name'] = 'userName'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Attribute AnalysisProfile.userOrganisation\n currentMap = {}\n contentMap['userOrganisation'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00002'] = currentMap\n loadMaps['ANAP.AnalysisProfile.userOrganisation'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.userOrganisation'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00002'\n currentMap['name'] = 'userOrganisation'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Attribute AnalysisProfile.webBrowser\n currentMap = {}\n contentMap['webBrowser'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00033'] = currentMap\n loadMaps['ANAP.AnalysisProfile.webBrowser'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.webBrowser'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00033'\n currentMap['name'] = 'webBrowser'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Role AnalysisProfile.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role AnalysisProfile.colorSchemes\n currentMap = {}\n contentMap['colorSchemes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00026'] = currentMap\n loadMaps['ANAP.AnalysisProfile.colorSchemes'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.colorSchemes'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00026'\n currentMap['name'] = 'colorSchemes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\n\n # Role AnalysisProfile.macros\n currentMap = {}\n contentMap['macros'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00022'] = currentMap\n loadMaps['ANAP.AnalysisProfile.macros'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.macros'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00022'\n currentMap['name'] = 'macros'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\n\n # Role AnalysisProfile.marksColor\n currentMap = {}\n contentMap['marksColor'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00028'] = currentMap\n loadMaps['ANAP.AnalysisProfile.marksColor'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.marksColor'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00028'\n currentMap['name'] = 'marksColor'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['implSkip'] = True\n currentMap['copyOverride'] = True\n\n # Role AnalysisProfile.refExpProfiles\n currentMap = {}\n contentMap['refExpProfiles'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00024'] = currentMap\n loadMaps['ANAP.AnalysisProfile.refExpProfiles'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.refExpProfiles'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00024'\n currentMap['name'] = 'refExpProfiles'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\n\n # Role AnalysisProfile.residueProfiles\n currentMap = {}\n contentMap['residueProfiles'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00020'] = currentMap\n loadMaps['ANAP.AnalysisProfile.residueProfiles'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.residueProfiles'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00020'\n currentMap['name'] = 'residueProfiles'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\n\n # Role AnalysisProfile.rulersColor\n currentMap = {}\n contentMap['rulersColor'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00027'] = currentMap\n loadMaps['ANAP.AnalysisProfile.rulersColor'] = currentMap\n currentMap['tag'] = 'ANAP.AnalysisProfile.rulersColor'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00027'\n currentMap['name'] = 'rulersColor'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['implSkip'] = True\n currentMap['copyOverride'] = True\n # End of AnalysisProfile\n\n currentMap = abstractTypes.get('AnalysisProfile')\n aList = ['createdBy', 'graphicsHandler', 'guid', 'isModifiable', 'lastUnlockedBy', 'name', 'panView', 'sendBugReports', 'transientDialogs', 'transientWindows', 'twoCharShortcuts', 'useCrosshair', 'useGlobalShortcuts', 'userEmail', 'webBrowser']\n currentMap['headerAttrs'] = aList\n aList = ['bgColor', 'fgColor', 'font', 'userName', 'userOrganisation', 'marksColor', 'rulersColor']\n currentMap['simpleAttrs'] = aList\n aList = ['residueProfiles', 'refExpProfiles', 'macros', 'colorSchemes', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['colorSchemes', 'macros', 'refExpProfiles', 'residueProfiles']\n currentMap['children'] = aList\n\n # Class ColorScheme\n currentMap = {}\n abstractTypes['ColorScheme'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00002'] = currentMap\n loadMaps['ANAP.ColorScheme'] = currentMap\n currentMap['tag'] = 'ANAP.ColorScheme'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00002'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'colorSchemes'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ColorScheme\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute ColorScheme.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute ColorScheme.colors\n currentMap = {}\n contentMap['colors'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00043'] = currentMap\n loadMaps['ANAP.ColorScheme.colors'] = currentMap\n currentMap['tag'] = 'ANAP.ColorScheme.colors'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00043'\n currentMap['name'] = 'colors'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\n\n # Attribute ColorScheme.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00007'] = currentMap\n loadMaps['ANAP.ColorScheme.name'] = currentMap\n currentMap['tag'] = 'ANAP.ColorScheme.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00007'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Role ColorScheme.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of ColorScheme\n\n currentMap = abstractTypes.get('ColorScheme')\n aList = ['colors', 'name']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class Macro\n currentMap = {}\n abstractTypes['Macro'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00001'] = currentMap\n loadMaps['ANAP.Macro'] = currentMap\n currentMap['tag'] = 'ANAP.Macro'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00001'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'macros'\n currentMap['objkey'] = 'serial'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.Macro\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute Macro.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute Macro.details\n currentMap = {}\n contentMap['details'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00005'] = currentMap\n loadMaps['ANAP.Macro.details'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.details'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00005'\n currentMap['name'] = 'details'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\n\n # Attribute Macro.function\n currentMap = {}\n contentMap['function'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00002'] = currentMap\n loadMaps['ANAP.Macro.function'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.function'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00002'\n currentMap['name'] = 'function'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute Macro.isInMenu\n currentMap = {}\n contentMap['isInMenu'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00005'] = currentMap\n loadMaps['ANAP.Macro.isInMenu'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.isInMenu'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00005'\n currentMap['name'] = 'isInMenu'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = False\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute Macro.isInMouseMenu\n currentMap = {}\n contentMap['isInMouseMenu'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00006'] = currentMap\n loadMaps['ANAP.Macro.isInMouseMenu'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.isInMouseMenu'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00006'\n currentMap['name'] = 'isInMouseMenu'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = False\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\n\n # Attribute Macro.module\n currentMap = {}\n contentMap['module'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00003'] = currentMap\n loadMaps['ANAP.Macro.module'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.module'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00003'\n currentMap['name'] = 'module'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute Macro.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:10_00001'] = currentMap\n loadMaps['ANAP.Macro.name'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:10_00001'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Attribute Macro.ordering\n currentMap = {}\n contentMap['ordering'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00004'] = currentMap\n loadMaps['ANAP.Macro.ordering'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.ordering'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00004'\n currentMap['name'] = 'ordering'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['proc'] = 'direct'\n currentMap['default'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\n\n # Attribute Macro.path\n currentMap = {}\n contentMap['path'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00001'] = currentMap\n loadMaps['ANAP.Macro.path'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.path'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00001'\n currentMap['name'] = 'path'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00003')\n\n # Attribute Macro.serial\n currentMap = {}\n contentMap['serial'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:09_00001'] = currentMap\n loadMaps['ANAP.Macro.serial'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.serial'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:09_00001'\n currentMap['name'] = 'serial'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\n\n # Attribute Macro.shortcut\n currentMap = {}\n contentMap['shortcut'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00006'] = currentMap\n loadMaps['ANAP.Macro.shortcut'] = currentMap\n currentMap['tag'] = 'ANAP.Macro.shortcut'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00006'\n currentMap['name'] = 'shortcut'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Role Macro.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of Macro\n\n currentMap = abstractTypes.get('Macro')\n aList = ['function', 'isInMenu', 'isInMouseMenu', 'module', 'ordering', 'serial', 'shortcut']\n currentMap['headerAttrs'] = aList\n aList = ['details', 'name', 'path']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class RefExpProfile\n currentMap = {}\n abstractTypes['RefExpProfile'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00006'] = currentMap\n loadMaps['ANAP.RefExpProfile'] = currentMap\n currentMap['tag'] = 'ANAP.RefExpProfile'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00006'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'refExpProfiles'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.RefExpProfile\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute RefExpProfile.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute RefExpProfile.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00046'] = currentMap\n loadMaps['ANAP.RefExpProfile.name'] = currentMap\n currentMap['tag'] = 'ANAP.RefExpProfile.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00046'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute RefExpProfile.peakSymbolColors\n currentMap = {}\n contentMap['peakSymbolColors'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00048'] = currentMap\n loadMaps['ANAP.RefExpProfile.peakSymbolColors'] = currentMap\n currentMap['tag'] = 'ANAP.RefExpProfile.peakSymbolColors'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00048'\n currentMap['name'] = 'peakSymbolColors'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\n\n # Attribute RefExpProfile.peakTextColors\n currentMap = {}\n contentMap['peakTextColors'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00049'] = currentMap\n loadMaps['ANAP.RefExpProfile.peakTextColors'] = currentMap\n currentMap['tag'] = 'ANAP.RefExpProfile.peakTextColors'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00049'\n currentMap['name'] = 'peakTextColors'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\n\n # Attribute RefExpProfile.refExpNames\n currentMap = {}\n contentMap['refExpNames'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00047'] = currentMap\n loadMaps['ANAP.RefExpProfile.refExpNames'] = currentMap\n currentMap['tag'] = 'ANAP.RefExpProfile.refExpNames'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00047'\n currentMap['name'] = 'refExpNames'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Role RefExpProfile.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role RefExpProfile.negColorSchemes\n currentMap = {}\n contentMap['negColorSchemes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00045'] = currentMap\n loadMaps['ANAP.RefExpProfile.negColorSchemes'] = currentMap\n currentMap['tag'] = 'ANAP.RefExpProfile.negColorSchemes'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00045'\n currentMap['name'] = 'negColorSchemes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n\n # Role RefExpProfile.posColorSchemes\n currentMap = {}\n contentMap['posColorSchemes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00041'] = currentMap\n loadMaps['ANAP.RefExpProfile.posColorSchemes'] = currentMap\n currentMap['tag'] = 'ANAP.RefExpProfile.posColorSchemes'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00041'\n currentMap['name'] = 'posColorSchemes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n # End of RefExpProfile\n\n currentMap = abstractTypes.get('RefExpProfile')\n aList = ['name']\n currentMap['headerAttrs'] = aList\n aList = ['peakSymbolColors', 'peakTextColors', 'refExpNames', 'negColorSchemes', 'posColorSchemes']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class ResidueProfile\n currentMap = {}\n abstractTypes['ResidueProfile'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00005'] = currentMap\n loadMaps['ANAP.ResidueProfile'] = currentMap\n currentMap['tag'] = 'ANAP.ResidueProfile'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00005'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'residueProfiles'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ResidueProfile\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute ResidueProfile.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute ResidueProfile.ccpCode\n currentMap = {}\n contentMap['ccpCode'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00051'] = currentMap\n loadMaps['ANAP.ResidueProfile.ccpCode'] = currentMap\n currentMap['tag'] = 'ANAP.ResidueProfile.ccpCode'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00051'\n currentMap['name'] = 'ccpCode'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\n\n # Attribute ResidueProfile.guiName\n currentMap = {}\n contentMap['guiName'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00052'] = currentMap\n loadMaps['ANAP.ResidueProfile.guiName'] = currentMap\n currentMap['tag'] = 'ANAP.ResidueProfile.guiName'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00052'\n currentMap['name'] = 'guiName'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\n\n # Attribute ResidueProfile.molType\n currentMap = {}\n contentMap['molType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00050'] = currentMap\n loadMaps['ANAP.ResidueProfile.molType'] = currentMap\n currentMap['tag'] = 'ANAP.ResidueProfile.molType'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00050'\n currentMap['name'] = 'molType'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024')\n\n # Role ResidueProfile.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of ResidueProfile\n\n currentMap = abstractTypes.get('ResidueProfile')\n aList = ['ccpCode', 'guiName', 'molType']\n currentMap['headerAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Out-of-package link to AnalysisProfile\n currentMap = {}\n exolinks['AnalysisProfile'] = currentMap\n loadMaps['ANAP.exo-AnalysisProfile'] = currentMap\n currentMap['tag'] = 'ANAP.exo-AnalysisProfile'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00004'\n currentMap['name'] = 'AnalysisProfile'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.AnalysisProfile\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n\n # Out-of-package link to ColorScheme\n currentMap = {}\n exolinks['ColorScheme'] = currentMap\n loadMaps['ANAP.exo-ColorScheme'] = currentMap\n currentMap['tag'] = 'ANAP.exo-ColorScheme'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00002'\n currentMap['name'] = 'ColorScheme'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ColorScheme\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to Macro\n currentMap = {}\n exolinks['Macro'] = currentMap\n loadMaps['ANAP.exo-Macro'] = currentMap\n currentMap['tag'] = 'ANAP.exo-Macro'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00001'\n currentMap['name'] = 'Macro'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.Macro\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\n\n # Out-of-package link to RefExpProfile\n currentMap = {}\n exolinks['RefExpProfile'] = currentMap\n loadMaps['ANAP.exo-RefExpProfile'] = currentMap\n currentMap['tag'] = 'ANAP.exo-RefExpProfile'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00006'\n currentMap['name'] = 'RefExpProfile'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.RefExpProfile\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\n\n # Out-of-package link to ResidueProfile\n currentMap = {}\n exolinks['ResidueProfile'] = currentMap\n loadMaps['ANAP.exo-ResidueProfile'] = currentMap\n currentMap['tag'] = 'ANAP.exo-ResidueProfile'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00005'\n currentMap['name'] = 'ResidueProfile'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ResidueProfile\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))","def makeMapping(globalMap):\n \n from memops.xml.Implementation import bool2str, str2bool\n\n # Set up top level dictionaries\n loadMaps = globalMap.get('loadMaps')\n mapsByGuid = globalMap.get('mapsByGuid')\n\n abstractTypes = globalMap.get('CCLB').get('abstractTypes')\n exolinks = globalMap.get('CCLB').get('exolinks')\n\n # Class AtomLabel\n currentMap = {}\n abstractTypes['AtomLabel'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00018'] = currentMap\n loadMaps['CCLB.AtomLabel'] = currentMap\n currentMap['tag'] = 'CCLB.AtomLabel'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00018'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'atomLabels'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.AtomLabel\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute AtomLabel.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute AtomLabel.isotopeCode\n currentMap = {}\n contentMap['isotopeCode'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00092'] = currentMap\n loadMaps['CCLB.AtomLabel.isotopeCode'] = currentMap\n currentMap['tag'] = 'CCLB.AtomLabel.isotopeCode'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00092'\n currentMap['name'] = 'isotopeCode'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute AtomLabel.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00003'] = currentMap\n loadMaps['CCLB.AtomLabel.name'] = currentMap\n currentMap['tag'] = 'CCLB.AtomLabel.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00003'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Attribute AtomLabel.subType\n currentMap = {}\n contentMap['subType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00004'] = currentMap\n loadMaps['CCLB.AtomLabel.subType'] = currentMap\n currentMap['tag'] = 'CCLB.AtomLabel.subType'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00004'\n currentMap['name'] = 'subType'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['proc'] = 'direct'\n currentMap['default'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\n\n # Attribute AtomLabel.weight\n currentMap = {}\n contentMap['weight'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00093'] = currentMap\n loadMaps['CCLB.AtomLabel.weight'] = currentMap\n currentMap['tag'] = 'CCLB.AtomLabel.weight'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00093'\n currentMap['name'] = 'weight'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 1.0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00009')\n\n # Role AtomLabel.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of AtomLabel\n\n currentMap = abstractTypes.get('AtomLabel')\n aList = ['isotopeCode', 'name', 'subType', 'weight']\n currentMap['headerAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class ChemCompLabel\n currentMap = {}\n abstractTypes['ChemCompLabel'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00014'] = currentMap\n loadMaps['CCLB.ChemCompLabel'] = currentMap\n currentMap['tag'] = 'CCLB.ChemCompLabel'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00014'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'chemCompLabels'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.ChemCompLabel\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute ChemCompLabel.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute ChemCompLabel.ccpCode\n currentMap = {}\n contentMap['ccpCode'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00073'] = currentMap\n loadMaps['CCLB.ChemCompLabel.ccpCode'] = currentMap\n currentMap['tag'] = 'CCLB.ChemCompLabel.ccpCode'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00073'\n currentMap['name'] = 'ccpCode'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\n\n # Attribute ChemCompLabel.molType\n currentMap = {}\n contentMap['molType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00072'] = currentMap\n loadMaps['CCLB.ChemCompLabel.molType'] = currentMap\n currentMap['tag'] = 'CCLB.ChemCompLabel.molType'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00072'\n currentMap['name'] = 'molType'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024')\n\n # Role ChemCompLabel.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role ChemCompLabel.isotopomers\n currentMap = {}\n contentMap['isotopomers'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:13_00001'] = currentMap\n loadMaps['CCLB.ChemCompLabel.isotopomers'] = currentMap\n currentMap['tag'] = 'CCLB.ChemCompLabel.isotopomers'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:13_00001'\n currentMap['name'] = 'isotopomers'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['content'] = globalMap.get('CCLB').get('abstractTypes')\n # End of ChemCompLabel\n\n currentMap = abstractTypes.get('ChemCompLabel')\n aList = ['ccpCode', 'molType']\n currentMap['headerAttrs'] = aList\n aList = ['isotopomers', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['isotopomers']\n currentMap['children'] = aList\n\n # Class Isotopomer\n currentMap = {}\n abstractTypes['Isotopomer'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:28:54_00001'] = currentMap\n loadMaps['CCLB.Isotopomer'] = currentMap\n currentMap['tag'] = 'CCLB.Isotopomer'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:28:54_00001'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'isotopomers'\n currentMap['objkey'] = 'serial'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.Isotopomer\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute Isotopomer.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute Isotopomer.serial\n currentMap = {}\n contentMap['serial'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00005'] = currentMap\n loadMaps['CCLB.Isotopomer.serial'] = currentMap\n currentMap['tag'] = 'CCLB.Isotopomer.serial'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00005'\n currentMap['name'] = 'serial'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\n\n # Attribute Isotopomer.weight\n currentMap = {}\n contentMap['weight'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00006'] = currentMap\n loadMaps['CCLB.Isotopomer.weight'] = currentMap\n currentMap['tag'] = 'CCLB.Isotopomer.weight'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00006'\n currentMap['name'] = 'weight'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 1.0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00009')\n\n # Role Isotopomer.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role Isotopomer.atomLabels\n currentMap = {}\n contentMap['atomLabels'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00001'] = currentMap\n loadMaps['CCLB.Isotopomer.atomLabels'] = currentMap\n currentMap['tag'] = 'CCLB.Isotopomer.atomLabels'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00001'\n currentMap['name'] = 'atomLabels'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['content'] = globalMap.get('CCLB').get('abstractTypes')\n # End of Isotopomer\n\n currentMap = abstractTypes.get('Isotopomer')\n aList = ['serial', 'weight']\n currentMap['headerAttrs'] = aList\n aList = ['atomLabels', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['atomLabels']\n currentMap['children'] = aList\n\n # Class LabelingScheme\n currentMap = {}\n abstractTypes['LabelingScheme'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:14_00001'] = currentMap\n loadMaps['CCLB.LabelingScheme'] = currentMap\n currentMap['tag'] = 'CCLB.LabelingScheme'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:14_00001'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'labelingSchemes'\n currentMap['isTop'] = True\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.LabelingScheme\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute LabelingScheme.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute LabelingScheme.createdBy\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute LabelingScheme.details\n currentMap = {}\n contentMap['details'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00005'] = currentMap\n loadMaps['CCLB.LabelingScheme.details'] = currentMap\n currentMap['tag'] = 'CCLB.LabelingScheme.details'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00005'\n currentMap['name'] = 'details'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\n\n # Attribute LabelingScheme.guid\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\n\n # Attribute LabelingScheme.isModifiable\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute LabelingScheme.lastUnlockedBy\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute LabelingScheme.longName\n currentMap = {}\n contentMap['longName'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00004'] = currentMap\n loadMaps['CCLB.LabelingScheme.longName'] = currentMap\n currentMap['tag'] = 'CCLB.LabelingScheme.longName'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00004'\n currentMap['name'] = 'longName'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Attribute LabelingScheme.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00003'] = currentMap\n loadMaps['CCLB.LabelingScheme.name'] = currentMap\n currentMap['tag'] = 'CCLB.LabelingScheme.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00003'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Role LabelingScheme.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role LabelingScheme.chemCompLabels\n currentMap = {}\n contentMap['chemCompLabels'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00002'] = currentMap\n loadMaps['CCLB.LabelingScheme.chemCompLabels'] = currentMap\n currentMap['tag'] = 'CCLB.LabelingScheme.chemCompLabels'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00002'\n currentMap['name'] = 'chemCompLabels'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CCLB').get('abstractTypes')\n # End of LabelingScheme\n\n currentMap = abstractTypes.get('LabelingScheme')\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy', 'name']\n currentMap['headerAttrs'] = aList\n aList = ['details', 'longName']\n currentMap['simpleAttrs'] = aList\n aList = ['chemCompLabels', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['chemCompLabels']\n currentMap['children'] = aList\n\n # Out-of-package link to AtomLabel\n currentMap = {}\n exolinks['AtomLabel'] = currentMap\n loadMaps['CCLB.exo-AtomLabel'] = currentMap\n currentMap['tag'] = 'CCLB.exo-AtomLabel'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00018'\n currentMap['name'] = 'AtomLabel'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.AtomLabel\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\n\n # Out-of-package link to ChemCompLabel\n currentMap = {}\n exolinks['ChemCompLabel'] = currentMap\n loadMaps['CCLB.exo-ChemCompLabel'] = currentMap\n currentMap['tag'] = 'CCLB.exo-ChemCompLabel'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00014'\n currentMap['name'] = 'ChemCompLabel'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.ChemCompLabel\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))\n\n # Out-of-package link to Isotopomer\n currentMap = {}\n exolinks['Isotopomer'] = currentMap\n loadMaps['CCLB.exo-Isotopomer'] = currentMap\n currentMap['tag'] = 'CCLB.exo-Isotopomer'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:28:54_00001'\n currentMap['name'] = 'Isotopomer'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.Isotopomer\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\n\n # Out-of-package link to LabelingScheme\n currentMap = {}\n exolinks['LabelingScheme'] = currentMap\n loadMaps['CCLB.exo-LabelingScheme'] = currentMap\n currentMap['tag'] = 'CCLB.exo-LabelingScheme'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:14_00001'\n currentMap['name'] = 'LabelingScheme'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.LabelingScheme\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))","def makeMapping(globalMap):\n \n from memops.xml.Implementation import bool2str, str2bool\n\n # Set up top level dictionaries\n loadMaps = globalMap.get('loadMaps')\n mapsByGuid = globalMap.get('mapsByGuid')\n\n abstractTypes = globalMap.get('CLAS').get('abstractTypes')\n exolinks = globalMap.get('CLAS').get('exolinks')\n\n # Class AbstractCategory\n currentMap = {}\n abstractTypes['AbstractCategory'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:19:17_00001'] = currentMap\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:19:17_00001'\n currentMap['eType'] = 'cplx'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.AbstractCategory\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute AbstractCategory.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute AbstractCategory.details\n currentMap = {}\n contentMap['details'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001'] = currentMap\n loadMaps['CLAS.AbstractCategory.details'] = currentMap\n currentMap['tag'] = 'CLAS.AbstractCategory.details'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001'\n currentMap['name'] = 'details'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\n\n # Attribute AbstractCategory.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014'] = currentMap\n loadMaps['CLAS.AbstractCategory.name'] = currentMap\n currentMap['tag'] = 'CLAS.AbstractCategory.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Role AbstractCategory.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of AbstractCategory\n\n currentMap = abstractTypes.get('AbstractCategory')\n aList = ['details', 'name']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class Classification\n currentMap = {}\n abstractTypes['Classification'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:18:54_00002'] = currentMap\n loadMaps['CLAS.Classification'] = currentMap\n currentMap['tag'] = 'CLAS.Classification'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:18:54_00002'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'classifications'\n currentMap['isTop'] = True\n currentMap['objkey'] = 'namingSystem'\n currentMap['class'] = ccp.api.lims.Classification.Classification\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute Classification.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute Classification.createdBy\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute Classification.guid\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\n\n # Attribute Classification.isModifiable\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute Classification.lastUnlockedBy\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute Classification.namingSystem\n currentMap = {}\n contentMap['namingSystem'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00013'] = currentMap\n loadMaps['CLAS.Classification.namingSystem'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.namingSystem'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00013'\n currentMap['name'] = 'namingSystem'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Role Classification.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role Classification.experimentTypes\n currentMap = {}\n contentMap['experimentTypes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00002'] = currentMap\n loadMaps['CLAS.Classification.experimentTypes'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.experimentTypes'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00002'\n currentMap['name'] = 'experimentTypes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n\n # Role Classification.hazardPhrases\n currentMap = {}\n contentMap['hazardPhrases'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00006'] = currentMap\n loadMaps['CLAS.Classification.hazardPhrases'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.hazardPhrases'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00006'\n currentMap['name'] = 'hazardPhrases'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n\n # Role Classification.holderCategorys\n currentMap = {}\n contentMap['holderCategorys'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:33:23_00002'] = currentMap\n loadMaps['CLAS.Classification.holderCategorys'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.holderCategorys'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:33:23_00002'\n currentMap['name'] = 'holderCategorys'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n\n # Role Classification.instrumentTypes\n currentMap = {}\n contentMap['instrumentTypes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:33_00001'] = currentMap\n loadMaps['CLAS.Classification.instrumentTypes'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.instrumentTypes'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:33_00001'\n currentMap['name'] = 'instrumentTypes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n\n # Role Classification.sampleCategories\n currentMap = {}\n contentMap['sampleCategories'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00008'] = currentMap\n loadMaps['CLAS.Classification.sampleCategories'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.sampleCategories'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00008'\n currentMap['name'] = 'sampleCategories'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n\n # Role Classification.sampleComponentCategory\n currentMap = {}\n contentMap['sampleComponentCategory'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00004'] = currentMap\n loadMaps['CLAS.Classification.sampleComponentCategory'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.sampleComponentCategory'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00004'\n currentMap['name'] = 'sampleComponentCategory'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n\n # Role Classification.targetScoreboards\n currentMap = {}\n contentMap['targetScoreboards'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00012'] = currentMap\n loadMaps['CLAS.Classification.targetScoreboards'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.targetScoreboards'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00012'\n currentMap['name'] = 'targetScoreboards'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n\n # Role Classification.targetStatus\n currentMap = {}\n contentMap['targetStatus'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00010'] = currentMap\n loadMaps['CLAS.Classification.targetStatus'] = currentMap\n currentMap['tag'] = 'CLAS.Classification.targetStatus'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00010'\n currentMap['name'] = 'targetStatus'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\n # End of Classification\n\n currentMap = abstractTypes.get('Classification')\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy']\n currentMap['headerAttrs'] = aList\n aList = ['namingSystem']\n currentMap['simpleAttrs'] = aList\n aList = ['targetStatus', 'targetScoreboards', 'sampleComponentCategory', 'sampleCategories', 'instrumentTypes', 'holderCategorys', 'hazardPhrases', 'experimentTypes', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['experimentTypes', 'hazardPhrases', 'holderCategorys', 'instrumentTypes', 'sampleCategories', 'sampleComponentCategory', 'targetScoreboards', 'targetStatus']\n currentMap['children'] = aList\n\n # Class SampleComponentCategory\n currentMap = {}\n abstractTypes['SampleComponentCategory'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00014'] = currentMap\n loadMaps['CLAS.SampleComponentCategory'] = currentMap\n currentMap['tag'] = 'CLAS.SampleComponentCategory'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00014'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'sampleComponentCategory'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.SampleComponentCategory\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute SampleComponentCategory.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute SampleComponentCategory.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute SampleComponentCategory.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Role SampleComponentCategory.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of SampleComponentCategory\n\n currentMap = abstractTypes.get('SampleComponentCategory')\n aList = ['details', 'name']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class ExperimentType\n currentMap = {}\n abstractTypes['ExperimentType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:45_00014'] = currentMap\n loadMaps['CLAS.ExperimentType'] = currentMap\n currentMap['tag'] = 'CLAS.ExperimentType'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:45_00014'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'experimentTypes'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.ExperimentType\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute ExperimentType.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute ExperimentType.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute ExperimentType.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Role ExperimentType.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role ExperimentType.instrumentTypes\n currentMap = {}\n contentMap['instrumentTypes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00002'] = currentMap\n loadMaps['CLAS.ExperimentType.instrumentTypes'] = currentMap\n currentMap['tag'] = 'CLAS.ExperimentType.instrumentTypes'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00002'\n currentMap['name'] = 'instrumentTypes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n\n # Role ExperimentType.sampleCategories\n currentMap = {}\n contentMap['sampleCategories'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00031'] = currentMap\n loadMaps['CLAS.ExperimentType.sampleCategories'] = currentMap\n currentMap['tag'] = 'CLAS.ExperimentType.sampleCategories'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00031'\n currentMap['name'] = 'sampleCategories'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n # End of ExperimentType\n\n currentMap = abstractTypes.get('ExperimentType')\n aList = ['details', 'name', 'instrumentTypes', 'sampleCategories']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class TargetScoreboard\n currentMap = {}\n abstractTypes['TargetScoreboard'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00040'] = currentMap\n loadMaps['CLAS.TargetScoreboard'] = currentMap\n currentMap['tag'] = 'CLAS.TargetScoreboard'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00040'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'targetScoreboards'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.TargetScoreboard\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute TargetScoreboard.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute TargetScoreboard.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute TargetScoreboard.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Role TargetScoreboard.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role TargetScoreboard.targetStatus\n currentMap = {}\n contentMap['targetStatus'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00039'] = currentMap\n loadMaps['CLAS.TargetScoreboard.targetStatus'] = currentMap\n currentMap['tag'] = 'CLAS.TargetScoreboard.targetStatus'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00039'\n currentMap['name'] = 'targetStatus'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n # End of TargetScoreboard\n\n currentMap = abstractTypes.get('TargetScoreboard')\n aList = ['details', 'name', 'targetStatus']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class HolderCategory\n currentMap = {}\n abstractTypes['HolderCategory'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00049'] = currentMap\n loadMaps['CLAS.HolderCategory'] = currentMap\n currentMap['tag'] = 'CLAS.HolderCategory'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00049'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'holderCategorys'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.HolderCategory\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute HolderCategory.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute HolderCategory.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute HolderCategory.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Role HolderCategory.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of HolderCategory\n\n currentMap = abstractTypes.get('HolderCategory')\n aList = ['details', 'name']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class HazardPhrase\n currentMap = {}\n abstractTypes['HazardPhrase'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00052'] = currentMap\n loadMaps['CLAS.HazardPhrase'] = currentMap\n currentMap['tag'] = 'CLAS.HazardPhrase'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00052'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'hazardPhrases'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.HazardPhrase\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute HazardPhrase.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute HazardPhrase.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute HazardPhrase.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Attribute HazardPhrase.phrase\n currentMap = {}\n contentMap['phrase'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:28_00017'] = currentMap\n loadMaps['CLAS.HazardPhrase.phrase'] = currentMap\n currentMap['tag'] = 'CLAS.HazardPhrase.phrase'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:28_00017'\n currentMap['name'] = 'phrase'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\n\n # Role HazardPhrase.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of HazardPhrase\n\n currentMap = abstractTypes.get('HazardPhrase')\n aList = ['details', 'name', 'phrase']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class InstrumentType\n currentMap = {}\n abstractTypes['InstrumentType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:46_00005'] = currentMap\n loadMaps['CLAS.InstrumentType'] = currentMap\n currentMap['tag'] = 'CLAS.InstrumentType'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:46_00005'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'instrumentTypes'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.InstrumentType\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute InstrumentType.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute InstrumentType.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute InstrumentType.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Role InstrumentType.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role InstrumentType.experimentTypes\n currentMap = {}\n contentMap['experimentTypes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00001'] = currentMap\n loadMaps['CLAS.InstrumentType.experimentTypes'] = currentMap\n currentMap['tag'] = 'CLAS.InstrumentType.experimentTypes'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00001'\n currentMap['name'] = 'experimentTypes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n # End of InstrumentType\n\n currentMap = abstractTypes.get('InstrumentType')\n aList = ['details', 'name', 'experimentTypes']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class SampleCategory\n currentMap = {}\n abstractTypes['SampleCategory'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:44_00009'] = currentMap\n loadMaps['CLAS.SampleCategory'] = currentMap\n currentMap['tag'] = 'CLAS.SampleCategory'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:44_00009'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'sampleCategories'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.SampleCategory\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute SampleCategory.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute SampleCategory.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute SampleCategory.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Role SampleCategory.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role SampleCategory.experimentTypes\n currentMap = {}\n contentMap['experimentTypes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00030'] = currentMap\n loadMaps['CLAS.SampleCategory.experimentTypes'] = currentMap\n currentMap['tag'] = 'CLAS.SampleCategory.experimentTypes'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00030'\n currentMap['name'] = 'experimentTypes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n # End of SampleCategory\n\n currentMap = abstractTypes.get('SampleCategory')\n aList = ['details', 'name', 'experimentTypes']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Class TargetStatus\n currentMap = {}\n abstractTypes['TargetStatus'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00039'] = currentMap\n loadMaps['CLAS.TargetStatus'] = currentMap\n currentMap['tag'] = 'CLAS.TargetStatus'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00039'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'targetStatus'\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.lims.Classification.TargetStatus\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute TargetStatus.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute TargetStatus.details\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\n\n # Attribute TargetStatus.name\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\n\n # Role TargetStatus.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role TargetStatus.targetScoreboards\n currentMap = {}\n contentMap['targetScoreboards'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00040'] = currentMap\n loadMaps['CLAS.TargetStatus.targetScoreboards'] = currentMap\n currentMap['tag'] = 'CLAS.TargetStatus.targetScoreboards'\n currentMap['type'] = 'link'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00040'\n currentMap['name'] = 'targetScoreboards'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['copyOverride'] = True\n # End of TargetStatus\n\n currentMap = abstractTypes.get('TargetStatus')\n aList = ['details', 'name', 'targetScoreboards']\n currentMap['simpleAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Out-of-package link to Classification\n currentMap = {}\n exolinks['Classification'] = currentMap\n loadMaps['CLAS.exo-Classification'] = currentMap\n currentMap['tag'] = 'CLAS.exo-Classification'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:18:54_00002'\n currentMap['name'] = 'Classification'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.Classification\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n\n # Out-of-package link to SampleComponentCategory\n currentMap = {}\n exolinks['SampleComponentCategory'] = currentMap\n loadMaps['CLAS.exo-SampleComponentCategory'] = currentMap\n currentMap['tag'] = 'CLAS.exo-SampleComponentCategory'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00014'\n currentMap['name'] = 'SampleComponentCategory'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.SampleComponentCategory\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to ExperimentType\n currentMap = {}\n exolinks['ExperimentType'] = currentMap\n loadMaps['CLAS.exo-ExperimentType'] = currentMap\n currentMap['tag'] = 'CLAS.exo-ExperimentType'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:45_00014'\n currentMap['name'] = 'ExperimentType'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.ExperimentType\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to TargetScoreboard\n currentMap = {}\n exolinks['TargetScoreboard'] = currentMap\n loadMaps['CLAS.exo-TargetScoreboard'] = currentMap\n currentMap['tag'] = 'CLAS.exo-TargetScoreboard'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00040'\n currentMap['name'] = 'TargetScoreboard'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.TargetScoreboard\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to HolderCategory\n currentMap = {}\n exolinks['HolderCategory'] = currentMap\n loadMaps['CLAS.exo-HolderCategory'] = currentMap\n currentMap['tag'] = 'CLAS.exo-HolderCategory'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00049'\n currentMap['name'] = 'HolderCategory'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.HolderCategory\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to HazardPhrase\n currentMap = {}\n exolinks['HazardPhrase'] = currentMap\n loadMaps['CLAS.exo-HazardPhrase'] = currentMap\n currentMap['tag'] = 'CLAS.exo-HazardPhrase'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00052'\n currentMap['name'] = 'HazardPhrase'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.HazardPhrase\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to InstrumentType\n currentMap = {}\n exolinks['InstrumentType'] = currentMap\n loadMaps['CLAS.exo-InstrumentType'] = currentMap\n currentMap['tag'] = 'CLAS.exo-InstrumentType'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:46_00005'\n currentMap['name'] = 'InstrumentType'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.InstrumentType\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to SampleCategory\n currentMap = {}\n exolinks['SampleCategory'] = currentMap\n loadMaps['CLAS.exo-SampleCategory'] = currentMap\n currentMap['tag'] = 'CLAS.exo-SampleCategory'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:44_00009'\n currentMap['name'] = 'SampleCategory'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.SampleCategory\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\n\n # Out-of-package link to TargetStatus\n currentMap = {}\n exolinks['TargetStatus'] = currentMap\n loadMaps['CLAS.exo-TargetStatus'] = currentMap\n currentMap['tag'] = 'CLAS.exo-TargetStatus'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00039'\n currentMap['name'] = 'TargetStatus'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.lims.Classification.TargetStatus\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))","def makeMapping(globalMap):\n \n from memops.xml.Implementation import bool2str, str2bool\n\n # Set up top level dictionaries\n loadMaps = globalMap.get('loadMaps')\n mapsByGuid = globalMap.get('mapsByGuid')\n\n abstractTypes = globalMap.get('CHEL').get('abstractTypes')\n exolinks = globalMap.get('CHEL').get('exolinks')\n\n # DataType HalfLifeType\n currentMap = {}\n abstractTypes['HalfLifeType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:10_00002'] = currentMap\n loadMaps['CHEL.HalfLifeType'] = currentMap\n currentMap['tag'] = 'CHEL.HalfLifeType'\n currentMap['type'] = 'simple'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:10_00002'\n currentMap['toStr'] = 'text'\n currentMap['cnvrt'] = 'text'\n\n # Class ChemElement\n currentMap = {}\n abstractTypes['ChemElement'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00004'] = currentMap\n loadMaps['CHEL.ChemElement'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00004'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'chemElements'\n currentMap['objkey'] = 'symbol'\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElement\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute ChemElement.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute ChemElement.atomNumber\n currentMap = {}\n contentMap['atomNumber'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00017'] = currentMap\n loadMaps['CHEL.ChemElement.atomNumber'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement.atomNumber'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00017'\n currentMap['name'] = 'atomNumber'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['proc'] = 'direct'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\n\n # Attribute ChemElement.atomicRadius\n currentMap = {}\n contentMap['atomicRadius'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00018'] = currentMap\n loadMaps['CHEL.ChemElement.atomicRadius'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement.atomicRadius'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00018'\n currentMap['name'] = 'atomicRadius'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute ChemElement.covalentRadius\n currentMap = {}\n contentMap['covalentRadius'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00019'] = currentMap\n loadMaps['CHEL.ChemElement.covalentRadius'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement.covalentRadius'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00019'\n currentMap['name'] = 'covalentRadius'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute ChemElement.mass\n currentMap = {}\n contentMap['mass'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00020'] = currentMap\n loadMaps['CHEL.ChemElement.mass'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement.mass'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00020'\n currentMap['name'] = 'mass'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute ChemElement.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00021'] = currentMap\n loadMaps['CHEL.ChemElement.name'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00021'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055')\n\n # Attribute ChemElement.symbol\n currentMap = {}\n contentMap['symbol'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00015'] = currentMap\n loadMaps['CHEL.ChemElement.symbol'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement.symbol'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00015'\n currentMap['name'] = 'symbol'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055')\n\n # Role ChemElement.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role ChemElement.isotopes\n currentMap = {}\n contentMap['isotopes'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00004'] = currentMap\n loadMaps['CHEL.ChemElement.isotopes'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElement.isotopes'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00004'\n currentMap['name'] = 'isotopes'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['content'] = globalMap.get('CHEL').get('abstractTypes')\n # End of ChemElement\n\n currentMap = abstractTypes.get('ChemElement')\n aList = ['atomNumber', 'atomicRadius', 'covalentRadius', 'mass', 'name', 'symbol']\n currentMap['headerAttrs'] = aList\n aList = ['isotopes', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['isotopes']\n currentMap['children'] = aList\n\n # Class ChemElementStore\n currentMap = {}\n abstractTypes['ChemElementStore'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00005'] = currentMap\n loadMaps['CHEL.ChemElementStore'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElementStore'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00005'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'chemElementStores'\n currentMap['isTop'] = True\n currentMap['objkey'] = 'name'\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElementStore\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute ChemElementStore.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute ChemElementStore.createdBy\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute ChemElementStore.guid\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\n\n # Attribute ChemElementStore.isModifiable\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute ChemElementStore.lastUnlockedBy\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\n\n # Attribute ChemElementStore.name\n currentMap = {}\n contentMap['name'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00024'] = currentMap\n loadMaps['CHEL.ChemElementStore.name'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElementStore.name'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00024'\n currentMap['name'] = 'name'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['eType'] = 'cplx'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\n\n # Role ChemElementStore.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n\n # Role ChemElementStore.chemElements\n currentMap = {}\n contentMap['chemElements'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00023'] = currentMap\n loadMaps['CHEL.ChemElementStore.chemElements'] = currentMap\n currentMap['tag'] = 'CHEL.ChemElementStore.chemElements'\n currentMap['type'] = 'child'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00023'\n currentMap['name'] = 'chemElements'\n currentMap['hicard'] = -1\n currentMap['locard'] = 0\n currentMap['eType'] = 'cplx'\n currentMap['implSkip'] = True\n currentMap['content'] = globalMap.get('CHEL').get('abstractTypes')\n # End of ChemElementStore\n\n currentMap = abstractTypes.get('ChemElementStore')\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy']\n currentMap['headerAttrs'] = aList\n aList = ['name']\n currentMap['simpleAttrs'] = aList\n aList = ['chemElements', 'access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n aList = ['chemElements']\n currentMap['children'] = aList\n\n # Class Isotope\n currentMap = {}\n abstractTypes['Isotope'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00003'] = currentMap\n loadMaps['CHEL.Isotope'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope'\n currentMap['type'] = 'class'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00003'\n currentMap['eType'] = 'cplx'\n currentMap['fromParent'] = 'isotopes'\n currentMap['objkey'] = 'massNumber'\n currentMap['class'] = ccp.api.molecule.ChemElement.Isotope\n contentMap = {}\n currentMap['content'] = contentMap\n\n # Attribute Isotope.abundance\n currentMap = {}\n contentMap['abundance'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00011'] = currentMap\n loadMaps['CHEL.Isotope.abundance'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.abundance'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00011'\n currentMap['name'] = 'abundance'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00058')\n\n # Attribute Isotope.applicationData\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\n\n # Attribute Isotope.gyroMagneticRatio\n currentMap = {}\n contentMap['gyroMagneticRatio'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00008'] = currentMap\n loadMaps['CHEL.Isotope.gyroMagneticRatio'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.gyroMagneticRatio'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00008'\n currentMap['name'] = 'gyroMagneticRatio'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute Isotope.halfLife\n currentMap = {}\n contentMap['halfLife'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00001'] = currentMap\n loadMaps['CHEL.Isotope.halfLife'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.halfLife'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00001'\n currentMap['name'] = 'halfLife'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00007')\n\n # Attribute Isotope.halfLifeError\n currentMap = {}\n contentMap['halfLifeError'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00002'] = currentMap\n loadMaps['CHEL.Isotope.halfLifeError'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.halfLifeError'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00002'\n currentMap['name'] = 'halfLifeError'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00007')\n\n # Attribute Isotope.halfLifeType\n currentMap = {}\n contentMap['halfLifeType'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00003'] = currentMap\n loadMaps['CHEL.Isotope.halfLifeType'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.halfLifeType'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00003'\n currentMap['name'] = 'halfLifeType'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['default'] = 'unknown'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-06-07-18:18:10_00002')\n\n # Attribute Isotope.magneticMoment\n currentMap = {}\n contentMap['magneticMoment'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00013'] = currentMap\n loadMaps['CHEL.Isotope.magneticMoment'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.magneticMoment'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00013'\n currentMap['name'] = 'magneticMoment'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute Isotope.mass\n currentMap = {}\n contentMap['mass'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00010'] = currentMap\n loadMaps['CHEL.Isotope.mass'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.mass'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00010'\n currentMap['name'] = 'mass'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute Isotope.massNumber\n currentMap = {}\n contentMap['massNumber'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00007'] = currentMap\n loadMaps['CHEL.Isotope.massNumber'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.massNumber'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00007'\n currentMap['name'] = 'massNumber'\n currentMap['hicard'] = 1\n currentMap['locard'] = 1\n currentMap['proc'] = 'direct'\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\n\n # Attribute Isotope.quadrupoleMoment\n currentMap = {}\n contentMap['quadrupoleMoment'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00014'] = currentMap\n loadMaps['CHEL.Isotope.quadrupoleMoment'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.quadrupoleMoment'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00014'\n currentMap['name'] = 'quadrupoleMoment'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute Isotope.receptivity\n currentMap = {}\n contentMap['receptivity'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00012'] = currentMap\n loadMaps['CHEL.Isotope.receptivity'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.receptivity'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00012'\n currentMap['name'] = 'receptivity'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\n\n # Attribute Isotope.spin\n currentMap = {}\n contentMap['spin'] = currentMap\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00009'] = currentMap\n loadMaps['CHEL.Isotope.spin'] = currentMap\n currentMap['tag'] = 'CHEL.Isotope.spin'\n currentMap['type'] = 'attr'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00009'\n currentMap['name'] = 'spin'\n currentMap['hicard'] = 1\n currentMap['locard'] = 0\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\n\n # Role Isotope.access\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\n # End of Isotope\n\n currentMap = abstractTypes.get('Isotope')\n aList = ['abundance', 'gyroMagneticRatio', 'halfLife', 'halfLifeError', 'halfLifeType', 'magneticMoment', 'mass', 'massNumber', 'quadrupoleMoment', 'receptivity', 'spin']\n currentMap['headerAttrs'] = aList\n aList = ['access', 'applicationData']\n currentMap['cplxAttrs'] = aList\n\n # Out-of-package link to ChemElement\n currentMap = {}\n exolinks['ChemElement'] = currentMap\n loadMaps['CHEL.exo-ChemElement'] = currentMap\n currentMap['tag'] = 'CHEL.exo-ChemElement'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00004'\n currentMap['name'] = 'ChemElement'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElement\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055'))\n\n # Out-of-package link to ChemElementStore\n currentMap = {}\n exolinks['ChemElementStore'] = currentMap\n loadMaps['CHEL.exo-ChemElementStore'] = currentMap\n currentMap['tag'] = 'CHEL.exo-ChemElementStore'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00005'\n currentMap['name'] = 'ChemElementStore'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElementStore\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n\n # Out-of-package link to Isotope\n currentMap = {}\n exolinks['Isotope'] = currentMap\n loadMaps['CHEL.exo-Isotope'] = currentMap\n currentMap['tag'] = 'CHEL.exo-Isotope'\n currentMap['type'] = 'exo'\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00003'\n currentMap['name'] = 'Isotope'\n currentMap['eType'] = 'cplx'\n currentMap['class'] = ccp.api.molecule.ChemElement.Isotope\n aList = list()\n currentMap['keyMaps'] = aList\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055'))\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))","def _generate_objects_file(self):\n xmls = glob(f'{ROOT}/Annotations/**/*.xml', recursive=True)","def getXML(self):\n\n def _getElementForMappingEntry(entry, mappingStyle):\n xmlDocTmp = Document()\n element = xmlDocTmp.createElement(mappingStyle)\n for k, v in viewitems(entry):\n # ignore empty, None or compiled regexp items into output\n if not v or (k == \"path-match-expr\"):\n continue\n element.setAttribute(k, str(v))\n return element\n\n xmlDoc = Document()\n root = xmlDoc.createElement(\"storage-mapping\") # root element name\n for mappingStyle, mappings in viewitems(self):\n for mapping in mappings:\n mapElem = _getElementForMappingEntry(mapping, mappingStyle)\n root.appendChild(mapElem)\n return root.toprettyxml()","def _output_dict(self):\n lang = self.ddnGuiLanguage.get()\n\n fileout = os.path.normpath('{}/{}-{}.xml'.\\\n format(self.MapCreator, self.Source, self.ddnCurProject.get()))\n linesout = ['<?xml version=\"1.0\" encoding=\"UTF-8\"?>', \\\n '<DictionarySet xmlns:mc=\"urn:fmosoft-map-creator\" xmlns=\"urn:fmosoft-map-creator\" Version=\"1\">', \\\n ' <Dictionary SourceLanguage=\"{}\" SourceLanguageIsPredefined=\"true\" TargetLanguage=\"{}\" TargetLanguageIsPredefined=\"false\">'.\\\n format(self.Source, self.ddnCurProject.get()), \\\n ]\n for child in self.tree.get_children('approved'):\n vv = self.tree.item(child)['values']\n linesout.append(' <Translation Source=\"{}\" Target=\"{}\"/>'.format(vv[0], vv[1]))\n linesout.append(' </Dictionary>')\n linesout.append('</DictionarySet>')\n linesout.append('')\n\n if os.path.exists(fileout):\n os.remove(fileout)\n\n if fileout:\n output = codecs.open(fileout, mode='w', encoding='utf-8')\n output.write('\\n'.join(linesout))\n output.close()\n pass","def convert_poi(input, output, map_category=(), map_icon=(), ignore_tags=()):\n\n # some tools write v2, others v3. Use RegExp to find which\n rx = re.compile(\"xmlns:([^= ]+) *=['\\\"]([^'\\\"]+/GpxExtensions/[^'\\\"]+)\")\n m = None\n for line in input:\n m = rx.search(line)\n if m:\n break\n\n if m:\n gpxx_ns = m.group(1)\n gpxx_url = m.group(2)\n logging.info(\"Found XML Namespace %s=%s\", gpxx_ns, gpxx_url)\n else:\n gpxx_ns = \"gpxx\"\n gpxx_url = \"http://www.garmin.com/xmlschemas/GpxExtensions/v3\"\n logging.warning(\"No XML Namespace for GpxExtensions! Using %s=%s\",\n gpxx_ns, gpxx_url)\n\n ns = {\n \"xsi\": \"http://www.w3.org/2001/XMLSchema-instance\",\n \"gpx\": \"http://www.topografix.com/GPX/1/1\",\n \"gpxd\": \"http://www.daimler.com/DaimlerGPXExtensions/V2.7.2\",\n gpxx_ns: gpxx_url\n }\n for k, v in ns.items():\n ET.register_namespace(k, v)\n\n\n logging.info(\"Parsing %s\", input)\n input.seek(0)\n indoc = ET.parse(input)\n outroot = ET.Element(\"{http://www.topografix.com/GPX/1/1}gpx\", attrib={\n \"version\": \"1.1\",\n \"creator\": indoc.getroot().get(\"creator\",\n \"gpx-poi-garmin-to-mercedesbenz\"),\n \"{http://www.w3.org/2001/XMLSchema-instance}schemaLocation\": \"http://www.topografix.com/GPX/1/1 http://www.topografix.com/GPX/1/1/gpx.xsd\",\n })\n\n it = []\n for i in ignore_tags:\n if i.startswith(\"{\"):\n it.append(i)\n else:\n i = i.split(':', 1)\n if len(i) == 1:\n i = ('gpx', i[0])\n it_ns = ns.get(i[0], i[0])\n it_tag = i[1]\n it.append(\"{%s}%s\" % (it_ns, it_tag))\n\n ignore_tags = it\n\n logging.info(\"Converting...\")\n for wpt in indoc.findall(\"gpx:wpt\", ns):\n wpt_out = ET.Element(wpt.tag, attrib=wpt.attrib)\n link_href = \"\"\n for cel in wpt:\n if cel.tag == \"{http://www.topografix.com/GPX/1/1}link\":\n link_href = cel.get(\"href\", \"\")\n\n for i in ignore_tags:\n if cel.tag == i:\n break\n else:\n if cel.tag == \"{http://www.topografix.com/GPX/1/1}extensions\":\n _convert_wpt_extension(cel, wpt_out, ns, link_href,\n map_category, map_icon, ignore_tags)\n else:\n _copy_gpx_tags(cel, wpt_out)\n\n if len(wpt_out):\n outroot.append(wpt_out)\n\n if len(outroot) > 30000:\n logging.warning(\"Writing %d entries (>30000!) to %s\", len(outroot), output)\n else:\n logging.info(\"Writing %d entries to %s\", len(outroot), output)\n ET.ElementTree(outroot).write(output, encoding=\"utf-8\", xml_declaration=True)\n logging.info(\"Finished\")","def generate(self, api):\n for namespace in api.namespaces.values():\n # One module per namespace is created. The module takes the name\n # of the namespace.\n with self.output_to_relative_path('{}.py'.format(namespace.name)):\n self._generate_namespace_module(namespace)","def map_doc():\n doc = DesignSpaceDocument()\n doc.addAxis(\n AxisDescriptor(\n tag=\"wght\",\n name=\"Weight\",\n minimum=100,\n maximum=900,\n default=100,\n map=[(100, 10), (900, 90)],\n )\n )\n doc.addAxis(\n AxisDescriptor(\n tag=\"wdth\",\n name=\"Width\",\n minimum=75,\n maximum=200,\n default=100,\n map=[(75, 7500), (100, 10000), (200, 20000)],\n )\n )\n doc.addAxis(\n AxisDescriptor(tag=\"CUST\", name=\"Custom\", minimum=1, maximum=2, default=1.5)\n )\n doc.addLocationLabel(\n LocationLabelDescriptor(\n name=\"Wonky\", userLocation={\"Weight\": 800, \"Custom\": 1.2}\n )\n )\n return doc","def resource_map(self):","def to_file_map(self, file_map=None):\n if file_map is None:\n file_map = self.file_map\n f = file_map['image'].get_prepare_fileobj('wb')\n f.write(self.to_xml())","def GenerateXML(dictionary, fileName=\"labelling.xml\") : \n root = gfg.Element(\"annotation\") \n #the big section is called Annotation\n for key in dictionary:\n #for every polygon list in inside object witho subelement name and attributes and the type \"polygon\"\n objectElement = gfg.Element(\"object\") \n root.append(objectElement) \n subElement1 = gfg.SubElement(objectElement, \"name:\".strip(\":\"))\n subElement1.text = str(dictionary[key][\"name\"])\n subElement2 = gfg.SubElement(objectElement, \"attributes\".strip(\":\"))\n subElement2.text = str(dictionary[key][\"attributes\"])\n subElement3 = gfg.SubElement(objectElement, \"polygon\")\n \n for i in range(0, len(dictionary[key])-2):\n #for every vertex of the polygon list it's rounded x, y on xml\n SubInsidePolygon = gfg.SubElement(subElement3, \"pt\")\n sub_x = gfg.SubElement(SubInsidePolygon, \"x\")\n sub_y = gfg.SubElement(SubInsidePolygon, \"y\")\n sub_x.text = str(int(round(dictionary[key][\"x_y_\" + str(i)][0])))\n sub_y.text = str(int(round(dictionary[key][\"x_y_\" + str(i)][1])))\n tree = gfg.ElementTree(root) \n #create the xml tree\n with open (fileName, \"wb\") as files : \n tree.write(files) \n #if xml does not exist create one otherwise rewrite to it","def export_representations(self):\n\n dbpath, config = self._start()\n self.logger.msg1(\"Loading ontology\")\n obo_path = check_file(config.obo, dbpath, \"obo\")\n self.obo = MinimalObo(obo_path, True)\n self._export_reference_representations()\n self._export_model_representations(config)\n self._end()","def _add_products_to_genmap(self, artifact, state):\n genmap = self.context.products.get('classes')\n for target, sources in artifact.sources_by_target.items():\n for source in sources:\n classes = state.classes_by_src.get(source, [])\n relsrc = os.path.relpath(source, target.target_base)\n genmap.add(relsrc, artifact.classes_dir, classes)\n genmap.add(target, artifact.classes_dir, classes)\n # TODO(John Sirois): Map target.resources in the same way\n # Create and Map scala plugin info files to the owning targets.\n if is_scalac_plugin(target) and target.classname:\n basedir, plugin_info_file = self._zinc_utils.write_plugin_info(self._resources_dir, target)\n genmap.add(target, basedir, [plugin_info_file])","def exportOrgs ( c ) :\n assert str(type(c)) == \"<type '_mysql.connection'>\"\n xml = \"\"\n o = sqlQuery ( c, \"select * from Organizations;\" )\n for i in o:\n oL = sqlQuery ( c, \"select * from OrganizationLocations where orgID = '\"+i[0]+\"';\" )\n oER = sqlQuery ( c, \"select * from OrganizationExternalResources where orgID = '\"+i[0]+\"';\" )\n oTC = sqlQuery ( c, \"select * from OrganizationsToCrises where orgID = '\"+i[0]+\"';\" )\n pTO = sqlQuery ( c, \"select * from PeopleToOrganizations where orgID = '\"+i[0]+\"';\" )\n xml += openTagAtt ( \"Organization\", \"organizationIdent\", i[0])\n xml += openCloseTag ( \"Name\", i[1])\n xml += closeTagAtt ( \"Kind\", \"organizationKindIdent\", i[2])\n for j in oL :\n xml += openTag ( \"Location\" )\n xml += openCloseTag ( \"Locality\", j [ 1 ] )\n xml += openCloseTag ( \"Region\", j [ 2 ] )\n xml += openCloseTag ( \"Country\", j [ 3 ] )\n xml += closeTag ( \"Location\" )\n xml += openCloseTag (\"History\", i[3])\n xml += openTag ( \"ContactInfo\" )\n xml += openCloseTag (\"Telephone\", i[4])\n xml += openCloseTag (\"Fax\", i[5])\n xml += openCloseTag (\"Email\", i[6])\n xml += openTag (\"PostalAddress\")\n xml += openCloseTag (\"StreetAddress\", i[7])\n xml += openCloseTag ( \"Locality\", i[8])\n xml += openCloseTag ( \"Region\", i[9])\n xml += openCloseTag ( \"PostalCode\", i[10])\n xml += openCloseTag ( \"Country\", i[11])\n xml += closeTag ( \"PostalAddress\" )\n xml += closeTag ( \"ContactInfo\" )\n xml += openTag (\"ExternalResources\")\n for j in oER:\n xml += openCloseTag ( j[1], j[2])\n xml += closeTag (\"ExternalResources\")\n xml += openTag (\"RelatedCrises\")\n for j in oTC:\n xml += closeTagAtt (\"RelatedCrisis\", \"crisisIdent\", j[1])\n xml += closeTag (\"RelatedCrises\")\n xml += openTag (\"RelatedPersons\")\n for j in pTO:\n xml += closeTagAtt (\"RelatedPerson\", \"personIdent\", j[0])\n xml += closeTag (\"RelatedPersons\")\n xml += closeTag (\"Organization\")\n assert str ( type ( xml ) ) == \"<type 'str'>\"\n return xml","def custom_package_xml_generator(directory, packagename=None, version='45.0', filename='package.xml'):\n\n METADATA_TYPE = {\n 'applications':'CustomApplication', 'aura':'AuraDefinitionBundle', 'classes':'ApexClass', 'customPermissions':'CustomPermission', \n 'flexipages':'FlexiPage', 'flows':'Flow', 'globalValueSets':'GlobalValueSet', 'labels':'CustomLabels', 'layouts':'Layout',\n 'lwc': 'LightningComponentBundle', 'objects':'CustomObject', 'pages':'ApexPage', 'permissionsets':'PermissionSet', 'profiles':'Profile',\n 'staticresources':'StaticResource', 'tabs':'CustomTab', 'triggers':'ApexTrigger', 'contentassets':'ContentAsset', 'pathAssistants':'PathAssistant',\n 'quickActions':'QuickAction', 'remoteSiteSettings':'RemoteSiteSetting', 'workflows':'Workflow', 'dashboards':'Dashboard', 'reports':'Report',\n 'cspTrustedSites':'CspTrustedSite',\n }\n\n \"\"\"\n Non-implemented Metadata:\n 'ApexComponent', 'CustomMetadata' (needs custom manipulation), 'CustomObjectTranslation', 'DuplicateRule', \n 'FlowCategory', 'GlobalValueSetTranslation', 'MatchingRules',\n \"\"\"\n #read directory structure\n\n mdtypedirs = os.listdir(directory)\n\n nested_mdt_object = ['ValidationRule', 'CompactLayout', 'ListView', 'SharingReason', 'RecordType']\n nested_mdt_workflow = ['WorkflowFieldUpdate', 'WorkflowKnowledgePublish', 'WorkflowTask', 'WorkflowAlert', 'WorkflowSend', 'WorkflowOutboundMessage', 'WorkflowRule']\n\n # start our xml structure\n root = xml.Element('Package')\n root.set('xmlns','http://soap.sforce.com/2006/04/metadata')\n\n for mdtype in mdtypedirs:\n # create child node for each type of component\n if mdtype in METADATA_TYPE.keys():\n etype = xml.SubElement(root, 'types')\n ename = xml.SubElement(etype, 'name')\n ename.text = str(METADATA_TYPE[mdtype])\n emember = xml.SubElement(etype, 'members')\n emember.text = str('*')\n \n if mdtype == 'objects':\n for nest_mdtyp in nested_mdt_object:\n etype = xml.SubElement(root, 'types')\n ename = xml.SubElement(etype, 'name')\n ename.text = nest_mdtyp\n emember = xml.SubElement(etype, 'members')\n emember.text = str('*')\n\n if mdtype == 'workflows':\n for nest_mdtyp in nested_mdt_workflow:\n etype = xml.SubElement(root, 'types')\n ename = xml.SubElement(etype, 'name')\n ename.text = nest_mdtyp\n emember = xml.SubElement(etype, 'members')\n emember.text = str('*')\n\n #Custom behavior for custom labels\n if mdtype == 'labels':\n etype = xml.SubElement(root, 'types')\n ename = xml.SubElement(etype, 'name')\n ename.text = 'CustomLabel'\n emember = xml.SubElement(etype, 'members')\n emember.text = str('*')\n\n # add the final xml node package.api_version\n eversion = xml.SubElement(root, 'version')\n eversion.text = str(version)\n\n #package name\n if packagename != None:\n efname = xml.SubElement(root, 'fullName')\n efname.text = str(packagename)\n\n #pretty format for xml\n xmlstring = xml.tostring(root)\n reparsed = minidom.parseString(xmlstring)\n prettyxml = reparsed.toprettyxml(indent=' ', newl='\\n', encoding='UTF-8')\n \n #generate xml file from string\n try:\n with open(os.path.join(directory, filename), \"bw\") as xml_file:\n xml_file.write(prettyxml)\n except IOError:\n pass","def open_output_files(self):\n if not os.path.exists(self.outputDictionaryPath):\n os.makedirs(self.outputDictionaryPath)\n\n self.XMLfile = open(os.path.join(self.outputDictionaryPath, 'MyDictionary.xml'), 'w+', encoding='utf-8') # this is the output file\n self.Makefile = open(os.path.join(self.outputDictionaryPath, 'Makefile'), 'w+', encoding='utf-8')\n self.MyInfoFile = open(os.path.join(self.outputDictionaryPath, 'MyInfo.plist'), 'w+', encoding='utf-8')","def _makeimap(self):\n self.map_['source'] = 'NAOJ'\n self.map_['provider'] = 'NRO'\n self.map_['instrument'] = 'NORH'\n self.map_['phyobs'] = ''","def _makeimap(self):\n self.map_['source'] = 'GOES'\n self.map_['provider'] = 'NOAA'\n self.map_['instrument'] = 'SUVI'\n self.map_['physobs'] = 'flux'","def write_maps(self):\n if np.allclose(self.xmap.origin, 0):\n ext = \"ccp4\"\n else:\n ext = \"mrc\"\n\n for q, coor, b in zip(self._occupancies, self._coor_set, self._bs):\n self.conformer.q = q\n self.conformer.coor = coor\n self.conformer.b = b\n self._transformer.density()\n fname = os.path.join(self.directory_name, f\"model.{ext}\")\n self._transformer.xmap.tofile(fname)\n self._transformer.xmap.array -= self.xmap.array\n fname = os.path.join(self.directory_name, f\"diff.{ext}\")\n self._transformer.xmap.tofile(fname)\n self._transformer.reset(full=True)","def aggregate_maps(self, G=[], node_tags={}):\n\n self.map = nx.DiGraph() \n \n # loop over graphs in list of graphs\n for g in G:\n # add nodes to graph\n self.map.add_nodes_from(g.nodes())\n # loop over edges \n for edge in g.edges(data=True):\n # if edge is already present, add weight to edge\n if self.map.has_edge(edge[0],edge[1]):\n self.map[edge[0]][edge[1]]['weight'] += edge[2]['weight']\n # if edge is not already present, add it with weight=1\n else:\n self.map.add_edge(edge[0],edge[1], weight=1)\n \n # relabel nodes according to mapping provided by 'node_tags'\n nx.set_node_attributes(self.map, name = 'node_label', values = node_tags) \n nx.relabel_nodes(self.map, mapping=node_tags, copy=False)\n\n # assign a random color to each node\n colour_list = np.random.choice( list(colors.get_named_colors_mapping().values()), len(self.map) )\n colour_dict = dict( zip(self.map.nodes, colour_list) )\n nx.set_node_attributes(self.map, name = 'node_color', values = colour_dict)\n\n # save node attributes to CoMap object\n self.node_labels = nx.get_node_attributes(self.map, name = 'node_label')\n self.node_colors = nx.get_node_attributes(self.map, name = 'node_color')\n \n return","def create_gen_xml(self, out_file):\n\n param_list = []\n msg = []\n msg_type = []\n dep_node = []\n for line in self.full_ed_lines:\n param_list.append(line.text())\n dep_pkg = param_list[6].split(', ')\n if dep_pkg[len(dep_pkg) - 1] == '':\n dep_pkg.pop()\n for dep in self.manager.wid.sub_list:\n dep_node.append(dep['msg_type'])\n for dep in self.manager.wid.pub_list:\n dep_node.append(dep['msg_type'])\n for dep in dep_node:\n a, b = dep.split('/')\n msg.append(a)\n msg_type.append(b)\n f = open('../genkernel/templates/package_rosgen.xml')\n o = open(out_file, 'a')\n flag = 0\n while 1:\n line = f.readline()\n if not line: break\n for i in range(6):\n line = line.replace('[{0}]'.format(i), param_list[i])\n line = line.replace('[7]', param_list[7])\n if line.find('[6]') != -1:\n for dep in dep_pkg:\n line_dep = '\\t<depend>{0}</depend>\\n'.format(dep)\n o.write(line_dep)\n flag = 1\n elif line.find('[8]') != -1:\n for dep, tp in zip(msg, msg_type):\n line_dep = '\\t\\t<depend type=\"{1}\">{0}</depend>\\n'.format(dep, tp)\n o.write(line_dep)\n flag = 1\n elif line.find('<subscribers>') != -1:\n o.write('\\t\\t<subscribers>\\n')\n for sub in self.manager.wid.sub_list:\n o.write('\\t\\t\\t<sub>\\n')\n o.write('\\t\\t\\t\\t<name>{0}</name>\\n'.format(sub['name']))\n o.write('\\t\\t\\t\\t<msg_type>{0}</msg_type>\\n'.format(sub['msg_type']))\n o.write('\\t\\t\\t\\t<topic_name>{0}</topic_name>\\n'.format(sub['topic_name']))\n o.write('\\t\\t\\t\\t<queue_size>{0}</queue_size>\\n'.format(sub['queue_size']))\n o.write('\\t\\t\\t</sub>\\n')\n o.write('\\t\\t</subscribers>\\n')\n flag = 1\n elif line.find('<publishers>') != -1:\n o.write('\\t\\t<publishers>\\n')\n for pub in self.manager.wid.pub_list:\n o.write('\\t\\t\\t<pub>\\n')\n o.write('\\t\\t\\t\\t<name>{0}</name>\\n'.format(pub['name']))\n o.write('\\t\\t\\t\\t<msg_type>{0}</msg_type>\\n'.format(pub['msg_type']))\n o.write('\\t\\t\\t\\t<topic_name>{0}</topic_name>\\n'.format(pub['topic_name']))\n o.write('\\t\\t\\t\\t<queue_size>{0}</queue_size>\\n'.format(pub['queue_size']))\n o.write('\\t\\t\\t</pub>\\n')\n o.write('\\t\\t</publishers>\\n')\n flag = 1\n if flag == 0:\n o.write(line)\n else:\n flag = 0\n o.close()\n f.close()\n self.changed = False","def serialize_mapping_to_file(\n file_path, alignment, onto_one=None, onto_two=None, extension=None\n):\n with open(file_path, \"w\", encoding=\"utf-8\") as out_file:\n out_file.write(__get_xml_intro(onto_one, onto_two, extension))\n for correspondence in alignment:\n out_file.write(__get_mapping_string(*correspondence))\n out_file.write(__get_xml_outro())","def map(self, app, node):","def build_mapping() -> str:\n templates = make_module_docstring(\"Template classes for GBD entities\", __file__)\n templates += make_import(\"typing\", [\"Union\", \"Tuple\"])\n templates += (\n make_import(\n \".id\",\n [\n \"c_id\",\n \"s_id\",\n \"hs_id\",\n \"me_id\",\n \"cov_id\",\n \"rei_id\",\n \"scalar\",\n ],\n )\n + SPACING\n )\n templates += make_gbd_record()\n\n for entity, info in get_base_types().items():\n templates += SPACING\n templates += make_record(entity, **info)\n\n return templates","def read_nuget(self, config, excl=None, mapping=None):\r\n\r\n\tif not self.env.MCS:\r\n\t\treturn\r\n\r\n\tsrc = self.path.find_resource(config)\r\n\tif src:\r\n\t\thandler = PackageHandler(self, excl, mapping)\r\n\t\tparser = xml.sax.make_parser()\r\n\t\tparser.setContentHandler(handler)\r\n\t\tparser.parse(src.abspath())","def DARP2016_Acoular_XML():\n XYZ_array, array_cal = DARP2016_MicArray()\n array_name = \"DARP2016\"\n array_pos = XML_mic(XYZ_array)\n array_file = '<?xml version=\"1.0\" encoding=\"utf-8\"?>\\n<MicArray name=\"{name}\">\\n{pos}</MicArray>'.format(\n name=array_name, pos=array_pos)\n calib_data = XML_calib(array_cal, 100)\n calib_file = '<?xml version=\"1.0\" encoding=\"utf-8\"?>\\n<Calib name=\"{name}\">\\n{data}</Calib>'.format(\n name=20160101, data=calib_data)\n\n save_XML(array_name, array_file)\n save_XML(array_name+\"_calib\", calib_file)","def WriteDepMappingFile(translated_source_files,\n objc_file_path,\n output_dependency_mapping_file,\n file_open=open):\n dep_mapping = dict()\n input_file_queue = Queue.Queue()\n output_dep_mapping_queue = Queue.Queue()\n error_message_queue = Queue.Queue()\n for output_file in translated_source_files.split(','):\n input_file_queue.put(output_file)\n\n for _ in xrange(multiprocessing.cpu_count()):\n t = threading.Thread(target=_ReadDepMapping, args=(input_file_queue,\n output_dep_mapping_queue,\n error_message_queue,\n objc_file_path,\n file_open))\n t.start()\n\n input_file_queue.join()\n\n if not error_message_queue.empty():\n error_messages = [error_message for error_message in\n error_message_queue.queue]\n raise RuntimeError('\\n'.join(error_messages))\n\n while not output_dep_mapping_queue.empty():\n entry_file, deps = output_dep_mapping_queue.get()\n dep_mapping[entry_file] = deps\n\n f = file_open(output_dependency_mapping_file, 'w')\n for entry in sorted(dep_mapping):\n for dep in dep_mapping[entry]:\n f.write(entry + ':' + dep + '\\n')\n f.close()","def mappings(input_report, **kwargs):\n ben = BoomerEngine()\n ben.load(input_report, prefix_map=global_prefix_map)\n writer = StreamingSssomWriter()\n for m in ben.mappings(**kwargs):\n writer.emit(m)\n writer.finish()","def outputs(self):\n return {\"path_to_mapping_json\": File_IO(\n self.node.outputs[0])}","def generate_header():\n header_file = AUTOGEN_WARNING\n header_file += \"/// /file atomic_nuclear_data.h\\n\"\n header_file += \"/// /author Andrew Davis (andrew.davis@wisc.edu)\\n\"\n header_file += \"///\\n\"\n header_file += (\n \"/// /brief Implements all the fundamental atomic & nuclear data data\\n\"\n )\n header_file += \"#include <map>\\n\"\n header_file += \"\\n\"\n header_file += \"namespace pyne\\n\"\n header_file += \"{\\n\"\n header_file += (\n \" /// main function to be called when you wish to load the nuclide data \\n\"\n )\n header_file += \" /// into memory \\n\"\n header_file += \" void _load_atomic_mass_map_memory();\\n\"\n header_file += \" /// function to create mapping from nuclides in id form\\n\"\n header_file += \" /// to their atomic masses\\n\"\n header_file += \" \\n\"\n header_file += \" void _insert_atomic_mass_map();\\n\"\n header_file += \" \\n\"\n header_file += \" /// function to create mapping from nuclides in id form \\n\"\n header_file += \" /// to their natural abundances\\n\"\n header_file += \" void _insert_abund_map();\\n\"\n header_file += \" \\n\"\n header_file += (\n \" /// Mapping from nuclides in id form to their natural abundances\\n\"\n )\n header_file += \" extern std::map<int,double> natural_abund_map;\\n\"\n header_file += \" \\n\"\n header_file += \" /// Mapping from nuclides in id form to their atomic masses.\\n\"\n header_file += \" extern std::map<int,double> atomic_mass_map;\\n\"\n header_file += \" \\n\"\n header_file += (\n \" /// Mapping from nuclides in id form to the associated error in \\n\"\n )\n header_file += \" /// abdundance \\n\"\n header_file += \" extern std::map<int,double> atomic_mass_error_map;\\n\"\n header_file += \"} // namespace pyne\\n\"\n return header_file","def OnInit(self):\n \n # load the xml resource\n script_dir = os.path.dirname ( __file__ )\n self.res = xrc.XmlResource ( \"%s/csv2ofx.xrc\" % script_dir )\n \n # load the frame from the resource \n self.frame = self.res.LoadFrame ( None, \"ID_CSV2OFX\")\n \n # associate the MenuBar\n self.frame.SetMenuBar (\n self.res.LoadMenuBar(\"ID_MENUBAR\")\n )\n\n # the grid\n self.grid = xrc.XRCCTRL(self.frame,\"ID_GRID\")\n self.grid.EnableEditing(False)\n \n # the mappings\n self.mappings = xrc.XRCCTRL(self.frame,\"ID_MAPPINGS\")\n\n try:\n try:\n # try current directory first\n if os.path.isfile ( 'csv2ofx_custom.py' ):\n execfile ( 'csv2ofx_custom.py', globals())\n for mapping in all_mappings:\n self.mappings.Append ( mapping, all_mappings[mapping] )\n else:\n raise # try the home directory\n except:\n homepath=os.path.expanduser('~')\n cust_mappings = \"%s/csv2ofx_custom.py\" % homepath\n if os.path.isfile( cust_mappings ):\n execfile ( cust_mappings, globals() )\n for mapping in all_mappings:\n self.mappings.Append ( mapping, all_mappings[mapping] )\n except: \n print_exc()\n # Use built in mappings as default/backup\n if self.mappings.IsEmpty():\n print \"Using Default Mappings\"\n import mappings\n for mapping in mappings.all_mappings:\n self.mappings.Append ( mapping, mappings.all_mappings[mapping] )\n self.mappings.SetSelection(0)\n\n # output formats\n self.exports = xrc.XRCCTRL(self.frame,\"ID_EXPORT\")\n \n # handle events\n self.Bind ( wx.EVT_MENU, self.OnCloseBtn, id=xrc.XRCID(\"ID_MENU_CLOSE\"))\n self.Bind ( wx.EVT_BUTTON, self.OnCloseBtn, id=xrc.XRCID(\"ID_BTN_CLOSE\"))\n self.Bind ( wx.EVT_MENU, self.OnImport, id=xrc.XRCID(\"ID_MENU_IMPORT\"))\n self.Bind ( wx.EVT_BUTTON, self.OnImport, id=xrc.XRCID(\"ID_BTN_IMPORT\"))\n self.Bind ( wx.EVT_MENU, self.OnExport, id=xrc.XRCID(\"ID_MENU_EXPORT\"))\n self.Bind ( wx.EVT_BUTTON, self.OnExport, id=xrc.XRCID(\"ID_BTN_EXPORT\"))\n self.frame.Bind ( wx.EVT_CLOSE, self.OnClose )\n self.frame.Bind ( wx.EVT_MOVE, self.OnMove )\n self.frame.Bind ( wx.EVT_SIZE, self.OnSize )\n \n \n # app preferences\n self.config = wx.Config ( \"csv2ofx\" )\n\n x=self.config.ReadInt(\"screenx\",100)\n y=self.config.ReadInt(\"screeny\",100)\n w=self.config.ReadInt(\"screenw\",600)\n h=self.config.ReadInt(\"screenh\",550)\n\n \n # show the frame \n self.SetTopWindow(self.frame)\n \n self.frame.SetPosition( (x,y) )\n self.frame.SetSize( (w,h) )\n self.frame.Show()\n return True","def _makeimap(self):\n self.map_[\"source\"] = \"nasa\"\n self.map_[\"instrument\"] = \"goes\"\n self.map_[\"physobs\"] = \"irradiance\"\n self.map_[\"provider\"] = \"sdac\"","def node_mapping(self):\n ...","def dump_packages(self, package_name='com.sagas.dss'):\n model_reader = oc.delegator.getModelReader()\n tree_map = model_reader.getEntitiesByPackage(None, None)\n # print(tree_map)\n py_tree_map={trim_prefix(k):to_list(vals) for (k,vals) in tree_map.items() }\n for (k,vals) in py_tree_map.items():\n print(k, vals)\n\n json_str=json.dumps(py_tree_map, ensure_ascii=False, indent=2)\n io_utils.write_to_file(\"dump_pkgs.json\", json_str)","def to_xml_dict(self):\n self._check_required_fields()\n\n root = odict[\n \"r:import\": odict[\n \"@xmlns:r\": \"http://resolver.nkp.cz/v3/\",\n \"r:monograph\": odict[\n \"r:titleInfo\": odict[\n \"r:title\": self.title,\n ],\n ],\n ]\n ]\n mono_root = root[\"r:import\"][\"r:monograph\"]\n\n # subtitle\n MonographComposer._assign_pattern(\n mono_root[\"r:titleInfo\"],\n \"r:subTitle\",\n self.subtitle\n )\n\n # handle ccnb, isbn, uuid\n self._add_identifier(mono_root, \"ccnb\")\n self._add_identifier(mono_root, \"isbn\")\n self._add_identifier(mono_root, \"other_id\", out=\"otherId\")\n\n # add form of the book\n MonographComposer._assign_pattern(\n mono_root,\n \"r:documentType\",\n self.document_type\n )\n\n mono_root[\"r:digitalBorn\"] = \"true\" if self.digital_born else \"false\"\n\n if self.author:\n mono_root[\"r:primaryOriginator\"] = odict[\n \"@type\": \"AUTHOR\",\n \"#text\": self.author\n ]\n\n if any([self.place, self.publisher, self.year]):\n publ = odict()\n\n MonographComposer._assign_pattern(\n publ,\n \"r:publisher\",\n self.publisher\n )\n MonographComposer._assign_pattern(publ, \"r:place\", self.place)\n MonographComposer._assign_pattern(publ, \"r:year\", self.year)\n\n mono_root[\"r:publication\"] = publ\n\n if self.format:\n format_dict = MonographComposer._create_path(\n root,\n odict,\n [\n \"r:import\",\n \"r:digitalDocument\",\n \"r:technicalMetadata\",\n \"r:format\",\n ]\n )\n\n format_dict[\"#text\"] = self.format\n\n return root","def _write_init(self):\n\n content = ('# -*- coding: utf-8 -*-'\n '\\n'\n '\\n'\n \"__version__ = '${version}'\"\n '\\n')\n intFile = os.path.join(self.packageDir, '__init__.py')\n return self.write_file(string.Template(content), intFile)","def _do_mapping(self):\n pass","def _generate_global_config() -> str:\n logger = getLogger(__name__)\n dst = os.path.join(os.path.expanduser(\"~\"),\n \".aiscalator/config/aiscalator.conf\")\n logger.info(\"Generating a new configuration file for aiscalator:\\n\\t%s\",\n dst)\n pattern = [\n \"testUserID\",\n \"generation_date\",\n ]\n replace_value = [\n generate_user_id(),\n '\"' + str(datetime\n .utcnow()\n .replace(tzinfo=timezone(\"UTC\"))) +\n '\" // in UTC timezone',\n ]\n dst_dir = os.path.dirname(dst)\n if dst_dir:\n os.makedirs(dst_dir, exist_ok=True)\n copy_replace(data_file(\"../config/template/aiscalator.conf\"),\n dst, pattern=pattern, replace_value=replace_value)\n open(os.path.join(dst_dir, \"apt_packages.txt\"), 'a').close()\n open(os.path.join(dst_dir, \"requirements.txt\"), 'a').close()\n open(os.path.join(dst_dir, \"lab_extensions.txt\"), 'a').close()\n return dst","def xml(self):\n xml_string = '<VendorMap>\\n'\n for vendorid, name in self.data.items():\n xml_string += \"<Vendor>\\n<Id>{}</Id>\\n<Name>{}</Name>\\n</Vendor>\\n\".format(vendorid, name)\n xml_string += '</VendorMap>\\n'\n return xml_string","def write_global_local_maps(dest,global_local,local_global):","def write(self, file_or_filename):\n etMap = revert(self)\n xmlTree = ET.ElementTree(etMap)\n xmlTree.write(file_or_filename)","def create_gen_json(self, out_file):\n\n params = self.create_package_dict()\n with open(out_file, 'w') as fp:\n json.dump(params, fp)","def output_map(self, x):\n\n return x","def make_mapping(self) -> None:\n start_mark = StreamMark('generated node', 0, 0, 0)\n end_mark = StreamMark('generated node', 0, 0, 0)\n self.yaml_node = yaml.MappingNode('tag:yaml.org,2002:map', list(),\n start_mark, end_mark)","def export_to_xml(self, resource_fs):\r\n\r\n # Set up runtime.export_fs so that it's available through future\r\n # uses of the pure xblock add_xml_to_node api\r\n self.runtime.export_fs = resource_fs\r\n\r\n # Get the definition\r\n xml_object = self.definition_to_xml(resource_fs)\r\n self.clean_metadata_from_xml(xml_object)\r\n\r\n # Set the tag so we get the file path right\r\n xml_object.tag = self.category\r\n\r\n # Add the non-inherited metadata\r\n for attr in sorted(own_metadata(self)):\r\n # don't want e.g. data_dir\r\n if attr not in self.metadata_to_strip and attr not in self.metadata_to_export_to_policy:\r\n val = serialize_field(self._field_data.get(self, attr))\r\n try:\r\n xml_object.set(attr, val)\r\n except Exception:\r\n logging.exception(\r\n u'Failed to serialize metadata attribute %s with value %s in module %s. This could mean data loss!!!',\r\n attr, val, self.url_name\r\n )\r\n\r\n for key, value in self.xml_attributes.items():\r\n if key not in self.metadata_to_strip:\r\n xml_object.set(key, value)\r\n\r\n if self.export_to_file():\r\n # Write the definition to a file\r\n url_path = name_to_pathname(self.url_name)\r\n filepath = self._format_filepath(self.category, url_path)\r\n resource_fs.makedir(os.path.dirname(filepath), recursive=True, allow_recreate=True)\r\n with resource_fs.open(filepath, 'w') as file:\r\n file.write(etree.tostring(xml_object, pretty_print=True, encoding='utf-8'))\r\n\r\n # And return just a pointer with the category and filename.\r\n record_object = etree.Element(self.category)\r\n else:\r\n record_object = xml_object\r\n\r\n record_object.set('url_name', self.url_name)\r\n\r\n # Special case for course pointers:\r\n if self.category == 'course':\r\n # add org and course attributes on the pointer tag\r\n record_object.set('org', self.location.org)\r\n record_object.set('course', self.location.course)\r\n\r\n return etree.tostring(record_object, pretty_print=True, encoding='utf-8')","def export_to_xml(self, resource_fs):\r\n raise NotImplementedError('Modules must implement export_to_xml to enable xml export')","def collect_olm_auto_generated_resources(namespace, ns_output_dir, k8s_cli):\n if not detect_if_olm_deployed(namespace, k8s_cli):\n return\n olm_output_dir = os.path.join(ns_output_dir, \"OLM\")\n make_dir(olm_output_dir)\n selector = f\"--selector={OLM_LABEL % namespace}\"\n logger.info(\"Namespace '%s': Collecting OLM autogenerated resources\", namespace)\n resources_out = OrderedDict()\n for resource in OLM_RESOURCES:\n output = run_get_resource_yaml(namespace, resource, k8s_cli, selector)\n if output:\n resources_out[resource] = output\n logger.info(\"Namespace '%s': + Collected %s\", namespace, resource)\n for entry, out in resources_out.items():\n with open(os.path.join(olm_output_dir,\n \"{}.yaml\".format(entry)), \"w+\", encoding='UTF-8') as file_handle:\n file_handle.write(out)","def create_mapping_file(options):\n\n mapping_file = open(os.path.splitext(options.bco)[0] + \"mapping.txt\", 'w')\n mapping_file.writelines(\n\"\"\"# Use this file to provide mapping values for a bco.\n# MISSING PROPERTIES/FIELDS lists properties/fields that are missing from bco\n# NONALLOWED PROPERTIES/FIELDS shows properties that are not allowed\n# Syntax for specifying values\n# To delete a value\n# PATH --> FIELD: DELETE\n# To add a value\n# PATH --> FIELD: ADD-value_to_add\n# To rename a field name\n# PATH --> FIELD: RENAME-new_field_name\n# To swap a field name with another current field name\n# PATH --> FIELD: SWAP-other_field_name\n# Blank values will be skipped. Data does not need to be double represented\n# For example, \n# if <bco_id> needs renamed to <object_id>, either\n# ['object_id'] --> object_id: \n# SWAP-bco_id\n# OR \n# ['bco_id'] --> bco_id: RENAME:object_id \n# will work. No need to fill out both values.\n\"\"\"\n)\n validate_bco(options)\n\n missing_reg = r'(.*?) is a required property' # missing required property\n additional_reg = r'Additional properties are not allowed (.*?)' # unalloewd extra property\n\n attribute_reg = r\"'(.*?)'\" # getting an attribute (field surronded by single quotes)\n index_reg = r\"On instance(.*?)\" # getting key path\n\n failed_validation_reg = r'Failed validating (.*?)' # invalid type\n\n missing = []\n additional = []\n invalid = []\n\n path = {}\n\n with open('error.log') as errors:\n for line in errors:\n if re.match(missing_reg, line): # if there is a missing property\n to_add = re.findall(attribute_reg, line)\n for match in to_add:\n missing.append(match)\n elif re.match(additional_reg, line): # if there is an additional property\n to_add = re.findall(attribute_reg, line)\n for match in to_add:\n additional.append(match)\n elif re.match(failed_validation_reg, line): # if a property is invalid\n # additional and required properties are already represnted by the above regexes,\n # so skip\n if line.__contains__(\"'additionalProperties'\") is False \\\n and line.__contains__(\"'required'\") is False:\n to_add = [line.split(\"schema\")[1].split(\"['\")[-1].strip(\"']:\\n\")]\n invalid.append(to_add[0])\n\n # field contains an index for some attribute\n # this attribute will be the last attribute found the above regexes, and is stored in\n # to_add\n if re.match(index_reg, line):\n keys = \"\"\n index_path = line.removeprefix(\"On instance\").removesuffix(\":\\n\")\n if index_path is not None:\n keys = str(index_path)\n if len(to_add) > 0: # if there are any attributes to add\n for item in to_add:\n add_or_update_list_HELPER(path, str(item), keys + \"['\" + str(item) +\n \"']\")\n to_add = [] # reset to_add\n mapping_file.write(\"====MISSING PROPERTIES/FIELDS====\\n\")\n for attribute in missing:\n mapping_file.write(str(path[attribute][0]) + \"-->\" + str(attribute) + \":\\n\")\n path[attribute].pop(0)\n\n mapping_file.write(\"====NONALLOWED PROPERTIES/FIELDS====\\n\")\n for attribute in additional:\n mapping_file.write(str(path[attribute][0]) + \"-->\" + str(attribute) + \":\\n\")\n path[attribute].pop(0)\n for attribute in invalid:\n mapping_file.write(str(path[attribute][0]).split(\"]\")[0]\n + \"]-->\" + str(attribute) + \":\\n\")\n path[attribute].pop(0)\n\n return mapping_file.name","def saveObjectMaps(self):\n if self.objectMaps == None: return\n path = os.path.join(self.dir,settings['mosh.modInfos.objectMaps'])\n outDir = os.path.split(path)[0]\n if not os.path.exists(outDir): os.makedirs(outDir)\n cPickle.dump(self.objectMaps,open(path,'wb'),2)","def setUp(self): \n \n self.obo = MinimalObo(obo_file)\n self.emapa = get_emapa_map(emapa_file, self.obo)","def configureGeoData(data,resultDir):\n xmlfiles = findfiles(['*.xml'],where=data[\"folder\"])\n xmlurls=[]\n fgdclist=[]\n #xmlselect=[]\n for xml in xmlfiles:\n shutil.copy(os.path.join(data['folder'],xml),resultDir)\n xmlurls.append(os.path.join(resulturl,resultDir.split('/')[-1],xml))\n #import xmltodict\n localfilename=os.path.join(data['folder'],xml)\n #xmlselect.append({\"file\":localfilename,\"url\":os.path.join(resulturl,resultDir.split('/')[-1],xml)})\n with open(os.path.join(data['folder'],xml)) as fd:\n stringxml = fd.read()\n #if 'FGDC' in stringxml.upper():\n fgdc={}\n fgdc['url']=os.path.join(resulturl,resultDir.split('/')[-1],xml)\n doc = xmltodict.parse(stringxml,cdata_key='text',attr_prefix='',dict_constructor=dict)\n fgdc['data']=doc\n fgdc['file']=localfilename\n fgdclist.append(fgdc)\n data['xmlurls']=xmlurls\n data['xml']={\"urls\":xmlurls,\"fgdc\":fgdclist,\"files\":xmlfiles}\n return data","def export_all_components(self) -> None:\n logging.message('Exporting current config.')\n for c in COMPONENT_MAP:\n self.export_components(c['file'], c['type'])\n\n aliases.export_aliases()\n export_groups(self)","def __init__(self, input_file, output_file, encoding='utf-8'):\n\n self.output_filename = output_file\n self.input_directory = input_file\n self.output_buffer = []\n self.output_dict = [{}]\n self.item_titles = {}\n self.output = None\n\n self._init_output_dict()\n\n # open the xml file for iteration\n # self.context = ETree.iterparse(input_file, events=(\"start\", \"end\"))\n\n # output file handle\n try:\n pass\n #self.output = codecs.open(output_file, \"w\", encoding=encoding)\n except:\n print(\"Failed to open the output file\")\n raise","def convert_to_annotation(file, output):\n from pybel.resources.definitions import write_annotation\n\n resource = parse_bel_resource(file)\n\n write_annotation(\n keyword=resource['Namespace']['Keyword'],\n values={k: '' for k in resource['Values']},\n citation_name=resource['Citation']['NameString'],\n description=resource['Namespace']['DescriptionString'],\n file=output\n )","def _load_objects(self):\n self._get_package()\n\n object_names = [name for name in dir(self._sdk) if name != \"GATDLSession\" and name != \"SDKInfo\" and name.startswith(\"GA\") and not name.endswith(\"Fetcher\")]\n\n for object_name in object_names:\n obj = getattr(self._sdk, object_name)\n self._objects_mapping[obj.rest_name] = object_name","def _po_packages(self):\n for name in self.distribution.po:\n source_dir = os.path.join(self.distribution.po_dir, name)\n build_dir = os.path.join(self.build_lib, name, 'locale')\n template = os.path.join(source_dir, name + '.pot')\n pkg = {'name': name,\n 'template': template,\n 'source_dir': source_dir,\n 'build_dir': build_dir}\n yield pkg","def getMappedSymbolsXML(self, addrstring: unicode) -> unicode:\n ...","def pathMap(self):\n pass","def _load_namespaces(self):\n nsdocs = self._docset.get_namespaces()\n for nsdoc in nsdocs:\n nsobj = Namespace(nsdoc)\n self._docmap[nsdoc] = nsobj\n self._namespaces.add(nsobj)","def to_xml(self, doc, element, pipe_type=None):\n\n # Add all simple python objects within the PipeContainer to the global element.\n global_element = doc.createElement('global')\n element.appendChild(global_element)\n global_element.setAttribute('desc', 'Global data located in the top level of the data pipe')\n fill_object_contents(doc, global_element, object=self, blacklist=['align_tensors', 'diff_tensor', 'exp_info', 'interatomic', 'hybrid_pipes', 'mol', 'pipe_type', 'structure'] + list(self.__class__.__dict__.keys()))\n\n # Hybrid info.\n self.xml_create_hybrid_element(doc, element)\n\n # Add the experimental information.\n if hasattr(self, 'exp_info'):\n self.exp_info.to_xml(doc, element)\n\n # Add the diffusion tensor data.\n if hasattr(self, 'diff_tensor'):\n self.diff_tensor.to_xml(doc, element)\n\n # Add the alignment tensor data.\n if hasattr(self, 'align_tensors'):\n self.align_tensors.to_xml(doc, element)\n\n # Add the molecule-residue-spin data.\n self.mol.to_xml(doc, element, pipe_type=pipe_type)\n\n # Add the interatomic data.\n self.interatomic.to_xml(doc, element, pipe_type=pipe_type)\n\n # Add the structural data, if it exists.\n if hasattr(self, 'structure'):\n self.structure.to_xml(doc, element)","def setup(self):\n # TODO : figure out how to make the map interface a singleton class\n\n if not hasattr(self, 'mapInterface'):\n self.mapInterface = MapInterface(settings['FILE_CONFIG']['filename'])","def create_master_mapping_file(options, bcos_to_map):\n mapping_dict = {\n \"missing\": {\n\n },\n\n \"additional\": {\n\n }\n }\n\n for bco in bcos_to_map:\n\n filename = os.path.splitext(bco)[0] + \"mapping.txt\"\n\n options.bco = bco\n create_mapping_file(options)\n\n addition_mode = False\n\n with open(filename) as mapping_file:\n for line in mapping_file:\n if line.startswith(\"#\"): # Skip comments explaining mapping file\n continue\n if line.startswith(\"====\"): # Switch modes. Starts false -> true -> false\n addition_mode = not addition_mode\n continue\n key_path = line.split(\"-->\")[0]\n value = line.split(\"-->\")[1].strip(\":\")\n\n if addition_mode:\n if key_path not in mapping_dict[\"missing\"]:\n mapping_dict[\"missing\"][key_path] = [value, [bco]]\n else:\n mapping_dict[\"missing\"][key_path][1].append(bco)\n else:\n if key_path not in mapping_dict[\"additional\"]:\n mapping_dict[\"additional\"][key_path] = [value, [bco]]\n else:\n mapping_dict[\"additional\"][key_path][1].append(bco)\n os.remove(filename)\n\n\n with open(\"mastermappingfile.txt\", \"w\") as masterfile: # write dict to\n # single file with\n masterfile.writelines(\n\"\"\"# Use this file to provide mapping values for multiple bcos.\n# fill out the values for each bco listed.\n# MISSING PROPERTIES/FIELDS lists properties/fields that are missing from bco\n# NONALLOWED PROPERTIES/FIELDS shows properties that are not allowed\n# Syntax for specifying values\n# To delete a value\n# PATH --> FIELD: DELETE\n# To add a value\n# PATH --> FIELD: ADD-value_to_add\n# To rename a field name\n# PATH --> FIELD: RENAME-new_field_name\n# To swap a field name with another current field name\n# PATH --> FIELD: SWAP-other_field_name\n# Blank values will be skipped. Data does not need to be double represented\n# For example, \n# if <bco_id> needs renamed to <object_id>, either\n# ['object_id'] --> object_id: \n# SWAP-bco_id\n# OR \n# ['bco_id'] --> bco_id: RENAME:object_id \n# will work. No need to fill out both values.\n\"\"\")\n masterfile.write(\"====MISSING PROPERTIES/FIELDS=====\\n\")\n for key in mapping_dict[\"missing\"]:\n attribute = mapping_dict[\"missing\"][key]\n value = attribute[0]\n\n masterfile.write(key + \"-->\" + value)\n for bco_name in attribute[1]:\n masterfile.write(\" \" + os.path.basename(bco_name) + \":\\n\")\n masterfile.write(\"-----------------------\\n\")\n masterfile.write(\"=====ADDITONAL PROPERTIES/FIELDS=====\\n\")\n for key in mapping_dict[\"additional\"]:\n attribute = mapping_dict[\"additional\"][key]\n value = attribute[0]\n masterfile.write(key + \"-->\" + value)\n for bco_name in attribute[1]:\n masterfile.write(\" \" + os.path.basename(bco_name) + \":\\n\")\n masterfile.write(\"-----------------------\\n\")","def build_I_map(self):\n raise NotImplementedError","def import_packages_global():\n return \"\"","def packages():","def _add_jet_maps(process, options, prefix, jet_algo_name):\n\n # -- jet-to-trigger-object association map\n from Karma.Common.Producers.JetMatchingProducers_cfi import karmaJetTriggerObjectMatchingProducer\n process.add_module(\n \"{}JetTriggerObjectMap{}\".format(prefix, jet_algo_name),\n karmaJetTriggerObjectMatchingProducer.clone(\n primaryCollectionSrc = cms.InputTag(\"{}Jets{}\".format(prefix, jet_algo_name)),\n secondaryCollectionSrc = cms.InputTag(\"karmaTriggerObjects\"),\n )\n )\n\n # -- jet-to-lepton association maps\n from Karma.Common.Producers.JetMatchingProducers_cfi import karmaJetMuonMatchingProducer\n process.add_module(\n \"{}JetMuonMap{}\".format(prefix, jet_algo_name),\n karmaJetMuonMatchingProducer.clone(\n primaryCollectionSrc = cms.InputTag(\"{}Jets{}\".format(prefix, jet_algo_name)),\n secondaryCollectionSrc = cms.InputTag(\"karmaMuons\"),\n )\n )\n from Karma.Common.Producers.JetMatchingProducers_cfi import karmaJetElectronMatchingProducer\n process.add_module(\n \"{}JetElectronMap{}\".format(prefix, jet_algo_name),\n karmaJetElectronMatchingProducer.clone(\n primaryCollectionSrc = cms.InputTag(\"{}Jets{}\".format(prefix, jet_algo_name)),\n secondaryCollectionSrc = cms.InputTag(\"karmaElectrons\"),\n )\n )\n\n if not options.isData:\n # -- jet-to-gen-jet association map\n from Karma.Common.Producers.JetMatchingProducers_cfi import karmaJetGenJetMatchingProducer\n process.add_module(\n \"{}JetGenJetMap{}\".format(prefix, jet_algo_name),\n karmaJetGenJetMatchingProducer.clone(\n primaryCollectionSrc = cms.InputTag(\"{}Jets{}\".format(prefix, jet_algo_name)),\n secondaryCollectionSrc = cms.InputTag(\"karmaGenJets{}\".format(jet_algo_name[:3])),\n maxDeltaR = cms.double(0.4 if 'AK8' in jet_algo_name else 0.2)\n )\n )","def write_global_attributes(self, attrs):\n raise NotImplementedError","def main(global_config, **settings):\n # http://ldh.clld.org/category/iso639_3/aap-para-arara/\n config = Configurator(settings=settings)\n config.include('clldmpg')\n config.include('clld_glottologfamily_plugin')\n config.registry.registerUtility(LanguageByFamilyMapMarker(), IMapMarker)\n\n # /feed/ -> /sources.atom\n \"\"\"\n/comments/feed/\n/feed/ -> /sources.atom\n \"\"\"\n config.add_301('/about/', lambda req: req.route_url('dataset', id='ldh'))\n config.add_301('/about/objective/', lambda req: req.route_url('about', _anchor='objectives'))\n config.add_301('/repository/', lambda req: req.route_url('about', _anchor='repository'))\n config.add_301('/archive/', lambda req: req.route_url('sources'))\n config.add_301('/category/iso639_3/', lambda req: req.route_url('languages'))\n config.add_301('/category/allgemein/', lambda req: req.route_url('languages'))\n config.add_301('/contact/', lambda req: req.route_url('contact'))\n config.add_301('/for-authors/', lambda req: req.route_url('help'))\n config.add_301('/for-authors/{path}/', lambda req: req.route_url('help'))\n config.add_301('/feed/', lambda req: req.route_url('sources_alt', ext='atom'))\n\n config.add_301(\n '/{year}/{month}/{day}/escidoc{id}/',\n lambda req: req.route_url(\n 'source',\n id='item_{0}'.format(req.matchdict['id'].split('-')[0]),\n _query=req.query_params),\n name='item')\n config.add_301(\n '/category/iso639_3/{id}/',\n lambda req: req.route_url(\n 'language',\n id=req.matchdict['id'].split('-')[0],\n _query=req.query_params),\n name='category')\n\n config.register_download(\n adapters.BibTeX(models.Description, 'ldh', description=\"Descriptions as BibTeX\"))\n\n return config.make_wsgi_app()","def DontuseThis():\n BCM_outputs = ['phi','rho','theta',\n 'r_probabilityMaps','l_probabilityMaps',\n 'models']\n BCM_Models = pe.Node(interface=nio.DataGrabber(input_names=['structures'],\n outfields=BCM_outputs),\n name='10_BCM_Models')\n BCM_Models.inputs.base_directory = atlas_fname_wpath\n BCM_Models.inputs.template_args['phi'] = [['spatialImages','phi','nii.gz']]\n BCM_Models.inputs.template_args['rho'] = [['spatialImages','rho','nii.gz']]\n BCM_Models.inputs.template_args['theta'] = [['spatialImages','theta','nii.gz']]\n BCM_Models.inputs.template_args['r_probabilityMaps'] = [['structures']]\n BCM_Models.inputs.template_args['l_probabilityMaps'] = [['structures']]\n BCM_Models.inputs.template_args['models'] = [['structures']]\n\n BRAINSCut_structures = ['caudate','thalamus','putamen','hippocampus']\n #BRAINSCut_structures = ['caudate','thalamus']\n BCM_Models.iterables = ( 'structures', BRAINSCut_structures )\n BCM_Models.inputs.template = '%s/%s.%s'\n BCM_Models.inputs.field_template = dict(\n r_probabilityMaps='probabilityMaps/r_%s_ProbabilityMap.nii.gz',\n l_probabilityMaps='probabilityMaps/l_%s_ProbabilityMap.nii.gz',\n models='modelFiles/%sModel*',\n )\n\n \"\"\"\n The xml creation and BRAINSCut need to be their own mini-pipeline that gets\n executed once for each of the structures in BRAINSCut_structures. This can be\n accomplished with a map node and a new pipeline.\n \"\"\"\n \"\"\"\n Create xml file for BRAINSCut\n \"\"\"\n\n\n BFitAtlasToSubject = pe.Node(interface=BRAINSFit(),name=\"BFitAtlasToSubject\")\n BFitAtlasToSubject.inputs.costMetric=\"MMI\"\n BFitAtlasToSubject.inputs.maskProcessingMode=\"ROI\"\n BFitAtlasToSubject.inputs.numberOfSamples=100000\n BFitAtlasToSubject.inputs.numberOfIterations=[1500,1500]\n BFitAtlasToSubject.inputs.numberOfHistogramBins=50\n BFitAtlasToSubject.inputs.maximumStepLength=0.2\n BFitAtlasToSubject.inputs.minimumStepLength=[0.005,0.005]\n BFitAtlasToSubject.inputs.transformType= [\"Affine\",\"BSpline\"]\n BFitAtlasToSubject.inputs.maxBSplineDisplacement= 7\n BFitAtlasToSubject.inputs.maskInferiorCutOffFromCenter=65\n BFitAtlasToSubject.inputs.splineGridSize=[28,20,24]\n BFitAtlasToSubject.inputs.outputVolume=\"Trial_Initializer_Output.nii.gz\"\n BFitAtlasToSubject.inputs.outputTransform=\"Trial_Initializer_Output.mat\"\n cutWF.connect(SplitAvgBABC,'avgBABCT1',BFitAtlasToSubject,'fixedVolume')\n cutWF.connect(BABC,'outputLabels',BFitAtlasToSubject,'fixedBinaryVolume')\n cutWF.connect(BAtlas,'template_t1',BFitAtlasToSubject,'movingVolume')\n cutWF.connect(BAtlas,'template_brain',BFitAtlasToSubject,'movingBinaryVolume')\n cutWF.connect(BLI,'outputTransformFilename',BFitAtlasToSubject,'initialTransform')\n\n CreateBRAINSCutXML = pe.Node(Function(input_names=['rho','phi','theta',\n 'model',\n 'r_probabilityMap',\n 'l_probabilityMap',\n 'atlasT1','atlasBrain',\n 'subjT1','subjT2',\n 'subjT1GAD','subjT2GAD',\n 'subjSGGAD','subjBrain',\n 'atlasToSubj','output_dir'],\n output_names=['xml_filename','rl_structure_filename_list'],\n function = create_BRAINSCut_XML),\n overwrite = True,\n name=\"CreateBRAINSCutXML\")\n\n ## HACK Makde better directory\n CreateBRAINSCutXML.inputs.output_dir = \".\" #os.path.join(cutWF.base_dir, \"BRAINSCut_output\")\n cutWF.connect(BCM_Models,'models',CreateBRAINSCutXML,'model')\n cutWF.connect(BCM_Models,'rho',CreateBRAINSCutXML,'rho')\n cutWF.connect(BCM_Models,'phi',CreateBRAINSCutXML,'phi')\n cutWF.connect(BCM_Models,'theta',CreateBRAINSCutXML,'theta')\n cutWF.connect(BCM_Models,'r_probabilityMaps',CreateBRAINSCutXML,'r_probabilityMap')\n cutWF.connect(BCM_Models,'l_probabilityMaps',CreateBRAINSCutXML,'l_probabilityMap')\n cutWF.connect(BAtlas,'template_t1',CreateBRAINSCutXML,'atlasT1')\n cutWF.connect(BAtlas,'template_brain',CreateBRAINSCutXML,'atlasBrain')\n cutWF.connect(SplitAvgBABC,'avgBABCT1',CreateBRAINSCutXML,'subjT1')\n cutWF.connect(SplitAvgBABC,'avgBABCT2',CreateBRAINSCutXML,'subjT2')\n cutWF.connect(GADT1,'outputVolume',CreateBRAINSCutXML,'subjT1GAD')\n cutWF.connect(GADT2,'outputVolume',CreateBRAINSCutXML,'subjT2GAD')\n cutWF.connect(SGI,'outputFileName',CreateBRAINSCutXML,'subjSGGAD')\n cutWF.connect(BABC,'outputLabels',CreateBRAINSCutXML,'subjBrain')\n cutWF.connect(BFitAtlasToSubject,'outputTransform',CreateBRAINSCutXML,'atlasToSubj')\n #CreateBRAINSCutXML.inputs.atlasToSubj = \"INTERNAL_REGISTER.mat\"\n #cutWF.connect(BABC,'atlasToSubjectTransform',CreateBRAINSCutXML,'atlasToSubj')\n\n \"\"\"\n ResampleNACLabels\n \"\"\"\n ResampleAtlasNACLabels=pe.Node(interface=BRAINSResample(),name=\"ResampleAtlasNACLabels\")\n ResampleAtlasNACLabels.inputs.interpolationMode = \"NearestNeighbor\"\n ResampleAtlasNACLabels.inputs.outputVolume = \"atlasToSubjectNACLabels.nii.gz\"\n\n cutWF.connect(cutWF,'OutputSpec.atlasToSubjectTransform',ResampleAtlasNACLabels,'warpTransform')\n cutWF.connect(cutWF,'OutputSpec.t1_corrected',ResampleAtlasNACLabels,'referenceVolume')\n cutWF.connect(BAtlas,'template_nac_lables',ResampleAtlasNACLabels,'inputVolume')\n\n \"\"\"\n BRAINSMush\n \"\"\"\n BMUSH=pe.Node(interface=BRAINSMush(),name=\"BMUSH\")\n BMUSH.inputs.outputVolume = \"MushImage.nii.gz\"\n BMUSH.inputs.outputMask = \"MushMask.nii.gz\"\n BMUSH.inputs.lowerThresholdFactor = 1.2\n BMUSH.inputs.upperThresholdFactor = 0.55\n\n cutWF.connect(myLocalTCWF,'OutputSpec.t1_corrected',BMUSH,'inputFirstVolume')\n cutWF.connect(myLocalTCWF,'OutputSpec.t2_corrected',BMUSH,'inputSecondVolume')\n cutWF.connect(myLocalTCWF,'OutputSpec.outputLabels',BMUSH,'inputMaskVolume')\n\n \"\"\"\n BRAINSROIAuto\n \"\"\"\n BROI = pe.Node(interface=BRAINSROIAuto(), name=\"BRAINSROIAuto\")\n BROI.inputs.closingSize=12\n BROI.inputs.otsuPercentileThreshold=0.01\n BROI.inputs.thresholdCorrectionFactor=1.0\n BROI.inputs.outputROIMaskVolume = \"temproiAuto_t1_ACPC_corrected_BRAINSABC.nii.gz\"\n cutWF.connect(myLocalTCWF,'OutputSpec.t1_corrected',BROI,'inputVolume')\n\n \"\"\"\n Split the implicit outputs of BABCext\n \"\"\"\n SplitAvgBABC = pe.Node(Function(input_names=['in_files','T1_count'], output_names=['avgBABCT1','avgBABCT2'],\n function = get_first_T1_and_T2), run_without_submitting=True, name=\"99_SplitAvgBABC\")\n SplitAvgBABC.inputs.T1_count = 1 ## There is only 1 average T1 image.\n\n cutWF.connect(myLocalTCWF,'OutputSpec.outputAverageImages',SplitAvgBABC,'in_files')\n\n\n\n def printFullPath(outFileFullPath):\n print(\"=\"*80)\n print(\"=\"*80)\n print(\"=\"*80)\n print(\"=\"*80)\n print(\"{0}\".format(outFileFullPath))\n return outFileFullPath\n printOutImage = pe.Node( Function(function=printFullPath, input_names = ['outFileFullPath'], output_names = ['genoutFileFullPath']), run_without_submitting=True, name=\"99_printOutImage\")\n cutWF.connect( GADT2, 'outputVolume', printOutImage, 'outFileFullPath' )","def export_robot_to_xacro_files():\n global robot, OUTPUT, NAME\n doc = Document()\n root = doc.createElement('robot')\n doc.appendChild(root)\n root.setAttribute(\"xmlns:xacro\", \"http://www.ros.org/wiki/xacro\")\n root.setAttribute(\"name\", robot.name)\n root.appendChild(ur.short(doc, 'xacro:include', 'filename', NAME +\n '_visual_collisions.xacro'))\n create_visual_xacro()\n for i in XACRO_DICO.keys():\n print('exporting ' + NAME + '_' + i + '.xacro')\n if i.find('eye') != -1:\n export_kinematic_chain_to_xacro(i, 'HeadRoll_link',\n 'HeadRoll_link')\n else:\n export_kinematic_chain_to_xacro(i)\n filenamerobot = NAME + '_' + i + '.xacro'\n root.appendChild(ur.short(doc, 'xacro:include', 'filename',\n filenamerobot))\n # Transmission elements not available from Aldebaran libraries yet\n export_robot_element('Transmission')\n root.appendChild(ur.short(doc, 'xacro:include', 'filename', NAME +\n '_Transmission.xacro'))\n # Gazebo Plugin not available from Aldebaran libraries yet\n export_robot_element('Gazebo')\n root.appendChild(ur.short(doc, 'xacro:include', 'filename', NAME +\n '_Gazebo.xacro'))\n root.appendChild(ur.short(doc, 'xacro:include', 'filename', NAME +\n '_sensors.xacro'))\n export_list_to_xacro(['_frame'], OUTPUT[0:OUTPUT.rfind('.')] +\n '_sensors.xacro')\n root.appendChild(ur.short(doc, 'xacro:include', 'filename', NAME +\n '_fingers.xacro'))\n export_list_to_xacro(['Finger', 'Thumb'], OUTPUT[0:OUTPUT.rfind('.')] +\n '_fingers.xacro')\n if NAME == 'pepper':\n root.appendChild(ur.short(doc, 'xacro:include', 'filename', NAME +\n '_wheels.xacro'))\n export_list_to_xacro(['Wheel'], OUTPUT[0:OUTPUT.rfind('.')] +\n '_wheels.xacro')\n if NAME == 'romeo':\n root.appendChild(ur.short(doc, 'xacro:include', 'filename',\n 'romeo_cap.xacro'))\n\n filename = OUTPUT[0:OUTPUT.rfind('.')] + '_robot.xacro'\n write_comments_in_xacro(doc, filename)\n print('output directory : ' + OUTPUT[0:OUTPUT.rfind('/') + 1])","def ref_mapping_directory(tmpdir_factory):\n basedir = tmpdir_factory.mktemp('data')\n mapdir = basedir.mkdir('mappings')\n\n template = textwrap.dedent(\"\"\"\n [ molecule ]\n dummy_{0}\n\n [ from ]\n {1}\n\n [ to ]\n {2}\n\n [ atoms ]\n 0 X1{0} A{0} B{0}\n 1 X2{0} C{0} D{0}\n \"\"\")\n\n mappings = collections.defaultdict(lambda: collections.defaultdict(dict))\n\n force_fields_from = ['ff{}'.format(i) for i in range(4)]\n force_fields_to = force_fields_from + ['only_to']\n force_fields_from = force_fields_from + ['only_from']\n iterate_on = itertools.product(force_fields_from, force_fields_to, range(3))\n for idx, (from_ff, to_ff, _) in enumerate(iterate_on):\n mapfile = mapdir / 'file{}.map'.format(idx)\n with open(str(mapfile), 'w') as outfile:\n outfile.write(template.format(idx, from_ff, to_ff))\n\n mapping = {\n (0, 'X1{}'.format(idx)): [(0, 'A{}'.format(idx)), (0, 'B{}'.format(idx))],\n (0, 'X2{}'.format(idx)): [(0, 'C{}'.format(idx)), (0, 'D{}'.format(idx))],\n }\n weights = {\n (0, 'A{}'.format(idx)): {(0, 'X1{}'.format(idx)): 0.5},\n (0, 'B{}'.format(idx)): {(0, 'X1{}'.format(idx)): 0.5},\n (0, 'C{}'.format(idx)): {(0, 'X2{}'.format(idx)): 0.5},\n (0, 'D{}'.format(idx)): {(0, 'X2{}'.format(idx)): 0.5},\n }\n extra = []\n mappings[from_ff][to_ff]['dummy_{}'.format(idx)] = (mapping, weights, extra)\n\n mappings = {from_ff: dict(to_ff) for from_ff, to_ff in mappings.items()}\n\n return Path(str(basedir)), mappings","def content(tmp_loc, ref_names_dict, order):\n \n fl = '[Content_Types].xml'\n inp_path = '/'.join([tmp_loc, fl])\n out_path = '/'.join([output_path, fl])\n \n cnt_lst = []\n asset_lst = []\n def_att = []\n d = dict()\n \n root1,tree1 = gen_tree(inp_path)\n root2,tree2 = gen_tree(out_path)\n \n # get all the extensions belongs to \"Default\" tag\n for relation in root2:\n if 'Default' in relation.tag:\n def_att.append(relation.attrib['Extension'])\n else:\n break\n \n for relation in root1:\n if 'Override' in relation.tag:\n attrib = relation.attrib['PartName'][1:]\n try:\n cnt = attrib.split('ppt/')[-1]\n ini = '/ppt/'\n except:\n cnt = attrib\n ini = '/'\n if cnt in ref_names_dict.keys():\n relation.attrib['PartName'] = f'{ini}{ref_names_dict[cnt]}'\n cnt_lst.append(relation)\n # asset_lst.append(relation.attrib['PartName'])\n else:\n cnt_lst.append(relation)\n if relation.attrib['PartName'] not in asset_lst:\n asset_lst.append(relation.attrib['PartName'])\n else:\n attrib = relation.attrib['Extension']\n if attrib not in def_att:\n cnt_lst.append(relation)\n # asset_lst.append(relation.attrib['Extension'])\n # deal with the assest_lst\n # print(\"AA: \", asset_lst)\n cnt_lst = natsort.natsorted(cnt_lst)\n for ele in cnt_lst:\n prev = tree2.find(ele.tag)\n prev.addnext(ele)\n \n tree2.write(out_path, pretty_print=True, xml_declaration=True, encoding='UTF-8', standalone=True)\n \n unq_attr = []\n for relation in root2:\n if 'Override' in relation.tag:\n if relation.attrib['PartName'] not in unq_attr:\n unq_attr.append(relation.attrib['PartName'])\n else:\n root2.remove(relation)\n tree2.write(out_path, pretty_print=True, xml_declaration=True, encoding='UTF-8', standalone=True)","def get_packages():\n\tapps=[]\n\tlspackages = glob.glob(u'/home/wpkg/packages/*.xml')\n\tfor fic in lspackages:\n #print fic\n\t\tif os.path.isfile(fic):\n\t\t\ttry:\n\t\t\t\txml = etree.parse(fic)\n\t\t\texcept:\n\t\t\t\t#si erreur on tente de corriger le fichier\n\t\t\t\toput = open(\"output.txt\",\"w\")\n\t\t\t\tdata = open(fic).read()\n\t\t\t\toput.write( re.sub('&(?!amp;|quot;|nbsp;|gt;|lt;|laquo;|raquo;|copy;|reg;|bul;|rsquo;)', '&', data) )\n\t\t\t\toput.close()\n\t\t\t\tshutil.move(\"output.txt\", fic)\n\n\t\t\tfinally:\n\t\t\t\tparser = etree.XMLParser(remove_comments=False)\n\t\t\t\txml = etree.parse(fic,parser)\n\t\t\t\tstrxml = etree.tostring(xml.getroot())\n\t\t\t\tfor group in xml.getiterator(u'package'):\n\t\t\t\t\tuid = group.get(u'id')\n\t\t\t\t\tname = group.get(u'name')\n\t\t\t\t\tdvars = dict()\n\t\t\t\t\tfor vars in xml.getiterator('variable'):\n\t\t\t\t\t\tdvars[vars.get('name').lower()] = vars.get('value').lower()\n\n\t\t\t\t#on remplit le dico des appli dispo\n\t\t\t\t#print fic,uid,name\n\t\t\t\tapps.append((fic,uid,name))\n\n\treturn apps","def create_osm_conf(self, stage_dir=None):\n result = INI_TEMPLATE.format(\n points_attributes=','.join(self.points),\n lines_attributes=','.join(self.lines),\n multipolygons_attributes=','.join(self.polygons)\n )\n with open(self.output_ini, 'w') as f:\n f.write(result)\n return self.output_ini","def exportDB ( c ) :\n assert str(type(c)) == \"<type '_mysql.connection'>\"\n xml = openTag ( \"WorldCrises\" )\n xml += exportCrises ( c )\n xml += exportOrgs ( c )\n xml += exportPeople ( c )\n xml += exportTypes ( c )\n xml += closeTag ( \"WorldCrises\" )\n assert str ( type ( xml ) ) == \"<type 'str'>\"\n return xml","def writeGromacsTopolFiles(self, amb2gmx = False):\n\n self.printMess(\"Writing GROMACS files\\n\")\n\n self.setAtomType4Gromacs()\n\n self.writeGroFile()\n\n self.writeGromacsTop(amb2gmx = amb2gmx)\n\n self.writeMdpFiles()","def group_by_namespace(self):\n package = self.container.config.output.package\n groups = collections.group_by(self.container, key=get_target_namespace)\n for namespace, classes in groups.items():\n self.assign(classes, package, namespace or \"\")","def write_map(self, file_name):\n\n if self.pixel == \"HEALPIX\":\n hp.fitsfunc.write_map(file_name, self.data, overwrite=True)\n if self.pixel == \"CAR\":\n enmap.write_map(file_name, self.data)","def main(*args):\r\n print(START_MESSAGE)\r\n print(\"Script Location:\", location)\r\n print(\"Arguments Passed:\", args)\r\n\r\n root = ET.parse(xmlfile).getroot()\r\n keys = []\r\n out = \"\"\r\n\r\n for child in root[1]:\r\n out += child.attrib['Name'] + \";\" + child[0].text + \"\\n\"\r\n\r\n with open(outputfile, 'w') as f:\r\n f.write(out)","def patch_well_known_namespaces(etree_module):\n etree_module._namespace_map.update({\n \"http://www.w3.org/1999/02/22-rdf-syntax-ns#\": \"rdf\", \n \"http://purl.org/rss/1.0/\": \"rss\", \n \"http://purl.org/rss/1.0/modules/taxonomy/\": \"taxo\", \n \"http://purl.org/dc/elements/1.1/\": \"dc\", \n \"http://purl.org/rss/1.0/modules/syndication/\": \"syn\", \n \"http://www.w3.org/2003/01/geo/wgs84_pos#\": \"geo\"})","def convert_to_annotation(file, output):\n resource = parse_bel_resource(file)\n\n write_annotation(\n keyword=resource['Namespace']['Keyword'],\n values={k: '' for k in resource['Values']},\n citation_name=resource['Citation']['NameString'],\n description=resource['Namespace']['DescriptionString'],\n file=output,\n )","def writeXml(self):\n curdir = os.getcwd()\n os.chdir(self.Imagedir)\n allImageLists = [self.sciImlist, self.ctxImlist, self.wgtImlist, self.rmsImlist]\n \n for imlist in allImageLists:\n for im in imlist:\n file = xmlUtil.markupImage(im,dataset=self.obsName)\n \n # Don't write these images as output of this module, which\n # really doesn't have any.\n \n #if file not in self.outputList.keys():\n # self.outputList[file] = [im]\n \n os.chdir(curdir)\n return","def _gtTSmap(self):\n if os.path.isfile(self.outtsmap):\n # Already exists\n return\n\n if self.csys == 'GAL':\n center_icrs = SkyCoord(ra=self.ra*u.degree, dec=self.dec*u.degree, frame='icrs')\n self.ra = center_icrs.galactic.l.deg\n self.dec = center_icrs.galactic.b.deg\n\n model = os.path.join(self.workpath, 'TSmapModel.xml') \n rfil = open(self.outmodel, 'r')\n wfil = open(model, 'w')\n isSrc = False\n isDif = False\n for line in rfil:\n if (isSrc) and ('<source name' in line):\n # Arrived to a new source, restart copying\n isSrc = False\n if (isDif) and ('<source name' in line) and ('PointSource' in line):\n isDif = False\n if 'TARGET' in line:\n isSrc = True\n if ('<source name=\"gll_iem_v06\"' in line) or ('<source name=\"iso_source_v06\"' in line): \n isDif = True\n \n if isSrc:\n # Do not copy the Target model to make it appear in the TS map\n pass\n else:\n if isDif:\n # Leave Diffuse model normalizations free\n wfil.write(line)\n else:\n # Make sur the gtlike output source model has all source parameters fixed\n wfil.write(line.replace('free=\"1\"', 'free=\"0\"'))\n rfil.close()\n wfil.close()\n\n # Launch the gttsmap tool \n if self.mode == 'binned':\n os.popen(\"gttsmap evfile={} scfile={} bexpmap={} expcube={} cmap={} srcmdl={}\\\n outfile={} evtype={} irfs=CALDB optimizer=NewMinuit statistic=BINNED ftol=1e-2\\\n coordsys={} proj=AIT nxpix={} nypix={} binsz={} xref={} yref={}\".format(self.outmktime,\n self.ft2, self.outbinexp, self.outltcube, self.outbincub, model, self.outtsmap, self.evtype,\n self.csys, self.imwid, self.imwid, self.binsz, self.ra, self.dec))\n elif self.mode == 'unbinned':\n os.popen(\"gttsmap evfile={} scfile={} expmap={} expcube={} srcmdl={}\\\n outfile={} evtype={} irfs=CALDB optimizer=NewMinuit statistic=UNBINNED ftol=1e-2\\\n coordsys={} proj=AIT nxpix={} nypix={} binsz={} xref={} yref={}\".format(self.outmktime,\n self.ft2, self.outexpmap, self.outltcube, model, self.outtsmap, self.evtype,\n self.csys, self.imwid, self.imwid, self.binsz, self.ra, self.dec))\n else:\n return\n\n if self.csys == 'GAL':\n self.ra = center_icrs.ra.deg\n self.dec = center_icrs.dec.deg\n return","def writeImports2File(self, file, indent = \" \"):\r\n # import each entity and its associated graphical file\r\n for obj in self.listNodes.keys():\r\n file.write(indent+\"from \"+obj+\" import \"+obj+\"\\n\")\r\n if not obj[0:4] == \"ASG_\":\r\n file.write(indent+\"from graph_\"+obj+\" import graph_\"+obj+\"\\n\")","def create_label_map(self, outpath):\n cnt = 1\n with open(outpath, 'w') as fp:\n for itm in self.clazzes:\n fp.write('item {\\n')\n fp.write('\\tname: \"{}\"\\n'.format(itm))\n fp.write('\\tid: {}\\n'.format(cnt))\n fp.write('}\\n')\n cnt += 1","def test_create_image_stream_mapping_for_all_namespaces(self):\n pass","def _write_packet_dict(ctx, package_dict):\n p4gf_util.write_dict_to_file(package_dict, _packet_filename(ctx.config.repo_name))","def meta2xml(meta, filename):\n\n # this is stupid, just use dict2xml\n xml = dict2xml(meta)\n with open(filename, 'w+') as output:\n output.write(xml)","def updateNamespace(self):\n import addict\n self.namespace['config'] = addict.Dict(self.namespace['config'])","def writeOrganisms( self ):\n\n self.logger.info( 'writeOrganisms: START' )\n\n # Generate inserts for meabolic pathways.\n self.importerOrganism.writeOrganisms()\n \n\n self.logger.info( 'writeOrganisms: keggreader.getAllOrganismEcs() : START' )\n\n # Get all organism ecs relations.\n organismEcs = self.reader.getAllOrganismEcs()\n\n self.logger.info( 'writeOrganisms: keggreader.getAllOrganismEcs() : DONE' )\n\n\n self.logger.info( 'writeOrganisms: keggreader.getAllOrganismMaps() : START' )\n\n # Get all organism maps relations.\n organismMaps = self.reader.getAllOrganismMaps()\n\n self.logger.info( 'writeOrganisms: keggreader.getAllOrganismMaps() : DONE' )\n\n\n self.logger.info( 'writeOrganisms: organismEcFile is organismEcsInsert.psql' )\n\n # Open protein_ecs insert file.\n organismEcFile = self.openInsertFile( 'organismEcsInsert.psql' )\n\n\n self.logger.info( 'writeOrganisms: organismMapFile is organismMapsInsert.psql' )\n\n # Open organism_maps insert file.\n organismMapFile = self.openInsertFile( 'organismMapsInsert.psql' )\n\n\n # Now we have to write organism_ecs table.\n for organism,relationalDatabaseId in self.importerOrganism.organismsInserted.iteritems():\n\n\n organismId = relationalDatabaseId\n\n if len( organismEcs[ organism ] ) > 0:\n \n self.logger.info( 'writeOrganisms: the organism: ' + organism + ' : FOUND ' + str(len(organismEcs[organism])) + ' EC numbers.' )\n for ec in organismEcs[ organism ]:\n ecId = self.importerEc.ecsInserted[ ec ]\n\n #self.writeOrganismEcsFile( organismEcFile, organismId , ecId )\n self.writeFile( organismEcFile, 'organism_ecs', [ str(organismId) , str(ecId) ] )\n else:\n self.logger.info( 'writeOrganisms: the organism: ' + organism + ' : doesnt have EC numbers associated.' )\n\n\n if len( organismMaps[ organism ] ) > 0:\n \n self.logger.info( 'writeOrganisms: the organism: ' + organism + ' : FOUND ' + str(len(organismMaps[organism])) + ' MAP numbers.' )\n for mapNumber in organismMaps[ organism ]:\n\n # We don't need maps that is not metabolic maps.\n if mapNumber in self.importerPathway.pathwayMapsInserted:\n mapId = self.importerPathway.pathwayMapsInserted[ mapNumber ]\n\n #self.writeOrganismMapsFile( organismMapFile, organismId , mapId )\n self.writeFile( organismMapFile, 'organism_maps', [ str(organismId) , str(mapId) ] )\n else:\n self.logger.info( 'writeOrganisms: the organism: ' + organism + ' : doesnt have MAP numbers associated.' )\n\n\n self.logger.info( 'writeOrganisms: DONE' )","def resource_mapping():\n return {\n 'OS::Heat::ResourceChain': ResourceChain,\n }","def from_ols(ontology, domain, encoding, ols_base_url, deploy, no_hash_check, output):\n ont = OlsNamespaceOntology(ontology, domain, encoding=encoding, ols_base=ols_base_url)\n ont.write_namespace(output)\n\n if deploy:\n ont.deploy_namespace(hash_check=(not no_hash_check))","def generate(self, conf, env, data):\n\n path = joinurl(conf['output_dir'], self.path)\n sm = Map()\n\n pages = []\n for i in env.globals.pages:\n pages.append(i.slug)\n for i in env.globals.entrylist:\n pages.append(i.slug)\n\n if exists(path) and not self.modified and not conf.modified:\n event.skip('sitemap', path)\n raise StopIteration\n for ns, fname in self.files:\n\n if ns == 'page' or ns == 'entry' or ns == 'index':\n pass\n else:\n continue\n\n permalink = '/' + fname.replace(conf['output_dir'], '')\n permalink = rchop(permalink, 'index.html')\n url = conf['www_root'] + permalink\n\n priority, changefreq = self.scores.get(ns, (0.5, 'weekly'))\n\n _pages = env.globals.entrylist + env.globals.pages\n\n for i in _pages:\n if permalink.strip('/') in pages:\n if i.slug.strip('/') == permalink.strip('/'):\n pages.remove(permalink.strip('/'))\n video = re.search('youtube.com/(v/|watch\\?v=|embed/)([a-zA-Z0-9\\-_]*)', i.content)\n if video:\n yt = video.group().split('/')[-1]\n sm.add(url, yt, i.title, i.metadesc, getmtime(fname), changefreq, priority)\n\n sm.finish()\n yield sm, path","def process(self):\n sections = Map.build_from_dirs(\n self.config_map,\n self.persist_fs,\n self.persist_fs.list_dirs(self.config_map.get_repo_path),\n )\n puml: PUML = PUML(self.config_map, self.persist_fs, sections)\n puml.write()","def init():\n\tprint(\"Initializing Application\")\n\tlabelDict = {}\n\tlabelsPath = os.path.sep.join([yolodir, \"coco.names\"])\n\tLABELS = open(labelsPath).read().strip().split(\"\\n\")\n\tfor label in LABELS:\n\t\tlabelDict[label] = 0\n\tjson.dump(labelDict, open(outputFile, \"w\"))\t\n\treturn \"Output Counter Initialized\"","def __genAttributesROOT( self, file, indent = ' ' ):\r\n # Go through all actively tracked ASG's (formalisms)\r\n for modelMetaName in self.__trackASG.keys():\r\n if( not self.__isASGbyNameEmpty( modelMetaName ) ):\r\n # This formalism has at least one entity, we NEED IT\r\n ASG = self.getASGbyName( modelMetaName )\r\n \r\n #todo: clean this up\r\n try:\r\n modelTracker = self.__trackAttributes[ modelMetaName ]\r\n except:\r\n print 'ERROR: see ASG.py in __genAttributesROOT(), caused by Buttons Model pointing to a Formalism with a different name'\r\n print 'modelMetaName', modelMetaName \r\n print 'self.__trackAttributes', self.__trackAttributes\r\n print 'self.__trackASG', self.__trackASG\r\n \r\n file.write( '\\n'+indent+'# --- Generating attributes code for ASG '\r\n +modelMetaName+' ---\\n' ) \r\n # DOH! Some formalisms may not have any attributes at all!\r\n if( len( modelTracker ) > 0 ):\r\n file.write( indent + 'if( ' + modelMetaName + 'RootNode ): ' )\r\n i2 = indent + ' '\r\n \r\n #Generates code for the attributes value\r\n for attr in modelTracker.keys():\r\n #print attr, ASG.getAttrValue(attr)\r\n file.write( '\\n'+i2+'# '+attr+'\\n' )\r\n \r\n # Write its value to file\r\n ASG.getAttrValue(attr).writeValue2File ( file = file, indent=i2, \r\n objName = modelMetaName+'RootNode.'+attr, depth = 1,\r\n generatingCode = 0) \r\n else: \r\n file.write( indent+'# No attributes to generate!\\n' )\r\n file.write( indent+'# --- ASG attributes over ---\\n\\n' )","def exportTypes( c ) :\n assert str(type(c)) == \"<type '_mysql.connection'>\"\n xml = \"\"\n cT = sqlQuery ( c, \"select * from CrisisKind;\" )\n oT = sqlQuery ( c, \"select * from OrganizationKind;\" )\n pT = sqlQuery ( c, \"select * from PersonKind;\" ) \n for i in cT:\n xml += openTagAtt (\"CrisisKind\", \"crisisKindIdent\", i[0])\n xml += openCloseTag (\"Name\", i[1])\n xml += openCloseTag (\"Description\", i[2])\n xml += closeTag (\"CrisisKind\") \n for i in oT:\n xml += openTagAtt (\"OrganizationKind\", \"organizationKindIdent\", i[0])\n xml += openCloseTag (\"Name\", i[1])\n xml += openCloseTag (\"Description\", i[2])\n xml += closeTag (\"OrganizationKind\")\n for i in pT:\n xml += openTagAtt (\"PersonKind\", \"personKindIdent\", i[0])\n xml += openCloseTag (\"Name\", i[1])\n xml += openCloseTag (\"Description\", i[2])\n xml += closeTag (\"PersonKind\")\n assert str ( type ( xml ) ) == \"<type 'str'>\"\n return xml","def _update_database_map(self, path):\n if path:\n filename = path + '/APD_MAP.txt'\n else:\n filename = 'APD_MAP.txt'\n filepointer = open(filename, 'w')\n for invariom, molecule in self.map.items():\n filepointer.write(invariom + ':' + molecule + '\\n')\n filepointer.close()"],"string":"[\n \"def makeMapping(globalMap):\\n \\n from memops.xml.Implementation import bool2str, str2bool\\n\\n # Set up top level dictionaries\\n loadMaps = globalMap.get('loadMaps')\\n mapsByGuid = globalMap.get('mapsByGuid')\\n\\n abstractTypes = globalMap.get('ANAP').get('abstractTypes')\\n exolinks = globalMap.get('ANAP').get('exolinks')\\n\\n # DataType GraphicsHandlerType\\n currentMap = {}\\n abstractTypes['GraphicsHandlerType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-03-11:26:03_00001'] = currentMap\\n loadMaps['ANAP.GraphicsHandlerType'] = currentMap\\n currentMap['tag'] = 'ANAP.GraphicsHandlerType'\\n currentMap['type'] = 'simple'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-03-11:26:03_00001'\\n currentMap['toStr'] = 'text'\\n currentMap['cnvrt'] = 'text'\\n\\n # Class AnalysisProfile\\n currentMap = {}\\n abstractTypes['AnalysisProfile'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00004'] = currentMap\\n loadMaps['ANAP.AnalysisProfile'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00004'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'analysisProfiles'\\n currentMap['isTop'] = True\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.AnalysisProfile\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute AnalysisProfile.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute AnalysisProfile.bgColor\\n currentMap = {}\\n contentMap['bgColor'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00031'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.bgColor'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.bgColor'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00031'\\n currentMap['name'] = 'bgColor'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['default'] = '#FFFFFF'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\\n\\n # Attribute AnalysisProfile.createdBy\\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute AnalysisProfile.fgColor\\n currentMap = {}\\n contentMap['fgColor'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00032'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.fgColor'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.fgColor'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00032'\\n currentMap['name'] = 'fgColor'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['default'] = '#000000'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\\n\\n # Attribute AnalysisProfile.font\\n currentMap = {}\\n contentMap['font'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00030'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.font'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.font'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00030'\\n currentMap['name'] = 'font'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Attribute AnalysisProfile.graphicsHandler\\n currentMap = {}\\n contentMap['graphicsHandler'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00040'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.graphicsHandler'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.graphicsHandler'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00040'\\n currentMap['name'] = 'graphicsHandler'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = 'Tk'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-10-03-11:26:03_00001')\\n\\n # Attribute AnalysisProfile.guid\\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\\n\\n # Attribute AnalysisProfile.isModifiable\\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute AnalysisProfile.lastUnlockedBy\\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute AnalysisProfile.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00029'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.name'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00029'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Attribute AnalysisProfile.panView\\n currentMap = {}\\n contentMap['panView'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00036'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.panView'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.panView'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00036'\\n currentMap['name'] = 'panView'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = True\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute AnalysisProfile.sendBugReports\\n currentMap = {}\\n contentMap['sendBugReports'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00004'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.sendBugReports'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.sendBugReports'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00004'\\n currentMap['name'] = 'sendBugReports'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = 'maybe'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2010-11-17-16:21:33_00001')\\n\\n # Attribute AnalysisProfile.transientDialogs\\n currentMap = {}\\n contentMap['transientDialogs'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00037'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.transientDialogs'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.transientDialogs'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00037'\\n currentMap['name'] = 'transientDialogs'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = True\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute AnalysisProfile.transientWindows\\n currentMap = {}\\n contentMap['transientWindows'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00038'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.transientWindows'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.transientWindows'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00038'\\n currentMap['name'] = 'transientWindows'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = False\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute AnalysisProfile.twoCharShortcuts\\n currentMap = {}\\n contentMap['twoCharShortcuts'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00039'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.twoCharShortcuts'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.twoCharShortcuts'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00039'\\n currentMap['name'] = 'twoCharShortcuts'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = False\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute AnalysisProfile.useCrosshair\\n currentMap = {}\\n contentMap['useCrosshair'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00034'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.useCrosshair'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.useCrosshair'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00034'\\n currentMap['name'] = 'useCrosshair'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = True\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute AnalysisProfile.useGlobalShortcuts\\n currentMap = {}\\n contentMap['useGlobalShortcuts'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00035'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.useGlobalShortcuts'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.useGlobalShortcuts'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00035'\\n currentMap['name'] = 'useGlobalShortcuts'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['default'] = False\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute AnalysisProfile.userEmail\\n currentMap = {}\\n contentMap['userEmail'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00003'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.userEmail'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.userEmail'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00003'\\n currentMap['name'] = 'userEmail'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\\n\\n # Attribute AnalysisProfile.userName\\n currentMap = {}\\n contentMap['userName'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00001'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.userName'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.userName'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00001'\\n currentMap['name'] = 'userName'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Attribute AnalysisProfile.userOrganisation\\n currentMap = {}\\n contentMap['userOrganisation'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00002'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.userOrganisation'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.userOrganisation'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2010-11-17-16:21:37_00002'\\n currentMap['name'] = 'userOrganisation'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Attribute AnalysisProfile.webBrowser\\n currentMap = {}\\n contentMap['webBrowser'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00033'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.webBrowser'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.webBrowser'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00033'\\n currentMap['name'] = 'webBrowser'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Role AnalysisProfile.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role AnalysisProfile.colorSchemes\\n currentMap = {}\\n contentMap['colorSchemes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00026'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.colorSchemes'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.colorSchemes'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00026'\\n currentMap['name'] = 'colorSchemes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\\n\\n # Role AnalysisProfile.macros\\n currentMap = {}\\n contentMap['macros'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00022'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.macros'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.macros'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00022'\\n currentMap['name'] = 'macros'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\\n\\n # Role AnalysisProfile.marksColor\\n currentMap = {}\\n contentMap['marksColor'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00028'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.marksColor'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.marksColor'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00028'\\n currentMap['name'] = 'marksColor'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['implSkip'] = True\\n currentMap['copyOverride'] = True\\n\\n # Role AnalysisProfile.refExpProfiles\\n currentMap = {}\\n contentMap['refExpProfiles'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00024'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.refExpProfiles'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.refExpProfiles'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00024'\\n currentMap['name'] = 'refExpProfiles'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\\n\\n # Role AnalysisProfile.residueProfiles\\n currentMap = {}\\n contentMap['residueProfiles'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00020'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.residueProfiles'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.residueProfiles'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00020'\\n currentMap['name'] = 'residueProfiles'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('ANAP').get('abstractTypes')\\n\\n # Role AnalysisProfile.rulersColor\\n currentMap = {}\\n contentMap['rulersColor'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00027'] = currentMap\\n loadMaps['ANAP.AnalysisProfile.rulersColor'] = currentMap\\n currentMap['tag'] = 'ANAP.AnalysisProfile.rulersColor'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00027'\\n currentMap['name'] = 'rulersColor'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['implSkip'] = True\\n currentMap['copyOverride'] = True\\n # End of AnalysisProfile\\n\\n currentMap = abstractTypes.get('AnalysisProfile')\\n aList = ['createdBy', 'graphicsHandler', 'guid', 'isModifiable', 'lastUnlockedBy', 'name', 'panView', 'sendBugReports', 'transientDialogs', 'transientWindows', 'twoCharShortcuts', 'useCrosshair', 'useGlobalShortcuts', 'userEmail', 'webBrowser']\\n currentMap['headerAttrs'] = aList\\n aList = ['bgColor', 'fgColor', 'font', 'userName', 'userOrganisation', 'marksColor', 'rulersColor']\\n currentMap['simpleAttrs'] = aList\\n aList = ['residueProfiles', 'refExpProfiles', 'macros', 'colorSchemes', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['colorSchemes', 'macros', 'refExpProfiles', 'residueProfiles']\\n currentMap['children'] = aList\\n\\n # Class ColorScheme\\n currentMap = {}\\n abstractTypes['ColorScheme'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00002'] = currentMap\\n loadMaps['ANAP.ColorScheme'] = currentMap\\n currentMap['tag'] = 'ANAP.ColorScheme'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00002'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'colorSchemes'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ColorScheme\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute ColorScheme.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute ColorScheme.colors\\n currentMap = {}\\n contentMap['colors'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00043'] = currentMap\\n loadMaps['ANAP.ColorScheme.colors'] = currentMap\\n currentMap['tag'] = 'ANAP.ColorScheme.colors'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00043'\\n currentMap['name'] = 'colors'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\\n\\n # Attribute ColorScheme.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00007'] = currentMap\\n loadMaps['ANAP.ColorScheme.name'] = currentMap\\n currentMap['tag'] = 'ANAP.ColorScheme.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00007'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Role ColorScheme.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of ColorScheme\\n\\n currentMap = abstractTypes.get('ColorScheme')\\n aList = ['colors', 'name']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class Macro\\n currentMap = {}\\n abstractTypes['Macro'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00001'] = currentMap\\n loadMaps['ANAP.Macro'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00001'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'macros'\\n currentMap['objkey'] = 'serial'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.Macro\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute Macro.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute Macro.details\\n currentMap = {}\\n contentMap['details'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00005'] = currentMap\\n loadMaps['ANAP.Macro.details'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.details'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00005'\\n currentMap['name'] = 'details'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\\n\\n # Attribute Macro.function\\n currentMap = {}\\n contentMap['function'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00002'] = currentMap\\n loadMaps['ANAP.Macro.function'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.function'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00002'\\n currentMap['name'] = 'function'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Attribute Macro.isInMenu\\n currentMap = {}\\n contentMap['isInMenu'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00005'] = currentMap\\n loadMaps['ANAP.Macro.isInMenu'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.isInMenu'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00005'\\n currentMap['name'] = 'isInMenu'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = False\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute Macro.isInMouseMenu\\n currentMap = {}\\n contentMap['isInMouseMenu'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00006'] = currentMap\\n loadMaps['ANAP.Macro.isInMouseMenu'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.isInMouseMenu'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-29-13:48:16_00006'\\n currentMap['name'] = 'isInMouseMenu'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = False\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00028')\\n\\n # Attribute Macro.module\\n currentMap = {}\\n contentMap['module'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00003'] = currentMap\\n loadMaps['ANAP.Macro.module'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.module'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00003'\\n currentMap['name'] = 'module'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Attribute Macro.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:10_00001'] = currentMap\\n loadMaps['ANAP.Macro.name'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:10_00001'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Attribute Macro.ordering\\n currentMap = {}\\n contentMap['ordering'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00004'] = currentMap\\n loadMaps['ANAP.Macro.ordering'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.ordering'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00004'\\n currentMap['name'] = 'ordering'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['proc'] = 'direct'\\n currentMap['default'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\\n\\n # Attribute Macro.path\\n currentMap = {}\\n contentMap['path'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00001'] = currentMap\\n loadMaps['ANAP.Macro.path'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.path'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00001'\\n currentMap['name'] = 'path'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00003')\\n\\n # Attribute Macro.serial\\n currentMap = {}\\n contentMap['serial'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:09_00001'] = currentMap\\n loadMaps['ANAP.Macro.serial'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.serial'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:09_00001'\\n currentMap['name'] = 'serial'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\\n\\n # Attribute Macro.shortcut\\n currentMap = {}\\n contentMap['shortcut'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00006'] = currentMap\\n loadMaps['ANAP.Macro.shortcut'] = currentMap\\n currentMap['tag'] = 'ANAP.Macro.shortcut'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-17-15:11:12_00006'\\n currentMap['name'] = 'shortcut'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Role Macro.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of Macro\\n\\n currentMap = abstractTypes.get('Macro')\\n aList = ['function', 'isInMenu', 'isInMouseMenu', 'module', 'ordering', 'serial', 'shortcut']\\n currentMap['headerAttrs'] = aList\\n aList = ['details', 'name', 'path']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class RefExpProfile\\n currentMap = {}\\n abstractTypes['RefExpProfile'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00006'] = currentMap\\n loadMaps['ANAP.RefExpProfile'] = currentMap\\n currentMap['tag'] = 'ANAP.RefExpProfile'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00006'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'refExpProfiles'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.RefExpProfile\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute RefExpProfile.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute RefExpProfile.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00046'] = currentMap\\n loadMaps['ANAP.RefExpProfile.name'] = currentMap\\n currentMap['tag'] = 'ANAP.RefExpProfile.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00046'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Attribute RefExpProfile.peakSymbolColors\\n currentMap = {}\\n contentMap['peakSymbolColors'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00048'] = currentMap\\n loadMaps['ANAP.RefExpProfile.peakSymbolColors'] = currentMap\\n currentMap['tag'] = 'ANAP.RefExpProfile.peakSymbolColors'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00048'\\n currentMap['name'] = 'peakSymbolColors'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\\n\\n # Attribute RefExpProfile.peakTextColors\\n currentMap = {}\\n contentMap['peakTextColors'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00049'] = currentMap\\n loadMaps['ANAP.RefExpProfile.peakTextColors'] = currentMap\\n currentMap['tag'] = 'ANAP.RefExpProfile.peakTextColors'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00049'\\n currentMap['name'] = 'peakTextColors'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00007')\\n\\n # Attribute RefExpProfile.refExpNames\\n currentMap = {}\\n contentMap['refExpNames'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00047'] = currentMap\\n loadMaps['ANAP.RefExpProfile.refExpNames'] = currentMap\\n currentMap['tag'] = 'ANAP.RefExpProfile.refExpNames'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00047'\\n currentMap['name'] = 'refExpNames'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Role RefExpProfile.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role RefExpProfile.negColorSchemes\\n currentMap = {}\\n contentMap['negColorSchemes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00045'] = currentMap\\n loadMaps['ANAP.RefExpProfile.negColorSchemes'] = currentMap\\n currentMap['tag'] = 'ANAP.RefExpProfile.negColorSchemes'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00045'\\n currentMap['name'] = 'negColorSchemes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n\\n # Role RefExpProfile.posColorSchemes\\n currentMap = {}\\n contentMap['posColorSchemes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00041'] = currentMap\\n loadMaps['ANAP.RefExpProfile.posColorSchemes'] = currentMap\\n currentMap['tag'] = 'ANAP.RefExpProfile.posColorSchemes'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00041'\\n currentMap['name'] = 'posColorSchemes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n # End of RefExpProfile\\n\\n currentMap = abstractTypes.get('RefExpProfile')\\n aList = ['name']\\n currentMap['headerAttrs'] = aList\\n aList = ['peakSymbolColors', 'peakTextColors', 'refExpNames', 'negColorSchemes', 'posColorSchemes']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class ResidueProfile\\n currentMap = {}\\n abstractTypes['ResidueProfile'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00005'] = currentMap\\n loadMaps['ANAP.ResidueProfile'] = currentMap\\n currentMap['tag'] = 'ANAP.ResidueProfile'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00005'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'residueProfiles'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ResidueProfile\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute ResidueProfile.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute ResidueProfile.ccpCode\\n currentMap = {}\\n contentMap['ccpCode'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00051'] = currentMap\\n loadMaps['ANAP.ResidueProfile.ccpCode'] = currentMap\\n currentMap['tag'] = 'ANAP.ResidueProfile.ccpCode'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00051'\\n currentMap['name'] = 'ccpCode'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\\n\\n # Attribute ResidueProfile.guiName\\n currentMap = {}\\n contentMap['guiName'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00052'] = currentMap\\n loadMaps['ANAP.ResidueProfile.guiName'] = currentMap\\n currentMap['tag'] = 'ANAP.ResidueProfile.guiName'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00052'\\n currentMap['name'] = 'guiName'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\\n\\n # Attribute ResidueProfile.molType\\n currentMap = {}\\n contentMap['molType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00050'] = currentMap\\n loadMaps['ANAP.ResidueProfile.molType'] = currentMap\\n currentMap['tag'] = 'ANAP.ResidueProfile.molType'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:52_00050'\\n currentMap['name'] = 'molType'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024')\\n\\n # Role ResidueProfile.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of ResidueProfile\\n\\n currentMap = abstractTypes.get('ResidueProfile')\\n aList = ['ccpCode', 'guiName', 'molType']\\n currentMap['headerAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Out-of-package link to AnalysisProfile\\n currentMap = {}\\n exolinks['AnalysisProfile'] = currentMap\\n loadMaps['ANAP.exo-AnalysisProfile'] = currentMap\\n currentMap['tag'] = 'ANAP.exo-AnalysisProfile'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00004'\\n currentMap['name'] = 'AnalysisProfile'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.AnalysisProfile\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n\\n # Out-of-package link to ColorScheme\\n currentMap = {}\\n exolinks['ColorScheme'] = currentMap\\n loadMaps['ANAP.exo-ColorScheme'] = currentMap\\n currentMap['tag'] = 'ANAP.exo-ColorScheme'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00002'\\n currentMap['name'] = 'ColorScheme'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ColorScheme\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to Macro\\n currentMap = {}\\n exolinks['Macro'] = currentMap\\n loadMaps['ANAP.exo-Macro'] = currentMap\\n currentMap['tag'] = 'ANAP.exo-Macro'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-08-11:06:22_00001'\\n currentMap['name'] = 'Macro'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.Macro\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\\n\\n # Out-of-package link to RefExpProfile\\n currentMap = {}\\n exolinks['RefExpProfile'] = currentMap\\n loadMaps['ANAP.exo-RefExpProfile'] = currentMap\\n currentMap['tag'] = 'ANAP.exo-RefExpProfile'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00006'\\n currentMap['name'] = 'RefExpProfile'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.RefExpProfile\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\\n\\n # Out-of-package link to ResidueProfile\\n currentMap = {}\\n exolinks['ResidueProfile'] = currentMap\\n loadMaps['ANAP.exo-ResidueProfile'] = currentMap\\n currentMap['tag'] = 'ANAP.exo-ResidueProfile'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2008-05-05-15:12:50_00005'\\n currentMap['name'] = 'ResidueProfile'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccpnmr.api.AnalysisProfile.ResidueProfile\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))\",\n \"def makeMapping(globalMap):\\n \\n from memops.xml.Implementation import bool2str, str2bool\\n\\n # Set up top level dictionaries\\n loadMaps = globalMap.get('loadMaps')\\n mapsByGuid = globalMap.get('mapsByGuid')\\n\\n abstractTypes = globalMap.get('CCLB').get('abstractTypes')\\n exolinks = globalMap.get('CCLB').get('exolinks')\\n\\n # Class AtomLabel\\n currentMap = {}\\n abstractTypes['AtomLabel'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00018'] = currentMap\\n loadMaps['CCLB.AtomLabel'] = currentMap\\n currentMap['tag'] = 'CCLB.AtomLabel'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00018'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'atomLabels'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.AtomLabel\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute AtomLabel.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute AtomLabel.isotopeCode\\n currentMap = {}\\n contentMap['isotopeCode'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00092'] = currentMap\\n loadMaps['CCLB.AtomLabel.isotopeCode'] = currentMap\\n currentMap['tag'] = 'CCLB.AtomLabel.isotopeCode'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00092'\\n currentMap['name'] = 'isotopeCode'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Attribute AtomLabel.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00003'] = currentMap\\n loadMaps['CCLB.AtomLabel.name'] = currentMap\\n currentMap['tag'] = 'CCLB.AtomLabel.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00003'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Attribute AtomLabel.subType\\n currentMap = {}\\n contentMap['subType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00004'] = currentMap\\n loadMaps['CCLB.AtomLabel.subType'] = currentMap\\n currentMap['tag'] = 'CCLB.AtomLabel.subType'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00004'\\n currentMap['name'] = 'subType'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['proc'] = 'direct'\\n currentMap['default'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\\n\\n # Attribute AtomLabel.weight\\n currentMap = {}\\n contentMap['weight'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00093'] = currentMap\\n loadMaps['CCLB.AtomLabel.weight'] = currentMap\\n currentMap['tag'] = 'CCLB.AtomLabel.weight'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00093'\\n currentMap['name'] = 'weight'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = 1.0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00009')\\n\\n # Role AtomLabel.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of AtomLabel\\n\\n currentMap = abstractTypes.get('AtomLabel')\\n aList = ['isotopeCode', 'name', 'subType', 'weight']\\n currentMap['headerAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class ChemCompLabel\\n currentMap = {}\\n abstractTypes['ChemCompLabel'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00014'] = currentMap\\n loadMaps['CCLB.ChemCompLabel'] = currentMap\\n currentMap['tag'] = 'CCLB.ChemCompLabel'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00014'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'chemCompLabels'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.ChemCompLabel\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute ChemCompLabel.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute ChemCompLabel.ccpCode\\n currentMap = {}\\n contentMap['ccpCode'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00073'] = currentMap\\n loadMaps['CCLB.ChemCompLabel.ccpCode'] = currentMap\\n currentMap['tag'] = 'CCLB.ChemCompLabel.ccpCode'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00073'\\n currentMap['name'] = 'ccpCode'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003')\\n\\n # Attribute ChemCompLabel.molType\\n currentMap = {}\\n contentMap['molType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00072'] = currentMap\\n loadMaps['CCLB.ChemCompLabel.molType'] = currentMap\\n currentMap['tag'] = 'CCLB.ChemCompLabel.molType'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:37_00072'\\n currentMap['name'] = 'molType'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024')\\n\\n # Role ChemCompLabel.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role ChemCompLabel.isotopomers\\n currentMap = {}\\n contentMap['isotopomers'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:13_00001'] = currentMap\\n loadMaps['CCLB.ChemCompLabel.isotopomers'] = currentMap\\n currentMap['tag'] = 'CCLB.ChemCompLabel.isotopomers'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:13_00001'\\n currentMap['name'] = 'isotopomers'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['content'] = globalMap.get('CCLB').get('abstractTypes')\\n # End of ChemCompLabel\\n\\n currentMap = abstractTypes.get('ChemCompLabel')\\n aList = ['ccpCode', 'molType']\\n currentMap['headerAttrs'] = aList\\n aList = ['isotopomers', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['isotopomers']\\n currentMap['children'] = aList\\n\\n # Class Isotopomer\\n currentMap = {}\\n abstractTypes['Isotopomer'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:28:54_00001'] = currentMap\\n loadMaps['CCLB.Isotopomer'] = currentMap\\n currentMap['tag'] = 'CCLB.Isotopomer'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:28:54_00001'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'isotopomers'\\n currentMap['objkey'] = 'serial'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.Isotopomer\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute Isotopomer.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute Isotopomer.serial\\n currentMap = {}\\n contentMap['serial'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00005'] = currentMap\\n loadMaps['CCLB.Isotopomer.serial'] = currentMap\\n currentMap['tag'] = 'CCLB.Isotopomer.serial'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00005'\\n currentMap['name'] = 'serial'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\\n\\n # Attribute Isotopomer.weight\\n currentMap = {}\\n contentMap['weight'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00006'] = currentMap\\n loadMaps['CCLB.Isotopomer.weight'] = currentMap\\n currentMap['tag'] = 'CCLB.Isotopomer.weight'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00006'\\n currentMap['name'] = 'weight'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = 1.0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00009')\\n\\n # Role Isotopomer.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role Isotopomer.atomLabels\\n currentMap = {}\\n contentMap['atomLabels'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00001'] = currentMap\\n loadMaps['CCLB.Isotopomer.atomLabels'] = currentMap\\n currentMap['tag'] = 'CCLB.Isotopomer.atomLabels'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:30:14_00001'\\n currentMap['name'] = 'atomLabels'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['content'] = globalMap.get('CCLB').get('abstractTypes')\\n # End of Isotopomer\\n\\n currentMap = abstractTypes.get('Isotopomer')\\n aList = ['serial', 'weight']\\n currentMap['headerAttrs'] = aList\\n aList = ['atomLabels', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['atomLabels']\\n currentMap['children'] = aList\\n\\n # Class LabelingScheme\\n currentMap = {}\\n abstractTypes['LabelingScheme'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:14_00001'] = currentMap\\n loadMaps['CCLB.LabelingScheme'] = currentMap\\n currentMap['tag'] = 'CCLB.LabelingScheme'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:14_00001'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'labelingSchemes'\\n currentMap['isTop'] = True\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.LabelingScheme\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute LabelingScheme.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute LabelingScheme.createdBy\\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute LabelingScheme.details\\n currentMap = {}\\n contentMap['details'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00005'] = currentMap\\n loadMaps['CCLB.LabelingScheme.details'] = currentMap\\n currentMap['tag'] = 'CCLB.LabelingScheme.details'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00005'\\n currentMap['name'] = 'details'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\\n\\n # Attribute LabelingScheme.guid\\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\\n\\n # Attribute LabelingScheme.isModifiable\\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute LabelingScheme.lastUnlockedBy\\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute LabelingScheme.longName\\n currentMap = {}\\n contentMap['longName'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00004'] = currentMap\\n loadMaps['CCLB.LabelingScheme.longName'] = currentMap\\n currentMap['tag'] = 'CCLB.LabelingScheme.longName'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00004'\\n currentMap['name'] = 'longName'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Attribute LabelingScheme.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00003'] = currentMap\\n loadMaps['CCLB.LabelingScheme.name'] = currentMap\\n currentMap['tag'] = 'CCLB.LabelingScheme.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00003'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Role LabelingScheme.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role LabelingScheme.chemCompLabels\\n currentMap = {}\\n contentMap['chemCompLabels'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00002'] = currentMap\\n loadMaps['CCLB.LabelingScheme.chemCompLabels'] = currentMap\\n currentMap['tag'] = 'CCLB.LabelingScheme.chemCompLabels'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:55_00002'\\n currentMap['name'] = 'chemCompLabels'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CCLB').get('abstractTypes')\\n # End of LabelingScheme\\n\\n currentMap = abstractTypes.get('LabelingScheme')\\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy', 'name']\\n currentMap['headerAttrs'] = aList\\n aList = ['details', 'longName']\\n currentMap['simpleAttrs'] = aList\\n aList = ['chemCompLabels', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['chemCompLabels']\\n currentMap['children'] = aList\\n\\n # Out-of-package link to AtomLabel\\n currentMap = {}\\n exolinks['AtomLabel'] = currentMap\\n loadMaps['CCLB.exo-AtomLabel'] = currentMap\\n currentMap['tag'] = 'CCLB.exo-AtomLabel'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00018'\\n currentMap['name'] = 'AtomLabel'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.AtomLabel\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037'))\\n\\n # Out-of-package link to ChemCompLabel\\n currentMap = {}\\n exolinks['ChemCompLabel'] = currentMap\\n loadMaps['CCLB.exo-ChemCompLabel'] = currentMap\\n currentMap['tag'] = 'CCLB.exo-ChemCompLabel'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:25:09_00014'\\n currentMap['name'] = 'ChemCompLabel'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.ChemCompLabel\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))\\n\\n # Out-of-package link to Isotopomer\\n currentMap = {}\\n exolinks['Isotopomer'] = currentMap\\n loadMaps['CCLB.exo-Isotopomer'] = currentMap\\n currentMap['tag'] = 'CCLB.exo-Isotopomer'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-10-24-11:28:54_00001'\\n currentMap['name'] = 'Isotopomer'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.Isotopomer\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00024'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-09-12-18:31:28_00003'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\\n\\n # Out-of-package link to LabelingScheme\\n currentMap = {}\\n exolinks['LabelingScheme'] = currentMap\\n loadMaps['CCLB.exo-LabelingScheme'] = currentMap\\n currentMap['tag'] = 'CCLB.exo-LabelingScheme'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-01-24-12:23:14_00001'\\n currentMap['name'] = 'LabelingScheme'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.molecule.ChemCompLabel.LabelingScheme\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\",\n \"def makeMapping(globalMap):\\n \\n from memops.xml.Implementation import bool2str, str2bool\\n\\n # Set up top level dictionaries\\n loadMaps = globalMap.get('loadMaps')\\n mapsByGuid = globalMap.get('mapsByGuid')\\n\\n abstractTypes = globalMap.get('CLAS').get('abstractTypes')\\n exolinks = globalMap.get('CLAS').get('exolinks')\\n\\n # Class AbstractCategory\\n currentMap = {}\\n abstractTypes['AbstractCategory'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:19:17_00001'] = currentMap\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:19:17_00001'\\n currentMap['eType'] = 'cplx'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.AbstractCategory\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute AbstractCategory.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute AbstractCategory.details\\n currentMap = {}\\n contentMap['details'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001'] = currentMap\\n loadMaps['CLAS.AbstractCategory.details'] = currentMap\\n currentMap['tag'] = 'CLAS.AbstractCategory.details'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001'\\n currentMap['name'] = 'details'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\\n\\n # Attribute AbstractCategory.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014'] = currentMap\\n loadMaps['CLAS.AbstractCategory.name'] = currentMap\\n currentMap['tag'] = 'CLAS.AbstractCategory.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Role AbstractCategory.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of AbstractCategory\\n\\n currentMap = abstractTypes.get('AbstractCategory')\\n aList = ['details', 'name']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class Classification\\n currentMap = {}\\n abstractTypes['Classification'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:18:54_00002'] = currentMap\\n loadMaps['CLAS.Classification'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:18:54_00002'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'classifications'\\n currentMap['isTop'] = True\\n currentMap['objkey'] = 'namingSystem'\\n currentMap['class'] = ccp.api.lims.Classification.Classification\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute Classification.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute Classification.createdBy\\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute Classification.guid\\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\\n\\n # Attribute Classification.isModifiable\\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute Classification.lastUnlockedBy\\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute Classification.namingSystem\\n currentMap = {}\\n contentMap['namingSystem'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00013'] = currentMap\\n loadMaps['CLAS.Classification.namingSystem'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.namingSystem'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00013'\\n currentMap['name'] = 'namingSystem'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Role Classification.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role Classification.experimentTypes\\n currentMap = {}\\n contentMap['experimentTypes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00002'] = currentMap\\n loadMaps['CLAS.Classification.experimentTypes'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.experimentTypes'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00002'\\n currentMap['name'] = 'experimentTypes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n\\n # Role Classification.hazardPhrases\\n currentMap = {}\\n contentMap['hazardPhrases'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00006'] = currentMap\\n loadMaps['CLAS.Classification.hazardPhrases'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.hazardPhrases'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00006'\\n currentMap['name'] = 'hazardPhrases'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n\\n # Role Classification.holderCategorys\\n currentMap = {}\\n contentMap['holderCategorys'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:33:23_00002'] = currentMap\\n loadMaps['CLAS.Classification.holderCategorys'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.holderCategorys'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:33:23_00002'\\n currentMap['name'] = 'holderCategorys'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n\\n # Role Classification.instrumentTypes\\n currentMap = {}\\n contentMap['instrumentTypes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:33_00001'] = currentMap\\n loadMaps['CLAS.Classification.instrumentTypes'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.instrumentTypes'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:33_00001'\\n currentMap['name'] = 'instrumentTypes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n\\n # Role Classification.sampleCategories\\n currentMap = {}\\n contentMap['sampleCategories'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00008'] = currentMap\\n loadMaps['CLAS.Classification.sampleCategories'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.sampleCategories'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00008'\\n currentMap['name'] = 'sampleCategories'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n\\n # Role Classification.sampleComponentCategory\\n currentMap = {}\\n contentMap['sampleComponentCategory'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00004'] = currentMap\\n loadMaps['CLAS.Classification.sampleComponentCategory'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.sampleComponentCategory'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00004'\\n currentMap['name'] = 'sampleComponentCategory'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n\\n # Role Classification.targetScoreboards\\n currentMap = {}\\n contentMap['targetScoreboards'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00012'] = currentMap\\n loadMaps['CLAS.Classification.targetScoreboards'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.targetScoreboards'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00012'\\n currentMap['name'] = 'targetScoreboards'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n\\n # Role Classification.targetStatus\\n currentMap = {}\\n contentMap['targetStatus'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00010'] = currentMap\\n loadMaps['CLAS.Classification.targetStatus'] = currentMap\\n currentMap['tag'] = 'CLAS.Classification.targetStatus'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00010'\\n currentMap['name'] = 'targetStatus'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CLAS').get('abstractTypes')\\n # End of Classification\\n\\n currentMap = abstractTypes.get('Classification')\\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy']\\n currentMap['headerAttrs'] = aList\\n aList = ['namingSystem']\\n currentMap['simpleAttrs'] = aList\\n aList = ['targetStatus', 'targetScoreboards', 'sampleComponentCategory', 'sampleCategories', 'instrumentTypes', 'holderCategorys', 'hazardPhrases', 'experimentTypes', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['experimentTypes', 'hazardPhrases', 'holderCategorys', 'instrumentTypes', 'sampleCategories', 'sampleComponentCategory', 'targetScoreboards', 'targetStatus']\\n currentMap['children'] = aList\\n\\n # Class SampleComponentCategory\\n currentMap = {}\\n abstractTypes['SampleComponentCategory'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00014'] = currentMap\\n loadMaps['CLAS.SampleComponentCategory'] = currentMap\\n currentMap['tag'] = 'CLAS.SampleComponentCategory'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00014'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'sampleComponentCategory'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.SampleComponentCategory\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute SampleComponentCategory.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute SampleComponentCategory.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute SampleComponentCategory.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Role SampleComponentCategory.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of SampleComponentCategory\\n\\n currentMap = abstractTypes.get('SampleComponentCategory')\\n aList = ['details', 'name']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class ExperimentType\\n currentMap = {}\\n abstractTypes['ExperimentType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:45_00014'] = currentMap\\n loadMaps['CLAS.ExperimentType'] = currentMap\\n currentMap['tag'] = 'CLAS.ExperimentType'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:45_00014'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'experimentTypes'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.ExperimentType\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute ExperimentType.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute ExperimentType.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute ExperimentType.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Role ExperimentType.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role ExperimentType.instrumentTypes\\n currentMap = {}\\n contentMap['instrumentTypes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00002'] = currentMap\\n loadMaps['CLAS.ExperimentType.instrumentTypes'] = currentMap\\n currentMap['tag'] = 'CLAS.ExperimentType.instrumentTypes'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00002'\\n currentMap['name'] = 'instrumentTypes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n\\n # Role ExperimentType.sampleCategories\\n currentMap = {}\\n contentMap['sampleCategories'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00031'] = currentMap\\n loadMaps['CLAS.ExperimentType.sampleCategories'] = currentMap\\n currentMap['tag'] = 'CLAS.ExperimentType.sampleCategories'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00031'\\n currentMap['name'] = 'sampleCategories'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n # End of ExperimentType\\n\\n currentMap = abstractTypes.get('ExperimentType')\\n aList = ['details', 'name', 'instrumentTypes', 'sampleCategories']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class TargetScoreboard\\n currentMap = {}\\n abstractTypes['TargetScoreboard'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00040'] = currentMap\\n loadMaps['CLAS.TargetScoreboard'] = currentMap\\n currentMap['tag'] = 'CLAS.TargetScoreboard'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00040'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'targetScoreboards'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.TargetScoreboard\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute TargetScoreboard.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute TargetScoreboard.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute TargetScoreboard.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Role TargetScoreboard.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role TargetScoreboard.targetStatus\\n currentMap = {}\\n contentMap['targetStatus'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00039'] = currentMap\\n loadMaps['CLAS.TargetScoreboard.targetStatus'] = currentMap\\n currentMap['tag'] = 'CLAS.TargetScoreboard.targetStatus'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00039'\\n currentMap['name'] = 'targetStatus'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n # End of TargetScoreboard\\n\\n currentMap = abstractTypes.get('TargetScoreboard')\\n aList = ['details', 'name', 'targetStatus']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class HolderCategory\\n currentMap = {}\\n abstractTypes['HolderCategory'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00049'] = currentMap\\n loadMaps['CLAS.HolderCategory'] = currentMap\\n currentMap['tag'] = 'CLAS.HolderCategory'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00049'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'holderCategorys'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.HolderCategory\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute HolderCategory.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute HolderCategory.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute HolderCategory.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Role HolderCategory.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of HolderCategory\\n\\n currentMap = abstractTypes.get('HolderCategory')\\n aList = ['details', 'name']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class HazardPhrase\\n currentMap = {}\\n abstractTypes['HazardPhrase'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00052'] = currentMap\\n loadMaps['CLAS.HazardPhrase'] = currentMap\\n currentMap['tag'] = 'CLAS.HazardPhrase'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00052'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'hazardPhrases'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.HazardPhrase\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute HazardPhrase.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute HazardPhrase.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute HazardPhrase.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Attribute HazardPhrase.phrase\\n currentMap = {}\\n contentMap['phrase'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:28_00017'] = currentMap\\n loadMaps['CLAS.HazardPhrase.phrase'] = currentMap\\n currentMap['tag'] = 'CLAS.HazardPhrase.phrase'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:28_00017'\\n currentMap['name'] = 'phrase'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00036')\\n\\n # Role HazardPhrase.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of HazardPhrase\\n\\n currentMap = abstractTypes.get('HazardPhrase')\\n aList = ['details', 'name', 'phrase']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class InstrumentType\\n currentMap = {}\\n abstractTypes['InstrumentType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:46_00005'] = currentMap\\n loadMaps['CLAS.InstrumentType'] = currentMap\\n currentMap['tag'] = 'CLAS.InstrumentType'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:46_00005'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'instrumentTypes'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.InstrumentType\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute InstrumentType.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute InstrumentType.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute InstrumentType.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Role InstrumentType.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role InstrumentType.experimentTypes\\n currentMap = {}\\n contentMap['experimentTypes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00001'] = currentMap\\n loadMaps['CLAS.InstrumentType.experimentTypes'] = currentMap\\n currentMap['tag'] = 'CLAS.InstrumentType.experimentTypes'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:57_00001'\\n currentMap['name'] = 'experimentTypes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n # End of InstrumentType\\n\\n currentMap = abstractTypes.get('InstrumentType')\\n aList = ['details', 'name', 'experimentTypes']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class SampleCategory\\n currentMap = {}\\n abstractTypes['SampleCategory'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:44_00009'] = currentMap\\n loadMaps['CLAS.SampleCategory'] = currentMap\\n currentMap['tag'] = 'CLAS.SampleCategory'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:44_00009'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'sampleCategories'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.SampleCategory\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute SampleCategory.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute SampleCategory.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute SampleCategory.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Role SampleCategory.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role SampleCategory.experimentTypes\\n currentMap = {}\\n contentMap['experimentTypes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00030'] = currentMap\\n loadMaps['CLAS.SampleCategory.experimentTypes'] = currentMap\\n currentMap['tag'] = 'CLAS.SampleCategory.experimentTypes'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:22:56_00030'\\n currentMap['name'] = 'experimentTypes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n # End of SampleCategory\\n\\n currentMap = abstractTypes.get('SampleCategory')\\n aList = ['details', 'name', 'experimentTypes']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Class TargetStatus\\n currentMap = {}\\n abstractTypes['TargetStatus'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00039'] = currentMap\\n loadMaps['CLAS.TargetStatus'] = currentMap\\n currentMap['tag'] = 'CLAS.TargetStatus'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00039'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'targetStatus'\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.lims.Classification.TargetStatus\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute TargetStatus.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute TargetStatus.details\\n contentMap['details'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:35_00001')\\n\\n # Attribute TargetStatus.name\\n contentMap['name'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-04-17:21:34_00014')\\n\\n # Role TargetStatus.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role TargetStatus.targetScoreboards\\n currentMap = {}\\n contentMap['targetScoreboards'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00040'] = currentMap\\n loadMaps['CLAS.TargetStatus.targetScoreboards'] = currentMap\\n currentMap['tag'] = 'CLAS.TargetStatus.targetScoreboards'\\n currentMap['type'] = 'link'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:18_00040'\\n currentMap['name'] = 'targetScoreboards'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['copyOverride'] = True\\n # End of TargetStatus\\n\\n currentMap = abstractTypes.get('TargetStatus')\\n aList = ['details', 'name', 'targetScoreboards']\\n currentMap['simpleAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Out-of-package link to Classification\\n currentMap = {}\\n exolinks['Classification'] = currentMap\\n loadMaps['CLAS.exo-Classification'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-Classification'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-09-04-17:18:54_00002'\\n currentMap['name'] = 'Classification'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.Classification\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n\\n # Out-of-package link to SampleComponentCategory\\n currentMap = {}\\n exolinks['SampleComponentCategory'] = currentMap\\n loadMaps['CLAS.exo-SampleComponentCategory'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-SampleComponentCategory'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00014'\\n currentMap['name'] = 'SampleComponentCategory'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.SampleComponentCategory\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to ExperimentType\\n currentMap = {}\\n exolinks['ExperimentType'] = currentMap\\n loadMaps['CLAS.exo-ExperimentType'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-ExperimentType'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:45_00014'\\n currentMap['name'] = 'ExperimentType'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.ExperimentType\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to TargetScoreboard\\n currentMap = {}\\n exolinks['TargetScoreboard'] = currentMap\\n loadMaps['CLAS.exo-TargetScoreboard'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-TargetScoreboard'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00040'\\n currentMap['name'] = 'TargetScoreboard'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.TargetScoreboard\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to HolderCategory\\n currentMap = {}\\n exolinks['HolderCategory'] = currentMap\\n loadMaps['CLAS.exo-HolderCategory'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-HolderCategory'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00049'\\n currentMap['name'] = 'HolderCategory'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.HolderCategory\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to HazardPhrase\\n currentMap = {}\\n exolinks['HazardPhrase'] = currentMap\\n loadMaps['CLAS.exo-HazardPhrase'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-HazardPhrase'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:52_00052'\\n currentMap['name'] = 'HazardPhrase'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.HazardPhrase\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to InstrumentType\\n currentMap = {}\\n exolinks['InstrumentType'] = currentMap\\n loadMaps['CLAS.exo-InstrumentType'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-InstrumentType'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:46_00005'\\n currentMap['name'] = 'InstrumentType'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.InstrumentType\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to SampleCategory\\n currentMap = {}\\n exolinks['SampleCategory'] = currentMap\\n loadMaps['CLAS.exo-SampleCategory'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-SampleCategory'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:44_00009'\\n currentMap['name'] = 'SampleCategory'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.SampleCategory\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\\n\\n # Out-of-package link to TargetStatus\\n currentMap = {}\\n exolinks['TargetStatus'] = currentMap\\n loadMaps['CLAS.exo-TargetStatus'] = currentMap\\n currentMap['tag'] = 'CLAS.exo-TargetStatus'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-14:22:51_00039'\\n currentMap['name'] = 'TargetStatus'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.lims.Classification.TargetStatus\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033'))\",\n \"def makeMapping(globalMap):\\n \\n from memops.xml.Implementation import bool2str, str2bool\\n\\n # Set up top level dictionaries\\n loadMaps = globalMap.get('loadMaps')\\n mapsByGuid = globalMap.get('mapsByGuid')\\n\\n abstractTypes = globalMap.get('CHEL').get('abstractTypes')\\n exolinks = globalMap.get('CHEL').get('exolinks')\\n\\n # DataType HalfLifeType\\n currentMap = {}\\n abstractTypes['HalfLifeType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:10_00002'] = currentMap\\n loadMaps['CHEL.HalfLifeType'] = currentMap\\n currentMap['tag'] = 'CHEL.HalfLifeType'\\n currentMap['type'] = 'simple'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:10_00002'\\n currentMap['toStr'] = 'text'\\n currentMap['cnvrt'] = 'text'\\n\\n # Class ChemElement\\n currentMap = {}\\n abstractTypes['ChemElement'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00004'] = currentMap\\n loadMaps['CHEL.ChemElement'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00004'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'chemElements'\\n currentMap['objkey'] = 'symbol'\\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElement\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute ChemElement.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute ChemElement.atomNumber\\n currentMap = {}\\n contentMap['atomNumber'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00017'] = currentMap\\n loadMaps['CHEL.ChemElement.atomNumber'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement.atomNumber'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00017'\\n currentMap['name'] = 'atomNumber'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['proc'] = 'direct'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\\n\\n # Attribute ChemElement.atomicRadius\\n currentMap = {}\\n contentMap['atomicRadius'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00018'] = currentMap\\n loadMaps['CHEL.ChemElement.atomicRadius'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement.atomicRadius'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00018'\\n currentMap['name'] = 'atomicRadius'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute ChemElement.covalentRadius\\n currentMap = {}\\n contentMap['covalentRadius'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00019'] = currentMap\\n loadMaps['CHEL.ChemElement.covalentRadius'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement.covalentRadius'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00019'\\n currentMap['name'] = 'covalentRadius'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute ChemElement.mass\\n currentMap = {}\\n contentMap['mass'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00020'] = currentMap\\n loadMaps['CHEL.ChemElement.mass'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement.mass'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00020'\\n currentMap['name'] = 'mass'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute ChemElement.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00021'] = currentMap\\n loadMaps['CHEL.ChemElement.name'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00021'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055')\\n\\n # Attribute ChemElement.symbol\\n currentMap = {}\\n contentMap['symbol'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00015'] = currentMap\\n loadMaps['CHEL.ChemElement.symbol'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement.symbol'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00015'\\n currentMap['name'] = 'symbol'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055')\\n\\n # Role ChemElement.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role ChemElement.isotopes\\n currentMap = {}\\n contentMap['isotopes'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00004'] = currentMap\\n loadMaps['CHEL.ChemElement.isotopes'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElement.isotopes'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00004'\\n currentMap['name'] = 'isotopes'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['content'] = globalMap.get('CHEL').get('abstractTypes')\\n # End of ChemElement\\n\\n currentMap = abstractTypes.get('ChemElement')\\n aList = ['atomNumber', 'atomicRadius', 'covalentRadius', 'mass', 'name', 'symbol']\\n currentMap['headerAttrs'] = aList\\n aList = ['isotopes', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['isotopes']\\n currentMap['children'] = aList\\n\\n # Class ChemElementStore\\n currentMap = {}\\n abstractTypes['ChemElementStore'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00005'] = currentMap\\n loadMaps['CHEL.ChemElementStore'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElementStore'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00005'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'chemElementStores'\\n currentMap['isTop'] = True\\n currentMap['objkey'] = 'name'\\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElementStore\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute ChemElementStore.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute ChemElementStore.createdBy\\n contentMap['createdBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00002__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute ChemElementStore.guid\\n contentMap['guid'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:26_00002')\\n\\n # Attribute ChemElementStore.isModifiable\\n contentMap['isModifiable'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-17-14:16:26_00010__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute ChemElementStore.lastUnlockedBy\\n contentMap['lastUnlockedBy'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:00:59_00003__www.ccpn.ac.uk_Fogh_2007-10-03-14:53:27_00001__www.ccpn.ac.uk_Fogh_2006-09-14-16:28:57_00002')\\n\\n # Attribute ChemElementStore.name\\n currentMap = {}\\n contentMap['name'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00024'] = currentMap\\n loadMaps['CHEL.ChemElementStore.name'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElementStore.name'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00024'\\n currentMap['name'] = 'name'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['eType'] = 'cplx'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00033')\\n\\n # Role ChemElementStore.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n\\n # Role ChemElementStore.chemElements\\n currentMap = {}\\n contentMap['chemElements'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00023'] = currentMap\\n loadMaps['CHEL.ChemElementStore.chemElements'] = currentMap\\n currentMap['tag'] = 'CHEL.ChemElementStore.chemElements'\\n currentMap['type'] = 'child'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00023'\\n currentMap['name'] = 'chemElements'\\n currentMap['hicard'] = -1\\n currentMap['locard'] = 0\\n currentMap['eType'] = 'cplx'\\n currentMap['implSkip'] = True\\n currentMap['content'] = globalMap.get('CHEL').get('abstractTypes')\\n # End of ChemElementStore\\n\\n currentMap = abstractTypes.get('ChemElementStore')\\n aList = ['createdBy', 'guid', 'isModifiable', 'lastUnlockedBy']\\n currentMap['headerAttrs'] = aList\\n aList = ['name']\\n currentMap['simpleAttrs'] = aList\\n aList = ['chemElements', 'access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n aList = ['chemElements']\\n currentMap['children'] = aList\\n\\n # Class Isotope\\n currentMap = {}\\n abstractTypes['Isotope'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00003'] = currentMap\\n loadMaps['CHEL.Isotope'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope'\\n currentMap['type'] = 'class'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00003'\\n currentMap['eType'] = 'cplx'\\n currentMap['fromParent'] = 'isotopes'\\n currentMap['objkey'] = 'massNumber'\\n currentMap['class'] = ccp.api.molecule.ChemElement.Isotope\\n contentMap = {}\\n currentMap['content'] = contentMap\\n\\n # Attribute Isotope.abundance\\n currentMap = {}\\n contentMap['abundance'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00011'] = currentMap\\n loadMaps['CHEL.Isotope.abundance'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.abundance'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00011'\\n currentMap['name'] = 'abundance'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00058')\\n\\n # Attribute Isotope.applicationData\\n contentMap['applicationData'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-09-14-18:48:27_00007')\\n\\n # Attribute Isotope.gyroMagneticRatio\\n currentMap = {}\\n contentMap['gyroMagneticRatio'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00008'] = currentMap\\n loadMaps['CHEL.Isotope.gyroMagneticRatio'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.gyroMagneticRatio'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00008'\\n currentMap['name'] = 'gyroMagneticRatio'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute Isotope.halfLife\\n currentMap = {}\\n contentMap['halfLife'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00001'] = currentMap\\n loadMaps['CHEL.Isotope.halfLife'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.halfLife'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00001'\\n currentMap['name'] = 'halfLife'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00007')\\n\\n # Attribute Isotope.halfLifeError\\n currentMap = {}\\n contentMap['halfLifeError'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00002'] = currentMap\\n loadMaps['CHEL.Isotope.halfLifeError'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.halfLifeError'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00002'\\n currentMap['name'] = 'halfLifeError'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:54_00007')\\n\\n # Attribute Isotope.halfLifeType\\n currentMap = {}\\n contentMap['halfLifeType'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00003'] = currentMap\\n loadMaps['CHEL.Isotope.halfLifeType'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.halfLifeType'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2007-06-07-18:18:13_00003'\\n currentMap['name'] = 'halfLifeType'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['default'] = 'unknown'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2007-06-07-18:18:10_00002')\\n\\n # Attribute Isotope.magneticMoment\\n currentMap = {}\\n contentMap['magneticMoment'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00013'] = currentMap\\n loadMaps['CHEL.Isotope.magneticMoment'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.magneticMoment'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00013'\\n currentMap['name'] = 'magneticMoment'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute Isotope.mass\\n currentMap = {}\\n contentMap['mass'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00010'] = currentMap\\n loadMaps['CHEL.Isotope.mass'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.mass'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00010'\\n currentMap['name'] = 'mass'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute Isotope.massNumber\\n currentMap = {}\\n contentMap['massNumber'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00007'] = currentMap\\n loadMaps['CHEL.Isotope.massNumber'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.massNumber'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00007'\\n currentMap['name'] = 'massNumber'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 1\\n currentMap['proc'] = 'direct'\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032')\\n\\n # Attribute Isotope.quadrupoleMoment\\n currentMap = {}\\n contentMap['quadrupoleMoment'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00014'] = currentMap\\n loadMaps['CHEL.Isotope.quadrupoleMoment'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.quadrupoleMoment'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00014'\\n currentMap['name'] = 'quadrupoleMoment'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute Isotope.receptivity\\n currentMap = {}\\n contentMap['receptivity'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00012'] = currentMap\\n loadMaps['CHEL.Isotope.receptivity'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.receptivity'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00012'\\n currentMap['name'] = 'receptivity'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00031')\\n\\n # Attribute Isotope.spin\\n currentMap = {}\\n contentMap['spin'] = currentMap\\n mapsByGuid['www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00009'] = currentMap\\n loadMaps['CHEL.Isotope.spin'] = currentMap\\n currentMap['tag'] = 'CHEL.Isotope.spin'\\n currentMap['type'] = 'attr'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:23:12_00009'\\n currentMap['name'] = 'spin'\\n currentMap['hicard'] = 1\\n currentMap['locard'] = 0\\n currentMap['data'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00037')\\n\\n # Role Isotope.access\\n contentMap['access'] = mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-12-31-09:03:01_00014')\\n # End of Isotope\\n\\n currentMap = abstractTypes.get('Isotope')\\n aList = ['abundance', 'gyroMagneticRatio', 'halfLife', 'halfLifeError', 'halfLifeType', 'magneticMoment', 'mass', 'massNumber', 'quadrupoleMoment', 'receptivity', 'spin']\\n currentMap['headerAttrs'] = aList\\n aList = ['access', 'applicationData']\\n currentMap['cplxAttrs'] = aList\\n\\n # Out-of-package link to ChemElement\\n currentMap = {}\\n exolinks['ChemElement'] = currentMap\\n loadMaps['CHEL.exo-ChemElement'] = currentMap\\n currentMap['tag'] = 'CHEL.exo-ChemElement'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00004'\\n currentMap['name'] = 'ChemElement'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElement\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055'))\\n\\n # Out-of-package link to ChemElementStore\\n currentMap = {}\\n exolinks['ChemElementStore'] = currentMap\\n loadMaps['CHEL.exo-ChemElementStore'] = currentMap\\n currentMap['tag'] = 'CHEL.exo-ChemElementStore'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00005'\\n currentMap['name'] = 'ChemElementStore'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.molecule.ChemElement.ChemElementStore\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n\\n # Out-of-package link to Isotope\\n currentMap = {}\\n exolinks['Isotope'] = currentMap\\n loadMaps['CHEL.exo-Isotope'] = currentMap\\n currentMap['tag'] = 'CHEL.exo-Isotope'\\n currentMap['type'] = 'exo'\\n currentMap['guid'] = 'www.ccpn.ac.uk_Fogh_2006-08-16-18:19:49_00003'\\n currentMap['name'] = 'Isotope'\\n currentMap['eType'] = 'cplx'\\n currentMap['class'] = ccp.api.molecule.ChemElement.Isotope\\n aList = list()\\n currentMap['keyMaps'] = aList\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2008-06-30-16:30:50_00001'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00055'))\\n aList.append(mapsByGuid.get('www.ccpn.ac.uk_Fogh_2006-08-16-14:22:53_00032'))\",\n \"def _generate_objects_file(self):\\n xmls = glob(f'{ROOT}/Annotations/**/*.xml', recursive=True)\",\n \"def getXML(self):\\n\\n def _getElementForMappingEntry(entry, mappingStyle):\\n xmlDocTmp = Document()\\n element = xmlDocTmp.createElement(mappingStyle)\\n for k, v in viewitems(entry):\\n # ignore empty, None or compiled regexp items into output\\n if not v or (k == \\\"path-match-expr\\\"):\\n continue\\n element.setAttribute(k, str(v))\\n return element\\n\\n xmlDoc = Document()\\n root = xmlDoc.createElement(\\\"storage-mapping\\\") # root element name\\n for mappingStyle, mappings in viewitems(self):\\n for mapping in mappings:\\n mapElem = _getElementForMappingEntry(mapping, mappingStyle)\\n root.appendChild(mapElem)\\n return root.toprettyxml()\",\n \"def _output_dict(self):\\n lang = self.ddnGuiLanguage.get()\\n\\n fileout = os.path.normpath('{}/{}-{}.xml'.\\\\\\n format(self.MapCreator, self.Source, self.ddnCurProject.get()))\\n linesout = ['<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>', \\\\\\n '<DictionarySet xmlns:mc=\\\"urn:fmosoft-map-creator\\\" xmlns=\\\"urn:fmosoft-map-creator\\\" Version=\\\"1\\\">', \\\\\\n ' <Dictionary SourceLanguage=\\\"{}\\\" SourceLanguageIsPredefined=\\\"true\\\" TargetLanguage=\\\"{}\\\" TargetLanguageIsPredefined=\\\"false\\\">'.\\\\\\n format(self.Source, self.ddnCurProject.get()), \\\\\\n ]\\n for child in self.tree.get_children('approved'):\\n vv = self.tree.item(child)['values']\\n linesout.append(' <Translation Source=\\\"{}\\\" Target=\\\"{}\\\"/>'.format(vv[0], vv[1]))\\n linesout.append(' </Dictionary>')\\n linesout.append('</DictionarySet>')\\n linesout.append('')\\n\\n if os.path.exists(fileout):\\n os.remove(fileout)\\n\\n if fileout:\\n output = codecs.open(fileout, mode='w', encoding='utf-8')\\n output.write('\\\\n'.join(linesout))\\n output.close()\\n pass\",\n \"def convert_poi(input, output, map_category=(), map_icon=(), ignore_tags=()):\\n\\n # some tools write v2, others v3. Use RegExp to find which\\n rx = re.compile(\\\"xmlns:([^= ]+) *=['\\\\\\\"]([^'\\\\\\\"]+/GpxExtensions/[^'\\\\\\\"]+)\\\")\\n m = None\\n for line in input:\\n m = rx.search(line)\\n if m:\\n break\\n\\n if m:\\n gpxx_ns = m.group(1)\\n gpxx_url = m.group(2)\\n logging.info(\\\"Found XML Namespace %s=%s\\\", gpxx_ns, gpxx_url)\\n else:\\n gpxx_ns = \\\"gpxx\\\"\\n gpxx_url = \\\"http://www.garmin.com/xmlschemas/GpxExtensions/v3\\\"\\n logging.warning(\\\"No XML Namespace for GpxExtensions! Using %s=%s\\\",\\n gpxx_ns, gpxx_url)\\n\\n ns = {\\n \\\"xsi\\\": \\\"http://www.w3.org/2001/XMLSchema-instance\\\",\\n \\\"gpx\\\": \\\"http://www.topografix.com/GPX/1/1\\\",\\n \\\"gpxd\\\": \\\"http://www.daimler.com/DaimlerGPXExtensions/V2.7.2\\\",\\n gpxx_ns: gpxx_url\\n }\\n for k, v in ns.items():\\n ET.register_namespace(k, v)\\n\\n\\n logging.info(\\\"Parsing %s\\\", input)\\n input.seek(0)\\n indoc = ET.parse(input)\\n outroot = ET.Element(\\\"{http://www.topografix.com/GPX/1/1}gpx\\\", attrib={\\n \\\"version\\\": \\\"1.1\\\",\\n \\\"creator\\\": indoc.getroot().get(\\\"creator\\\",\\n \\\"gpx-poi-garmin-to-mercedesbenz\\\"),\\n \\\"{http://www.w3.org/2001/XMLSchema-instance}schemaLocation\\\": \\\"http://www.topografix.com/GPX/1/1 http://www.topografix.com/GPX/1/1/gpx.xsd\\\",\\n })\\n\\n it = []\\n for i in ignore_tags:\\n if i.startswith(\\\"{\\\"):\\n it.append(i)\\n else:\\n i = i.split(':', 1)\\n if len(i) == 1:\\n i = ('gpx', i[0])\\n it_ns = ns.get(i[0], i[0])\\n it_tag = i[1]\\n it.append(\\\"{%s}%s\\\" % (it_ns, it_tag))\\n\\n ignore_tags = it\\n\\n logging.info(\\\"Converting...\\\")\\n for wpt in indoc.findall(\\\"gpx:wpt\\\", ns):\\n wpt_out = ET.Element(wpt.tag, attrib=wpt.attrib)\\n link_href = \\\"\\\"\\n for cel in wpt:\\n if cel.tag == \\\"{http://www.topografix.com/GPX/1/1}link\\\":\\n link_href = cel.get(\\\"href\\\", \\\"\\\")\\n\\n for i in ignore_tags:\\n if cel.tag == i:\\n break\\n else:\\n if cel.tag == \\\"{http://www.topografix.com/GPX/1/1}extensions\\\":\\n _convert_wpt_extension(cel, wpt_out, ns, link_href,\\n map_category, map_icon, ignore_tags)\\n else:\\n _copy_gpx_tags(cel, wpt_out)\\n\\n if len(wpt_out):\\n outroot.append(wpt_out)\\n\\n if len(outroot) > 30000:\\n logging.warning(\\\"Writing %d entries (>30000!) to %s\\\", len(outroot), output)\\n else:\\n logging.info(\\\"Writing %d entries to %s\\\", len(outroot), output)\\n ET.ElementTree(outroot).write(output, encoding=\\\"utf-8\\\", xml_declaration=True)\\n logging.info(\\\"Finished\\\")\",\n \"def generate(self, api):\\n for namespace in api.namespaces.values():\\n # One module per namespace is created. The module takes the name\\n # of the namespace.\\n with self.output_to_relative_path('{}.py'.format(namespace.name)):\\n self._generate_namespace_module(namespace)\",\n \"def map_doc():\\n doc = DesignSpaceDocument()\\n doc.addAxis(\\n AxisDescriptor(\\n tag=\\\"wght\\\",\\n name=\\\"Weight\\\",\\n minimum=100,\\n maximum=900,\\n default=100,\\n map=[(100, 10), (900, 90)],\\n )\\n )\\n doc.addAxis(\\n AxisDescriptor(\\n tag=\\\"wdth\\\",\\n name=\\\"Width\\\",\\n minimum=75,\\n maximum=200,\\n default=100,\\n map=[(75, 7500), (100, 10000), (200, 20000)],\\n )\\n )\\n doc.addAxis(\\n AxisDescriptor(tag=\\\"CUST\\\", name=\\\"Custom\\\", minimum=1, maximum=2, default=1.5)\\n )\\n doc.addLocationLabel(\\n LocationLabelDescriptor(\\n name=\\\"Wonky\\\", userLocation={\\\"Weight\\\": 800, \\\"Custom\\\": 1.2}\\n )\\n )\\n return doc\",\n \"def resource_map(self):\",\n \"def to_file_map(self, file_map=None):\\n if file_map is None:\\n file_map = self.file_map\\n f = file_map['image'].get_prepare_fileobj('wb')\\n f.write(self.to_xml())\",\n \"def GenerateXML(dictionary, fileName=\\\"labelling.xml\\\") : \\n root = gfg.Element(\\\"annotation\\\") \\n #the big section is called Annotation\\n for key in dictionary:\\n #for every polygon list in inside object witho subelement name and attributes and the type \\\"polygon\\\"\\n objectElement = gfg.Element(\\\"object\\\") \\n root.append(objectElement) \\n subElement1 = gfg.SubElement(objectElement, \\\"name:\\\".strip(\\\":\\\"))\\n subElement1.text = str(dictionary[key][\\\"name\\\"])\\n subElement2 = gfg.SubElement(objectElement, \\\"attributes\\\".strip(\\\":\\\"))\\n subElement2.text = str(dictionary[key][\\\"attributes\\\"])\\n subElement3 = gfg.SubElement(objectElement, \\\"polygon\\\")\\n \\n for i in range(0, len(dictionary[key])-2):\\n #for every vertex of the polygon list it's rounded x, y on xml\\n SubInsidePolygon = gfg.SubElement(subElement3, \\\"pt\\\")\\n sub_x = gfg.SubElement(SubInsidePolygon, \\\"x\\\")\\n sub_y = gfg.SubElement(SubInsidePolygon, \\\"y\\\")\\n sub_x.text = str(int(round(dictionary[key][\\\"x_y_\\\" + str(i)][0])))\\n sub_y.text = str(int(round(dictionary[key][\\\"x_y_\\\" + str(i)][1])))\\n tree = gfg.ElementTree(root) \\n #create the xml tree\\n with open (fileName, \\\"wb\\\") as files : \\n tree.write(files) \\n #if xml does not exist create one otherwise rewrite to it\",\n \"def export_representations(self):\\n\\n dbpath, config = self._start()\\n self.logger.msg1(\\\"Loading ontology\\\")\\n obo_path = check_file(config.obo, dbpath, \\\"obo\\\")\\n self.obo = MinimalObo(obo_path, True)\\n self._export_reference_representations()\\n self._export_model_representations(config)\\n self._end()\",\n \"def _add_products_to_genmap(self, artifact, state):\\n genmap = self.context.products.get('classes')\\n for target, sources in artifact.sources_by_target.items():\\n for source in sources:\\n classes = state.classes_by_src.get(source, [])\\n relsrc = os.path.relpath(source, target.target_base)\\n genmap.add(relsrc, artifact.classes_dir, classes)\\n genmap.add(target, artifact.classes_dir, classes)\\n # TODO(John Sirois): Map target.resources in the same way\\n # Create and Map scala plugin info files to the owning targets.\\n if is_scalac_plugin(target) and target.classname:\\n basedir, plugin_info_file = self._zinc_utils.write_plugin_info(self._resources_dir, target)\\n genmap.add(target, basedir, [plugin_info_file])\",\n \"def exportOrgs ( c ) :\\n assert str(type(c)) == \\\"<type '_mysql.connection'>\\\"\\n xml = \\\"\\\"\\n o = sqlQuery ( c, \\\"select * from Organizations;\\\" )\\n for i in o:\\n oL = sqlQuery ( c, \\\"select * from OrganizationLocations where orgID = '\\\"+i[0]+\\\"';\\\" )\\n oER = sqlQuery ( c, \\\"select * from OrganizationExternalResources where orgID = '\\\"+i[0]+\\\"';\\\" )\\n oTC = sqlQuery ( c, \\\"select * from OrganizationsToCrises where orgID = '\\\"+i[0]+\\\"';\\\" )\\n pTO = sqlQuery ( c, \\\"select * from PeopleToOrganizations where orgID = '\\\"+i[0]+\\\"';\\\" )\\n xml += openTagAtt ( \\\"Organization\\\", \\\"organizationIdent\\\", i[0])\\n xml += openCloseTag ( \\\"Name\\\", i[1])\\n xml += closeTagAtt ( \\\"Kind\\\", \\\"organizationKindIdent\\\", i[2])\\n for j in oL :\\n xml += openTag ( \\\"Location\\\" )\\n xml += openCloseTag ( \\\"Locality\\\", j [ 1 ] )\\n xml += openCloseTag ( \\\"Region\\\", j [ 2 ] )\\n xml += openCloseTag ( \\\"Country\\\", j [ 3 ] )\\n xml += closeTag ( \\\"Location\\\" )\\n xml += openCloseTag (\\\"History\\\", i[3])\\n xml += openTag ( \\\"ContactInfo\\\" )\\n xml += openCloseTag (\\\"Telephone\\\", i[4])\\n xml += openCloseTag (\\\"Fax\\\", i[5])\\n xml += openCloseTag (\\\"Email\\\", i[6])\\n xml += openTag (\\\"PostalAddress\\\")\\n xml += openCloseTag (\\\"StreetAddress\\\", i[7])\\n xml += openCloseTag ( \\\"Locality\\\", i[8])\\n xml += openCloseTag ( \\\"Region\\\", i[9])\\n xml += openCloseTag ( \\\"PostalCode\\\", i[10])\\n xml += openCloseTag ( \\\"Country\\\", i[11])\\n xml += closeTag ( \\\"PostalAddress\\\" )\\n xml += closeTag ( \\\"ContactInfo\\\" )\\n xml += openTag (\\\"ExternalResources\\\")\\n for j in oER:\\n xml += openCloseTag ( j[1], j[2])\\n xml += closeTag (\\\"ExternalResources\\\")\\n xml += openTag (\\\"RelatedCrises\\\")\\n for j in oTC:\\n xml += closeTagAtt (\\\"RelatedCrisis\\\", \\\"crisisIdent\\\", j[1])\\n xml += closeTag (\\\"RelatedCrises\\\")\\n xml += openTag (\\\"RelatedPersons\\\")\\n for j in pTO:\\n xml += closeTagAtt (\\\"RelatedPerson\\\", \\\"personIdent\\\", j[0])\\n xml += closeTag (\\\"RelatedPersons\\\")\\n xml += closeTag (\\\"Organization\\\")\\n assert str ( type ( xml ) ) == \\\"<type 'str'>\\\"\\n return xml\",\n \"def custom_package_xml_generator(directory, packagename=None, version='45.0', filename='package.xml'):\\n\\n METADATA_TYPE = {\\n 'applications':'CustomApplication', 'aura':'AuraDefinitionBundle', 'classes':'ApexClass', 'customPermissions':'CustomPermission', \\n 'flexipages':'FlexiPage', 'flows':'Flow', 'globalValueSets':'GlobalValueSet', 'labels':'CustomLabels', 'layouts':'Layout',\\n 'lwc': 'LightningComponentBundle', 'objects':'CustomObject', 'pages':'ApexPage', 'permissionsets':'PermissionSet', 'profiles':'Profile',\\n 'staticresources':'StaticResource', 'tabs':'CustomTab', 'triggers':'ApexTrigger', 'contentassets':'ContentAsset', 'pathAssistants':'PathAssistant',\\n 'quickActions':'QuickAction', 'remoteSiteSettings':'RemoteSiteSetting', 'workflows':'Workflow', 'dashboards':'Dashboard', 'reports':'Report',\\n 'cspTrustedSites':'CspTrustedSite',\\n }\\n\\n \\\"\\\"\\\"\\n Non-implemented Metadata:\\n 'ApexComponent', 'CustomMetadata' (needs custom manipulation), 'CustomObjectTranslation', 'DuplicateRule', \\n 'FlowCategory', 'GlobalValueSetTranslation', 'MatchingRules',\\n \\\"\\\"\\\"\\n #read directory structure\\n\\n mdtypedirs = os.listdir(directory)\\n\\n nested_mdt_object = ['ValidationRule', 'CompactLayout', 'ListView', 'SharingReason', 'RecordType']\\n nested_mdt_workflow = ['WorkflowFieldUpdate', 'WorkflowKnowledgePublish', 'WorkflowTask', 'WorkflowAlert', 'WorkflowSend', 'WorkflowOutboundMessage', 'WorkflowRule']\\n\\n # start our xml structure\\n root = xml.Element('Package')\\n root.set('xmlns','http://soap.sforce.com/2006/04/metadata')\\n\\n for mdtype in mdtypedirs:\\n # create child node for each type of component\\n if mdtype in METADATA_TYPE.keys():\\n etype = xml.SubElement(root, 'types')\\n ename = xml.SubElement(etype, 'name')\\n ename.text = str(METADATA_TYPE[mdtype])\\n emember = xml.SubElement(etype, 'members')\\n emember.text = str('*')\\n \\n if mdtype == 'objects':\\n for nest_mdtyp in nested_mdt_object:\\n etype = xml.SubElement(root, 'types')\\n ename = xml.SubElement(etype, 'name')\\n ename.text = nest_mdtyp\\n emember = xml.SubElement(etype, 'members')\\n emember.text = str('*')\\n\\n if mdtype == 'workflows':\\n for nest_mdtyp in nested_mdt_workflow:\\n etype = xml.SubElement(root, 'types')\\n ename = xml.SubElement(etype, 'name')\\n ename.text = nest_mdtyp\\n emember = xml.SubElement(etype, 'members')\\n emember.text = str('*')\\n\\n #Custom behavior for custom labels\\n if mdtype == 'labels':\\n etype = xml.SubElement(root, 'types')\\n ename = xml.SubElement(etype, 'name')\\n ename.text = 'CustomLabel'\\n emember = xml.SubElement(etype, 'members')\\n emember.text = str('*')\\n\\n # add the final xml node package.api_version\\n eversion = xml.SubElement(root, 'version')\\n eversion.text = str(version)\\n\\n #package name\\n if packagename != None:\\n efname = xml.SubElement(root, 'fullName')\\n efname.text = str(packagename)\\n\\n #pretty format for xml\\n xmlstring = xml.tostring(root)\\n reparsed = minidom.parseString(xmlstring)\\n prettyxml = reparsed.toprettyxml(indent=' ', newl='\\\\n', encoding='UTF-8')\\n \\n #generate xml file from string\\n try:\\n with open(os.path.join(directory, filename), \\\"bw\\\") as xml_file:\\n xml_file.write(prettyxml)\\n except IOError:\\n pass\",\n \"def open_output_files(self):\\n if not os.path.exists(self.outputDictionaryPath):\\n os.makedirs(self.outputDictionaryPath)\\n\\n self.XMLfile = open(os.path.join(self.outputDictionaryPath, 'MyDictionary.xml'), 'w+', encoding='utf-8') # this is the output file\\n self.Makefile = open(os.path.join(self.outputDictionaryPath, 'Makefile'), 'w+', encoding='utf-8')\\n self.MyInfoFile = open(os.path.join(self.outputDictionaryPath, 'MyInfo.plist'), 'w+', encoding='utf-8')\",\n \"def _makeimap(self):\\n self.map_['source'] = 'NAOJ'\\n self.map_['provider'] = 'NRO'\\n self.map_['instrument'] = 'NORH'\\n self.map_['phyobs'] = ''\",\n \"def _makeimap(self):\\n self.map_['source'] = 'GOES'\\n self.map_['provider'] = 'NOAA'\\n self.map_['instrument'] = 'SUVI'\\n self.map_['physobs'] = 'flux'\",\n \"def write_maps(self):\\n if np.allclose(self.xmap.origin, 0):\\n ext = \\\"ccp4\\\"\\n else:\\n ext = \\\"mrc\\\"\\n\\n for q, coor, b in zip(self._occupancies, self._coor_set, self._bs):\\n self.conformer.q = q\\n self.conformer.coor = coor\\n self.conformer.b = b\\n self._transformer.density()\\n fname = os.path.join(self.directory_name, f\\\"model.{ext}\\\")\\n self._transformer.xmap.tofile(fname)\\n self._transformer.xmap.array -= self.xmap.array\\n fname = os.path.join(self.directory_name, f\\\"diff.{ext}\\\")\\n self._transformer.xmap.tofile(fname)\\n self._transformer.reset(full=True)\",\n \"def aggregate_maps(self, G=[], node_tags={}):\\n\\n self.map = nx.DiGraph() \\n \\n # loop over graphs in list of graphs\\n for g in G:\\n # add nodes to graph\\n self.map.add_nodes_from(g.nodes())\\n # loop over edges \\n for edge in g.edges(data=True):\\n # if edge is already present, add weight to edge\\n if self.map.has_edge(edge[0],edge[1]):\\n self.map[edge[0]][edge[1]]['weight'] += edge[2]['weight']\\n # if edge is not already present, add it with weight=1\\n else:\\n self.map.add_edge(edge[0],edge[1], weight=1)\\n \\n # relabel nodes according to mapping provided by 'node_tags'\\n nx.set_node_attributes(self.map, name = 'node_label', values = node_tags) \\n nx.relabel_nodes(self.map, mapping=node_tags, copy=False)\\n\\n # assign a random color to each node\\n colour_list = np.random.choice( list(colors.get_named_colors_mapping().values()), len(self.map) )\\n colour_dict = dict( zip(self.map.nodes, colour_list) )\\n nx.set_node_attributes(self.map, name = 'node_color', values = colour_dict)\\n\\n # save node attributes to CoMap object\\n self.node_labels = nx.get_node_attributes(self.map, name = 'node_label')\\n self.node_colors = nx.get_node_attributes(self.map, name = 'node_color')\\n \\n return\",\n \"def create_gen_xml(self, out_file):\\n\\n param_list = []\\n msg = []\\n msg_type = []\\n dep_node = []\\n for line in self.full_ed_lines:\\n param_list.append(line.text())\\n dep_pkg = param_list[6].split(', ')\\n if dep_pkg[len(dep_pkg) - 1] == '':\\n dep_pkg.pop()\\n for dep in self.manager.wid.sub_list:\\n dep_node.append(dep['msg_type'])\\n for dep in self.manager.wid.pub_list:\\n dep_node.append(dep['msg_type'])\\n for dep in dep_node:\\n a, b = dep.split('/')\\n msg.append(a)\\n msg_type.append(b)\\n f = open('../genkernel/templates/package_rosgen.xml')\\n o = open(out_file, 'a')\\n flag = 0\\n while 1:\\n line = f.readline()\\n if not line: break\\n for i in range(6):\\n line = line.replace('[{0}]'.format(i), param_list[i])\\n line = line.replace('[7]', param_list[7])\\n if line.find('[6]') != -1:\\n for dep in dep_pkg:\\n line_dep = '\\\\t<depend>{0}</depend>\\\\n'.format(dep)\\n o.write(line_dep)\\n flag = 1\\n elif line.find('[8]') != -1:\\n for dep, tp in zip(msg, msg_type):\\n line_dep = '\\\\t\\\\t<depend type=\\\"{1}\\\">{0}</depend>\\\\n'.format(dep, tp)\\n o.write(line_dep)\\n flag = 1\\n elif line.find('<subscribers>') != -1:\\n o.write('\\\\t\\\\t<subscribers>\\\\n')\\n for sub in self.manager.wid.sub_list:\\n o.write('\\\\t\\\\t\\\\t<sub>\\\\n')\\n o.write('\\\\t\\\\t\\\\t\\\\t<name>{0}</name>\\\\n'.format(sub['name']))\\n o.write('\\\\t\\\\t\\\\t\\\\t<msg_type>{0}</msg_type>\\\\n'.format(sub['msg_type']))\\n o.write('\\\\t\\\\t\\\\t\\\\t<topic_name>{0}</topic_name>\\\\n'.format(sub['topic_name']))\\n o.write('\\\\t\\\\t\\\\t\\\\t<queue_size>{0}</queue_size>\\\\n'.format(sub['queue_size']))\\n o.write('\\\\t\\\\t\\\\t</sub>\\\\n')\\n o.write('\\\\t\\\\t</subscribers>\\\\n')\\n flag = 1\\n elif line.find('<publishers>') != -1:\\n o.write('\\\\t\\\\t<publishers>\\\\n')\\n for pub in self.manager.wid.pub_list:\\n o.write('\\\\t\\\\t\\\\t<pub>\\\\n')\\n o.write('\\\\t\\\\t\\\\t\\\\t<name>{0}</name>\\\\n'.format(pub['name']))\\n o.write('\\\\t\\\\t\\\\t\\\\t<msg_type>{0}</msg_type>\\\\n'.format(pub['msg_type']))\\n o.write('\\\\t\\\\t\\\\t\\\\t<topic_name>{0}</topic_name>\\\\n'.format(pub['topic_name']))\\n o.write('\\\\t\\\\t\\\\t\\\\t<queue_size>{0}</queue_size>\\\\n'.format(pub['queue_size']))\\n o.write('\\\\t\\\\t\\\\t</pub>\\\\n')\\n o.write('\\\\t\\\\t</publishers>\\\\n')\\n flag = 1\\n if flag == 0:\\n o.write(line)\\n else:\\n flag = 0\\n o.close()\\n f.close()\\n self.changed = False\",\n \"def serialize_mapping_to_file(\\n file_path, alignment, onto_one=None, onto_two=None, extension=None\\n):\\n with open(file_path, \\\"w\\\", encoding=\\\"utf-8\\\") as out_file:\\n out_file.write(__get_xml_intro(onto_one, onto_two, extension))\\n for correspondence in alignment:\\n out_file.write(__get_mapping_string(*correspondence))\\n out_file.write(__get_xml_outro())\",\n \"def map(self, app, node):\",\n \"def build_mapping() -> str:\\n templates = make_module_docstring(\\\"Template classes for GBD entities\\\", __file__)\\n templates += make_import(\\\"typing\\\", [\\\"Union\\\", \\\"Tuple\\\"])\\n templates += (\\n make_import(\\n \\\".id\\\",\\n [\\n \\\"c_id\\\",\\n \\\"s_id\\\",\\n \\\"hs_id\\\",\\n \\\"me_id\\\",\\n \\\"cov_id\\\",\\n \\\"rei_id\\\",\\n \\\"scalar\\\",\\n ],\\n )\\n + SPACING\\n )\\n templates += make_gbd_record()\\n\\n for entity, info in get_base_types().items():\\n templates += SPACING\\n templates += make_record(entity, **info)\\n\\n return templates\",\n \"def read_nuget(self, config, excl=None, mapping=None):\\r\\n\\r\\n\\tif not self.env.MCS:\\r\\n\\t\\treturn\\r\\n\\r\\n\\tsrc = self.path.find_resource(config)\\r\\n\\tif src:\\r\\n\\t\\thandler = PackageHandler(self, excl, mapping)\\r\\n\\t\\tparser = xml.sax.make_parser()\\r\\n\\t\\tparser.setContentHandler(handler)\\r\\n\\t\\tparser.parse(src.abspath())\",\n \"def DARP2016_Acoular_XML():\\n XYZ_array, array_cal = DARP2016_MicArray()\\n array_name = \\\"DARP2016\\\"\\n array_pos = XML_mic(XYZ_array)\\n array_file = '<?xml version=\\\"1.0\\\" encoding=\\\"utf-8\\\"?>\\\\n<MicArray name=\\\"{name}\\\">\\\\n{pos}</MicArray>'.format(\\n name=array_name, pos=array_pos)\\n calib_data = XML_calib(array_cal, 100)\\n calib_file = '<?xml version=\\\"1.0\\\" encoding=\\\"utf-8\\\"?>\\\\n<Calib name=\\\"{name}\\\">\\\\n{data}</Calib>'.format(\\n name=20160101, data=calib_data)\\n\\n save_XML(array_name, array_file)\\n save_XML(array_name+\\\"_calib\\\", calib_file)\",\n \"def WriteDepMappingFile(translated_source_files,\\n objc_file_path,\\n output_dependency_mapping_file,\\n file_open=open):\\n dep_mapping = dict()\\n input_file_queue = Queue.Queue()\\n output_dep_mapping_queue = Queue.Queue()\\n error_message_queue = Queue.Queue()\\n for output_file in translated_source_files.split(','):\\n input_file_queue.put(output_file)\\n\\n for _ in xrange(multiprocessing.cpu_count()):\\n t = threading.Thread(target=_ReadDepMapping, args=(input_file_queue,\\n output_dep_mapping_queue,\\n error_message_queue,\\n objc_file_path,\\n file_open))\\n t.start()\\n\\n input_file_queue.join()\\n\\n if not error_message_queue.empty():\\n error_messages = [error_message for error_message in\\n error_message_queue.queue]\\n raise RuntimeError('\\\\n'.join(error_messages))\\n\\n while not output_dep_mapping_queue.empty():\\n entry_file, deps = output_dep_mapping_queue.get()\\n dep_mapping[entry_file] = deps\\n\\n f = file_open(output_dependency_mapping_file, 'w')\\n for entry in sorted(dep_mapping):\\n for dep in dep_mapping[entry]:\\n f.write(entry + ':' + dep + '\\\\n')\\n f.close()\",\n \"def mappings(input_report, **kwargs):\\n ben = BoomerEngine()\\n ben.load(input_report, prefix_map=global_prefix_map)\\n writer = StreamingSssomWriter()\\n for m in ben.mappings(**kwargs):\\n writer.emit(m)\\n writer.finish()\",\n \"def outputs(self):\\n return {\\\"path_to_mapping_json\\\": File_IO(\\n self.node.outputs[0])}\",\n \"def generate_header():\\n header_file = AUTOGEN_WARNING\\n header_file += \\\"/// /file atomic_nuclear_data.h\\\\n\\\"\\n header_file += \\\"/// /author Andrew Davis (andrew.davis@wisc.edu)\\\\n\\\"\\n header_file += \\\"///\\\\n\\\"\\n header_file += (\\n \\\"/// /brief Implements all the fundamental atomic & nuclear data data\\\\n\\\"\\n )\\n header_file += \\\"#include <map>\\\\n\\\"\\n header_file += \\\"\\\\n\\\"\\n header_file += \\\"namespace pyne\\\\n\\\"\\n header_file += \\\"{\\\\n\\\"\\n header_file += (\\n \\\" /// main function to be called when you wish to load the nuclide data \\\\n\\\"\\n )\\n header_file += \\\" /// into memory \\\\n\\\"\\n header_file += \\\" void _load_atomic_mass_map_memory();\\\\n\\\"\\n header_file += \\\" /// function to create mapping from nuclides in id form\\\\n\\\"\\n header_file += \\\" /// to their atomic masses\\\\n\\\"\\n header_file += \\\" \\\\n\\\"\\n header_file += \\\" void _insert_atomic_mass_map();\\\\n\\\"\\n header_file += \\\" \\\\n\\\"\\n header_file += \\\" /// function to create mapping from nuclides in id form \\\\n\\\"\\n header_file += \\\" /// to their natural abundances\\\\n\\\"\\n header_file += \\\" void _insert_abund_map();\\\\n\\\"\\n header_file += \\\" \\\\n\\\"\\n header_file += (\\n \\\" /// Mapping from nuclides in id form to their natural abundances\\\\n\\\"\\n )\\n header_file += \\\" extern std::map<int,double> natural_abund_map;\\\\n\\\"\\n header_file += \\\" \\\\n\\\"\\n header_file += \\\" /// Mapping from nuclides in id form to their atomic masses.\\\\n\\\"\\n header_file += \\\" extern std::map<int,double> atomic_mass_map;\\\\n\\\"\\n header_file += \\\" \\\\n\\\"\\n header_file += (\\n \\\" /// Mapping from nuclides in id form to the associated error in \\\\n\\\"\\n )\\n header_file += \\\" /// abdundance \\\\n\\\"\\n header_file += \\\" extern std::map<int,double> atomic_mass_error_map;\\\\n\\\"\\n header_file += \\\"} // namespace pyne\\\\n\\\"\\n return header_file\",\n \"def OnInit(self):\\n \\n # load the xml resource\\n script_dir = os.path.dirname ( __file__ )\\n self.res = xrc.XmlResource ( \\\"%s/csv2ofx.xrc\\\" % script_dir )\\n \\n # load the frame from the resource \\n self.frame = self.res.LoadFrame ( None, \\\"ID_CSV2OFX\\\")\\n \\n # associate the MenuBar\\n self.frame.SetMenuBar (\\n self.res.LoadMenuBar(\\\"ID_MENUBAR\\\")\\n )\\n\\n # the grid\\n self.grid = xrc.XRCCTRL(self.frame,\\\"ID_GRID\\\")\\n self.grid.EnableEditing(False)\\n \\n # the mappings\\n self.mappings = xrc.XRCCTRL(self.frame,\\\"ID_MAPPINGS\\\")\\n\\n try:\\n try:\\n # try current directory first\\n if os.path.isfile ( 'csv2ofx_custom.py' ):\\n execfile ( 'csv2ofx_custom.py', globals())\\n for mapping in all_mappings:\\n self.mappings.Append ( mapping, all_mappings[mapping] )\\n else:\\n raise # try the home directory\\n except:\\n homepath=os.path.expanduser('~')\\n cust_mappings = \\\"%s/csv2ofx_custom.py\\\" % homepath\\n if os.path.isfile( cust_mappings ):\\n execfile ( cust_mappings, globals() )\\n for mapping in all_mappings:\\n self.mappings.Append ( mapping, all_mappings[mapping] )\\n except: \\n print_exc()\\n # Use built in mappings as default/backup\\n if self.mappings.IsEmpty():\\n print \\\"Using Default Mappings\\\"\\n import mappings\\n for mapping in mappings.all_mappings:\\n self.mappings.Append ( mapping, mappings.all_mappings[mapping] )\\n self.mappings.SetSelection(0)\\n\\n # output formats\\n self.exports = xrc.XRCCTRL(self.frame,\\\"ID_EXPORT\\\")\\n \\n # handle events\\n self.Bind ( wx.EVT_MENU, self.OnCloseBtn, id=xrc.XRCID(\\\"ID_MENU_CLOSE\\\"))\\n self.Bind ( wx.EVT_BUTTON, self.OnCloseBtn, id=xrc.XRCID(\\\"ID_BTN_CLOSE\\\"))\\n self.Bind ( wx.EVT_MENU, self.OnImport, id=xrc.XRCID(\\\"ID_MENU_IMPORT\\\"))\\n self.Bind ( wx.EVT_BUTTON, self.OnImport, id=xrc.XRCID(\\\"ID_BTN_IMPORT\\\"))\\n self.Bind ( wx.EVT_MENU, self.OnExport, id=xrc.XRCID(\\\"ID_MENU_EXPORT\\\"))\\n self.Bind ( wx.EVT_BUTTON, self.OnExport, id=xrc.XRCID(\\\"ID_BTN_EXPORT\\\"))\\n self.frame.Bind ( wx.EVT_CLOSE, self.OnClose )\\n self.frame.Bind ( wx.EVT_MOVE, self.OnMove )\\n self.frame.Bind ( wx.EVT_SIZE, self.OnSize )\\n \\n \\n # app preferences\\n self.config = wx.Config ( \\\"csv2ofx\\\" )\\n\\n x=self.config.ReadInt(\\\"screenx\\\",100)\\n y=self.config.ReadInt(\\\"screeny\\\",100)\\n w=self.config.ReadInt(\\\"screenw\\\",600)\\n h=self.config.ReadInt(\\\"screenh\\\",550)\\n\\n \\n # show the frame \\n self.SetTopWindow(self.frame)\\n \\n self.frame.SetPosition( (x,y) )\\n self.frame.SetSize( (w,h) )\\n self.frame.Show()\\n return True\",\n \"def _makeimap(self):\\n self.map_[\\\"source\\\"] = \\\"nasa\\\"\\n self.map_[\\\"instrument\\\"] = \\\"goes\\\"\\n self.map_[\\\"physobs\\\"] = \\\"irradiance\\\"\\n self.map_[\\\"provider\\\"] = \\\"sdac\\\"\",\n \"def node_mapping(self):\\n ...\",\n \"def dump_packages(self, package_name='com.sagas.dss'):\\n model_reader = oc.delegator.getModelReader()\\n tree_map = model_reader.getEntitiesByPackage(None, None)\\n # print(tree_map)\\n py_tree_map={trim_prefix(k):to_list(vals) for (k,vals) in tree_map.items() }\\n for (k,vals) in py_tree_map.items():\\n print(k, vals)\\n\\n json_str=json.dumps(py_tree_map, ensure_ascii=False, indent=2)\\n io_utils.write_to_file(\\\"dump_pkgs.json\\\", json_str)\",\n \"def to_xml_dict(self):\\n self._check_required_fields()\\n\\n root = odict[\\n \\\"r:import\\\": odict[\\n \\\"@xmlns:r\\\": \\\"http://resolver.nkp.cz/v3/\\\",\\n \\\"r:monograph\\\": odict[\\n \\\"r:titleInfo\\\": odict[\\n \\\"r:title\\\": self.title,\\n ],\\n ],\\n ]\\n ]\\n mono_root = root[\\\"r:import\\\"][\\\"r:monograph\\\"]\\n\\n # subtitle\\n MonographComposer._assign_pattern(\\n mono_root[\\\"r:titleInfo\\\"],\\n \\\"r:subTitle\\\",\\n self.subtitle\\n )\\n\\n # handle ccnb, isbn, uuid\\n self._add_identifier(mono_root, \\\"ccnb\\\")\\n self._add_identifier(mono_root, \\\"isbn\\\")\\n self._add_identifier(mono_root, \\\"other_id\\\", out=\\\"otherId\\\")\\n\\n # add form of the book\\n MonographComposer._assign_pattern(\\n mono_root,\\n \\\"r:documentType\\\",\\n self.document_type\\n )\\n\\n mono_root[\\\"r:digitalBorn\\\"] = \\\"true\\\" if self.digital_born else \\\"false\\\"\\n\\n if self.author:\\n mono_root[\\\"r:primaryOriginator\\\"] = odict[\\n \\\"@type\\\": \\\"AUTHOR\\\",\\n \\\"#text\\\": self.author\\n ]\\n\\n if any([self.place, self.publisher, self.year]):\\n publ = odict()\\n\\n MonographComposer._assign_pattern(\\n publ,\\n \\\"r:publisher\\\",\\n self.publisher\\n )\\n MonographComposer._assign_pattern(publ, \\\"r:place\\\", self.place)\\n MonographComposer._assign_pattern(publ, \\\"r:year\\\", self.year)\\n\\n mono_root[\\\"r:publication\\\"] = publ\\n\\n if self.format:\\n format_dict = MonographComposer._create_path(\\n root,\\n odict,\\n [\\n \\\"r:import\\\",\\n \\\"r:digitalDocument\\\",\\n \\\"r:technicalMetadata\\\",\\n \\\"r:format\\\",\\n ]\\n )\\n\\n format_dict[\\\"#text\\\"] = self.format\\n\\n return root\",\n \"def _write_init(self):\\n\\n content = ('# -*- coding: utf-8 -*-'\\n '\\\\n'\\n '\\\\n'\\n \\\"__version__ = '${version}'\\\"\\n '\\\\n')\\n intFile = os.path.join(self.packageDir, '__init__.py')\\n return self.write_file(string.Template(content), intFile)\",\n \"def _do_mapping(self):\\n pass\",\n \"def _generate_global_config() -> str:\\n logger = getLogger(__name__)\\n dst = os.path.join(os.path.expanduser(\\\"~\\\"),\\n \\\".aiscalator/config/aiscalator.conf\\\")\\n logger.info(\\\"Generating a new configuration file for aiscalator:\\\\n\\\\t%s\\\",\\n dst)\\n pattern = [\\n \\\"testUserID\\\",\\n \\\"generation_date\\\",\\n ]\\n replace_value = [\\n generate_user_id(),\\n '\\\"' + str(datetime\\n .utcnow()\\n .replace(tzinfo=timezone(\\\"UTC\\\"))) +\\n '\\\" // in UTC timezone',\\n ]\\n dst_dir = os.path.dirname(dst)\\n if dst_dir:\\n os.makedirs(dst_dir, exist_ok=True)\\n copy_replace(data_file(\\\"../config/template/aiscalator.conf\\\"),\\n dst, pattern=pattern, replace_value=replace_value)\\n open(os.path.join(dst_dir, \\\"apt_packages.txt\\\"), 'a').close()\\n open(os.path.join(dst_dir, \\\"requirements.txt\\\"), 'a').close()\\n open(os.path.join(dst_dir, \\\"lab_extensions.txt\\\"), 'a').close()\\n return dst\",\n \"def xml(self):\\n xml_string = '<VendorMap>\\\\n'\\n for vendorid, name in self.data.items():\\n xml_string += \\\"<Vendor>\\\\n<Id>{}</Id>\\\\n<Name>{}</Name>\\\\n</Vendor>\\\\n\\\".format(vendorid, name)\\n xml_string += '</VendorMap>\\\\n'\\n return xml_string\",\n \"def write_global_local_maps(dest,global_local,local_global):\",\n \"def write(self, file_or_filename):\\n etMap = revert(self)\\n xmlTree = ET.ElementTree(etMap)\\n xmlTree.write(file_or_filename)\",\n \"def create_gen_json(self, out_file):\\n\\n params = self.create_package_dict()\\n with open(out_file, 'w') as fp:\\n json.dump(params, fp)\",\n \"def output_map(self, x):\\n\\n return x\",\n \"def make_mapping(self) -> None:\\n start_mark = StreamMark('generated node', 0, 0, 0)\\n end_mark = StreamMark('generated node', 0, 0, 0)\\n self.yaml_node = yaml.MappingNode('tag:yaml.org,2002:map', list(),\\n start_mark, end_mark)\",\n \"def export_to_xml(self, resource_fs):\\r\\n\\r\\n # Set up runtime.export_fs so that it's available through future\\r\\n # uses of the pure xblock add_xml_to_node api\\r\\n self.runtime.export_fs = resource_fs\\r\\n\\r\\n # Get the definition\\r\\n xml_object = self.definition_to_xml(resource_fs)\\r\\n self.clean_metadata_from_xml(xml_object)\\r\\n\\r\\n # Set the tag so we get the file path right\\r\\n xml_object.tag = self.category\\r\\n\\r\\n # Add the non-inherited metadata\\r\\n for attr in sorted(own_metadata(self)):\\r\\n # don't want e.g. data_dir\\r\\n if attr not in self.metadata_to_strip and attr not in self.metadata_to_export_to_policy:\\r\\n val = serialize_field(self._field_data.get(self, attr))\\r\\n try:\\r\\n xml_object.set(attr, val)\\r\\n except Exception:\\r\\n logging.exception(\\r\\n u'Failed to serialize metadata attribute %s with value %s in module %s. This could mean data loss!!!',\\r\\n attr, val, self.url_name\\r\\n )\\r\\n\\r\\n for key, value in self.xml_attributes.items():\\r\\n if key not in self.metadata_to_strip:\\r\\n xml_object.set(key, value)\\r\\n\\r\\n if self.export_to_file():\\r\\n # Write the definition to a file\\r\\n url_path = name_to_pathname(self.url_name)\\r\\n filepath = self._format_filepath(self.category, url_path)\\r\\n resource_fs.makedir(os.path.dirname(filepath), recursive=True, allow_recreate=True)\\r\\n with resource_fs.open(filepath, 'w') as file:\\r\\n file.write(etree.tostring(xml_object, pretty_print=True, encoding='utf-8'))\\r\\n\\r\\n # And return just a pointer with the category and filename.\\r\\n record_object = etree.Element(self.category)\\r\\n else:\\r\\n record_object = xml_object\\r\\n\\r\\n record_object.set('url_name', self.url_name)\\r\\n\\r\\n # Special case for course pointers:\\r\\n if self.category == 'course':\\r\\n # add org and course attributes on the pointer tag\\r\\n record_object.set('org', self.location.org)\\r\\n record_object.set('course', self.location.course)\\r\\n\\r\\n return etree.tostring(record_object, pretty_print=True, encoding='utf-8')\",\n \"def export_to_xml(self, resource_fs):\\r\\n raise NotImplementedError('Modules must implement export_to_xml to enable xml export')\",\n \"def collect_olm_auto_generated_resources(namespace, ns_output_dir, k8s_cli):\\n if not detect_if_olm_deployed(namespace, k8s_cli):\\n return\\n olm_output_dir = os.path.join(ns_output_dir, \\\"OLM\\\")\\n make_dir(olm_output_dir)\\n selector = f\\\"--selector={OLM_LABEL % namespace}\\\"\\n logger.info(\\\"Namespace '%s': Collecting OLM autogenerated resources\\\", namespace)\\n resources_out = OrderedDict()\\n for resource in OLM_RESOURCES:\\n output = run_get_resource_yaml(namespace, resource, k8s_cli, selector)\\n if output:\\n resources_out[resource] = output\\n logger.info(\\\"Namespace '%s': + Collected %s\\\", namespace, resource)\\n for entry, out in resources_out.items():\\n with open(os.path.join(olm_output_dir,\\n \\\"{}.yaml\\\".format(entry)), \\\"w+\\\", encoding='UTF-8') as file_handle:\\n file_handle.write(out)\",\n \"def create_mapping_file(options):\\n\\n mapping_file = open(os.path.splitext(options.bco)[0] + \\\"mapping.txt\\\", 'w')\\n mapping_file.writelines(\\n\\\"\\\"\\\"# Use this file to provide mapping values for a bco.\\n# MISSING PROPERTIES/FIELDS lists properties/fields that are missing from bco\\n# NONALLOWED PROPERTIES/FIELDS shows properties that are not allowed\\n# Syntax for specifying values\\n# To delete a value\\n# PATH --> FIELD: DELETE\\n# To add a value\\n# PATH --> FIELD: ADD-value_to_add\\n# To rename a field name\\n# PATH --> FIELD: RENAME-new_field_name\\n# To swap a field name with another current field name\\n# PATH --> FIELD: SWAP-other_field_name\\n# Blank values will be skipped. Data does not need to be double represented\\n# For example, \\n# if <bco_id> needs renamed to <object_id>, either\\n# ['object_id'] --> object_id: \\n# SWAP-bco_id\\n# OR \\n# ['bco_id'] --> bco_id: RENAME:object_id \\n# will work. No need to fill out both values.\\n\\\"\\\"\\\"\\n)\\n validate_bco(options)\\n\\n missing_reg = r'(.*?) is a required property' # missing required property\\n additional_reg = r'Additional properties are not allowed (.*?)' # unalloewd extra property\\n\\n attribute_reg = r\\\"'(.*?)'\\\" # getting an attribute (field surronded by single quotes)\\n index_reg = r\\\"On instance(.*?)\\\" # getting key path\\n\\n failed_validation_reg = r'Failed validating (.*?)' # invalid type\\n\\n missing = []\\n additional = []\\n invalid = []\\n\\n path = {}\\n\\n with open('error.log') as errors:\\n for line in errors:\\n if re.match(missing_reg, line): # if there is a missing property\\n to_add = re.findall(attribute_reg, line)\\n for match in to_add:\\n missing.append(match)\\n elif re.match(additional_reg, line): # if there is an additional property\\n to_add = re.findall(attribute_reg, line)\\n for match in to_add:\\n additional.append(match)\\n elif re.match(failed_validation_reg, line): # if a property is invalid\\n # additional and required properties are already represnted by the above regexes,\\n # so skip\\n if line.__contains__(\\\"'additionalProperties'\\\") is False \\\\\\n and line.__contains__(\\\"'required'\\\") is False:\\n to_add = [line.split(\\\"schema\\\")[1].split(\\\"['\\\")[-1].strip(\\\"']:\\\\n\\\")]\\n invalid.append(to_add[0])\\n\\n # field contains an index for some attribute\\n # this attribute will be the last attribute found the above regexes, and is stored in\\n # to_add\\n if re.match(index_reg, line):\\n keys = \\\"\\\"\\n index_path = line.removeprefix(\\\"On instance\\\").removesuffix(\\\":\\\\n\\\")\\n if index_path is not None:\\n keys = str(index_path)\\n if len(to_add) > 0: # if there are any attributes to add\\n for item in to_add:\\n add_or_update_list_HELPER(path, str(item), keys + \\\"['\\\" + str(item) +\\n \\\"']\\\")\\n to_add = [] # reset to_add\\n mapping_file.write(\\\"====MISSING PROPERTIES/FIELDS====\\\\n\\\")\\n for attribute in missing:\\n mapping_file.write(str(path[attribute][0]) + \\\"-->\\\" + str(attribute) + \\\":\\\\n\\\")\\n path[attribute].pop(0)\\n\\n mapping_file.write(\\\"====NONALLOWED PROPERTIES/FIELDS====\\\\n\\\")\\n for attribute in additional:\\n mapping_file.write(str(path[attribute][0]) + \\\"-->\\\" + str(attribute) + \\\":\\\\n\\\")\\n path[attribute].pop(0)\\n for attribute in invalid:\\n mapping_file.write(str(path[attribute][0]).split(\\\"]\\\")[0]\\n + \\\"]-->\\\" + str(attribute) + \\\":\\\\n\\\")\\n path[attribute].pop(0)\\n\\n return mapping_file.name\",\n \"def saveObjectMaps(self):\\n if self.objectMaps == None: return\\n path = os.path.join(self.dir,settings['mosh.modInfos.objectMaps'])\\n outDir = os.path.split(path)[0]\\n if not os.path.exists(outDir): os.makedirs(outDir)\\n cPickle.dump(self.objectMaps,open(path,'wb'),2)\",\n \"def setUp(self): \\n \\n self.obo = MinimalObo(obo_file)\\n self.emapa = get_emapa_map(emapa_file, self.obo)\",\n \"def configureGeoData(data,resultDir):\\n xmlfiles = findfiles(['*.xml'],where=data[\\\"folder\\\"])\\n xmlurls=[]\\n fgdclist=[]\\n #xmlselect=[]\\n for xml in xmlfiles:\\n shutil.copy(os.path.join(data['folder'],xml),resultDir)\\n xmlurls.append(os.path.join(resulturl,resultDir.split('/')[-1],xml))\\n #import xmltodict\\n localfilename=os.path.join(data['folder'],xml)\\n #xmlselect.append({\\\"file\\\":localfilename,\\\"url\\\":os.path.join(resulturl,resultDir.split('/')[-1],xml)})\\n with open(os.path.join(data['folder'],xml)) as fd:\\n stringxml = fd.read()\\n #if 'FGDC' in stringxml.upper():\\n fgdc={}\\n fgdc['url']=os.path.join(resulturl,resultDir.split('/')[-1],xml)\\n doc = xmltodict.parse(stringxml,cdata_key='text',attr_prefix='',dict_constructor=dict)\\n fgdc['data']=doc\\n fgdc['file']=localfilename\\n fgdclist.append(fgdc)\\n data['xmlurls']=xmlurls\\n data['xml']={\\\"urls\\\":xmlurls,\\\"fgdc\\\":fgdclist,\\\"files\\\":xmlfiles}\\n return data\",\n \"def export_all_components(self) -> None:\\n logging.message('Exporting current config.')\\n for c in COMPONENT_MAP:\\n self.export_components(c['file'], c['type'])\\n\\n aliases.export_aliases()\\n export_groups(self)\",\n \"def __init__(self, input_file, output_file, encoding='utf-8'):\\n\\n self.output_filename = output_file\\n self.input_directory = input_file\\n self.output_buffer = []\\n self.output_dict = [{}]\\n self.item_titles = {}\\n self.output = None\\n\\n self._init_output_dict()\\n\\n # open the xml file for iteration\\n # self.context = ETree.iterparse(input_file, events=(\\\"start\\\", \\\"end\\\"))\\n\\n # output file handle\\n try:\\n pass\\n #self.output = codecs.open(output_file, \\\"w\\\", encoding=encoding)\\n except:\\n print(\\\"Failed to open the output file\\\")\\n raise\",\n \"def convert_to_annotation(file, output):\\n from pybel.resources.definitions import write_annotation\\n\\n resource = parse_bel_resource(file)\\n\\n write_annotation(\\n keyword=resource['Namespace']['Keyword'],\\n values={k: '' for k in resource['Values']},\\n citation_name=resource['Citation']['NameString'],\\n description=resource['Namespace']['DescriptionString'],\\n file=output\\n )\",\n \"def _load_objects(self):\\n self._get_package()\\n\\n object_names = [name for name in dir(self._sdk) if name != \\\"GATDLSession\\\" and name != \\\"SDKInfo\\\" and name.startswith(\\\"GA\\\") and not name.endswith(\\\"Fetcher\\\")]\\n\\n for object_name in object_names:\\n obj = getattr(self._sdk, object_name)\\n self._objects_mapping[obj.rest_name] = object_name\",\n \"def _po_packages(self):\\n for name in self.distribution.po:\\n source_dir = os.path.join(self.distribution.po_dir, name)\\n build_dir = os.path.join(self.build_lib, name, 'locale')\\n template = os.path.join(source_dir, name + '.pot')\\n pkg = {'name': name,\\n 'template': template,\\n 'source_dir': source_dir,\\n 'build_dir': build_dir}\\n yield pkg\",\n \"def getMappedSymbolsXML(self, addrstring: unicode) -> unicode:\\n ...\",\n \"def pathMap(self):\\n pass\",\n \"def _load_namespaces(self):\\n nsdocs = self._docset.get_namespaces()\\n for nsdoc in nsdocs:\\n nsobj = Namespace(nsdoc)\\n self._docmap[nsdoc] = nsobj\\n self._namespaces.add(nsobj)\",\n \"def to_xml(self, doc, element, pipe_type=None):\\n\\n # Add all simple python objects within the PipeContainer to the global element.\\n global_element = doc.createElement('global')\\n element.appendChild(global_element)\\n global_element.setAttribute('desc', 'Global data located in the top level of the data pipe')\\n fill_object_contents(doc, global_element, object=self, blacklist=['align_tensors', 'diff_tensor', 'exp_info', 'interatomic', 'hybrid_pipes', 'mol', 'pipe_type', 'structure'] + list(self.__class__.__dict__.keys()))\\n\\n # Hybrid info.\\n self.xml_create_hybrid_element(doc, element)\\n\\n # Add the experimental information.\\n if hasattr(self, 'exp_info'):\\n self.exp_info.to_xml(doc, element)\\n\\n # Add the diffusion tensor data.\\n if hasattr(self, 'diff_tensor'):\\n self.diff_tensor.to_xml(doc, element)\\n\\n # Add the alignment tensor data.\\n if hasattr(self, 'align_tensors'):\\n self.align_tensors.to_xml(doc, element)\\n\\n # Add the molecule-residue-spin data.\\n self.mol.to_xml(doc, element, pipe_type=pipe_type)\\n\\n # Add the interatomic data.\\n self.interatomic.to_xml(doc, element, pipe_type=pipe_type)\\n\\n # Add the structural data, if it exists.\\n if hasattr(self, 'structure'):\\n self.structure.to_xml(doc, element)\",\n \"def setup(self):\\n # TODO : figure out how to make the map interface a singleton class\\n\\n if not hasattr(self, 'mapInterface'):\\n self.mapInterface = MapInterface(settings['FILE_CONFIG']['filename'])\",\n \"def create_master_mapping_file(options, bcos_to_map):\\n mapping_dict = {\\n \\\"missing\\\": {\\n\\n },\\n\\n \\\"additional\\\": {\\n\\n }\\n }\\n\\n for bco in bcos_to_map:\\n\\n filename = os.path.splitext(bco)[0] + \\\"mapping.txt\\\"\\n\\n options.bco = bco\\n create_mapping_file(options)\\n\\n addition_mode = False\\n\\n with open(filename) as mapping_file:\\n for line in mapping_file:\\n if line.startswith(\\\"#\\\"): # Skip comments explaining mapping file\\n continue\\n if line.startswith(\\\"====\\\"): # Switch modes. Starts false -> true -> false\\n addition_mode = not addition_mode\\n continue\\n key_path = line.split(\\\"-->\\\")[0]\\n value = line.split(\\\"-->\\\")[1].strip(\\\":\\\")\\n\\n if addition_mode:\\n if key_path not in mapping_dict[\\\"missing\\\"]:\\n mapping_dict[\\\"missing\\\"][key_path] = [value, [bco]]\\n else:\\n mapping_dict[\\\"missing\\\"][key_path][1].append(bco)\\n else:\\n if key_path not in mapping_dict[\\\"additional\\\"]:\\n mapping_dict[\\\"additional\\\"][key_path] = [value, [bco]]\\n else:\\n mapping_dict[\\\"additional\\\"][key_path][1].append(bco)\\n os.remove(filename)\\n\\n\\n with open(\\\"mastermappingfile.txt\\\", \\\"w\\\") as masterfile: # write dict to\\n # single file with\\n masterfile.writelines(\\n\\\"\\\"\\\"# Use this file to provide mapping values for multiple bcos.\\n# fill out the values for each bco listed.\\n# MISSING PROPERTIES/FIELDS lists properties/fields that are missing from bco\\n# NONALLOWED PROPERTIES/FIELDS shows properties that are not allowed\\n# Syntax for specifying values\\n# To delete a value\\n# PATH --> FIELD: DELETE\\n# To add a value\\n# PATH --> FIELD: ADD-value_to_add\\n# To rename a field name\\n# PATH --> FIELD: RENAME-new_field_name\\n# To swap a field name with another current field name\\n# PATH --> FIELD: SWAP-other_field_name\\n# Blank values will be skipped. Data does not need to be double represented\\n# For example, \\n# if <bco_id> needs renamed to <object_id>, either\\n# ['object_id'] --> object_id: \\n# SWAP-bco_id\\n# OR \\n# ['bco_id'] --> bco_id: RENAME:object_id \\n# will work. No need to fill out both values.\\n\\\"\\\"\\\")\\n masterfile.write(\\\"====MISSING PROPERTIES/FIELDS=====\\\\n\\\")\\n for key in mapping_dict[\\\"missing\\\"]:\\n attribute = mapping_dict[\\\"missing\\\"][key]\\n value = attribute[0]\\n\\n masterfile.write(key + \\\"-->\\\" + value)\\n for bco_name in attribute[1]:\\n masterfile.write(\\\" \\\" + os.path.basename(bco_name) + \\\":\\\\n\\\")\\n masterfile.write(\\\"-----------------------\\\\n\\\")\\n masterfile.write(\\\"=====ADDITONAL PROPERTIES/FIELDS=====\\\\n\\\")\\n for key in mapping_dict[\\\"additional\\\"]:\\n attribute = mapping_dict[\\\"additional\\\"][key]\\n value = attribute[0]\\n masterfile.write(key + \\\"-->\\\" + value)\\n for bco_name in attribute[1]:\\n masterfile.write(\\\" \\\" + os.path.basename(bco_name) + \\\":\\\\n\\\")\\n masterfile.write(\\\"-----------------------\\\\n\\\")\",\n \"def build_I_map(self):\\n raise NotImplementedError\",\n \"def import_packages_global():\\n return \\\"\\\"\",\n \"def packages():\",\n \"def _add_jet_maps(process, options, prefix, jet_algo_name):\\n\\n # -- jet-to-trigger-object association map\\n from Karma.Common.Producers.JetMatchingProducers_cfi import karmaJetTriggerObjectMatchingProducer\\n process.add_module(\\n \\\"{}JetTriggerObjectMap{}\\\".format(prefix, jet_algo_name),\\n karmaJetTriggerObjectMatchingProducer.clone(\\n primaryCollectionSrc = cms.InputTag(\\\"{}Jets{}\\\".format(prefix, jet_algo_name)),\\n secondaryCollectionSrc = cms.InputTag(\\\"karmaTriggerObjects\\\"),\\n )\\n )\\n\\n # -- jet-to-lepton association maps\\n from Karma.Common.Producers.JetMatchingProducers_cfi import karmaJetMuonMatchingProducer\\n process.add_module(\\n \\\"{}JetMuonMap{}\\\".format(prefix, jet_algo_name),\\n karmaJetMuonMatchingProducer.clone(\\n primaryCollectionSrc = cms.InputTag(\\\"{}Jets{}\\\".format(prefix, jet_algo_name)),\\n secondaryCollectionSrc = cms.InputTag(\\\"karmaMuons\\\"),\\n )\\n )\\n from Karma.Common.Producers.JetMatchingProducers_cfi import karmaJetElectronMatchingProducer\\n process.add_module(\\n \\\"{}JetElectronMap{}\\\".format(prefix, jet_algo_name),\\n karmaJetElectronMatchingProducer.clone(\\n primaryCollectionSrc = cms.InputTag(\\\"{}Jets{}\\\".format(prefix, jet_algo_name)),\\n secondaryCollectionSrc = cms.InputTag(\\\"karmaElectrons\\\"),\\n )\\n )\\n\\n if not options.isData:\\n # -- jet-to-gen-jet association map\\n from Karma.Common.Producers.JetMatchingProducers_cfi import karmaJetGenJetMatchingProducer\\n process.add_module(\\n \\\"{}JetGenJetMap{}\\\".format(prefix, jet_algo_name),\\n karmaJetGenJetMatchingProducer.clone(\\n primaryCollectionSrc = cms.InputTag(\\\"{}Jets{}\\\".format(prefix, jet_algo_name)),\\n secondaryCollectionSrc = cms.InputTag(\\\"karmaGenJets{}\\\".format(jet_algo_name[:3])),\\n maxDeltaR = cms.double(0.4 if 'AK8' in jet_algo_name else 0.2)\\n )\\n )\",\n \"def write_global_attributes(self, attrs):\\n raise NotImplementedError\",\n \"def main(global_config, **settings):\\n # http://ldh.clld.org/category/iso639_3/aap-para-arara/\\n config = Configurator(settings=settings)\\n config.include('clldmpg')\\n config.include('clld_glottologfamily_plugin')\\n config.registry.registerUtility(LanguageByFamilyMapMarker(), IMapMarker)\\n\\n # /feed/ -> /sources.atom\\n \\\"\\\"\\\"\\n/comments/feed/\\n/feed/ -> /sources.atom\\n \\\"\\\"\\\"\\n config.add_301('/about/', lambda req: req.route_url('dataset', id='ldh'))\\n config.add_301('/about/objective/', lambda req: req.route_url('about', _anchor='objectives'))\\n config.add_301('/repository/', lambda req: req.route_url('about', _anchor='repository'))\\n config.add_301('/archive/', lambda req: req.route_url('sources'))\\n config.add_301('/category/iso639_3/', lambda req: req.route_url('languages'))\\n config.add_301('/category/allgemein/', lambda req: req.route_url('languages'))\\n config.add_301('/contact/', lambda req: req.route_url('contact'))\\n config.add_301('/for-authors/', lambda req: req.route_url('help'))\\n config.add_301('/for-authors/{path}/', lambda req: req.route_url('help'))\\n config.add_301('/feed/', lambda req: req.route_url('sources_alt', ext='atom'))\\n\\n config.add_301(\\n '/{year}/{month}/{day}/escidoc{id}/',\\n lambda req: req.route_url(\\n 'source',\\n id='item_{0}'.format(req.matchdict['id'].split('-')[0]),\\n _query=req.query_params),\\n name='item')\\n config.add_301(\\n '/category/iso639_3/{id}/',\\n lambda req: req.route_url(\\n 'language',\\n id=req.matchdict['id'].split('-')[0],\\n _query=req.query_params),\\n name='category')\\n\\n config.register_download(\\n adapters.BibTeX(models.Description, 'ldh', description=\\\"Descriptions as BibTeX\\\"))\\n\\n return config.make_wsgi_app()\",\n \"def DontuseThis():\\n BCM_outputs = ['phi','rho','theta',\\n 'r_probabilityMaps','l_probabilityMaps',\\n 'models']\\n BCM_Models = pe.Node(interface=nio.DataGrabber(input_names=['structures'],\\n outfields=BCM_outputs),\\n name='10_BCM_Models')\\n BCM_Models.inputs.base_directory = atlas_fname_wpath\\n BCM_Models.inputs.template_args['phi'] = [['spatialImages','phi','nii.gz']]\\n BCM_Models.inputs.template_args['rho'] = [['spatialImages','rho','nii.gz']]\\n BCM_Models.inputs.template_args['theta'] = [['spatialImages','theta','nii.gz']]\\n BCM_Models.inputs.template_args['r_probabilityMaps'] = [['structures']]\\n BCM_Models.inputs.template_args['l_probabilityMaps'] = [['structures']]\\n BCM_Models.inputs.template_args['models'] = [['structures']]\\n\\n BRAINSCut_structures = ['caudate','thalamus','putamen','hippocampus']\\n #BRAINSCut_structures = ['caudate','thalamus']\\n BCM_Models.iterables = ( 'structures', BRAINSCut_structures )\\n BCM_Models.inputs.template = '%s/%s.%s'\\n BCM_Models.inputs.field_template = dict(\\n r_probabilityMaps='probabilityMaps/r_%s_ProbabilityMap.nii.gz',\\n l_probabilityMaps='probabilityMaps/l_%s_ProbabilityMap.nii.gz',\\n models='modelFiles/%sModel*',\\n )\\n\\n \\\"\\\"\\\"\\n The xml creation and BRAINSCut need to be their own mini-pipeline that gets\\n executed once for each of the structures in BRAINSCut_structures. This can be\\n accomplished with a map node and a new pipeline.\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n Create xml file for BRAINSCut\\n \\\"\\\"\\\"\\n\\n\\n BFitAtlasToSubject = pe.Node(interface=BRAINSFit(),name=\\\"BFitAtlasToSubject\\\")\\n BFitAtlasToSubject.inputs.costMetric=\\\"MMI\\\"\\n BFitAtlasToSubject.inputs.maskProcessingMode=\\\"ROI\\\"\\n BFitAtlasToSubject.inputs.numberOfSamples=100000\\n BFitAtlasToSubject.inputs.numberOfIterations=[1500,1500]\\n BFitAtlasToSubject.inputs.numberOfHistogramBins=50\\n BFitAtlasToSubject.inputs.maximumStepLength=0.2\\n BFitAtlasToSubject.inputs.minimumStepLength=[0.005,0.005]\\n BFitAtlasToSubject.inputs.transformType= [\\\"Affine\\\",\\\"BSpline\\\"]\\n BFitAtlasToSubject.inputs.maxBSplineDisplacement= 7\\n BFitAtlasToSubject.inputs.maskInferiorCutOffFromCenter=65\\n BFitAtlasToSubject.inputs.splineGridSize=[28,20,24]\\n BFitAtlasToSubject.inputs.outputVolume=\\\"Trial_Initializer_Output.nii.gz\\\"\\n BFitAtlasToSubject.inputs.outputTransform=\\\"Trial_Initializer_Output.mat\\\"\\n cutWF.connect(SplitAvgBABC,'avgBABCT1',BFitAtlasToSubject,'fixedVolume')\\n cutWF.connect(BABC,'outputLabels',BFitAtlasToSubject,'fixedBinaryVolume')\\n cutWF.connect(BAtlas,'template_t1',BFitAtlasToSubject,'movingVolume')\\n cutWF.connect(BAtlas,'template_brain',BFitAtlasToSubject,'movingBinaryVolume')\\n cutWF.connect(BLI,'outputTransformFilename',BFitAtlasToSubject,'initialTransform')\\n\\n CreateBRAINSCutXML = pe.Node(Function(input_names=['rho','phi','theta',\\n 'model',\\n 'r_probabilityMap',\\n 'l_probabilityMap',\\n 'atlasT1','atlasBrain',\\n 'subjT1','subjT2',\\n 'subjT1GAD','subjT2GAD',\\n 'subjSGGAD','subjBrain',\\n 'atlasToSubj','output_dir'],\\n output_names=['xml_filename','rl_structure_filename_list'],\\n function = create_BRAINSCut_XML),\\n overwrite = True,\\n name=\\\"CreateBRAINSCutXML\\\")\\n\\n ## HACK Makde better directory\\n CreateBRAINSCutXML.inputs.output_dir = \\\".\\\" #os.path.join(cutWF.base_dir, \\\"BRAINSCut_output\\\")\\n cutWF.connect(BCM_Models,'models',CreateBRAINSCutXML,'model')\\n cutWF.connect(BCM_Models,'rho',CreateBRAINSCutXML,'rho')\\n cutWF.connect(BCM_Models,'phi',CreateBRAINSCutXML,'phi')\\n cutWF.connect(BCM_Models,'theta',CreateBRAINSCutXML,'theta')\\n cutWF.connect(BCM_Models,'r_probabilityMaps',CreateBRAINSCutXML,'r_probabilityMap')\\n cutWF.connect(BCM_Models,'l_probabilityMaps',CreateBRAINSCutXML,'l_probabilityMap')\\n cutWF.connect(BAtlas,'template_t1',CreateBRAINSCutXML,'atlasT1')\\n cutWF.connect(BAtlas,'template_brain',CreateBRAINSCutXML,'atlasBrain')\\n cutWF.connect(SplitAvgBABC,'avgBABCT1',CreateBRAINSCutXML,'subjT1')\\n cutWF.connect(SplitAvgBABC,'avgBABCT2',CreateBRAINSCutXML,'subjT2')\\n cutWF.connect(GADT1,'outputVolume',CreateBRAINSCutXML,'subjT1GAD')\\n cutWF.connect(GADT2,'outputVolume',CreateBRAINSCutXML,'subjT2GAD')\\n cutWF.connect(SGI,'outputFileName',CreateBRAINSCutXML,'subjSGGAD')\\n cutWF.connect(BABC,'outputLabels',CreateBRAINSCutXML,'subjBrain')\\n cutWF.connect(BFitAtlasToSubject,'outputTransform',CreateBRAINSCutXML,'atlasToSubj')\\n #CreateBRAINSCutXML.inputs.atlasToSubj = \\\"INTERNAL_REGISTER.mat\\\"\\n #cutWF.connect(BABC,'atlasToSubjectTransform',CreateBRAINSCutXML,'atlasToSubj')\\n\\n \\\"\\\"\\\"\\n ResampleNACLabels\\n \\\"\\\"\\\"\\n ResampleAtlasNACLabels=pe.Node(interface=BRAINSResample(),name=\\\"ResampleAtlasNACLabels\\\")\\n ResampleAtlasNACLabels.inputs.interpolationMode = \\\"NearestNeighbor\\\"\\n ResampleAtlasNACLabels.inputs.outputVolume = \\\"atlasToSubjectNACLabels.nii.gz\\\"\\n\\n cutWF.connect(cutWF,'OutputSpec.atlasToSubjectTransform',ResampleAtlasNACLabels,'warpTransform')\\n cutWF.connect(cutWF,'OutputSpec.t1_corrected',ResampleAtlasNACLabels,'referenceVolume')\\n cutWF.connect(BAtlas,'template_nac_lables',ResampleAtlasNACLabels,'inputVolume')\\n\\n \\\"\\\"\\\"\\n BRAINSMush\\n \\\"\\\"\\\"\\n BMUSH=pe.Node(interface=BRAINSMush(),name=\\\"BMUSH\\\")\\n BMUSH.inputs.outputVolume = \\\"MushImage.nii.gz\\\"\\n BMUSH.inputs.outputMask = \\\"MushMask.nii.gz\\\"\\n BMUSH.inputs.lowerThresholdFactor = 1.2\\n BMUSH.inputs.upperThresholdFactor = 0.55\\n\\n cutWF.connect(myLocalTCWF,'OutputSpec.t1_corrected',BMUSH,'inputFirstVolume')\\n cutWF.connect(myLocalTCWF,'OutputSpec.t2_corrected',BMUSH,'inputSecondVolume')\\n cutWF.connect(myLocalTCWF,'OutputSpec.outputLabels',BMUSH,'inputMaskVolume')\\n\\n \\\"\\\"\\\"\\n BRAINSROIAuto\\n \\\"\\\"\\\"\\n BROI = pe.Node(interface=BRAINSROIAuto(), name=\\\"BRAINSROIAuto\\\")\\n BROI.inputs.closingSize=12\\n BROI.inputs.otsuPercentileThreshold=0.01\\n BROI.inputs.thresholdCorrectionFactor=1.0\\n BROI.inputs.outputROIMaskVolume = \\\"temproiAuto_t1_ACPC_corrected_BRAINSABC.nii.gz\\\"\\n cutWF.connect(myLocalTCWF,'OutputSpec.t1_corrected',BROI,'inputVolume')\\n\\n \\\"\\\"\\\"\\n Split the implicit outputs of BABCext\\n \\\"\\\"\\\"\\n SplitAvgBABC = pe.Node(Function(input_names=['in_files','T1_count'], output_names=['avgBABCT1','avgBABCT2'],\\n function = get_first_T1_and_T2), run_without_submitting=True, name=\\\"99_SplitAvgBABC\\\")\\n SplitAvgBABC.inputs.T1_count = 1 ## There is only 1 average T1 image.\\n\\n cutWF.connect(myLocalTCWF,'OutputSpec.outputAverageImages',SplitAvgBABC,'in_files')\\n\\n\\n\\n def printFullPath(outFileFullPath):\\n print(\\\"=\\\"*80)\\n print(\\\"=\\\"*80)\\n print(\\\"=\\\"*80)\\n print(\\\"=\\\"*80)\\n print(\\\"{0}\\\".format(outFileFullPath))\\n return outFileFullPath\\n printOutImage = pe.Node( Function(function=printFullPath, input_names = ['outFileFullPath'], output_names = ['genoutFileFullPath']), run_without_submitting=True, name=\\\"99_printOutImage\\\")\\n cutWF.connect( GADT2, 'outputVolume', printOutImage, 'outFileFullPath' )\",\n \"def export_robot_to_xacro_files():\\n global robot, OUTPUT, NAME\\n doc = Document()\\n root = doc.createElement('robot')\\n doc.appendChild(root)\\n root.setAttribute(\\\"xmlns:xacro\\\", \\\"http://www.ros.org/wiki/xacro\\\")\\n root.setAttribute(\\\"name\\\", robot.name)\\n root.appendChild(ur.short(doc, 'xacro:include', 'filename', NAME +\\n '_visual_collisions.xacro'))\\n create_visual_xacro()\\n for i in XACRO_DICO.keys():\\n print('exporting ' + NAME + '_' + i + '.xacro')\\n if i.find('eye') != -1:\\n export_kinematic_chain_to_xacro(i, 'HeadRoll_link',\\n 'HeadRoll_link')\\n else:\\n export_kinematic_chain_to_xacro(i)\\n filenamerobot = NAME + '_' + i + '.xacro'\\n root.appendChild(ur.short(doc, 'xacro:include', 'filename',\\n filenamerobot))\\n # Transmission elements not available from Aldebaran libraries yet\\n export_robot_element('Transmission')\\n root.appendChild(ur.short(doc, 'xacro:include', 'filename', NAME +\\n '_Transmission.xacro'))\\n # Gazebo Plugin not available from Aldebaran libraries yet\\n export_robot_element('Gazebo')\\n root.appendChild(ur.short(doc, 'xacro:include', 'filename', NAME +\\n '_Gazebo.xacro'))\\n root.appendChild(ur.short(doc, 'xacro:include', 'filename', NAME +\\n '_sensors.xacro'))\\n export_list_to_xacro(['_frame'], OUTPUT[0:OUTPUT.rfind('.')] +\\n '_sensors.xacro')\\n root.appendChild(ur.short(doc, 'xacro:include', 'filename', NAME +\\n '_fingers.xacro'))\\n export_list_to_xacro(['Finger', 'Thumb'], OUTPUT[0:OUTPUT.rfind('.')] +\\n '_fingers.xacro')\\n if NAME == 'pepper':\\n root.appendChild(ur.short(doc, 'xacro:include', 'filename', NAME +\\n '_wheels.xacro'))\\n export_list_to_xacro(['Wheel'], OUTPUT[0:OUTPUT.rfind('.')] +\\n '_wheels.xacro')\\n if NAME == 'romeo':\\n root.appendChild(ur.short(doc, 'xacro:include', 'filename',\\n 'romeo_cap.xacro'))\\n\\n filename = OUTPUT[0:OUTPUT.rfind('.')] + '_robot.xacro'\\n write_comments_in_xacro(doc, filename)\\n print('output directory : ' + OUTPUT[0:OUTPUT.rfind('/') + 1])\",\n \"def ref_mapping_directory(tmpdir_factory):\\n basedir = tmpdir_factory.mktemp('data')\\n mapdir = basedir.mkdir('mappings')\\n\\n template = textwrap.dedent(\\\"\\\"\\\"\\n [ molecule ]\\n dummy_{0}\\n\\n [ from ]\\n {1}\\n\\n [ to ]\\n {2}\\n\\n [ atoms ]\\n 0 X1{0} A{0} B{0}\\n 1 X2{0} C{0} D{0}\\n \\\"\\\"\\\")\\n\\n mappings = collections.defaultdict(lambda: collections.defaultdict(dict))\\n\\n force_fields_from = ['ff{}'.format(i) for i in range(4)]\\n force_fields_to = force_fields_from + ['only_to']\\n force_fields_from = force_fields_from + ['only_from']\\n iterate_on = itertools.product(force_fields_from, force_fields_to, range(3))\\n for idx, (from_ff, to_ff, _) in enumerate(iterate_on):\\n mapfile = mapdir / 'file{}.map'.format(idx)\\n with open(str(mapfile), 'w') as outfile:\\n outfile.write(template.format(idx, from_ff, to_ff))\\n\\n mapping = {\\n (0, 'X1{}'.format(idx)): [(0, 'A{}'.format(idx)), (0, 'B{}'.format(idx))],\\n (0, 'X2{}'.format(idx)): [(0, 'C{}'.format(idx)), (0, 'D{}'.format(idx))],\\n }\\n weights = {\\n (0, 'A{}'.format(idx)): {(0, 'X1{}'.format(idx)): 0.5},\\n (0, 'B{}'.format(idx)): {(0, 'X1{}'.format(idx)): 0.5},\\n (0, 'C{}'.format(idx)): {(0, 'X2{}'.format(idx)): 0.5},\\n (0, 'D{}'.format(idx)): {(0, 'X2{}'.format(idx)): 0.5},\\n }\\n extra = []\\n mappings[from_ff][to_ff]['dummy_{}'.format(idx)] = (mapping, weights, extra)\\n\\n mappings = {from_ff: dict(to_ff) for from_ff, to_ff in mappings.items()}\\n\\n return Path(str(basedir)), mappings\",\n \"def content(tmp_loc, ref_names_dict, order):\\n \\n fl = '[Content_Types].xml'\\n inp_path = '/'.join([tmp_loc, fl])\\n out_path = '/'.join([output_path, fl])\\n \\n cnt_lst = []\\n asset_lst = []\\n def_att = []\\n d = dict()\\n \\n root1,tree1 = gen_tree(inp_path)\\n root2,tree2 = gen_tree(out_path)\\n \\n # get all the extensions belongs to \\\"Default\\\" tag\\n for relation in root2:\\n if 'Default' in relation.tag:\\n def_att.append(relation.attrib['Extension'])\\n else:\\n break\\n \\n for relation in root1:\\n if 'Override' in relation.tag:\\n attrib = relation.attrib['PartName'][1:]\\n try:\\n cnt = attrib.split('ppt/')[-1]\\n ini = '/ppt/'\\n except:\\n cnt = attrib\\n ini = '/'\\n if cnt in ref_names_dict.keys():\\n relation.attrib['PartName'] = f'{ini}{ref_names_dict[cnt]}'\\n cnt_lst.append(relation)\\n # asset_lst.append(relation.attrib['PartName'])\\n else:\\n cnt_lst.append(relation)\\n if relation.attrib['PartName'] not in asset_lst:\\n asset_lst.append(relation.attrib['PartName'])\\n else:\\n attrib = relation.attrib['Extension']\\n if attrib not in def_att:\\n cnt_lst.append(relation)\\n # asset_lst.append(relation.attrib['Extension'])\\n # deal with the assest_lst\\n # print(\\\"AA: \\\", asset_lst)\\n cnt_lst = natsort.natsorted(cnt_lst)\\n for ele in cnt_lst:\\n prev = tree2.find(ele.tag)\\n prev.addnext(ele)\\n \\n tree2.write(out_path, pretty_print=True, xml_declaration=True, encoding='UTF-8', standalone=True)\\n \\n unq_attr = []\\n for relation in root2:\\n if 'Override' in relation.tag:\\n if relation.attrib['PartName'] not in unq_attr:\\n unq_attr.append(relation.attrib['PartName'])\\n else:\\n root2.remove(relation)\\n tree2.write(out_path, pretty_print=True, xml_declaration=True, encoding='UTF-8', standalone=True)\",\n \"def get_packages():\\n\\tapps=[]\\n\\tlspackages = glob.glob(u'/home/wpkg/packages/*.xml')\\n\\tfor fic in lspackages:\\n #print fic\\n\\t\\tif os.path.isfile(fic):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\txml = etree.parse(fic)\\n\\t\\t\\texcept:\\n\\t\\t\\t\\t#si erreur on tente de corriger le fichier\\n\\t\\t\\t\\toput = open(\\\"output.txt\\\",\\\"w\\\")\\n\\t\\t\\t\\tdata = open(fic).read()\\n\\t\\t\\t\\toput.write( re.sub('&(?!amp;|quot;|nbsp;|gt;|lt;|laquo;|raquo;|copy;|reg;|bul;|rsquo;)', '&', data) )\\n\\t\\t\\t\\toput.close()\\n\\t\\t\\t\\tshutil.move(\\\"output.txt\\\", fic)\\n\\n\\t\\t\\tfinally:\\n\\t\\t\\t\\tparser = etree.XMLParser(remove_comments=False)\\n\\t\\t\\t\\txml = etree.parse(fic,parser)\\n\\t\\t\\t\\tstrxml = etree.tostring(xml.getroot())\\n\\t\\t\\t\\tfor group in xml.getiterator(u'package'):\\n\\t\\t\\t\\t\\tuid = group.get(u'id')\\n\\t\\t\\t\\t\\tname = group.get(u'name')\\n\\t\\t\\t\\t\\tdvars = dict()\\n\\t\\t\\t\\t\\tfor vars in xml.getiterator('variable'):\\n\\t\\t\\t\\t\\t\\tdvars[vars.get('name').lower()] = vars.get('value').lower()\\n\\n\\t\\t\\t\\t#on remplit le dico des appli dispo\\n\\t\\t\\t\\t#print fic,uid,name\\n\\t\\t\\t\\tapps.append((fic,uid,name))\\n\\n\\treturn apps\",\n \"def create_osm_conf(self, stage_dir=None):\\n result = INI_TEMPLATE.format(\\n points_attributes=','.join(self.points),\\n lines_attributes=','.join(self.lines),\\n multipolygons_attributes=','.join(self.polygons)\\n )\\n with open(self.output_ini, 'w') as f:\\n f.write(result)\\n return self.output_ini\",\n \"def exportDB ( c ) :\\n assert str(type(c)) == \\\"<type '_mysql.connection'>\\\"\\n xml = openTag ( \\\"WorldCrises\\\" )\\n xml += exportCrises ( c )\\n xml += exportOrgs ( c )\\n xml += exportPeople ( c )\\n xml += exportTypes ( c )\\n xml += closeTag ( \\\"WorldCrises\\\" )\\n assert str ( type ( xml ) ) == \\\"<type 'str'>\\\"\\n return xml\",\n \"def writeGromacsTopolFiles(self, amb2gmx = False):\\n\\n self.printMess(\\\"Writing GROMACS files\\\\n\\\")\\n\\n self.setAtomType4Gromacs()\\n\\n self.writeGroFile()\\n\\n self.writeGromacsTop(amb2gmx = amb2gmx)\\n\\n self.writeMdpFiles()\",\n \"def group_by_namespace(self):\\n package = self.container.config.output.package\\n groups = collections.group_by(self.container, key=get_target_namespace)\\n for namespace, classes in groups.items():\\n self.assign(classes, package, namespace or \\\"\\\")\",\n \"def write_map(self, file_name):\\n\\n if self.pixel == \\\"HEALPIX\\\":\\n hp.fitsfunc.write_map(file_name, self.data, overwrite=True)\\n if self.pixel == \\\"CAR\\\":\\n enmap.write_map(file_name, self.data)\",\n \"def main(*args):\\r\\n print(START_MESSAGE)\\r\\n print(\\\"Script Location:\\\", location)\\r\\n print(\\\"Arguments Passed:\\\", args)\\r\\n\\r\\n root = ET.parse(xmlfile).getroot()\\r\\n keys = []\\r\\n out = \\\"\\\"\\r\\n\\r\\n for child in root[1]:\\r\\n out += child.attrib['Name'] + \\\";\\\" + child[0].text + \\\"\\\\n\\\"\\r\\n\\r\\n with open(outputfile, 'w') as f:\\r\\n f.write(out)\",\n \"def patch_well_known_namespaces(etree_module):\\n etree_module._namespace_map.update({\\n \\\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\\\": \\\"rdf\\\", \\n \\\"http://purl.org/rss/1.0/\\\": \\\"rss\\\", \\n \\\"http://purl.org/rss/1.0/modules/taxonomy/\\\": \\\"taxo\\\", \\n \\\"http://purl.org/dc/elements/1.1/\\\": \\\"dc\\\", \\n \\\"http://purl.org/rss/1.0/modules/syndication/\\\": \\\"syn\\\", \\n \\\"http://www.w3.org/2003/01/geo/wgs84_pos#\\\": \\\"geo\\\"})\",\n \"def convert_to_annotation(file, output):\\n resource = parse_bel_resource(file)\\n\\n write_annotation(\\n keyword=resource['Namespace']['Keyword'],\\n values={k: '' for k in resource['Values']},\\n citation_name=resource['Citation']['NameString'],\\n description=resource['Namespace']['DescriptionString'],\\n file=output,\\n )\",\n \"def writeXml(self):\\n curdir = os.getcwd()\\n os.chdir(self.Imagedir)\\n allImageLists = [self.sciImlist, self.ctxImlist, self.wgtImlist, self.rmsImlist]\\n \\n for imlist in allImageLists:\\n for im in imlist:\\n file = xmlUtil.markupImage(im,dataset=self.obsName)\\n \\n # Don't write these images as output of this module, which\\n # really doesn't have any.\\n \\n #if file not in self.outputList.keys():\\n # self.outputList[file] = [im]\\n \\n os.chdir(curdir)\\n return\",\n \"def _gtTSmap(self):\\n if os.path.isfile(self.outtsmap):\\n # Already exists\\n return\\n\\n if self.csys == 'GAL':\\n center_icrs = SkyCoord(ra=self.ra*u.degree, dec=self.dec*u.degree, frame='icrs')\\n self.ra = center_icrs.galactic.l.deg\\n self.dec = center_icrs.galactic.b.deg\\n\\n model = os.path.join(self.workpath, 'TSmapModel.xml') \\n rfil = open(self.outmodel, 'r')\\n wfil = open(model, 'w')\\n isSrc = False\\n isDif = False\\n for line in rfil:\\n if (isSrc) and ('<source name' in line):\\n # Arrived to a new source, restart copying\\n isSrc = False\\n if (isDif) and ('<source name' in line) and ('PointSource' in line):\\n isDif = False\\n if 'TARGET' in line:\\n isSrc = True\\n if ('<source name=\\\"gll_iem_v06\\\"' in line) or ('<source name=\\\"iso_source_v06\\\"' in line): \\n isDif = True\\n \\n if isSrc:\\n # Do not copy the Target model to make it appear in the TS map\\n pass\\n else:\\n if isDif:\\n # Leave Diffuse model normalizations free\\n wfil.write(line)\\n else:\\n # Make sur the gtlike output source model has all source parameters fixed\\n wfil.write(line.replace('free=\\\"1\\\"', 'free=\\\"0\\\"'))\\n rfil.close()\\n wfil.close()\\n\\n # Launch the gttsmap tool \\n if self.mode == 'binned':\\n os.popen(\\\"gttsmap evfile={} scfile={} bexpmap={} expcube={} cmap={} srcmdl={}\\\\\\n outfile={} evtype={} irfs=CALDB optimizer=NewMinuit statistic=BINNED ftol=1e-2\\\\\\n coordsys={} proj=AIT nxpix={} nypix={} binsz={} xref={} yref={}\\\".format(self.outmktime,\\n self.ft2, self.outbinexp, self.outltcube, self.outbincub, model, self.outtsmap, self.evtype,\\n self.csys, self.imwid, self.imwid, self.binsz, self.ra, self.dec))\\n elif self.mode == 'unbinned':\\n os.popen(\\\"gttsmap evfile={} scfile={} expmap={} expcube={} srcmdl={}\\\\\\n outfile={} evtype={} irfs=CALDB optimizer=NewMinuit statistic=UNBINNED ftol=1e-2\\\\\\n coordsys={} proj=AIT nxpix={} nypix={} binsz={} xref={} yref={}\\\".format(self.outmktime,\\n self.ft2, self.outexpmap, self.outltcube, model, self.outtsmap, self.evtype,\\n self.csys, self.imwid, self.imwid, self.binsz, self.ra, self.dec))\\n else:\\n return\\n\\n if self.csys == 'GAL':\\n self.ra = center_icrs.ra.deg\\n self.dec = center_icrs.dec.deg\\n return\",\n \"def writeImports2File(self, file, indent = \\\" \\\"):\\r\\n # import each entity and its associated graphical file\\r\\n for obj in self.listNodes.keys():\\r\\n file.write(indent+\\\"from \\\"+obj+\\\" import \\\"+obj+\\\"\\\\n\\\")\\r\\n if not obj[0:4] == \\\"ASG_\\\":\\r\\n file.write(indent+\\\"from graph_\\\"+obj+\\\" import graph_\\\"+obj+\\\"\\\\n\\\")\",\n \"def create_label_map(self, outpath):\\n cnt = 1\\n with open(outpath, 'w') as fp:\\n for itm in self.clazzes:\\n fp.write('item {\\\\n')\\n fp.write('\\\\tname: \\\"{}\\\"\\\\n'.format(itm))\\n fp.write('\\\\tid: {}\\\\n'.format(cnt))\\n fp.write('}\\\\n')\\n cnt += 1\",\n \"def test_create_image_stream_mapping_for_all_namespaces(self):\\n pass\",\n \"def _write_packet_dict(ctx, package_dict):\\n p4gf_util.write_dict_to_file(package_dict, _packet_filename(ctx.config.repo_name))\",\n \"def meta2xml(meta, filename):\\n\\n # this is stupid, just use dict2xml\\n xml = dict2xml(meta)\\n with open(filename, 'w+') as output:\\n output.write(xml)\",\n \"def updateNamespace(self):\\n import addict\\n self.namespace['config'] = addict.Dict(self.namespace['config'])\",\n \"def writeOrganisms( self ):\\n\\n self.logger.info( 'writeOrganisms: START' )\\n\\n # Generate inserts for meabolic pathways.\\n self.importerOrganism.writeOrganisms()\\n \\n\\n self.logger.info( 'writeOrganisms: keggreader.getAllOrganismEcs() : START' )\\n\\n # Get all organism ecs relations.\\n organismEcs = self.reader.getAllOrganismEcs()\\n\\n self.logger.info( 'writeOrganisms: keggreader.getAllOrganismEcs() : DONE' )\\n\\n\\n self.logger.info( 'writeOrganisms: keggreader.getAllOrganismMaps() : START' )\\n\\n # Get all organism maps relations.\\n organismMaps = self.reader.getAllOrganismMaps()\\n\\n self.logger.info( 'writeOrganisms: keggreader.getAllOrganismMaps() : DONE' )\\n\\n\\n self.logger.info( 'writeOrganisms: organismEcFile is organismEcsInsert.psql' )\\n\\n # Open protein_ecs insert file.\\n organismEcFile = self.openInsertFile( 'organismEcsInsert.psql' )\\n\\n\\n self.logger.info( 'writeOrganisms: organismMapFile is organismMapsInsert.psql' )\\n\\n # Open organism_maps insert file.\\n organismMapFile = self.openInsertFile( 'organismMapsInsert.psql' )\\n\\n\\n # Now we have to write organism_ecs table.\\n for organism,relationalDatabaseId in self.importerOrganism.organismsInserted.iteritems():\\n\\n\\n organismId = relationalDatabaseId\\n\\n if len( organismEcs[ organism ] ) > 0:\\n \\n self.logger.info( 'writeOrganisms: the organism: ' + organism + ' : FOUND ' + str(len(organismEcs[organism])) + ' EC numbers.' )\\n for ec in organismEcs[ organism ]:\\n ecId = self.importerEc.ecsInserted[ ec ]\\n\\n #self.writeOrganismEcsFile( organismEcFile, organismId , ecId )\\n self.writeFile( organismEcFile, 'organism_ecs', [ str(organismId) , str(ecId) ] )\\n else:\\n self.logger.info( 'writeOrganisms: the organism: ' + organism + ' : doesnt have EC numbers associated.' )\\n\\n\\n if len( organismMaps[ organism ] ) > 0:\\n \\n self.logger.info( 'writeOrganisms: the organism: ' + organism + ' : FOUND ' + str(len(organismMaps[organism])) + ' MAP numbers.' )\\n for mapNumber in organismMaps[ organism ]:\\n\\n # We don't need maps that is not metabolic maps.\\n if mapNumber in self.importerPathway.pathwayMapsInserted:\\n mapId = self.importerPathway.pathwayMapsInserted[ mapNumber ]\\n\\n #self.writeOrganismMapsFile( organismMapFile, organismId , mapId )\\n self.writeFile( organismMapFile, 'organism_maps', [ str(organismId) , str(mapId) ] )\\n else:\\n self.logger.info( 'writeOrganisms: the organism: ' + organism + ' : doesnt have MAP numbers associated.' )\\n\\n\\n self.logger.info( 'writeOrganisms: DONE' )\",\n \"def resource_mapping():\\n return {\\n 'OS::Heat::ResourceChain': ResourceChain,\\n }\",\n \"def from_ols(ontology, domain, encoding, ols_base_url, deploy, no_hash_check, output):\\n ont = OlsNamespaceOntology(ontology, domain, encoding=encoding, ols_base=ols_base_url)\\n ont.write_namespace(output)\\n\\n if deploy:\\n ont.deploy_namespace(hash_check=(not no_hash_check))\",\n \"def generate(self, conf, env, data):\\n\\n path = joinurl(conf['output_dir'], self.path)\\n sm = Map()\\n\\n pages = []\\n for i in env.globals.pages:\\n pages.append(i.slug)\\n for i in env.globals.entrylist:\\n pages.append(i.slug)\\n\\n if exists(path) and not self.modified and not conf.modified:\\n event.skip('sitemap', path)\\n raise StopIteration\\n for ns, fname in self.files:\\n\\n if ns == 'page' or ns == 'entry' or ns == 'index':\\n pass\\n else:\\n continue\\n\\n permalink = '/' + fname.replace(conf['output_dir'], '')\\n permalink = rchop(permalink, 'index.html')\\n url = conf['www_root'] + permalink\\n\\n priority, changefreq = self.scores.get(ns, (0.5, 'weekly'))\\n\\n _pages = env.globals.entrylist + env.globals.pages\\n\\n for i in _pages:\\n if permalink.strip('/') in pages:\\n if i.slug.strip('/') == permalink.strip('/'):\\n pages.remove(permalink.strip('/'))\\n video = re.search('youtube.com/(v/|watch\\\\?v=|embed/)([a-zA-Z0-9\\\\-_]*)', i.content)\\n if video:\\n yt = video.group().split('/')[-1]\\n sm.add(url, yt, i.title, i.metadesc, getmtime(fname), changefreq, priority)\\n\\n sm.finish()\\n yield sm, path\",\n \"def process(self):\\n sections = Map.build_from_dirs(\\n self.config_map,\\n self.persist_fs,\\n self.persist_fs.list_dirs(self.config_map.get_repo_path),\\n )\\n puml: PUML = PUML(self.config_map, self.persist_fs, sections)\\n puml.write()\",\n \"def init():\\n\\tprint(\\\"Initializing Application\\\")\\n\\tlabelDict = {}\\n\\tlabelsPath = os.path.sep.join([yolodir, \\\"coco.names\\\"])\\n\\tLABELS = open(labelsPath).read().strip().split(\\\"\\\\n\\\")\\n\\tfor label in LABELS:\\n\\t\\tlabelDict[label] = 0\\n\\tjson.dump(labelDict, open(outputFile, \\\"w\\\"))\\t\\n\\treturn \\\"Output Counter Initialized\\\"\",\n \"def __genAttributesROOT( self, file, indent = ' ' ):\\r\\n # Go through all actively tracked ASG's (formalisms)\\r\\n for modelMetaName in self.__trackASG.keys():\\r\\n if( not self.__isASGbyNameEmpty( modelMetaName ) ):\\r\\n # This formalism has at least one entity, we NEED IT\\r\\n ASG = self.getASGbyName( modelMetaName )\\r\\n \\r\\n #todo: clean this up\\r\\n try:\\r\\n modelTracker = self.__trackAttributes[ modelMetaName ]\\r\\n except:\\r\\n print 'ERROR: see ASG.py in __genAttributesROOT(), caused by Buttons Model pointing to a Formalism with a different name'\\r\\n print 'modelMetaName', modelMetaName \\r\\n print 'self.__trackAttributes', self.__trackAttributes\\r\\n print 'self.__trackASG', self.__trackASG\\r\\n \\r\\n file.write( '\\\\n'+indent+'# --- Generating attributes code for ASG '\\r\\n +modelMetaName+' ---\\\\n' ) \\r\\n # DOH! Some formalisms may not have any attributes at all!\\r\\n if( len( modelTracker ) > 0 ):\\r\\n file.write( indent + 'if( ' + modelMetaName + 'RootNode ): ' )\\r\\n i2 = indent + ' '\\r\\n \\r\\n #Generates code for the attributes value\\r\\n for attr in modelTracker.keys():\\r\\n #print attr, ASG.getAttrValue(attr)\\r\\n file.write( '\\\\n'+i2+'# '+attr+'\\\\n' )\\r\\n \\r\\n # Write its value to file\\r\\n ASG.getAttrValue(attr).writeValue2File ( file = file, indent=i2, \\r\\n objName = modelMetaName+'RootNode.'+attr, depth = 1,\\r\\n generatingCode = 0) \\r\\n else: \\r\\n file.write( indent+'# No attributes to generate!\\\\n' )\\r\\n file.write( indent+'# --- ASG attributes over ---\\\\n\\\\n' )\",\n \"def exportTypes( c ) :\\n assert str(type(c)) == \\\"<type '_mysql.connection'>\\\"\\n xml = \\\"\\\"\\n cT = sqlQuery ( c, \\\"select * from CrisisKind;\\\" )\\n oT = sqlQuery ( c, \\\"select * from OrganizationKind;\\\" )\\n pT = sqlQuery ( c, \\\"select * from PersonKind;\\\" ) \\n for i in cT:\\n xml += openTagAtt (\\\"CrisisKind\\\", \\\"crisisKindIdent\\\", i[0])\\n xml += openCloseTag (\\\"Name\\\", i[1])\\n xml += openCloseTag (\\\"Description\\\", i[2])\\n xml += closeTag (\\\"CrisisKind\\\") \\n for i in oT:\\n xml += openTagAtt (\\\"OrganizationKind\\\", \\\"organizationKindIdent\\\", i[0])\\n xml += openCloseTag (\\\"Name\\\", i[1])\\n xml += openCloseTag (\\\"Description\\\", i[2])\\n xml += closeTag (\\\"OrganizationKind\\\")\\n for i in pT:\\n xml += openTagAtt (\\\"PersonKind\\\", \\\"personKindIdent\\\", i[0])\\n xml += openCloseTag (\\\"Name\\\", i[1])\\n xml += openCloseTag (\\\"Description\\\", i[2])\\n xml += closeTag (\\\"PersonKind\\\")\\n assert str ( type ( xml ) ) == \\\"<type 'str'>\\\"\\n return xml\",\n \"def _update_database_map(self, path):\\n if path:\\n filename = path + '/APD_MAP.txt'\\n else:\\n filename = 'APD_MAP.txt'\\n filepointer = open(filename, 'w')\\n for invariom, molecule in self.map.items():\\n filepointer.write(invariom + ':' + molecule + '\\\\n')\\n filepointer.close()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64070976","0.6367374","0.62000525","0.6076145","0.56269336","0.540561","0.54037416","0.5370882","0.53047323","0.51571155","0.5128653","0.5097843","0.50681853","0.5067503","0.5059321","0.50549144","0.5013462","0.50095636","0.50081456","0.49879944","0.49847412","0.49812582","0.49756378","0.4946094","0.49370292","0.49026048","0.48832655","0.48647475","0.48623577","0.48453248","0.4844509","0.48404142","0.48379508","0.4836441","0.48340115","0.48267025","0.48210335","0.48053676","0.4791238","0.4787718","0.47780046","0.4770183","0.47578147","0.47461554","0.4733791","0.4732264","0.4730456","0.47282168","0.47107893","0.47099042","0.4709202","0.47064707","0.46973324","0.4690402","0.4689963","0.46843097","0.4679357","0.46641147","0.46439883","0.46434972","0.46412128","0.46350184","0.46317396","0.4631269","0.46154782","0.4611108","0.46082678","0.4607891","0.4607816","0.46027815","0.46005866","0.45907766","0.45870084","0.45808575","0.4572564","0.457032","0.45658776","0.4561102","0.45610693","0.45599008","0.45581383","0.4548859","0.45450434","0.4532557","0.45283708","0.45279568","0.4515127","0.45031548","0.449916","0.44910067","0.4489694","0.4488968","0.4488948","0.44864723","0.44848576","0.4482884","0.44797492","0.44782576","0.4477629","0.44775403"],"string":"[\n \"0.64070976\",\n \"0.6367374\",\n \"0.62000525\",\n \"0.6076145\",\n \"0.56269336\",\n \"0.540561\",\n \"0.54037416\",\n \"0.5370882\",\n \"0.53047323\",\n \"0.51571155\",\n \"0.5128653\",\n \"0.5097843\",\n \"0.50681853\",\n \"0.5067503\",\n \"0.5059321\",\n \"0.50549144\",\n \"0.5013462\",\n \"0.50095636\",\n \"0.50081456\",\n \"0.49879944\",\n \"0.49847412\",\n \"0.49812582\",\n \"0.49756378\",\n \"0.4946094\",\n \"0.49370292\",\n \"0.49026048\",\n \"0.48832655\",\n \"0.48647475\",\n \"0.48623577\",\n \"0.48453248\",\n \"0.4844509\",\n \"0.48404142\",\n \"0.48379508\",\n \"0.4836441\",\n \"0.48340115\",\n \"0.48267025\",\n \"0.48210335\",\n \"0.48053676\",\n \"0.4791238\",\n \"0.4787718\",\n \"0.47780046\",\n \"0.4770183\",\n \"0.47578147\",\n \"0.47461554\",\n \"0.4733791\",\n \"0.4732264\",\n \"0.4730456\",\n \"0.47282168\",\n \"0.47107893\",\n \"0.47099042\",\n \"0.4709202\",\n \"0.47064707\",\n \"0.46973324\",\n \"0.4690402\",\n \"0.4689963\",\n \"0.46843097\",\n \"0.4679357\",\n \"0.46641147\",\n \"0.46439883\",\n \"0.46434972\",\n \"0.46412128\",\n \"0.46350184\",\n \"0.46317396\",\n \"0.4631269\",\n \"0.46154782\",\n \"0.4611108\",\n \"0.46082678\",\n \"0.4607891\",\n \"0.4607816\",\n \"0.46027815\",\n \"0.46005866\",\n \"0.45907766\",\n \"0.45870084\",\n \"0.45808575\",\n \"0.4572564\",\n \"0.457032\",\n \"0.45658776\",\n \"0.4561102\",\n \"0.45610693\",\n \"0.45599008\",\n \"0.45581383\",\n \"0.4548859\",\n \"0.45450434\",\n \"0.4532557\",\n \"0.45283708\",\n \"0.45279568\",\n \"0.4515127\",\n \"0.45031548\",\n \"0.449916\",\n \"0.44910067\",\n \"0.4489694\",\n \"0.4488968\",\n \"0.4488948\",\n \"0.44864723\",\n \"0.44848576\",\n \"0.4482884\",\n \"0.44797492\",\n \"0.44782576\",\n \"0.4477629\",\n \"0.44775403\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65873575"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":76691,"cells":{"query":{"kind":"string","value":"Performs Encoding and Decoding at once"},"document":{"kind":"string","value":"def img_recolor(self, args, input_image_path):\n \n ec = encoder.Encoder(output_path=args.intermediate_representation, method=args.method,\n size=args.size, p=args.p, grid_size=args.grid_size, plot=args.plot, quantize=args.quantize)\n dc = decoder.Decoder(output_path=args.output_path, method=args.method, size=args.size, p=args.p, gpu_id=args.gpu_id, plot=args.plot)\n\n ec.encode(input_image_path)\n img_gray_name = ar_utils.gen_new_gray_filename(input_image_path)\n img_gray_path = os.path.join(args.intermediate_representation, img_gray_name)\n dc.decode(img_gray_path)\n\n if args.delete_gray and os.path.exists(img_gray_path):\n os.remove(img_gray_path)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def decode(self, encoded):","def encode(self, decoded):","def handle_encode(self, results):\n \n config.COD_PROMPT = config.ENC_PROMPT\n print config.ENC_PROMPT + \" encoding results...\"\n \n # while there is another decoder, run each item through the next decoder\n data = results\n success = False\n for encoder in self.encoder_list:\n current_encoder = encoder()\n full_body = getattr(current_encoder,'full_body_encode',False)\n success, data = self.recursive_decoder(current_encoder.encode, data, full_body)\n if not success:\n break\n print config.ENC_PROMPT + \"%s encoded to '%s'\" % ( current_encoder.name(),data)\n return success, data","def decode(data): #@NoSelf","def decode(self): # pragma: no cover\n pass","def decoder(self):\n pass","def test_decode(self):\n pass # TODO(tlarsen)","def decode(self):\n self.decoded_content = []\n if self.encoded_content:\n s = self.encoded_content\n if self.encoding:\n if self.encoding.lower() == u'base64':\n s = decode_base64(s)\n elif self.encoding.lower() == u'csv':\n list_of_lines = s.split()\n for line in list_of_lines:\n self.decoded_content.extend(line.split(','))\n self.decoded_content = map(int, [val for val in self.decoded_content if val])\n s = \"\"\n else:\n raise Exception(u'unknown data encoding %s' % (self.encoding))\n else:\n # in the case of xml the encoded_content already contains a list of integers\n self.decoded_content = map(int, self.encoded_content)\n s = \"\"\n if self.compression:\n if self.compression == u'gzip':\n s = decompress_gzip(s)\n elif self.compression == u'zlib':\n s = decompress_zlib(s)\n else:\n raise Exception(u'unknown data compression %s' %(self.compression))\n else:\n raise Exception(u'no encoded content to decode')\n for idx in xrange(0, len(s), 4):\n val = ord(str(s[idx])) | (ord(str(s[idx + 1])) << 8) | \\\n (ord(str(s[idx + 2])) << 16) | (ord(str(s[idx + 3])) << 24)\n self.decoded_content.append(val)\n #print len(self.decoded_content)\n # generate the 2D version\n self._gen_2D()","def decode(self, s):","def decode(self, s):","def testEncodeDecode(self):\n test_cases = [\n '\\n',\n ' ',\n 'aaabbb',\n 'a b',\n '\\n\\n\\n',\n '122333',\n 'aaaaaaaaaa',\n 'aaaaaaaaaabbbbbbbbbbb',\n 'a'*1001,\n ''.join(['a'*1001, 'b'*909, 'c'*65, 'd'*2]),\n ]\n for data in test_cases:\n encoded_result = ASCIITransportFormat.encode_data(data)\n decoded_result = ASCIITransportFormat.decode_data(encoded_result)\n self.assertEqual(decoded_result, data)","def _dinamic_decode(self):\n raise NotImplementedError","def encode(self, strs):","def encode(self, strs):","def decode(self):\n for layer in self.layers:\n layer.decode()","def decode(self):\n for layer in self.layers:\n layer.decode()","def test_encode_decode(self):\n assert self._test == pybinn.loads(pybinn.dumps(self._test))","def decode(self, coded_set):","def decode(self):\n s = self.encoded_content\n if self.encoded_content:\n if self.encoding:\n if self.encoding == u'base64':\n s = decode_base64(s)\n else:\n raise Exception(u'unknown data encoding %s' % (self.encoding))\n if self.compression:\n if self.compression == u'gzip':\n s = decompress_gzip(s)\n else:\n raise Exception(u'unknown data compression %s' %(self.compression))\n else:\n raise Exception(u'no encoded content to decode')\n self.decoded_content = []\n for idx in xrange(0, len(s), 4):\n val = ord(str(s[idx])) | (ord(str(s[idx + 1])) << 8) | \\\n (ord(str(s[idx + 2])) << 16) | (ord(str(s[idx + 3])) << 24)\n self.decoded_content.append(val)\n # generate the 2D version\n self._gen_2D()","def refresh(self):\n\n self._codec = {\n 'datas': self._datas,\n 'formats': create_encode_decode_formats(self._datas,\n self._length)\n }","def test_decode():","def runit():\n print encode(\"no\")\n\n print encode(\"yes\")\n\n print encode(\"OMG\")\n\n print encode(\"O M G\")\n\n print encode(\"mindblowingly\")\n\n print encode(\"Testing, 1 2 3, testing.\")\n\n print encode(\"Truth is fiction.\")\n\n print encode(\"The quick brown fox jumps over the lazy dog.\")\n\n print decode(\"vcvix rhn\")\n\n print decode(\"zmlyh gzxov rhlug vmzhg vkkrm thglm v\")","def test_encoders(encoder):\n assert encoding.decode(None, encoder) is None\n assert encoding.encode(None, encoder) is None\n\n assert b\"\" == encoding.decode(b\"\", encoder)\n\n assert b\"string\" == encoding.decode(\n encoding.encode(\n b\"string\",\n encoder\n ),\n encoder\n )\n\n with pytest.raises(TypeError):\n encoding.encode(\"string\", encoder)\n\n with pytest.raises(TypeError):\n encoding.decode(\"string\", encoder)\n with pytest.raises(ValueError):\n encoding.decode(b\"foobar\", encoder)","def test_decode(self):\n assert url_encoder.decode('TheStakeOut') == 1\n assert url_encoder.decode('TheStockTip-TheSeven') == 800\n assert url_encoder.decode('MaleUnbonding-TheConversion-TheAndreaDoria') == 99999","def test_encode(self):\n pass # TODO(tlarsen)","def fit_transform(self):\n if self.enc_types == \"label\":\n return self._label_encoding()\n elif self.enc_types == \"ohe\":\n return self._one_hot_encoder()\n elif self.enc_types == \"binary\":\n return self._binarization()\n else:\n raise Exception(\"Encoding type not understood\")","def __init__(self, encoding):\n self.trans = {}\n for char in u\"ÀÁÂẦẤẪẨẬÃĀĂẰẮẴẶẲȦǠẠḀȂĄǍẢ\":\n self.trans[char] = u\"A\"\n for char in u\"ȀǞ\":\n self.trans[char] = u\"Ä\"\n self.trans[u\"Ǻ\"] = u\"Å\"\n self.trans[u\"Ä\"] = u\"Ae\"\n self.trans[u\"Å\"] = u\"Aa\"\n for char in u\"àáâầấẫẩậãāăằắẵặẳȧǡạḁȃąǎảẚ\":\n self.trans[char] = u\"a\"\n for char in u\"ȁǟ\":\n self.trans[char] = u\"ä\"\n self.trans[u\"ǻ\"] = u\"å\"\n self.trans[u\"ä\"] = u\"ae\"\n self.trans[u\"å\"] = u\"aa\"\n for char in u\"ḂḄḆƁƂ\":\n self.trans[char] = u\"B\"\n for char in u\"ḃḅḇƀɓƃ\":\n self.trans[char] = u\"b\"\n for char in u\"ĆĈĊÇČƇ\":\n self.trans[char] = u\"C\"\n for char in u\"ćĉċçčƈȼ\":\n self.trans[char] = u\"c\"\n self.trans[u\"Ḉ\"] = u\"Ç\"\n self.trans[u\"ḉ\"] = u\"ç\"\n self.trans[u\"Ð\"] = u\"Dh\"\n self.trans[u\"ð\"] = u\"dh\"\n for char in u\"ĎḊḌḎḐḒĐƉƊƋ\":\n self.trans[char] = u\"D\"\n for char in u\"ďḋḍḏḑḓđɖɗƌ\":\n self.trans[char] = u\"d\"\n for char in u\"ÈȄÉÊḚËĒḔḖĔĖẸE̩ȆȨḜĘĚẼḘẺ\":\n self.trans[char] = u\"E\"\n for char in u\"ỀẾỄỆỂ\":\n self.trans[char] = u\"Ê\"\n for char in u\"èȅéêḛëēḕḗĕėẹe̩ȇȩḝęěẽḙẻ\":\n self.trans[char] = u\"e\"\n for char in u\"ềếễệể\":\n self.trans[char] = u\"ê\"\n for char in u\"ḞƑ\":\n self.trans[char] = u\"F\"\n for char in u\"ḟƒ\":\n self.trans[char] = u\"f\"\n for char in u\"ǴḠĞĠĢǦǤƓ\":\n self.trans[char] = u\"G\"\n for char in u\"ǵḡğġģǧǥɠ\":\n self.trans[char] = u\"g\"\n self.trans[u\"Ĝ\"] = u\"Gx\"\n self.trans[u\"ĝ\"] = u\"gx\"\n for char in u\"ḢḤḦȞḨḪH̱ĦǶ\":\n self.trans[char] = u\"H\"\n for char in u\"ḣḥḧȟḩḫ̱ẖħƕ\":\n self.trans[char] = u\"h\"\n for char in u\"IÌȈÍÎĨḬÏḮĪĬȊĮǏİỊỈƗ\":\n self.trans[char] = u\"I\"\n for char in u\"ıìȉíîĩḭïḯīĭȋįǐiịỉɨ\":\n self.trans[char] = u\"i\"\n for char in u\"ĴJ\":\n self.trans[char] = u\"J\"\n for char in u\"ɟĵ̌ǰ\":\n self.trans[char] = u\"j\"\n for char in u\"ḰǨĶḲḴƘ\":\n self.trans[char] = u\"K\"\n for char in u\"ḱǩķḳḵƙ\":\n self.trans[char] = u\"k\"\n for char in u\"ĹĻĽḶḸḺḼȽŁ\":\n self.trans[char] = u\"L\"\n for char in u\"ĺļľḷḹḻḽƚłɫ\":\n self.trans[char] = u\"l\"\n for char in u\"ḾṀṂ\":\n self.trans[char] = u\"M\"\n for char in u\"ḿṁṃɱ\":\n self.trans[char] = u\"m\"\n for char in u\"ǸŃÑŅŇṄṆṈṊŊƝɲȠ\":\n self.trans[char] = u\"N\"\n for char in u\"ǹńñņňṅṇṉṋŋɲƞ\":\n self.trans[char] = u\"n\"\n for char in u\"ÒÓÔÕṌṎȬÖŌṐṒŎǑȮȰỌǪǬƠỜỚỠỢỞỎƟØǾ\":\n self.trans[char] = u\"O\"\n for char in u\"òóôõṍṏȭöōṑṓŏǒȯȱọǫǭơờớỡợởỏɵøǿ\":\n self.trans[char] = u\"o\"\n for char in u\"ȌŐȪ\":\n self.trans[char] = u\"Ö\"\n for char in u\"ȍőȫ\":\n self.trans[char] = u\"ö\"\n for char in u\"ỒỐỖỘỔȎ\":\n self.trans[char] = u\"Ô\"\n for char in u\"ồốỗộổȏ\":\n self.trans[char] = u\"ô\"\n for char in u\"ṔṖƤ\":\n self.trans[char] = u\"P\"\n for char in u\"ṕṗƥ\":\n self.trans[char] = u\"p\"\n self.trans[u\"ᵽ\"] = u\"q\"\n for char in u\"ȐŔŖŘȒṘṚṜṞ\":\n self.trans[char] = u\"R\"\n for char in u\"ȑŕŗřȓṙṛṝṟɽ\":\n self.trans[char] = u\"r\"\n for char in u\"ŚṤŞȘŠṦṠṢṨ\":\n self.trans[char] = u\"S\"\n for char in u\"śṥşșšṧṡṣṩȿ\":\n self.trans[char] = u\"s\"\n self.trans[u\"Ŝ\"] = u\"Sx\"\n self.trans[u\"ŝ\"] = u\"sx\"\n for char in u\"ŢȚŤṪṬṮṰŦƬƮ\":\n self.trans[char] = u\"T\"\n for char in u\"ţțťṫṭṯṱŧȾƭʈ\":\n self.trans[char] = u\"t\"\n for char in u\"ÙÚŨṸṴÜṲŪṺŬỤŮŲǓṶỦƯỮỰỬ\":\n self.trans[char] = u\"U\"\n for char in u\"ùúũṹṵüṳūṻŭụůųǔṷủưữựửʉ\":\n self.trans[char] = u\"u\"\n for char in u\"ȔŰǛǗǕǙ\":\n self.trans[char] = u\"Ü\"\n for char in u\"ȕűǜǘǖǚ\":\n self.trans[char] = u\"ü\"\n self.trans[u\"Û\"] = u\"Ux\"\n self.trans[u\"û\"] = u\"ux\"\n self.trans[u\"Ȗ\"] = u\"Û\"\n self.trans[u\"ȗ\"] = u\"û\"\n self.trans[u\"Ừ\"] = u\"Ù\"\n self.trans[u\"ừ\"] = u\"ù\"\n self.trans[u\"Ứ\"] = u\"Ú\"\n self.trans[u\"ứ\"] = u\"ú\"\n for char in u\"ṼṾ\":\n self.trans[char] = u\"V\"\n for char in u\"ṽṿ\":\n self.trans[char] = u\"v\"\n for char in u\"ẀẂŴẄẆẈ\":\n self.trans[char] = u\"W\"\n for char in u\"ẁẃŵẅẇẉ\":\n self.trans[char] = u\"w\"\n for char in u\"ẊẌ\":\n self.trans[char] = u\"X\"\n for char in u\"ẋẍ\":\n self.trans[char] = u\"x\"\n for char in u\"ỲÝŶŸỸȲẎỴỶƳ\":\n self.trans[char] = u\"Y\"\n for char in u\"ỳýŷÿỹȳẏỵỷƴ\":\n self.trans[char] = u\"y\"\n for char in u\"ŹẐŻẒŽẔƵȤ\":\n self.trans[char] = u\"Z\"\n for char in u\"źẑżẓžẕƶȥ\":\n self.trans[char] = u\"z\"\n self.trans[u\"ɀ\"] = u\"zv\"\n\n # Latin: extended Latin alphabet\n self.trans[u\"ɑ\"] = u\"a\"\n for char in u\"ÆǼǢ\":\n self.trans[char] = u\"AE\"\n for char in u\"æǽǣ\":\n self.trans[char] = u\"ae\"\n self.trans[u\"Ð\"] = u\"Dh\"\n self.trans[u\"ð\"] = u\"dh\"\n for char in u\"ƎƏƐ\":\n self.trans[char] = u\"E\"\n for char in u\"ǝəɛ\":\n self.trans[char] = u\"e\"\n for char in u\"ƔƢ\":\n self.trans[char] = u\"G\"\n for char in u\"ᵷɣƣᵹ\":\n self.trans[char] = u\"g\"\n self.trans[u\"Ƅ\"] = u\"H\"\n self.trans[u\"ƅ\"] = u\"h\"\n self.trans[u\"Ƕ\"] = u\"Wh\"\n self.trans[u\"ƕ\"] = u\"wh\"\n self.trans[u\"Ɩ\"] = u\"I\"\n self.trans[u\"ɩ\"] = u\"i\"\n self.trans[u\"Ŋ\"] = u\"Ng\"\n self.trans[u\"ŋ\"] = u\"ng\"\n self.trans[u\"Œ\"] = u\"OE\"\n self.trans[u\"œ\"] = u\"oe\"\n self.trans[u\"Ɔ\"] = u\"O\"\n self.trans[u\"ɔ\"] = u\"o\"\n self.trans[u\"Ȣ\"] = u\"Ou\"\n self.trans[u\"ȣ\"] = u\"ou\"\n self.trans[u\"Ƽ\"] = u\"Q\"\n for char in u\"ĸƽ\":\n self.trans[char] = u\"q\"\n self.trans[u\"ȹ\"] = u\"qp\"\n self.trans[u\"\"] = u\"r\"\n self.trans[u\"ſ\"] = u\"s\"\n self.trans[u\"ß\"] = u\"ss\"\n self.trans[u\"Ʃ\"] = u\"Sh\"\n for char in u\"ʃᶋ\":\n self.trans[char] = u\"sh\"\n self.trans[u\"Ʉ\"] = u\"U\"\n self.trans[u\"ʉ\"] = u\"u\"\n self.trans[u\"Ʌ\"] = u\"V\"\n self.trans[u\"ʌ\"] = u\"v\"\n for char in u\"ƜǷ\":\n self.trans[char] = u\"W\"\n for char in u\"ɯƿ\":\n self.trans[char] = u\"w\"\n self.trans[u\"Ȝ\"] = u\"Y\"\n self.trans[u\"ȝ\"] = u\"y\"\n self.trans[u\"IJ\"] = u\"IJ\"\n self.trans[u\"ij\"] = u\"ij\"\n self.trans[u\"Ƨ\"] = u\"Z\"\n for char in u\"ʮƨ\":\n self.trans[char] = u\"z\"\n self.trans[u\"Ʒ\"] = u\"Zh\"\n self.trans[u\"ʒ\"] = u\"zh\"\n self.trans[u\"Ǯ\"] = u\"Dzh\"\n self.trans[u\"ǯ\"] = u\"dzh\"\n for char in u\"ƸƹʔˀɁɂ\":\n self.trans[char] = u\"'\"\n self.trans['Þ'] = 'Th'\n self.trans['þ'] = 'th'\n for char in u\"Cʗǃ\":\n self.trans[char] = u\"!\"\n\n # Punctuation and typography\n for char in u\"«»“”„¨\":\n self.trans[char] = u'\"'\n for char in u\"‘’′\":\n self.trans[char] = u\"'\"\n self.trans[u\"•\"] = u\"*\"\n self.trans[u\"@\"] = u\"(at)\"\n self.trans[u\"¤\"] = u\"$\"\n self.trans[u\"¢\"] = u\"c\"\n self.trans[u\"€\"] = u\"E\"\n self.trans[u\"£\"] = u\"L\"\n self.trans[u\"¥\"] = u\"yen\"\n self.trans[u\"†\"] = u\"+\"\n self.trans[u\"‡\"] = u\"++\"\n self.trans[u\"°\"] = u\":\"\n self.trans[u\"¡\"] = u\"!\"\n self.trans[u\"¿\"] = u\"?\"\n self.trans[u\"‰\"] = u\"o/oo\"\n self.trans[u\"‱\"] = u\"o/ooo\"\n for char in u\"¶§\":\n self.trans[char] = u\">\"\n self.trans['…'] = '...'\n for char in u\"‒–—―\":\n self.trans[char] = u\"-\"\n self.trans['·'] = ' '\n self.trans[u\"¦\"] = u\"|\"\n self.trans[u\"⁂\"] = u\"***\"\n self.trans[u\"◊\"] = u\"<>\"\n self.trans[u\"‽\"] = u\"?!\"\n self.trans[u\"؟\"] = u\";-)\"\n self.trans[u\"¹\"] = u\"1\"\n self.trans[u\"²\"] = u\"2\"\n self.trans[u\"³\"] = u\"3\"\n\n # Cyrillic\n self.trans.update({u\"А\": u\"A\", u\"а\": u\"a\", u\"Б\": u\"B\", u\"б\": u\"b\",\n u\"В\": u\"V\", u\"в\": u\"v\", u\"Г\": u\"G\", u\"г\": u\"g\",\n u\"Д\": u\"D\", u\"д\": u\"d\", u\"Е\": u\"E\", u\"е\": u\"e\",\n u\"Ж\": u\"Zh\", u\"ж\": u\"zh\", u\"З\": u\"Z\", u\"з\": u\"z\",\n u\"И\": u\"I\", u\"и\": u\"i\", u\"Й\": u\"J\", u\"й\": u\"j\",\n u\"К\": u\"K\", u\"к\": u\"k\", u\"Л\": u\"L\", u\"л\": u\"l\",\n u\"М\": u\"M\", u\"м\": u\"m\", u\"Н\": u\"N\", u\"н\": u\"n\",\n u\"О\": u\"O\", u\"о\": u\"o\", u\"П\": u\"P\", u\"п\": u\"p\",\n u\"Р\": u\"R\", u\"р\": u\"r\", u\"С\": u\"S\", u\"с\": u\"s\",\n u\"Т\": u\"T\", u\"т\": u\"t\", u\"У\": u\"U\", u\"у\": u\"u\",\n u\"Ф\": u\"F\", u\"ф\": u\"f\", u\"х\": u\"kh\", u\"Ц\": u\"C\",\n u\"ц\": u\"c\", u\"Ч\": u\"Ch\", u\"ч\": u\"ch\", u\"Ш\": u\"Sh\",\n u\"ш\": u\"sh\", u\"Щ\": u\"Shch\", u\"щ\": u\"shch\", u\"Ь\": u\"'\",\n u\"ь\": \"'\", u\"Ъ\": u'\"', u\"ъ\": '\"', u\"Ю\": u\"Yu\",\n u\"ю\": u\"yu\", u\"Я\": u\"Ya\", u\"я\": u\"ya\", u\"Х\": u\"Kh\",\n u\"Χ\": u\"Kh\"})\n\n # Additional Cyrillic letters, most occuring in only one or a few languages\n self.trans.update({u\"Ы\": u\"Y\", u\"ы\": u\"y\", u\"Ё\": u\"Ë\", u\"ё\": u\"ë\",\n u\"Э\": u\"È\", u\"Ѐ\": u\"È\", u\"э\": u\"è\", u\"ѐ\": u\"è\",\n u\"І\": u\"I\", u\"і\": u\"i\", u\"Ї\": u\"Ji\", u\"ї\": u\"ji\",\n u\"Є\": u\"Je\", u\"є\": u\"je\", u\"Ґ\": u\"G\", u\"Ҝ\": u\"G\",\n u\"ґ\": u\"g\", u\"ҝ\": u\"g\", u\"Ђ\": u\"Dj\", u\"ђ\": u\"dj\",\n \"Љ\": \"Lj\", \"љ\": \"lj\",\n u\"Њ\": u\"Nj\", u\"њ\": u\"nj\", u\"Ћ\": u\"Cj\", u\"ћ\": u\"cj\",\n 'Җ': 'Zhj', 'Ѓ': 'Gj', 'ѓ': 'gj',\n u\"Ќ\": u\"Kj\", u\"ќ\": u\"kj\", u\"Ӣ\": u\"Ii\", u\"ӣ\": u\"ii\",\n \"Ҳ\": \"H\", \"ҳ\": \"h\",\n u\"Ҷ\": u\"Dz\", u\"ҷ\": u\"dz\", u\"Ө\": u\"Ô\", u\"Ӫ\": u\"Ô\",\n u\"ө\": u\"ô\", u\"ӫ\": u\"ô\", u\"Ү\": u\"Y\", u\"ү\": u\"y\", u\"Һ\": u\"H\",\n u\"һ\": u\"h\", u\"Ә\": u\"AE\", u\"Ӕ\": u\"AE\", u\"ә\": u\"ae\",\n 'Ӛ': 'Ë', 'Ӭ': 'Ë', 'ӛ': 'ë', 'ӭ': 'ë',\n 'җ': 'zhj', 'Ұ': 'U', 'ў': 'ù', 'Ў': 'Ù',\n u\"ѝ\": u\"ì\", u\"Ѝ\": u\"Ì\", u\"Ӑ\": u\"A\", u\"ă\": u\"a\", u\"Ӓ\": u\"Ä\",\n \"Ҽ\": \"Ts\", \"Ҿ\": \"Ts\", \"ҽ\": \"ts\", \"ҿ\": \"ts\",\n u\"Ҙ\": u\"Dh\", u\"ҙ\": u\"dh\", u\"Ӏ\": u\"\", u\"ӏ\": u\"\", u\"Ӆ\": u\"L\",\n u\"ӆ\": u\"l\", u\"Ӎ\": u\"M\", u\"ӎ\": u\"m\", u\"Ӧ\": u\"Ö\", u\"ӧ\": u\"ö\",\n u\"Ҩ\": u\"u\", u\"ҩ\": u\"u\", u\"Ҧ\": u\"Ph\", u\"ҧ\": u\"ph\", u\"Ҏ\": u\"R\",\n u\"ҏ\": u\"r\", u\"Ҫ\": u\"Th\", u\"ҫ\": u\"th\", u\"Ҭ\": u\"T\", u\"ҭ\": u\"t\",\n 'Ӯ': 'Û', 'ӯ': 'û', 'Ӹ': 'U', 'ұ': 'u',\n u\"ӹ\": u\"u\", u\"Ҵ\": u\"Tts\", u\"ҵ\": u\"tts\", u\"Ӵ\": u\"Ch\", u\"ӵ\": u\"ch\"})\n\n for char in u\"ЈӤҊ\":\n self.trans[char] = u\"J\"\n for char in u\"јӥҋ\":\n self.trans[char] = u\"j\"\n for char in u\"ЏӁӜҶ\":\n self.trans[char] = u\"Dzh\"\n for char in u\"џӂӝҷ\":\n self.trans[char] = u\"dzh\"\n for char in u\"ЅӞӠӋҸ\":\n self.trans[char] = u\"Dz\"\n for char in u\"ѕӟӡӌҹ\":\n self.trans[char] = u\"dz\"\n for char in u\"ҒӶҔ\":\n self.trans[char] = u\"G\"\n for char in u\"ғӷҕ\":\n self.trans[char] = u\"g\"\n for char in u\"ҚҞҠӃ\":\n self.trans[char] = u\"Q\"\n for char in u\"қҟҡӄ\":\n self.trans[char] = u\"q\"\n for char in u\"ҢҤӉӇ\":\n self.trans[char] = u\"Ng\"\n for char in u\"ңҥӊӈ\":\n self.trans[char] = u\"ng\"\n for char in u\"ӖѢҌ\":\n self.trans[char] = u\"E\"\n for char in u\"ӗѣҍ\":\n self.trans[char] = u\"e\"\n for char in u\"ӲӰҮ\":\n self.trans[char] = u\"Ü\"\n for char in u\"ӳӱү\":\n self.trans[char] = u\"ü\"\n\n # Archaic Cyrillic letters\n self.trans.update({u\"Ѹ\": u\"Ou\", u\"ѹ\": u\"ou\", u\"Ѡ\": u\"O\", u\"Ѻ\": u\"O\", u\"ѡ\": u\"o\",\n u\"ѻ\": u\"o\", u\"Ѿ\": u\"Ot\", u\"ѿ\": u\"ot\", u\"Ѣ\": u\"E\", u\"ѣ\": u\"e\",\n u\"Ѥ\": u\"Ei\", u\"Ѧ\": u\"Ei\", u\"ѥ\": u\"ei\", u\"ѧ\": u\"ei\", u\"Ѫ\": u\"Ai\",\n u\"ѫ\": u\"ai\", u\"Ѯ\": u\"X\", u\"ѯ\": u\"x\", u\"Ѱ\": u\"Ps\", u\"ѱ\": u\"ps\",\n u\"Ѳ\": u\"Th\", u\"ѳ\": u\"th\", u\"Ѵ\": u\"Ü\", u\"Ѷ\": u\"Ü\", u\"ѵ\": u\"ü\"})\n\n # Hebrew alphabet\n for char in u\"אע\":\n self.trans[char] = u\"'\"\n self.trans[u\"ב\"] = u\"b\"\n self.trans[u\"ג\"] = u\"g\"\n self.trans[u\"ד\"] = u\"d\"\n self.trans[u\"ה\"] = u\"h\"\n self.trans[u\"ו\"] = u\"v\"\n self.trans[u\"ז\"] = u\"z\"\n self.trans[u\"ח\"] = u\"kh\"\n self.trans[u\"ט\"] = u\"t\"\n self.trans[u\"י\"] = u\"y\"\n for char in u\"ךכ\":\n self.trans[char] = u\"k\"\n self.trans[u\"ל\"] = u\"l\"\n for char in u\"םמ\":\n self.trans[char] = u\"m\"\n for char in u\"ןנ\":\n self.trans[char] = u\"n\"\n self.trans[u\"ס\"] = u\"s\"\n for char in u\"ףפ\":\n self.trans[char] = u\"ph\"\n for char in u\"ץצ\":\n self.trans[char] = u\"ts\"\n self.trans[u\"ק\"] = u\"q\"\n self.trans[u\"ר\"] = u\"r\"\n self.trans[u\"ש\"] = u\"sh\"\n self.trans[u\"ת\"] = u\"th\"\n\n # Arab alphabet\n for char in u\"اﺍﺎ\":\n self.trans[char] = u\"a\"\n for char in u\"بﺏﺐﺒﺑ\":\n self.trans[char] = u\"b\"\n for char in u\"تﺕﺖﺘﺗ\":\n self.trans[char] = u\"t\"\n for char in u\"ثﺙﺚﺜﺛ\":\n self.trans[char] = u\"th\"\n for char in u\"جﺝﺞﺠﺟ\":\n self.trans[char] = u\"g\"\n for char in u\"حﺡﺢﺤﺣ\":\n self.trans[char] = u\"h\"\n for char in u\"خﺥﺦﺨﺧ\":\n self.trans[char] = u\"kh\"\n for char in u\"دﺩﺪ\":\n self.trans[char] = u\"d\"\n for char in u\"ذﺫﺬ\":\n self.trans[char] = u\"dh\"\n for char in u\"رﺭﺮ\":\n self.trans[char] = u\"r\"\n for char in u\"زﺯﺰ\":\n self.trans[char] = u\"z\"\n for char in u\"سﺱﺲﺴﺳ\":\n self.trans[char] = u\"s\"\n for char in u\"شﺵﺶﺸﺷ\":\n self.trans[char] = u\"sh\"\n for char in u\"صﺹﺺﺼﺻ\":\n self.trans[char] = u\"s\"\n for char in u\"ضﺽﺾﻀﺿ\":\n self.trans[char] = u\"d\"\n for char in u\"طﻁﻂﻄﻃ\":\n self.trans[char] = u\"t\"\n for char in u\"ظﻅﻆﻈﻇ\":\n self.trans[char] = u\"z\"\n for char in u\"عﻉﻊﻌﻋ\":\n self.trans[char] = u\"'\"\n for char in u\"غﻍﻎﻐﻏ\":\n self.trans[char] = u\"gh\"\n for char in u\"فﻑﻒﻔﻓ\":\n self.trans[char] = u\"f\"\n for char in u\"قﻕﻖﻘﻗ\":\n self.trans[char] = u\"q\"\n for char in u\"كﻙﻚﻜﻛک\":\n self.trans[char] = u\"k\"\n for char in u\"لﻝﻞﻠﻟ\":\n self.trans[char] = u\"l\"\n for char in u\"مﻡﻢﻤﻣ\":\n self.trans[char] = u\"m\"\n for char in u\"نﻥﻦﻨﻧ\":\n self.trans[char] = u\"n\"\n for char in u\"هﻩﻪﻬﻫ\":\n self.trans[char] = u\"h\"\n for char in u\"وﻭﻮ\":\n self.trans[char] = u\"w\"\n for char in u\"یيﻱﻲﻴﻳ\":\n self.trans[char] = u\"y\"\n # Arabic - additional letters, modified letters and ligatures\n self.trans[u\"ﺀ\"] = u\"'\"\n for char in u\"آﺁﺂ\":\n self.trans[char] = u\"'a\"\n for char in u\"ةﺓﺔ\":\n self.trans[char] = u\"th\"\n for char in u\"ىﻯﻰ\":\n self.trans[char] = u\"á\"\n for char in u\"یﯼﯽﯿﯾ\":\n self.trans[char] = u\"y\"\n self.trans[u\"؟\"] = u\"?\"\n # Arabic - ligatures\n for char in u\"ﻻﻼ\":\n self.trans[char] = u\"la\"\n self.trans[u\"ﷲ\"] = u\"llah\"\n for char in u\"إأ\":\n self.trans[char] = u\"a'\"\n self.trans[u\"ؤ\"] = u\"w'\"\n self.trans[u\"ئ\"] = u\"y'\"\n for char in u\"◌◌\":\n self.trans[char] = u\"\" # indicates absence of vowels\n # Arabic vowels\n self.trans[u\"◌\"] = u\"a\"\n self.trans[u\"◌\"] = u\"u\"\n self.trans[u\"◌\"] = u\"i\"\n self.trans[u\"◌\"] = u\"a\"\n self.trans[u\"◌\"] = u\"ay\"\n self.trans[u\"◌\"] = u\"ay\"\n self.trans[u\"◌\"] = u\"u\"\n self.trans[u\"◌\"] = u\"iy\"\n # Arab numerals\n for char in u\"٠۰\":\n self.trans[char] = u\"0\"\n for char in u\"١۱\":\n self.trans[char] = u\"1\"\n for char in u\"٢۲\":\n self.trans[char] = u\"2\"\n for char in u\"٣۳\":\n self.trans[char] = u\"3\"\n for char in u\"٤۴\":\n self.trans[char] = u\"4\"\n for char in u\"٥۵\":\n self.trans[char] = u\"5\"\n for char in u\"٦۶\":\n self.trans[char] = u\"6\"\n for char in u\"٧۷\":\n self.trans[char] = u\"7\"\n for char in u\"٨۸\":\n self.trans[char] = u\"8\"\n for char in u\"٩۹\":\n self.trans[char] = u\"9\"\n # Perso-Arabic\n for char in u\"پﭙﭙپ\":\n self.trans[char] = u\"p\"\n for char in u\"چچچچ\":\n self.trans[char] = u\"ch\"\n for char in u\"ژژ\":\n self.trans[char] = u\"zh\"\n for char in u\"گﮔﮕﮓ\":\n self.trans[char] = u\"g\"\n\n # Greek\n self.trans.update({u\"Α\": u\"A\", u\"α\": u\"a\", u\"Β\": u\"B\", u\"β\": u\"b\", u\"Γ\": u\"G\",\n u\"γ\": u\"g\", u\"Δ\": u\"D\", u\"δ\": u\"d\", u\"Ε\": u\"E\", u\"ε\": u\"e\",\n u\"Ζ\": u\"Z\", u\"ζ\": u\"z\", u\"Η\": u\"I\", u\"η\": u\"i\", u\"θ\": u\"th\",\n u\"Θ\": u\"Th\", u\"Ι\": u\"I\", u\"ι\": u\"i\", u\"Κ\": u\"K\", u\"κ\": u\"k\",\n u\"Λ\": u\"L\", u\"λ\": u\"l\", u\"Μ\": u\"M\", u\"μ\": u\"m\", u\"Ν\": u\"N\",\n u\"ν\": u\"n\", u\"Ξ\": u\"X\", u\"ξ\": u\"x\", u\"Ο\": u\"O\", u\"ο\": u\"o\",\n u\"Π\": u\"P\", u\"π\": u\"p\", u\"Ρ\": u\"R\", u\"ρ\": u\"r\", u\"Σ\": u\"S\",\n u\"σ\": u\"s\", u\"ς\": u\"s\", u\"Τ\": u\"T\", u\"τ\": u\"t\", u\"Υ\": u\"Y\",\n u\"υ\": u\"y\", u\"Φ\": u\"F\", u\"φ\": u\"f\", u\"Ψ\": u\"Ps\", u\"ψ\": u\"ps\",\n u\"Ω\": u\"O\", u\"ω\": u\"o\", u\"ϗ\": u\"&\", u\"Ϛ\": u\"St\", u\"ϛ\": u\"st\",\n u\"Ϙ\": u\"Q\", u\"Ϟ\": u\"Q\", u\"ϙ\": u\"q\", u\"ϟ\": u\"q\", u\"Ϻ\": u\"S\",\n u\"ϻ\": u\"s\", u\"Ϡ\": u\"Ss\", u\"ϡ\": u\"ss\", u\"Ϸ\": u\"Sh\", u\"ϸ\": u\"sh\",\n u\"·\": u\":\", u\"Ά\": u\"Á\", u\"ά\": u\"á\", u\"Έ\": u\"É\", u\"Ή\": u\"É\",\n u\"έ\": u\"é\", u\"ή\": u\"é\", u\"Ί\": u\"Í\", u\"ί\": u\"í\", u\"Ϊ\": u\"Ï\",\n u\"ϊ\": u\"ï\", u\"ΐ\": u\"ï\", u\"Ό\": u\"Ó\", u\"ό\": u\"ó\", u\"Ύ\": u\"Ý\",\n u\"ύ\": u\"ý\", u\"Ϋ\": u\"Y\", u\"ϋ\": u\"ÿ\", u\"ΰ\": u\"ÿ\", u\"Ώ\": u\"Ó\",\n u\"ώ\": u\"ó\"})\n\n # Japanese (katakana and hiragana)\n for char in u\"アァあ\":\n self.trans[char] = u\"a\"\n for char in u\"イィい\":\n self.trans[char] = u\"i\"\n for char in u\"ウう\":\n self.trans[char] = u\"u\"\n for char in u\"エェえ\":\n self.trans[char] = u\"e\"\n for char in u\"オォお\":\n self.trans[char] = u\"o\"\n for char in u\"ャや\":\n self.trans[char] = u\"ya\"\n for char in u\"ュゆ\":\n self.trans[char] = u\"yu\"\n for char in u\"ョよ\":\n self.trans[char] = u\"yo\"\n for char in u\"カか\":\n self.trans[char] = u\"ka\"\n for char in u\"キき\":\n self.trans[char] = u\"ki\"\n for char in u\"クく\":\n self.trans[char] = u\"ku\"\n for char in u\"ケけ\":\n self.trans[char] = u\"ke\"\n for char in u\"コこ\":\n self.trans[char] = u\"ko\"\n for char in u\"サさ\":\n self.trans[char] = u\"sa\"\n for char in u\"シし\":\n self.trans[char] = u\"shi\"\n for char in u\"スす\":\n self.trans[char] = u\"su\"\n for char in u\"セせ\":\n self.trans[char] = u\"se\"\n for char in u\"ソそ\":\n self.trans[char] = u\"so\"\n for char in u\"タた\":\n self.trans[char] = u\"ta\"\n for char in u\"チち\":\n self.trans[char] = u\"chi\"\n for char in u\"ツつ\":\n self.trans[char] = u\"tsu\"\n for char in u\"テて\":\n self.trans[char] = u\"te\"\n for char in u\"トと\":\n self.trans[char] = u\"to\"\n for char in u\"ナな\":\n self.trans[char] = u\"na\"\n for char in u\"ニに\":\n self.trans[char] = u\"ni\"\n for char in u\"ヌぬ\":\n self.trans[char] = u\"nu\"\n for char in u\"ネね\":\n self.trans[char] = u\"ne\"\n for char in u\"ノの\":\n self.trans[char] = u\"no\"\n for char in u\"ハは\":\n self.trans[char] = u\"ha\"\n for char in u\"ヒひ\":\n self.trans[char] = u\"hi\"\n for char in u\"フふ\":\n self.trans[char] = u\"fu\"\n for char in u\"ヘへ\":\n self.trans[char] = u\"he\"\n for char in u\"ホほ\":\n self.trans[char] = u\"ho\"\n for char in u\"マま\":\n self.trans[char] = u\"ma\"\n for char in u\"ミみ\":\n self.trans[char] = u\"mi\"\n for char in u\"ムむ\":\n self.trans[char] = u\"mu\"\n for char in u\"メめ\":\n self.trans[char] = u\"me\"\n for char in u\"モも\":\n self.trans[char] = u\"mo\"\n for char in u\"ラら\":\n self.trans[char] = u\"ra\"\n for char in u\"リり\":\n self.trans[char] = u\"ri\"\n for char in u\"ルる\":\n self.trans[char] = u\"ru\"\n for char in u\"レれ\":\n self.trans[char] = u\"re\"\n for char in u\"ロろ\":\n self.trans[char] = u\"ro\"\n for char in u\"ワわ\":\n self.trans[char] = u\"wa\"\n for char in u\"ヰゐ\":\n self.trans[char] = u\"wi\"\n for char in u\"ヱゑ\":\n self.trans[char] = u\"we\"\n for char in u\"ヲを\":\n self.trans[char] = u\"wo\"\n for char in u\"ンん\":\n self.trans[char] = u\"n\"\n for char in u\"ガが\":\n self.trans[char] = u\"ga\"\n for char in u\"ギぎ\":\n self.trans[char] = u\"gi\"\n for char in u\"グぐ\":\n self.trans[char] = u\"gu\"\n for char in u\"ゲげ\":\n self.trans[char] = u\"ge\"\n for char in u\"ゴご\":\n self.trans[char] = u\"go\"\n for char in u\"ザざ\":\n self.trans[char] = u\"za\"\n for char in u\"ジじ\":\n self.trans[char] = u\"ji\"\n for char in u\"ズず\":\n self.trans[char] = u\"zu\"\n for char in u\"ゼぜ\":\n self.trans[char] = u\"ze\"\n for char in u\"ゾぞ\":\n self.trans[char] = u\"zo\"\n for char in u\"ダだ\":\n self.trans[char] = u\"da\"\n for char in u\"ヂぢ\":\n self.trans[char] = u\"dji\"\n for char in u\"ヅづ\":\n self.trans[char] = u\"dzu\"\n for char in u\"デで\":\n self.trans[char] = u\"de\"\n for char in u\"ドど\":\n self.trans[char] = u\"do\"\n for char in u\"バば\":\n self.trans[char] = u\"ba\"\n for char in u\"ビび\":\n self.trans[char] = u\"bi\"\n for char in u\"ブぶ\":\n self.trans[char] = u\"bu\"\n for char in u\"ベべ\":\n self.trans[char] = u\"be\"\n for char in u\"ボぼ\":\n self.trans[char] = u\"bo\"\n for char in u\"パぱ\":\n self.trans[char] = u\"pa\"\n for char in u\"ピぴ\":\n self.trans[char] = u\"pi\"\n for char in u\"プぷ\":\n self.trans[char] = u\"pu\"\n for char in u\"ペぺ\":\n self.trans[char] = u\"pe\"\n for char in u\"ポぽ\":\n self.trans[char] = u\"po\"\n for char in u\"ヴゔ\":\n self.trans[char] = u\"vu\"\n self.trans[u\"ヷ\"] = u\"va\"\n self.trans[u\"ヸ\"] = u\"vi\"\n self.trans[u\"ヹ\"] = u\"ve\"\n self.trans[u\"ヺ\"] = u\"vo\"\n\n # Japanese and Chinese punctuation and typography\n for char in u\"・·\":\n self.trans[char] = u\" \"\n for char in u\"〃『』《》\":\n self.trans[char] = u'\"'\n for char in u\"「」〈〉〘〙〚〛\":\n self.trans[char] = u\"'\"\n for char in u\"（〔\":\n self.trans[char] = u\"(\"\n for char in u\"）〕\":\n self.trans[char] = u\")\"\n for char in u\"［【〖\":\n self.trans[char] = u\"[\"\n for char in u\"］】〗\":\n self.trans[char] = u\"]\"\n self.trans['｛'] = '{'\n self.trans['｝'] = '}'\n self.trans['っ'] = ':'\n self.trans['ー'] = 'h'\n self.trans['゛'] = \"'\"\n self.trans['゜'] = 'p'\n self.trans['。'] = '. '\n self.trans['、'] = ', '\n self.trans['・'] = ' '\n self.trans['〆'] = 'shime'\n self.trans['〜'] = '-'\n self.trans['…'] = '...'\n self.trans['‥'] = '..'\n self.trans['ヶ'] = 'months'\n for char in u\"•◦\":\n self.trans[char] = u\"_\"\n for char in u\"※＊\":\n self.trans[char] = u\"*\"\n self.trans['Ⓧ'] = '(X)'\n self.trans['Ⓨ'] = '(Y)'\n self.trans['！'] = '!'\n self.trans['？'] = '?'\n self.trans['；'] = ';'\n self.trans['：'] = ':'\n self.trans['。'] = '.'\n for char in u\"，、\":\n self.trans[char] = u\",\"\n\n # Georgian\n self.trans['ა'] = 'a'\n self.trans['ბ'] = 'b'\n self.trans['გ'] = 'g'\n self.trans['დ'] = 'd'\n for char in u\"ეჱ\":\n self.trans[char] = u\"e\"\n self.trans['ვ'] = 'v'\n self.trans['ზ'] = 'z'\n self.trans['თ'] = 'th'\n self.trans['ი'] = 'i'\n self.trans['კ'] = 'k'\n self.trans['ლ'] = 'l'\n self.trans['მ'] = 'm'\n self.trans['ნ'] = 'n'\n self.trans['ო'] = 'o'\n self.trans['პ'] = 'p'\n self.trans['ჟ'] = 'zh'\n self.trans['რ'] = 'r'\n self.trans['ს'] = 's'\n self.trans['ტ'] = 't'\n self.trans['უ'] = 'u'\n self.trans['ფ'] = 'ph'\n self.trans['ქ'] = 'q'\n self.trans['ღ'] = 'gh'\n for char in u\"ყ\":\n self.trans[char] = u\"q'\"\n self.trans['შ'] = 'sh'\n self.trans['ჩ'] = 'ch'\n self.trans['ც'] = 'ts'\n self.trans['ძ'] = 'dz'\n for char in u\"წ\":\n self.trans[char] = u\"ts'\"\n for char in u\"ჭ\":\n self.trans[char] = u\"ch'\"\n self.trans['ხ'] = 'kh'\n self.trans['ჯ'] = 'j'\n self.trans['ჰ'] = 'h'\n self.trans['ჳ'] = 'w'\n self.trans['ჵ'] = 'o'\n self.trans['ჶ'] = 'f'\n\n # Devanagari\n for char in u\"पप\":\n self.trans[char] = u\"p\"\n self.trans['अ'] = 'a'\n for char in u\"आा\":\n self.trans[char] = u\"aa\"\n self.trans['प'] = 'pa'\n for char in u\"इि\":\n self.trans[char] = u\"i\"\n for char in u\"ईी\":\n self.trans[char] = u\"ii\"\n for char in u\"उु\":\n self.trans[char] = u\"u\"\n for char in u\"ऊू\":\n self.trans[char] = u\"uu\"\n for char in u\"एे\":\n self.trans[char] = u\"e\"\n for char in u\"ऐै\":\n self.trans[char] = u\"ai\"\n for char in u\"ओो\":\n self.trans[char] = u\"o\"\n for char in u\"औौ\":\n self.trans[char] = u\"au\"\n for char in u\"ऋृर\":\n self.trans[char] = u\"r\"\n for char in u\"ॠॄ\":\n self.trans[char] = u\"rr\"\n for char in u\"ऌॢल\":\n self.trans[char] = u\"l\"\n for char in u\"ॡॣ\":\n self.trans[char] = u\"ll\"\n self.trans['क'] = 'k'\n self.trans['ख'] = 'kh'\n self.trans['ग'] = 'g'\n self.trans['घ'] = 'gh'\n self.trans['ङ'] = 'ng'\n self.trans['च'] = 'c'\n self.trans['छ'] = 'ch'\n self.trans['ज'] = 'j'\n self.trans['झ'] = 'jh'\n self.trans['ञ'] = 'ñ'\n for char in u\"टत\":\n self.trans[char] = u\"t\"\n for char in u\"ठथ\":\n self.trans[char] = u\"th\"\n for char in u\"डद\":\n self.trans[char] = u\"d\"\n for char in u\"ढध\":\n self.trans[char] = u\"dh\"\n for char in u\"णन\":\n self.trans[char] = u\"n\"\n self.trans['फ'] = 'ph'\n self.trans['ब'] = 'b'\n self.trans['भ'] = 'bh'\n self.trans['म'] = 'm'\n self.trans['य'] = 'y'\n self.trans['व'] = 'v'\n self.trans['श'] = 'sh'\n for char in u\"षस\":\n self.trans[char] = u\"s\"\n self.trans['ह'] = 'h'\n self.trans['क'] = 'x'\n self.trans['त'] = 'tr'\n self.trans['ज'] = 'gj'\n for char in u\"क़\":\n self.trans[char] = u\"q\"\n self.trans['फ'] = 'f'\n self.trans['ख'] = 'hh'\n self.trans['H'] = 'gh'\n self.trans['ज'] = 'z'\n for char in u\"डढ\":\n self.trans[char] = u\"r\"\n # Devanagari ligatures (possibly incomplete and/or incorrect)\n for char in u\"ख्\":\n self.trans[char] = u\"khn\"\n self.trans['त'] = 'tn'\n for char in u\"द्\":\n self.trans[char] = u\"dn\"\n self.trans['श'] = 'cn'\n for char in u\"ह्\":\n self.trans[char] = u\"fn\"\n for char in u\"अँ\":\n self.trans[char] = u\"m\"\n for char in u\"॒॑\":\n self.trans[char] = u\"\"\n self.trans['०'] = '0'\n self.trans['१'] = '1'\n self.trans['२'] = '2'\n self.trans['३'] = '3'\n self.trans['४'] = '4'\n self.trans['५'] = '5'\n self.trans['६'] = '6'\n self.trans['७'] = '7'\n self.trans['८'] = '8'\n self.trans['९'] = '9'\n\n # Armenian\n self.trans['Ա'] = 'A'\n self.trans['ա'] = 'a'\n self.trans['Բ'] = 'B'\n self.trans['բ'] = 'b'\n self.trans['Գ'] = 'G'\n self.trans['գ'] = 'g'\n self.trans['Դ'] = 'D'\n self.trans['դ'] = 'd'\n self.trans['Ե'] = 'Je'\n self.trans['ե'] = 'e'\n self.trans['Զ'] = 'Z'\n self.trans['զ'] = 'z'\n self.trans['Է'] = 'É'\n self.trans['է'] = 'é'\n self.trans['Ը'] = 'Ë'\n self.trans['ը'] = 'ë'\n self.trans['Թ'] = 'Th'\n self.trans['թ'] = 'th'\n self.trans['Ժ'] = 'Zh'\n self.trans['ժ'] = 'zh'\n self.trans['Ի'] = 'I'\n self.trans['ի'] = 'i'\n self.trans['Լ'] = 'L'\n self.trans['լ'] = 'l'\n self.trans['Խ'] = 'Ch'\n self.trans['խ'] = 'ch'\n self.trans['Ծ'] = 'Ts'\n self.trans['ծ'] = 'ts'\n self.trans['Կ'] = 'K'\n self.trans['կ'] = 'k'\n self.trans['Հ'] = 'H'\n self.trans['հ'] = 'h'\n self.trans['Ձ'] = 'Dz'\n self.trans['ձ'] = 'dz'\n self.trans['Ղ'] = 'R'\n self.trans['ղ'] = 'r'\n self.trans['Ճ'] = 'Cz'\n self.trans['ճ'] = 'cz'\n self.trans['Մ'] = 'M'\n self.trans['մ'] = 'm'\n self.trans['Յ'] = 'J'\n self.trans['յ'] = 'j'\n self.trans['Ն'] = 'N'\n self.trans['ն'] = 'n'\n self.trans['Շ'] = 'S'\n self.trans['շ'] = 's'\n self.trans['Շ'] = 'Vo'\n self.trans['շ'] = 'o'\n self.trans['Չ'] = 'Tsh'\n self.trans['չ'] = 'tsh'\n self.trans['Պ'] = 'P'\n self.trans['պ'] = 'p'\n self.trans['Ջ'] = 'Dz'\n self.trans['ջ'] = 'dz'\n self.trans['Ռ'] = 'R'\n self.trans['ռ'] = 'r'\n self.trans['Ս'] = 'S'\n self.trans['ս'] = 's'\n self.trans['Վ'] = 'V'\n self.trans['վ'] = 'v'\n for char in u\"Տ\":\n self.trans[char] = u\"T'\"\n for char in u\"տ\":\n self.trans[char] = u\"t'\"\n self.trans['Ր'] = 'R'\n self.trans['ր'] = 'r'\n self.trans['Ց'] = 'Tsh'\n self.trans['ց'] = 'tsh'\n self.trans['Ւ'] = 'V'\n self.trans['ւ'] = 'v'\n self.trans['Փ'] = 'Ph'\n self.trans['փ'] = 'ph'\n self.trans['Ք'] = 'Kh'\n self.trans['ք'] = 'kh'\n self.trans['Օ'] = 'O'\n self.trans['օ'] = 'o'\n self.trans['Ֆ'] = 'F'\n self.trans['ֆ'] = 'f'\n self.trans['և'] = '&'\n self.trans['՟'] = '.'\n self.trans['՞'] = '?'\n self.trans['՝'] = ';'\n self.trans['՛'] = ''\n\n # Tamil\n for char in u\"க்\":\n self.trans[char] = u\"k\"\n for char in u\"ஙண்ந்ன்\":\n self.trans[char] = u\"n\"\n self.trans['ச'] = 'c'\n for char in u\"ஞ்\":\n self.trans[char] = u\"ñ\"\n for char in u\"ட்\":\n self.trans[char] = u\"th\"\n self.trans['த'] = 't'\n self.trans['ப'] = 'p'\n for char in u\"ம்\":\n self.trans[char] = u\"m\"\n for char in u\"ய்\":\n self.trans[char] = u\"y\"\n for char in u\"ர்ழ்ற\":\n self.trans[char] = u\"r\"\n for char in u\"ல்ள\":\n self.trans[char] = u\"l\"\n for char in u\"வ்\":\n self.trans[char] = u\"v\"\n self.trans['ஜ'] = 'j'\n self.trans['ஷ'] = 'sh'\n self.trans['ஸ'] = 's'\n self.trans['ஹ'] = 'h'\n for char in u\"க்ஷ\":\n self.trans[char] = u\"x\"\n self.trans['அ'] = 'a'\n self.trans['ஆ'] = 'aa'\n self.trans['இ'] = 'i'\n self.trans['ஈ'] = 'ii'\n self.trans['உ'] = 'u'\n self.trans['ஊ'] = 'uu'\n self.trans['எ'] = 'e'\n self.trans['ஏ'] = 'ee'\n self.trans['ஐ'] = 'ai'\n self.trans['ஒ'] = 'o'\n self.trans['ஓ'] = 'oo'\n self.trans['ஔ'] = 'au'\n self.trans['ஃ'] = ''\n\n # Bengali\n self.trans['অ'] = 'ô'\n for char in u\"আা\":\n self.trans[char] = u\"a\"\n for char in u\"ইিঈী\":\n self.trans[char] = u\"i\"\n for char in u\"উুঊূ\":\n self.trans[char] = u\"u\"\n for char in u\"ঋৃ\":\n self.trans[char] = u\"ri\"\n for char in u\"এেয়\":\n self.trans[char] = u\"e\"\n for char in u\"ঐৈ\":\n self.trans[char] = u\"oi\"\n for char in u\"ওো\":\n self.trans[char] = u\"o\"\n for char in u\"ঔৌ\":\n self.trans[char] = \"ou\"\n self.trans['্'] = ''\n self.trans['ৎ'] = 't'\n self.trans['ং'] = 'n'\n self.trans['ঃ'] = 'h'\n self.trans['ঁ'] = 'ñ'\n self.trans['ক'] = 'k'\n self.trans['খ'] = 'kh'\n self.trans['গ'] = 'g'\n self.trans['ঘ'] = 'gh'\n self.trans['ঙ'] = 'ng'\n self.trans['চ'] = 'ch'\n self.trans['ছ'] = 'chh'\n self.trans['জ'] = 'j'\n self.trans['ঝ'] = 'jh'\n self.trans['ঞ'] = 'n'\n for char in u\"টত\":\n self.trans[char] = u\"t\"\n for char in u\"ঠথ\":\n self.trans[char] = u\"th\"\n for char in u\"ডদ\":\n self.trans[char] = u\"d\"\n for char in u\"ঢধ\":\n self.trans[char] = u\"dh\"\n for char in u\"ণন\":\n self.trans[char] = u\"n\"\n self.trans['প'] = 'p'\n self.trans['ফ'] = 'ph'\n self.trans['ব'] = 'b'\n self.trans['ভ'] = 'bh'\n self.trans['ম'] = 'm'\n self.trans['য'] = 'dzh'\n self.trans['র'] = 'r'\n self.trans['ল'] = 'l'\n self.trans['শ'] = 's'\n self.trans['হ'] = 'h'\n for char in u\"য়\":\n self.trans[char] = u\"-\"\n for char in u\"ড়\":\n self.trans[char] = u\"r\"\n self.trans['ঢ'] = 'rh'\n self.trans['০'] = '0'\n self.trans['১'] = '1'\n self.trans['২'] = '2'\n self.trans['৩'] = '3'\n self.trans['৪'] = '4'\n self.trans['৫'] = '5'\n self.trans['৬'] = '6'\n self.trans['৭'] = '7'\n self.trans['৮'] = '8'\n self.trans['৯'] = '9'\n\n # Thai (because of complications of the alphabet, self.transliterations\n # are very imprecise here)\n self.trans['ก'] = 'k'\n for char in u\"ขฃคฅฆ\":\n self.trans[char] = u\"kh\"\n self.trans['ง'] = 'ng'\n for char in u\"จฉชฌ\":\n self.trans[char] = u\"ch\"\n for char in u\"ซศษส\":\n self.trans[char] = u\"s\"\n for char in u\"ญย\":\n self.trans[char] = u\"y\"\n for char in u\"ฎด\":\n self.trans[char] = u\"d\"\n for char in u\"ฏต\":\n self.trans[char] = u\"t\"\n for char in u\"ฐฑฒถทธ\":\n self.trans[char] = u\"th\"\n for char in u\"ณน\":\n self.trans[char] = u\"n\"\n self.trans['บ'] = 'b'\n self.trans['ป'] = 'p'\n for char in u\"ผพภ\":\n self.trans[char] = u\"ph\"\n for char in u\"ฝฟ\":\n self.trans[char] = u\"f\"\n self.trans['ม'] = 'm'\n self.trans['ร'] = 'r'\n self.trans['ฤ'] = 'rue'\n self.trans['ๅ'] = ':'\n for char in u\"ลฬ\":\n self.trans[char] = u\"l\"\n self.trans['ฦ'] = 'lue'\n self.trans['ว'] = 'w'\n for char in u\"หฮ\":\n self.trans[char] = u\"h\"\n self.trans['อ'] = ''\n self.trans['ร'] = 'ü'\n self.trans['ว'] = 'ua'\n for char in u\"อวโิ\":\n self.trans[char] = u\"o\"\n for char in u\"ะัา\":\n self.trans[char] = u\"a\"\n self.trans['ว'] = 'u'\n self.trans['ำ'] = 'am'\n self.trans['ิ'] = 'i'\n self.trans['ี'] = 'i:'\n self.trans['ึ'] = 'ue'\n self.trans['ื'] = 'ue:'\n self.trans['ุ'] = 'u'\n self.trans['ู'] = 'u:'\n for char in u\"เ็\":\n self.trans[char] = u\"e\"\n self.trans['แ'] = 'ae'\n for char in u\"ใไ\":\n self.trans[char] = u\"ai\"\n for char in u\"่้๊๋็์\":\n self.trans[char] = u\"\"\n self.trans['ฯ'] = '.'\n self.trans['ๆ'] = '(2)'\n\n # Korean (Revised Romanization system within possible, incomplete)\n self.trans['국'] = 'guk'\n self.trans['명'] = 'myeong'\n self.trans['검'] = 'geom'\n self.trans['타'] = 'ta'\n self.trans['분'] = 'bun'\n self.trans['사'] = 'sa'\n self.trans['류'] = 'ryu'\n self.trans['포'] = 'po'\n self.trans['르'] = 'reu'\n self.trans['투'] = 'tu'\n self.trans['갈'] = 'gal'\n self.trans['어'] = 'eo'\n self.trans['노'] = 'no'\n self.trans['웨'] = 'we'\n self.trans['이'] = 'i'\n self.trans['라'] = 'ra'\n self.trans['틴'] = 'tin'\n self.trans['루'] = 'ru'\n self.trans['마'] = 'ma'\n self.trans['니'] = 'ni'\n self.trans['아'] = 'a'\n self.trans['독'] = 'dok'\n self.trans['일'] = 'il'\n self.trans['모'] = 'mo'\n self.trans['크'] = 'keu'\n self.trans['샤'] = 'sya'\n self.trans['영'] = 'yeong'\n self.trans['불'] = 'bul'\n self.trans['가'] = 'ga'\n self.trans['리'] = 'ri'\n self.trans['그'] = 'geu'\n self.trans['지'] = 'ji'\n self.trans['야'] = 'ya'\n self.trans['바'] = 'ba'\n self.trans['슈'] = 'syu'\n self.trans['키'] = 'ki'\n self.trans['프'] = 'peu'\n self.trans['랑'] = 'rang'\n self.trans['스'] = 'seu'\n self.trans['로'] = 'ro'\n self.trans['메'] = 'me'\n self.trans['역'] = 'yeok'\n self.trans['도'] = 'do'\n\n # Kannada\n self.trans[u\"ಅ\"] = u\"a\"\n for char in u\"ಆಾ\":\n self.trans[char] = u\"aa\"\n for char in u\"ಇಿ\":\n self.trans[char] = u\"i\"\n for char in u\"ಈೀ\":\n self.trans[char] = u\"ii\"\n for char in u\"ಉು\":\n self.trans[char] = u\"u\"\n for char in u\"ಊೂ\":\n self.trans[char] = u\"uu\"\n for char in u\"ಋೂ\":\n self.trans[char] = u\"r'\"\n for char in u\"ಎೆ\":\n self.trans[char] = u\"e\"\n for char in u\"ಏೇ\":\n self.trans[char] = u\"ee\"\n for char in u\"ಐೈ\":\n self.trans[char] = u\"ai\"\n for char in u\"ಒೊ\":\n self.trans[char] = u\"o\"\n for char in u\"ಓೋ\":\n self.trans[char] = u\"oo\"\n for char in u\"ಔೌ\":\n self.trans[char] = u\"au\"\n self.trans[u\"ಂ\"] = u\"m'\"\n self.trans[u\"ಃ\"] = u\"h'\"\n self.trans[u\"ಕ\"] = u\"k\"\n self.trans[u\"ಖ\"] = u\"kh\"\n self.trans[u\"ಗ\"] = u\"g\"\n self.trans[u\"ಘ\"] = u\"gh\"\n self.trans[u\"ಙ\"] = u\"ng\"\n self.trans[u\"ಚ\"] = u\"c\"\n self.trans[u\"ಛ\"] = u\"ch\"\n self.trans[u\"ಜ\"] = u\"j\"\n self.trans[u\"ಝ\"] = u\"ny\"\n self.trans[u\"ಟ\"] = u\"tt\"\n self.trans[u\"ಠ\"] = u\"tth\"\n self.trans[u\"ಡ\"] = u\"dd\"\n self.trans[u\"ಢ\"] = u\"ddh\"\n self.trans[u\"ಣ\"] = u\"nn\"\n self.trans[u\"ತ\"] = u\"t\"\n self.trans[u\"ಥ\"] = u\"th\"\n self.trans[u\"ದ\"] = u\"d\"\n self.trans[u\"ಧ\"] = u\"dh\"\n self.trans[u\"ನ\"] = u\"n\"\n self.trans[u\"ಪ\"] = u\"p\"\n self.trans[u\"ಫ\"] = u\"ph\"\n self.trans[u\"ಬ\"] = u\"b\"\n self.trans[u\"ಭ\"] = u\"bh\"\n self.trans[u\"ಮ\"] = u\"m\"\n self.trans[u\"ಯ\"] = u\"y\"\n self.trans[u\"ರ\"] = u\"r\"\n self.trans[u\"ಲ\"] = u\"l\"\n self.trans[u\"ವ\"] = u\"v\"\n self.trans[u\"ಶ\"] = u\"sh\"\n self.trans[u\"ಷ\"] = u\"ss\"\n self.trans[u\"ಸ\"] = u\"s\"\n self.trans[u\"ಹ\"] = u\"h\"\n self.trans[u\"ಳ\"] = u\"ll\"\n self.trans[u\"೦\"] = u\"0\"\n self.trans[u\"೧\"] = u\"1\"\n self.trans[u\"೨\"] = u\"2\"\n self.trans[u\"೩\"] = u\"3\"\n self.trans[u\"೪\"] = u\"4\"\n self.trans[u\"೫\"] = u\"5\"\n self.trans[u\"೬\"] = u\"6\"\n self.trans[u\"೭\"] = u\"7\"\n self.trans[u\"೮\"] = u\"8\"\n self.trans[u\"೯\"] = u\"9\"\n # Telugu\n self.trans['అ'] = 'a'\n for char in u\"ఆా\":\n self.trans[char] = u\"aa\"\n for char in u\"ఇి\":\n self.trans[char] = u\"i\"\n for char in u\"ఈీ\":\n self.trans[char] = u\"ii\"\n for char in u\"ఉు\":\n self.trans[char] = u\"u\"\n for char in u\"ఊూ\":\n self.trans[char] = u\"uu\"\n for char in u\"ఋృ\":\n self.trans[char] = u\"r'\"\n for char in u\"ౠౄ\":\n self.trans[char] = u'r\"'\n self.trans[u\"ఌ\"] = u\"l'\"\n self.trans[u\"ౡ\"] = u'l\"'\n for char in u\"ఎె\":\n self.trans[char] = u\"e\"\n for char in u\"ఏే\":\n self.trans[char] = u\"ee\"\n for char in u\"ఐై\":\n self.trans[char] = u\"ai\"\n for char in u\"ఒొ\":\n self.trans[char] = u\"o\"\n for char in u\"ఓో\":\n self.trans[char] = u\"oo\"\n for char in u\"ఔౌ\":\n self.trans[char] = u\"au\"\n self.trans[u\"ం\"] = u\"'\"\n self.trans[u\"ః\"] = u'\"'\n self.trans[u\"క\"] = u\"k\"\n self.trans[u\"ఖ\"] = u\"kh\"\n self.trans[u\"గ\"] = u\"g\"\n self.trans[u\"ఘ\"] = u\"gh\"\n self.trans[u\"ఙ\"] = u\"ng\"\n self.trans[u\"చ\"] = u\"ts\"\n self.trans[u\"ఛ\"] = u\"tsh\"\n self.trans[u\"జ\"] = u\"j\"\n self.trans[u\"ఝ\"] = u\"jh\"\n self.trans[u\"ఞ\"] = u\"ñ\"\n for char in u\"టత\":\n self.trans[char] = u\"t\"\n for char in u\"ఠథ\":\n self.trans[char] = u\"th\"\n for char in u\"డద\":\n self.trans[char] = u\"d\"\n for char in u\"ఢధ\":\n self.trans[char] = u\"dh\"\n for char in u\"ణన\":\n self.trans[char] = u\"n\"\n self.trans[u\"ప\"] = u\"p\"\n self.trans[u\"ఫ\"] = u\"ph\"\n self.trans[u\"బ\"] = u\"b\"\n self.trans[u\"భ\"] = u\"bh\"\n self.trans[u\"మ\"] = u\"m\"\n self.trans[u\"య\"] = u\"y\"\n for char in u\"రఱ\":\n self.trans[char] = u\"r\"\n for char in u\"లళ\":\n self.trans[char] = u\"l\"\n self.trans[u\"వ\"] = u\"v\"\n self.trans[u\"శ\"] = u\"sh\"\n for char in u\"షస\":\n self.trans[char] = u\"s\"\n self.trans[u\"హ\"] = u\"h\"\n self.trans[u\"్\"] = \"\"\n for char in u\"ంఁ\":\n self.trans[char] = u\"^\"\n self.trans[u\"ః\"] = u\"-\"\n self.trans[u\"౦\"] = u\"0\"\n self.trans[u\"౧\"] = u\"1\"\n self.trans[u\"౨\"] = u\"2\"\n self.trans[u\"౩\"] = u\"3\"\n self.trans[u\"౪\"] = u\"4\"\n self.trans[u\"౫\"] = u\"5\"\n self.trans[u\"౬\"] = u\"6\"\n self.trans[u\"౭\"] = u\"7\"\n self.trans[u\"౮\"] = u\"8\"\n self.trans[u\"౯\"] = u\"9\"\n self.trans[u\"౹\"] = u\"1/4\"\n self.trans[u\"౺\"] = u\"1/2\"\n self.trans[u\"౻\"] = u\"3/4\"\n self.trans[u\"౼\"] = u\"1/16\"\n self.trans[u\"౽\"] = u\"1/8\"\n self.trans[u\"౾\"] = u\"3/16\"\n # Lao - note: pronounciation in initial position is used;\n # different pronounciation in final position is ignored\n self.trans[u\"ກ\"] = \"k\"\n for char in u\"ຂຄ\":\n self.trans[char] = \"kh\"\n self.trans[u\"ງ\"] = \"ng\"\n self.trans[u\"ຈ\"] = \"ch\"\n for char in u\"ສຊ\":\n self.trans[char] = \"s\"\n self.trans[u\"ຍ\"] = \"ny\"\n self.trans[u\"ດ\"] = \"d\"\n self.trans[u\"ຕ\"] = \"t\"\n for char in u\"ຖທ\":\n self.trans[char] = \"th\"\n self.trans[u\"ນ\"] = \"n\"\n self.trans[u\"ບ\"] = \"b\"\n self.trans[u\"ປ\"] = \"p\"\n for char in u\"ຜພ\":\n self.trans[char] = \"ph\"\n for char in u\"ຝຟ\":\n self.trans[char] = \"f\"\n for char in u\"ມໝ\":\n self.trans[char] = \"m\"\n self.trans[u\"ຢ\"] = \"y\"\n for char in u\"ຣຼ\":\n self.trans[char] = \"r\"\n for char in u\"ລຼ\":\n self.trans[char] = \"l\"\n self.trans[u\"ວ\"] = \"v\"\n self.trans['ຮ'] = 'h'\n self.trans[u\"ອ\"] = \"'\"\n for char in u\"ະັ\":\n self.trans[char] = \"a\"\n self.trans[u\"ິ\"] = \"i\"\n self.trans[u\"ຶ\"] = \"ue\"\n self.trans[u\"ຸ\"] = \"u\"\n self.trans[u\"ເ\"] = u\"é\"\n self.trans[u\"ແ\"] = u\"è\"\n for char in u\"ໂົາໍ\":\n self.trans[char] = \"o\"\n self.trans[u\"ຽ\"] = \"ia\"\n self.trans[u\"ເຶ\"] = \"uea\"\n self.trans[u\"ຍ\"] = \"i\"\n for char in u\"ໄໃ\":\n self.trans[char] = \"ai\"\n self.trans[u\"ຳ\"] = \"am\"\n self.trans[u\"າ\"] = \"aa\"\n self.trans[u\"ີ\"] = \"ii\"\n self.trans[u\"ື\"] = \"yy\"\n self.trans[u\"ູ\"] = \"uu\"\n self.trans[u\"ເ\"] = \"e\"\n self.trans[u\"ແ\"] = \"ei\"\n self.trans[u\"໐\"] = \"0\"\n self.trans[u\"໑\"] = \"1\"\n self.trans[u\"໒\"] = \"2\"\n self.trans[u\"໓\"] = \"3\"\n self.trans[u\"໔\"] = \"4\"\n self.trans[u\"໕\"] = \"5\"\n self.trans[u\"໖\"] = \"6\"\n self.trans[u\"໗\"] = \"7\"\n self.trans[u\"໘\"] = \"8\"\n self.trans[u\"໙\"] = \"9\"\n # Chinese -- note: incomplete\n for char in u\"埃挨哎唉哀皑癌蔼矮艾碍爱隘\":\n self.trans[char] = u\"ai\"\n for char in u\"鞍氨安俺按暗岸胺案\":\n self.trans[char] = u\"an\"\n for char in u\"肮昂盎\":\n self.trans[char] = u\"ang\"\n for char in u\"凹敖熬翱袄傲奥懊澳\":\n self.trans[char] = u\"ao\"\n for char in u\"芭捌扒叭吧笆八疤巴拔跋靶把耙坝霸罢爸\":\n self.trans[char] = u\"ba\"\n for char in u\"白柏百摆佰败拜稗\":\n self.trans[char] = u\"bai\"\n for char in u\"斑班搬扳般颁板版扮拌伴瓣半办绊\":\n self.trans[char] = u\"ban\"\n for char in u\"邦帮梆榜膀绑棒磅蚌镑傍谤\":\n self.trans[char] = u\"bang\"\n for char in u\"苞胞包褒剥薄雹保堡饱宝抱报暴豹鲍爆\":\n self.trans[char] = u\"bao\"\n for char in u\"杯碑悲卑北辈背贝钡倍狈备惫焙被\":\n self.trans[char] = u\"bei\"\n for char in u\"奔苯本笨\":\n self.trans[char] = u\"ben\"\n for char in u\"崩绷甭泵蹦迸\":\n self.trans[char] = u\"beng\"\n for char in u\"逼鼻比鄙笔彼碧蓖蔽毕毙毖币庇痹闭敝弊必辟壁臂避陛\":\n self.trans[char] = u\"bi\"\n for char in u\"鞭边编贬扁便变卞辨辩辫遍\":\n self.trans[char] = u\"bian\"\n for char in u\"标彪膘表\":\n self.trans[char] = u\"biao\"\n for char in u\"鳖憋别瘪\":\n self.trans[char] = u\"bie\"\n for char in u\"彬斌濒滨宾摈\":\n self.trans[char] = u\"bin\"\n for char in u\"兵冰柄丙秉饼炳病并\":\n self.trans[char] = u\"bing\"\n for char in u\"玻菠播拨钵波博勃搏铂箔伯帛舶脖膊渤泊驳捕卜亳\":\n self.trans[char] = u\"bo\"\n for char in u\"哺补埠不布步簿部怖\":\n self.trans[char] = u\"bu\"\n for char in u\"猜裁材才财睬踩采彩菜蔡\":\n self.trans[char] = u\"cai\"\n for char in u\"餐参蚕残惭惨灿\":\n self.trans[char] = u\"can\"\n for char in u\"苍舱仓沧藏\":\n self.trans[char] = u\"cang\"\n for char in u\"操糙槽曹草\":\n self.trans[char] = u\"cao\"\n for char in u\"厕策侧册测\":\n self.trans[char] = u\"ce\"\n for char in u\"层蹭\":\n self.trans[char] = u\"ceng\"\n for char in u\"插叉茬茶查碴搽察岔差诧\":\n self.trans[char] = u\"cha\"\n for char in u\"拆柴豺\":\n self.trans[char] = u\"chai\"\n for char in u\"搀掺蝉馋谗缠铲产阐颤\":\n self.trans[char] = u\"chan\"\n for char in u\"昌猖场尝常长偿肠厂敞畅唱倡\":\n self.trans[char] = u\"chang\"\n for char in u\"超抄钞朝嘲潮巢吵炒\":\n self.trans[char] = u\"chao\"\n for char in u\"车扯撤掣彻澈\":\n self.trans[char] = u\"che\"\n for char in u\"郴臣辰尘晨忱沉陈趁衬\":\n self.trans[char] = u\"chen\"\n for char in u\"撑称城橙成呈乘程惩澄诚承逞骋秤\":\n self.trans[char] = u\"cheng\"\n for char in u\"吃痴持匙池迟弛驰耻齿侈尺赤翅斥炽\":\n self.trans[char] = u\"chi\"\n for char in u\"充冲虫崇宠\":\n self.trans[char] = u\"chong\"\n for char in u\"抽酬畴踌稠愁筹仇绸瞅丑臭\":\n self.trans[char] = u\"chou\"\n for char in u\"初出橱厨躇锄雏滁除楚储矗搐触处\":\n self.trans[char] = u\"chu\"\n self.trans['揣'] = 'chuai'\n for char in u\"川穿椽传船喘串\":\n self.trans[char] = u\"chuan\"\n for char in u\"疮窗幢床闯创\":\n self.trans[char] = u\"chuang\"\n for char in u\"吹炊捶锤垂\":\n self.trans[char] = u\"chui\"\n for char in u\"春椿醇唇淳纯蠢\":\n self.trans[char] = u\"chun\"\n for char in u\"戳绰\":\n self.trans[char] = u\"chuo\"\n for char in u\"疵茨磁雌辞慈瓷词此刺赐次\":\n self.trans[char] = u\"ci\"\n for char in u\"聪葱囱匆从丛\":\n self.trans[char] = u\"cong\"\n self.trans['凑'] = 'cou'\n for char in u\"粗醋簇促\":\n self.trans[char] = u\"cu\"\n for char in u\"蹿篡窜\":\n self.trans[char] = u\"cuan\"\n for char in u\"摧崔催脆瘁粹淬翠\":\n self.trans[char] = u\"cui\"\n for char in u\"村存寸\":\n self.trans[char] = u\"cun\"\n for char in u\"磋撮搓措挫错\":\n self.trans[char] = u\"cuo\"\n for char in u\"搭达答瘩打大\":\n self.trans[char] = u\"da\"\n for char in u\"呆歹傣戴带殆代贷袋待逮怠\":\n self.trans[char] = u\"dai\"\n for char in u\"耽担丹单郸掸胆旦氮但惮淡诞弹蛋儋\":\n self.trans[char] = u\"dan\"\n for char in u\"当挡党荡档\":\n self.trans[char] = u\"dang\"\n for char in u\"刀捣蹈倒岛祷导到稻悼道盗\":\n self.trans[char] = u\"dao\"\n for char in u\"德得的\":\n self.trans[char] = u\"de\"\n for char in u\"蹬灯登等瞪凳邓\":\n self.trans[char] = u\"deng\"\n for char in u\"堤低滴迪敌笛狄涤翟嫡抵底地蒂第帝弟递缔\":\n self.trans[char] = u\"di\"\n for char in u\"颠掂滇碘点典靛垫电佃甸店惦奠淀殿\":\n self.trans[char] = u\"dian\"\n for char in u\"碉叼雕凋刁掉吊钓调\":\n self.trans[char] = u\"diao\"\n for char in u\"跌爹碟蝶迭谍叠\":\n self.trans[char] = u\"die\"\n for char in u\"丁盯叮钉顶鼎锭定订\":\n self.trans[char] = u\"ding\"\n self.trans['丢'] = 'diu'\n for char in u\"东冬董懂动栋侗恫冻洞\":\n self.trans[char] = u\"dong\"\n for char in u\"兜抖斗陡豆逗痘\":\n self.trans[char] = u\"dou\"\n for char in u\"都督毒犊独读堵睹赌杜镀肚度渡妒\":\n self.trans[char] = u\"du\"\n for char in u\"端短锻段断缎\":\n self.trans[char] = u\"duan\"\n for char in u\"堆兑队对\":\n self.trans[char] = u\"dui\"\n for char in u\"墩吨蹲敦顿囤钝盾遁\":\n self.trans[char] = u\"dun\"\n for char in u\"掇哆多夺垛躲朵跺舵剁惰堕\":\n self.trans[char] = u\"duo\"\n for char in u\"蛾峨鹅俄额讹娥恶厄扼遏鄂饿\":\n self.trans[char] = u\"e\"\n for char in u\"恩嗯\":\n self.trans[char] = u\"en\"\n for char in u\"而儿耳尔饵洱二贰\":\n self.trans[char] = u\"er\"\n for char in u\"发罚筏伐乏阀法珐\":\n self.trans[char] = u\"fa\"\n for char in u\"藩帆番翻樊矾钒繁凡烦反返范贩犯饭泛\":\n self.trans[char] = u\"fan\"\n for char in u\"坊芳方肪房防妨仿访纺放\":\n self.trans[char] = u\"fang\"\n for char in u\"菲非啡飞肥匪诽吠肺废沸费\":\n self.trans[char] = u\"fei\"\n for char in u\"芬酚吩氛分纷坟焚汾粉奋份忿愤粪\":\n self.trans[char] = u\"fen\"\n for char in u\"丰封枫蜂峰锋风疯烽逢冯缝讽奉凤\":\n self.trans[char] = u\"feng\"\n self.trans['佛'] = 'fo'\n self.trans['否'] = 'fou'\n for char in u\"夫敷肤孵扶拂辐幅氟符伏俘服浮涪福袱弗甫抚辅俯釜斧脯腑府腐赴副覆赋复傅付阜父腹负富讣附妇缚咐\":\n self.trans[char] = u\"fu\"\n for char in u\"噶嘎\":\n self.trans[char] = u\"ga\"\n for char in u\"该改概钙盖溉\":\n self.trans[char] = u\"gai\"\n for char in u\"干甘杆柑竿肝赶感秆敢赣\":\n self.trans[char] = u\"gan\"\n for char in u\"冈刚钢缸肛纲岗港杠\":\n self.trans[char] = u\"gang\"\n for char in u\"篙皋高膏羔糕搞镐稿告\":\n self.trans[char] = u\"gao\"\n for char in u\"哥歌搁戈鸽胳疙割革葛格蛤阁隔铬个各\":\n self.trans[char] = u\"ge\"\n self.trans['给'] = 'gei'\n for char in u\"根跟\":\n self.trans[char] = u\"gen\"\n for char in u\"耕更庚羹埂耿梗\":\n self.trans[char] = u\"geng\"\n for char in u\"工攻功恭龚供躬公宫弓巩汞拱贡共\":\n self.trans[char] = u\"gong\"\n for char in u\"钩勾沟苟狗垢构购够\":\n self.trans[char] = u\"gou\"\n for char in u\"辜菇咕箍估沽孤姑鼓古蛊骨谷股故顾固雇\":\n self.trans[char] = u\"gu\"\n for char in u\"刮瓜剐寡挂褂\":\n self.trans[char] = u\"gua\"\n for char in u\"乖拐怪\":\n self.trans[char] = u\"guai\"\n for char in u\"棺关官冠观管馆罐惯灌贯\":\n self.trans[char] = u\"guan\"\n for char in u\"光广逛\":\n self.trans[char] = u\"guang\"\n for char in u\"瑰规圭硅归龟闺轨鬼诡癸桂柜跪贵刽\":\n self.trans[char] = u\"gui\"\n for char in u\"辊滚棍\":\n self.trans[char] = u\"gun\"\n for char in u\"锅郭国果裹过\":\n self.trans[char] = u\"guo\"\n self.trans['哈'] = 'ha'\n for char in u\"骸孩海氦亥害骇\":\n self.trans[char] = u\"hai\"\n for char in u\"酣憨邯韩含涵寒函喊罕翰撼捍旱憾悍焊汗汉\":\n self.trans[char] = u\"han\"\n for char in u\"夯杭航\":\n self.trans[char] = u\"hang\"\n for char in u\"壕嚎豪毫郝好耗号浩\":\n self.trans[char] = u\"hao\"\n for char in u\"呵喝荷菏核禾和何合盒貉阂河涸赫褐鹤贺\":\n self.trans[char] = u\"he\"\n for char in u\"嘿黑\":\n self.trans[char] = u\"hei\"\n for char in u\"痕很狠恨\":\n self.trans[char] = u\"hen\"\n for char in u\"哼亨横衡恒\":\n self.trans[char] = u\"heng\"\n for char in u\"轰哄烘虹鸿洪宏弘红\":\n self.trans[char] = u\"hong\"\n for char in u\"喉侯猴吼厚候后\":\n self.trans[char] = u\"hou\"\n for char in u\"呼乎忽瑚壶葫胡蝴狐糊湖弧虎唬护互沪户\":\n self.trans[char] = u\"hu\"\n for char in u\"花哗华猾滑画划化话\":\n self.trans[char] = u\"hua\"\n for char in u\"槐徊怀淮坏\":\n self.trans[char] = u\"huai\"\n for char in u\"欢环桓还缓换患唤痪豢焕涣宦幻\":\n self.trans[char] = u\"huan\"\n for char in u\"荒慌黄磺蝗簧皇凰惶煌晃幌恍谎\":\n self.trans[char] = u\"huang\"\n for char in u\"灰挥辉徽恢蛔回毁悔慧卉惠晦贿秽会烩汇讳诲绘\":\n self.trans[char] = u\"hui\"\n for char in u\"荤昏婚魂浑混\":\n self.trans[char] = u\"hun\"\n for char in u\"豁活伙火获或惑霍货祸\":\n self.trans[char] = u\"huo\"\n for char in u\"击圾基机畸稽积箕肌饥迹激讥鸡姬绩缉吉极棘辑籍集及急疾汲即嫉级挤几脊己蓟技冀季伎祭剂悸济寄寂计记既忌际妓继纪\":\n self.trans[char] = u\"ji\"\n for char in u\"嘉枷夹佳家加荚颊贾甲钾假稼价架驾嫁\":\n self.trans[char] = u\"jia\"\n for char in u\"歼监坚尖笺间煎兼肩艰奸缄茧检柬碱硷拣捡简俭剪减荐槛鉴践贱见键箭件健舰剑饯渐溅涧建\":\n self.trans[char] = u\"jian\"\n for char in u\"僵姜将浆江疆蒋桨奖讲匠酱降\":\n self.trans[char] = u\"jiang\"\n for char in u\"蕉椒礁焦胶交郊浇骄娇嚼搅铰矫侥脚狡角饺缴绞剿教酵轿较叫窖\":\n self.trans[char] = u\"jiao\"\n for char in u\"揭接皆秸街阶截劫节桔杰捷睫竭洁结解姐戒藉芥界借介疥诫届\":\n self.trans[char] = u\"jie\"\n for char in u\"巾筋斤金今津襟紧锦仅谨进靳晋禁近烬浸尽劲\":\n self.trans[char] = u\"jin\"\n for char in u\"荆兢茎睛晶鲸京惊精粳经井警景颈静境敬镜径痉靖竟竞净\":\n self.trans[char] = u\"jing\"\n for char in u\"囧炯窘\":\n self.trans[char] = u\"jiong\"\n for char in u\"揪究纠玖韭久灸九酒厩救旧臼舅咎就疚\":\n self.trans[char] = u\"jiu\"\n for char in u\"鞠拘狙疽居驹菊局咀矩举沮聚拒据巨具距踞锯俱句惧炬剧\":\n self.trans[char] = u\"ju\"\n for char in u\"捐鹃娟倦眷卷绢\":\n self.trans[char] = u\"juan\"\n for char in u\"撅攫抉掘倔爵觉决诀绝\":\n self.trans[char] = u\"jue\"\n for char in u\"均菌钧军君峻俊竣浚郡骏\":\n self.trans[char] = u\"jun\"\n for char in u\"喀咖卡咯\":\n self.trans[char] = u\"ka\"\n for char in u\"开揩楷凯慨\":\n self.trans[char] = u\"kai\"\n for char in u\"刊堪勘坎砍看\":\n self.trans[char] = u\"kan\"\n for char in u\"康慷糠扛抗亢炕\":\n self.trans[char] = u\"kang\"\n for char in u\"考拷烤靠\":\n self.trans[char] = u\"kao\"\n for char in u\"坷苛柯棵磕颗科壳咳可渴克刻客课\":\n self.trans[char] = u\"ke\"\n for char in u\"肯啃垦恳\":\n self.trans[char] = u\"ken\"\n for char in u\"坑吭\":\n self.trans[char] = u\"keng\"\n for char in u\"空恐孔控\":\n self.trans[char] = u\"kong\"\n for char in u\"抠口扣寇\":\n self.trans[char] = u\"kou\"\n for char in u\"枯哭窟苦酷库裤\":\n self.trans[char] = u\"ku\"\n for char in u\"夸垮挎跨胯\":\n self.trans[char] = u\"kua\"\n for char in u\"块筷侩快\":\n self.trans[char] = u\"kuai\"\n for char in u\"宽款\":\n self.trans[char] = u\"kuan\"\n for char in u\"匡筐狂框矿眶旷况\":\n self.trans[char] = u\"kuang\"\n for char in u\"亏盔岿窥葵奎魁傀馈愧溃\":\n self.trans[char] = u\"kui\"\n for char in u\"坤昆捆困\":\n self.trans[char] = u\"kun\"\n for char in u\"括扩廓阔\":\n self.trans[char] = u\"kuo\"\n for char in u\"垃拉喇蜡腊辣啦\":\n self.trans[char] = u\"la\"\n for char in u\"莱来赖\":\n self.trans[char] = u\"lai\"\n for char in u\"蓝婪栏拦篮阑兰澜谰揽览懒缆烂滥\":\n self.trans[char] = u\"lan\"\n for char in u\"琅榔狼廊郎朗浪\":\n self.trans[char] = u\"lang\"\n for char in u\"捞劳牢老佬姥酪烙涝\":\n self.trans[char] = u\"lao\"\n for char in u\"勒乐\":\n self.trans[char] = u\"le\"\n for char in u\"雷镭蕾磊累儡垒擂肋类泪\":\n self.trans[char] = u\"lei\"\n for char in u\"棱楞冷\":\n self.trans[char] = u\"leng\"\n for char in u\"厘梨犁黎篱狸离漓理李里鲤礼莉荔吏栗丽厉励砾历利傈例俐痢立粒沥隶力璃哩\":\n self.trans[char] = u\"li\"\n self.trans['俩'] = 'lia'\n for char in u\"联莲连镰廉怜涟帘敛脸链恋炼练\":\n self.trans[char] = u\"lian\"\n for char in u\"粮凉梁粱良两辆量晾亮谅\":\n self.trans[char] = u\"liang\"\n for char in u\"撩聊僚疗燎寥辽潦了撂镣廖料\":\n self.trans[char] = u\"liao\"\n for char in u\"列裂烈劣猎\":\n self.trans[char] = u\"lie\"\n for char in u\"琳林磷霖临邻鳞淋凛赁吝拎\":\n self.trans[char] = u\"lin\"\n for char in u\"玲菱零龄铃伶羚凌灵陵岭领另令\":\n self.trans[char] = u\"ling\"\n for char in u\"溜琉榴硫馏留刘瘤流柳六\":\n self.trans[char] = u\"liu\"\n for char in u\"龙聋咙笼窿隆垄拢陇\":\n self.trans[char] = u\"long\"\n for char in u\"楼娄搂篓漏陋\":\n self.trans[char] = u\"lou\"\n for char in u\"芦卢颅庐炉掳卤虏鲁麓碌露路赂鹿潞禄录陆戮泸\":\n self.trans[char] = u\"lu\"\n for char in u\"峦挛孪滦卵乱\":\n self.trans[char] = u\"luan\"\n for char in u\"掠略\":\n self.trans[char] = u\"lue\"\n for char in u\"抡轮伦仑沦纶论\":\n self.trans[char] = u\"lun\"\n for char in u\"萝螺罗逻锣箩骡裸落洛骆络漯\":\n self.trans[char] = u\"luo\"\n for char in u\"驴吕铝侣旅履屡缕虑氯律率滤绿\":\n self.trans[char] = u\"lv\"\n for char in u\"妈麻玛码蚂马骂嘛吗\":\n self.trans[char] = u\"ma\"\n for char in u\"埋买麦卖迈脉\":\n self.trans[char] = u\"mai\"\n for char in u\"瞒馒蛮满蔓曼慢漫谩\":\n self.trans[char] = u\"man\"\n for char in u\"芒茫盲氓忙莽\":\n self.trans[char] = u\"mang\"\n for char in u\"猫茅锚毛矛铆卯茂冒帽貌贸\":\n self.trans[char] = u\"mao\"\n self.trans['么'] = 'me'\n for char in u\"玫枚梅酶霉煤没眉媒镁每美昧寐妹媚\":\n self.trans[char] = u\"mei\"\n for char in u\"门闷们\":\n self.trans[char] = u\"men\"\n for char in u\"萌蒙檬盟锰猛梦孟\":\n self.trans[char] = u\"meng\"\n for char in u\"眯醚靡糜迷谜弥米秘觅泌蜜密幂\":\n self.trans[char] = u\"mi\"\n for char in u\"棉眠绵冕免勉娩缅面\":\n self.trans[char] = u\"mian\"\n for char in u\"苗描瞄藐秒渺庙妙\":\n self.trans[char] = u\"miao\"\n for char in u\"蔑灭\":\n self.trans[char] = u\"mie\"\n for char in u\"民抿皿敏悯闽\":\n self.trans[char] = u\"min\"\n for char in u\"明螟鸣铭名命\":\n self.trans[char] = u\"ming\"\n self.trans['谬'] = 'miu'\n for char in u\"摸摹蘑模膜磨摩魔抹末莫墨默沫漠寞陌\":\n self.trans[char] = u\"mo\"\n for char in u\"谋牟某\":\n self.trans[char] = u\"mou\"\n for char in u\"拇牡亩姆母墓暮幕募慕木目睦牧穆\":\n self.trans[char] = u\"mu\"\n for char in u\"拿哪呐钠那娜纳\":\n self.trans[char] = u\"na\"\n for char in u\"氖乃奶耐奈\":\n self.trans[char] = u\"nai\"\n for char in u\"南男难\":\n self.trans[char] = u\"nan\"\n self.trans['囊'] = 'nang'\n for char in u\"挠脑恼闹淖\":\n self.trans[char] = u\"nao\"\n self.trans['呢'] = 'ne'\n for char in u\"馁内\":\n self.trans[char] = u\"nei\"\n self.trans['嫩'] = 'nen'\n self.trans['能'] = 'neng'\n for char in u\"妮霓倪泥尼拟你匿腻逆溺\":\n self.trans[char] = u\"ni\"\n for char in u\"蔫拈年碾撵捻念\":\n self.trans[char] = u\"nian\"\n for char in u\"娘酿\":\n self.trans[char] = u\"niang\"\n for char in u\"鸟尿\":\n self.trans[char] = u\"niao\"\n for char in u\"捏聂孽啮镊镍涅\":\n self.trans[char] = u\"nie\"\n self.trans['您'] = 'nin'\n for char in u\"柠狞凝宁拧泞\":\n self.trans[char] = u\"ning\"\n for char in u\"牛扭钮纽\":\n self.trans[char] = u\"niu\"\n for char in u\"脓浓农弄\":\n self.trans[char] = u\"nong\"\n for char in u\"奴努怒\":\n self.trans[char] = u\"nu\"\n self.trans['暖'] = 'nuan'\n for char in u\"虐疟\":\n self.trans[char] = u\"nue\"\n for char in u\"挪懦糯诺\":\n self.trans[char] = u\"nuo\"\n self.trans['女'] = 'nv'\n self.trans['哦'] = 'o'\n for char in u\"欧鸥殴藕呕偶沤\":\n self.trans[char] = u\"ou\"\n for char in u\"啪趴爬帕怕琶\":\n self.trans[char] = u\"pa\"\n for char in u\"拍排牌徘湃派\":\n self.trans[char] = u\"pai\"\n for char in u\"攀潘盘磐盼畔判叛\":\n self.trans[char] = u\"pan\"\n for char in u\"乓庞旁耪胖\":\n self.trans[char] = u\"pang\"\n for char in u\"抛咆刨炮袍跑泡\":\n self.trans[char] = u\"pao\"\n for char in u\"呸胚培裴赔陪配佩沛\":\n self.trans[char] = u\"pei\"\n for char in u\"喷盆\":\n self.trans[char] = u\"pen\"\n for char in u\"砰抨烹澎彭蓬棚硼篷膨朋鹏捧碰\":\n self.trans[char] = u\"peng\"\n for char in u\"坯砒霹批披劈琵毗啤脾疲皮匹痞僻屁譬\":\n self.trans[char] = u\"pi\"\n for char in u\"篇偏片骗\":\n self.trans[char] = u\"pian\"\n for char in u\"飘漂瓢票\":\n self.trans[char] = u\"piao\"\n for char in u\"撇瞥\":\n self.trans[char] = u\"pie\"\n for char in u\"拼频贫品聘\":\n self.trans[char] = u\"pin\"\n for char in u\"乒坪苹萍平凭瓶评屏\":\n self.trans[char] = u\"ping\"\n for char in u\"坡泼颇婆破魄迫粕剖\":\n self.trans[char] = u\"po\"\n for char in u\"扑铺仆莆葡菩蒲埔朴圃普浦谱曝瀑濮\":\n self.trans[char] = u\"pu\"\n for char in u\"期欺栖戚妻七凄漆柒沏其棋奇歧畦崎脐齐旗祈祁骑起岂乞企启契砌器气迄弃汽泣讫\":\n self.trans[char] = u\"qi\"\n for char in u\"掐恰洽\":\n self.trans[char] = u\"qia\"\n for char in u\"牵扦钎铅千迁签仟谦乾黔钱钳前潜遣浅谴堑嵌欠歉\":\n self.trans[char] = u\"qian\"\n for char in u\"枪呛腔羌墙蔷强抢\":\n self.trans[char] = u\"qiang\"\n for char in u\"橇锹敲悄桥瞧乔侨巧鞘撬翘峭俏窍\":\n self.trans[char] = u\"qiao\"\n for char in u\"切茄且怯窃\":\n self.trans[char] = u\"qie\"\n for char in u\"钦侵亲秦琴勤芹擒禽寝沁\":\n self.trans[char] = u\"qin\"\n for char in u\"青轻氢倾卿清擎晴氰情顷请庆\":\n self.trans[char] = u\"qing\"\n for char in u\"琼穷\":\n self.trans[char] = u\"qiong\"\n for char in u\"秋丘邱球求囚酋泅\":\n self.trans[char] = u\"qiu\"\n for char in u\"趋区蛆曲躯屈驱渠取娶龋趣去\":\n self.trans[char] = u\"qu\"\n for char in u\"圈颧权醛泉全痊拳犬券劝\":\n self.trans[char] = u\"quan\"\n for char in u\"缺炔瘸却鹊榷确雀\":\n self.trans[char] = u\"que\"\n for char in u\"裙群\":\n self.trans[char] = u\"qun\"\n for char in u\"然燃冉染\":\n self.trans[char] = u\"ran\"\n for char in u\"瓤壤攘嚷让\":\n self.trans[char] = u\"rang\"\n for char in u\"饶扰绕\":\n self.trans[char] = u\"rao\"\n for char in u\"惹热\":\n self.trans[char] = u\"re\"\n for char in u\"壬仁人忍韧任认刃妊纫\":\n self.trans[char] = u\"ren\"\n for char in u\"扔仍\":\n self.trans[char] = u\"reng\"\n self.trans['日'] = 'ri'\n for char in u\"戎茸蓉荣融熔溶容绒冗\":\n self.trans[char] = u\"rong\"\n for char in u\"揉柔肉\":\n self.trans[char] = u\"rou\"\n for char in u\"茹蠕儒孺如辱乳汝入褥\":\n self.trans[char] = u\"ru\"\n for char in u\"软阮\":\n self.trans[char] = u\"ruan\"\n for char in u\"蕊瑞锐\":\n self.trans[char] = u\"rui\"\n for char in u\"闰润\":\n self.trans[char] = u\"run\"\n for char in u\"若弱\":\n self.trans[char] = u\"ruo\"\n for char in u\"撒洒萨\":\n self.trans[char] = u\"sa\"\n for char in u\"腮鳃塞赛\":\n self.trans[char] = u\"sai\"\n for char in u\"三叁伞散\":\n self.trans[char] = u\"san\"\n for char in u\"桑嗓丧\":\n self.trans[char] = u\"sang\"\n for char in u\"搔骚扫嫂\":\n self.trans[char] = u\"sao\"\n for char in u\"瑟色涩\":\n self.trans[char] = u\"se\"\n self.trans['森'] = 'sen'\n self.trans['僧'] = 'seng'\n for char in u\"莎砂杀刹沙纱傻啥煞\":\n self.trans[char] = u\"sha\"\n for char in u\"筛晒\":\n self.trans[char] = u\"shai\"\n for char in u\"珊苫杉山删煽衫闪陕擅赡膳善汕扇缮\":\n self.trans[char] = u\"shan\"\n for char in u\"墒伤商赏晌上尚裳\":\n self.trans[char] = u\"shang\"\n for char in u\"梢捎稍烧芍勺韶少哨邵绍\":\n self.trans[char] = u\"shao\"\n for char in u\"奢赊蛇舌舍赦摄射慑涉社设\":\n self.trans[char] = u\"she\"\n for char in u\"砷申呻伸身深娠绅神沈审婶甚肾慎渗\":\n self.trans[char] = u\"shen\"\n for char in u\"声生甥牲升绳省盛剩胜圣\":\n self.trans[char] = u\"sheng\"\n for char in u\"师失狮施湿诗尸虱十石拾时什食蚀实识史矢使屎驶始式示士世柿事拭誓逝势是嗜噬适仕侍释饰氏市恃室视试\":\n self.trans[char] = u\"shi\"\n for char in u\"收手首守寿授售受瘦兽\":\n self.trans[char] = u\"shou\"\n for char in u\"蔬枢梳殊抒输叔舒淑疏书赎孰熟薯暑曙署蜀黍鼠属术述树束戍竖墅庶数漱恕\":\n self.trans[char] = u\"shu\"\n for char in u\"刷耍\":\n self.trans[char] = u\"shua\"\n for char in u\"摔衰甩帅\":\n self.trans[char] = u\"shuai\"\n for char in u\"栓拴\":\n self.trans[char] = u\"shuan\"\n for char in u\"霜双爽\":\n self.trans[char] = u\"shuang\"\n for char in u\"谁水睡税\":\n self.trans[char] = u\"shui\"\n for char in u\"吮瞬顺舜\":\n self.trans[char] = u\"shun\"\n for char in u\"说硕朔烁\":\n self.trans[char] = u\"shuo\"\n for char in u\"斯撕嘶思私司丝死肆寺嗣四伺似饲巳\":\n self.trans[char] = u\"si\"\n for char in u\"松耸怂颂送宋讼诵\":\n self.trans[char] = u\"song\"\n for char in u\"搜艘擞\":\n self.trans[char] = u\"sou\"\n for char in u\"嗽苏酥俗素速粟僳塑溯宿诉肃\":\n self.trans[char] = u\"su\"\n for char in u\"酸蒜算\":\n self.trans[char] = u\"suan\"\n for char in u\"虽隋随绥髓碎岁穗遂隧祟\":\n self.trans[char] = u\"sui\"\n for char in u\"孙损笋\":\n self.trans[char] = u\"sun\"\n for char in u\"蓑梭唆缩琐索锁所\":\n self.trans[char] = u\"suo\"\n for char in u\"塌他它她塔獭挞蹋踏\":\n self.trans[char] = u\"ta\"\n for char in u\"胎苔抬台泰酞太态汰\":\n self.trans[char] = u\"tai\"\n for char in u\"坍摊贪瘫滩坛檀痰潭谭谈坦毯袒碳探叹炭\":\n self.trans[char] = u\"tan\"\n for char in u\"汤塘搪堂棠膛唐糖倘躺淌趟烫\":\n self.trans[char] = u\"tang\"\n for char in u\"掏涛滔绦萄桃逃淘陶讨套\":\n self.trans[char] = u\"tao\"\n self.trans['特'] = 'te'\n for char in u\"藤腾疼誊\":\n self.trans[char] = u\"teng\"\n for char in u\"梯剔踢锑提题蹄啼体替嚏惕涕剃屉\":\n self.trans[char] = u\"ti\"\n for char in u\"兲天添填田甜恬舔腆\":\n self.trans[char] = u\"tian\"\n for char in u\"挑条迢眺跳\":\n self.trans[char] = u\"tiao\"\n for char in u\"贴铁帖\":\n self.trans[char] = u\"tie\"\n for char in u\"厅听烃汀廷停亭庭挺艇\":\n self.trans[char] = u\"ting\"\n for char in u\"通桐酮瞳同铜彤童桶捅筒统痛\":\n self.trans[char] = u\"tong\"\n for char in u\"偷投头透\":\n self.trans[char] = u\"tou\"\n for char in u\"凸秃突图徒途涂屠土吐兔\":\n self.trans[char] = u\"tu\"\n for char in u\"湍团\":\n self.trans[char] = u\"tuan\"\n for char in u\"推颓腿蜕褪退\":\n self.trans[char] = u\"tui\"\n for char in u\"吞屯臀\":\n self.trans[char] = u\"tun\"\n for char in u\"拖托脱鸵陀驮驼椭妥拓唾\":\n self.trans[char] = u\"tuo\"\n for char in u\"挖哇蛙洼娃瓦袜\":\n self.trans[char] = u\"wa\"\n for char in u\"歪外\":\n self.trans[char] = u\"wai\"\n for char in u\"豌弯湾玩顽丸烷完碗挽晚皖惋宛婉万腕莞\":\n self.trans[char] = u\"wan\"\n for char in u\"汪王亡枉网往旺望忘妄\":\n self.trans[char] = u\"wang\"\n for char in u\"威巍微危韦违桅围唯惟为潍维苇萎委伟伪尾纬未蔚味畏胃喂魏位渭谓尉慰卫\":\n self.trans[char] = u\"wei\"\n for char in u\"瘟温蚊文闻纹吻稳紊问\":\n self.trans[char] = u\"wen\"\n for char in u\"嗡翁瓮\":\n self.trans[char] = u\"weng\"\n for char in u\"挝蜗涡窝我斡卧握沃\":\n self.trans[char] = u\"wo\"\n for char in u\"巫呜钨乌污诬屋无芜梧吾吴毋武五捂午舞伍侮坞戊雾晤物勿务悟误\":\n self.trans[char] = u\"wu\"\n for char in u\"昔熙析西硒矽晰嘻吸锡牺稀息希悉膝夕惜熄烯溪汐犀檄袭席习媳喜铣洗系隙戏细\":\n self.trans[char] = u\"xi\"\n for char in u\"瞎虾匣霞辖暇峡侠狭下厦夏吓\":\n self.trans[char] = u\"xia\"\n for char in u\"掀锨先仙鲜纤咸贤衔舷闲涎弦嫌显险现献县腺馅羡宪陷限线\":\n self.trans[char] = u\"xian\"\n for char in u\"相厢镶香箱襄湘乡翔祥详想响享项巷橡像向象\":\n self.trans[char] = u\"xiang\"\n for char in u\"萧硝霄削哮嚣销消宵淆晓小孝校肖啸笑效\":\n self.trans[char] = u\"xiao\"\n for char in u\"楔些歇蝎鞋协挟携邪斜胁谐写械卸蟹懈泄泻谢屑\":\n self.trans[char] = u\"xie\"\n for char in u\"薪芯锌欣辛新忻心信衅\":\n self.trans[char] = u\"xin\"\n for char in u\"星腥猩惺兴刑型形邢行醒幸杏性姓\":\n self.trans[char] = u\"xing\"\n for char in u\"兄凶胸匈汹雄熊\":\n self.trans[char] = u\"xiong\"\n for char in u\"休修羞朽嗅锈秀袖绣\":\n self.trans[char] = u\"xiu\"\n for char in u\"墟戌需虚嘘须徐许蓄酗叙旭序畜恤絮婿绪续\":\n self.trans[char] = u\"xu\"\n for char in u\"轩喧宣悬旋玄选癣眩绚\":\n self.trans[char] = u\"xuan\"\n for char in u\"靴薛学穴雪血\":\n self.trans[char] = u\"xue\"\n for char in u\"勋熏循旬询寻驯巡殉汛训讯逊迅\":\n self.trans[char] = u\"xun\"\n for char in u\"压押鸦鸭呀丫芽牙蚜崖衙涯雅哑亚讶\":\n self.trans[char] = u\"ya\"\n for char in u\"焉咽阉烟淹盐严研蜒岩延言颜阎炎沿奄掩眼衍演艳堰燕厌砚雁唁彦焰宴谚验\":\n self.trans[char] = u\"yan\"\n for char in u\"殃央鸯秧杨扬佯疡羊洋阳氧仰痒养样漾\":\n self.trans[char] = u\"yang\"\n for char in u\"邀腰妖瑶摇尧遥窑谣姚咬舀药要耀\":\n self.trans[char] = u\"yao\"\n for char in u\"椰噎耶爷野冶也页掖业叶曳腋夜液\":\n self.trans[char] = u\"ye\"\n for char in u\"一壹医揖铱依伊衣颐夷遗移仪胰疑沂宜姨彝椅蚁倚已乙矣以艺抑易邑屹亿役臆逸肄疫亦裔意毅忆义益溢诣议谊译异翼翌绎\":\n self.trans[char] = u\"yi\"\n for char in u\"茵荫因殷音阴姻吟银淫寅饮尹引隐印\":\n self.trans[char] = u\"yin\"\n for char in u\"英樱婴鹰应缨莹萤营荧蝇迎赢盈影颖硬映\":\n self.trans[char] = u\"ying\"\n self.trans['哟'] = 'yo'\n for char in u\"拥佣臃痈庸雍踊蛹咏泳涌永恿勇用\":\n self.trans[char] = u\"yong\"\n for char in u\"幽优悠忧尤由邮铀犹油游酉有友右佑釉诱又幼迂\":\n self.trans[char] = u\"you\"\n for char in u\"淤于盂榆虞愚舆余俞逾鱼愉渝渔隅予娱雨与屿禹宇语羽玉域芋郁吁遇喻峪御愈欲狱育誉浴寓裕预豫驭\":\n self.trans[char] = u\"yu\"\n for char in u\"鸳渊冤元垣袁原援辕园员圆猿源缘远苑愿怨院\":\n self.trans[char] = u\"yuan\"\n for char in u\"曰约越跃钥岳粤月悦阅\":\n self.trans[char] = u\"yue\"\n for char in u\"耘云郧匀陨允运蕴酝晕韵孕\":\n self.trans[char] = u\"yun\"\n for char in u\"匝砸杂\":\n self.trans[char] = u\"za\"\n for char in u\"栽哉灾宰载再在\":\n self.trans[char] = u\"zai\"\n for char in u\"咱攒暂赞\":\n self.trans[char] = u\"zan\"\n for char in u\"赃脏葬\":\n self.trans[char] = u\"zang\"\n for char in u\"遭糟凿藻枣早澡蚤躁噪造皂灶燥\":\n self.trans[char] = u\"zao\"\n for char in u\"责择则泽\":\n self.trans[char] = u\"ze\"\n self.trans['贼'] = 'zei'\n self.trans['怎'] = 'zen'\n for char in u\"增憎曾赠\":\n self.trans[char] = u\"zeng\"\n for char in u\"扎喳渣札轧铡闸眨栅榨咋乍炸诈\":\n self.trans[char] = u\"zha\"\n for char in u\"摘斋宅窄债寨\":\n self.trans[char] = u\"zhai\"\n for char in u\"瞻毡詹粘沾盏斩辗崭展蘸栈占战站湛绽\":\n self.trans[char] = u\"zhan\"\n for char in u\"樟章彰漳张掌涨杖丈帐账仗胀瘴障\":\n self.trans[char] = u\"zhang\"\n for char in u\"招昭找沼赵照罩兆肇召\":\n self.trans[char] = u\"zhao\"\n for char in u\"遮折哲蛰辙者锗蔗这浙\":\n self.trans[char] = u\"zhe\"\n for char in u\"珍斟真甄砧臻贞针侦枕疹诊震振镇阵圳\":\n self.trans[char] = u\"zhen\"\n for char in u\"蒸挣睁征狰争怔整拯正政帧症郑证\":\n self.trans[char] = u\"zheng\"\n for char in u\"芝枝支吱蜘知肢脂汁之织职直植殖执值侄址指止趾只旨纸志挚掷至致置帜峙制智秩稚质炙痔滞治窒\":\n self.trans[char] = u\"zhi\"\n for char in u\"中盅忠钟衷终种肿重仲众\":\n self.trans[char] = u\"zhong\"\n for char in u\"舟周州洲诌粥轴肘帚咒皱宙昼骤\":\n self.trans[char] = u\"zhou\"\n for char in u\"珠株蛛朱猪诸诛逐竹烛煮拄瞩嘱主著柱助蛀贮铸筑住注祝驻\":\n self.trans[char] = u\"zhu\"\n for char in u\"抓爪\":\n self.trans[char] = u\"zhua\"\n self.trans['拽'] = 'zhuai'\n for char in u\"专砖转撰赚篆\":\n self.trans[char] = u\"zhuan\"\n for char in u\"桩庄装妆撞壮状\":\n self.trans[char] = u\"zhuang\"\n for char in u\"椎锥追赘坠缀\":\n self.trans[char] = u\"zhui\"\n for char in u\"谆准\":\n self.trans[char] = u\"zhun\"\n for char in u\"捉拙卓桌琢茁酌啄着灼浊\":\n self.trans[char] = u\"zhuo\"\n for char in u\"兹咨资姿滋淄孜紫仔籽滓子自渍字\":\n self.trans[char] = u\"zi\"\n for char in u\"鬃棕踪宗综总纵\":\n self.trans[char] = u\"zong\"\n for char in u\"邹走奏揍\":\n self.trans[char] = u\"zou\"\n for char in u\"租足卒族祖诅阻组\":\n self.trans[char] = u\"zu\"\n for char in u\"钻纂\":\n self.trans[char] = u\"zuan\"\n for char in u\"嘴醉最罪\":\n self.trans[char] = u\"zui\"\n for char in u\"尊遵\":\n self.trans[char] = u\"zun\"\n for char in u\"昨左佐柞做作坐座\":\n self.trans[char] = u\"zuo\"\n # from: https://www.wikidata.org/wiki/MediaWiki:Gadget-SimpleTransliterate.js\n self.trans[u\"ଂ\"] = \"anusvara\"\n self.trans[u\"ઇ\"] = \"i\"\n self.trans[u\"എ\"] = \"e\"\n self.trans[u\"ગ\"] = \"ga\"\n self.trans[u\"ਜ\"] = \"ja\"\n self.trans[u\"ഞ\"] = \"nya\"\n self.trans[u\"ଢ\"] = \"ddha\"\n self.trans[u\"ધ\"] = \"dha\"\n self.trans[u\"ਬ\"] = \"ba\"\n self.trans[u\"മ\"] = \"ma\"\n self.trans[u\"ଲ\"] = \"la\"\n self.trans[u\"ષ\"] = \"ssa\"\n self.trans[u\"਼\"] = \"nukta\"\n self.trans[u\"ാ\"] = \"aa\"\n self.trans[u\"ୂ\"] = \"uu\"\n self.trans[u\"ે\"] = \"e\"\n self.trans[u\"ੌ\"] = \"au\"\n self.trans[u\"ൎ\"] = \"reph\"\n self.trans[u\"ੜ\"] = \"rra\"\n self.trans[u\"՞\"] = \"?\"\n self.trans[u\"ୢ\"] = \"l\"\n self.trans[u\"૧\"] = \"1\"\n self.trans[u\"੬\"] = \"6\"\n self.trans[u\"൮\"] = \"8\"\n self.trans[u\"୲\"] = \"quarter\"\n self.trans[u\"ൾ\"] = \"ll\"\n self.trans[u\"ਇ\"] = \"i\"\n self.trans[u\"ഉ\"] = \"u\"\n self.trans[u\"ઌ\"] = \"l\"\n self.trans[u\"ਗ\"] = \"ga\"\n self.trans[u\"ങ\"] = \"nga\"\n self.trans[u\"ଝ\"] = \"jha\"\n self.trans[u\"જ\"] = \"ja\"\n self.trans[u\"؟\"] = \"?\"\n self.trans[u\"ਧ\"] = \"dha\"\n self.trans[u\"ഩ\"] = \"nnna\"\n self.trans[u\"ଭ\"] = \"bha\"\n self.trans[u\"બ\"] = \"ba\"\n self.trans[u\"ഹ\"] = \"ha\"\n self.trans[u\"ଽ\"] = \"avagraha\"\n self.trans[u\"઼\"] = \"nukta\"\n self.trans[u\"ੇ\"] = \"ee\"\n self.trans[u\"୍\"] = \"virama\"\n self.trans[u\"ૌ\"] = \"au\"\n self.trans[u\"੧\"] = \"1\"\n self.trans[u\"൩\"] = \"3\"\n self.trans[u\"୭\"] = \"7\"\n self.trans[u\"૬\"] = \"6\"\n self.trans[u\"൹\"] = \"mark\"\n self.trans[u\"ਖ਼\"] = \"khha\"\n self.trans[u\"ਂ\"] = \"bindi\"\n self.trans[u\"ഈ\"] = \"ii\"\n self.trans[u\"ઍ\"] = \"e\"\n self.trans[u\"ଌ\"] = \"l\"\n self.trans[u\"ഘ\"] = \"gha\"\n self.trans[u\"ઝ\"] = \"jha\"\n self.trans[u\"ଡ଼\"] = \"rra\"\n self.trans[u\"ਢ\"] = \"ddha\"\n self.trans[u\"ന\"] = \"na\"\n self.trans[u\"ભ\"] = \"bha\"\n self.trans[u\"ବ\"] = \"ba\"\n self.trans[u\"ਲ\"] = \"la\"\n self.trans[u\"സ\"] = \"sa\"\n self.trans[u\"ઽ\"] = \"avagraha\"\n self.trans[u\"଼\"] = \"nukta\"\n self.trans[u\"ੂ\"] = \"uu\"\n self.trans[u\"ൈ\"] = \"ai\"\n self.trans[u\"્\"] = \"virama\"\n self.trans[u\"ୌ\"] = \"au\"\n self.trans[u\"൨\"] = \"2\"\n self.trans[u\"૭\"] = \"7\"\n self.trans[u\"୬\"] = \"6\"\n self.trans[u\"ੲ\"] = \"iri\"\n self.trans[u\"ഃ\"] = \"visarga\"\n self.trans[u\"ં\"] = \"anusvara\"\n self.trans[u\"ଇ\"] = \"i\"\n self.trans[u\"ഓ\"] = \"oo\"\n self.trans[u\"ଗ\"] = \"ga\"\n self.trans[u\"ਝ\"] = \"jha\"\n self.trans[u\"？\"] = \"?\"\n self.trans[u\"ണ\"] = \"nna\"\n self.trans[u\"ઢ\"] = \"ddha\"\n self.trans[u\"ଧ\"] = \"dha\"\n self.trans[u\"ਭ\"] = \"bha\"\n self.trans[u\"ള\"] = \"lla\"\n self.trans[u\"લ\"] = \"la\"\n self.trans[u\"ଷ\"] = \"ssa\"\n self.trans[u\"ൃ\"] = \"r\"\n self.trans[u\"ૂ\"] = \"uu\"\n self.trans[u\"େ\"] = \"e\"\n self.trans[u\"੍\"] = \"virama\"\n self.trans[u\"ୗ\"] = \"mark\"\n self.trans[u\"ൣ\"] = \"ll\"\n self.trans[u\"ૢ\"] = \"l\"\n self.trans[u\"୧\"] = \"1\"\n self.trans[u\"੭\"] = \"7\"\n self.trans[u\"൳\"] = \"1/4\"\n self.trans[u\"୷\"] = \"sixteenths\"\n self.trans[u\"ଆ\"] = \"aa\"\n self.trans[u\"ઋ\"] = \"r\"\n self.trans[u\"ഊ\"] = \"uu\"\n self.trans[u\"ਐ\"] = \"ai\"\n self.trans[u\"ଖ\"] = \"kha\"\n self.trans[u\"છ\"] = \"cha\"\n self.trans[u\"ച\"] = \"ca\"\n self.trans[u\"ਠ\"] = \"ttha\"\n self.trans[u\"ଦ\"] = \"da\"\n self.trans[u\"ફ\"] = \"pha\"\n self.trans[u\"പ\"] = \"pa\"\n self.trans[u\"ਰ\"] = \"ra\"\n self.trans[u\"ଶ\"] = \"sha\"\n self.trans[u\"ഺ\"] = \"ttta\"\n self.trans[u\"ੀ\"] = \"ii\"\n self.trans[u\"ો\"] = \"o\"\n self.trans[u\"ൊ\"] = \"o\"\n self.trans[u\"ୖ\"] = \"mark\"\n self.trans[u\"୦\"] = \"0\"\n self.trans[u\"૫\"] = \"5\"\n self.trans[u\"൪\"] = \"4\"\n self.trans[u\"ੰ\"] = \"tippi\"\n self.trans[u\"୶\"] = \"eighth\"\n self.trans[u\"ൺ\"] = \"nn\"\n self.trans[u\"ଁ\"] = \"candrabindu\"\n self.trans[u\"അ\"] = \"a\"\n self.trans[u\"ઐ\"] = \"ai\"\n self.trans[u\"ക\"] = \"ka\"\n self.trans[u\"ਸ਼\"] = \"sha\"\n self.trans[u\"ਛ\"] = \"cha\"\n self.trans[u\"ଡ\"] = \"dda\"\n self.trans[u\"ઠ\"] = \"ttha\"\n self.trans[u\"ഥ\"] = \"tha\"\n self.trans[u\"ਫ\"] = \"pha\"\n self.trans[u\"ર\"] = \"ra\"\n self.trans[u\"വ\"] = \"va\"\n self.trans[u\"ୁ\"] = \"u\"\n self.trans[u\"ી\"] = \"ii\"\n self.trans[u\"ੋ\"] = \"oo\"\n self.trans[u\"ૐ\"] = \"om\"\n self.trans[u\"ୡ\"] = \"ll\"\n self.trans[u\"ૠ\"] = \"rr\"\n self.trans[u\"੫\"] = \"5\"\n self.trans[u\"ୱ\"] = \"wa\"\n self.trans[u\"૰\"] = \"sign\"\n self.trans[u\"൵\"] = \"quarters\"\n self.trans[u\"ਫ਼\"] = \"fa\"\n self.trans[u\"ઁ\"] = \"candrabindu\"\n self.trans[u\"ਆ\"] = \"aa\"\n self.trans[u\"ઑ\"] = \"o\"\n self.trans[u\"ଐ\"] = \"ai\"\n self.trans[u\"ഔ\"] = \"au\"\n self.trans[u\"ਖ\"] = \"kha\"\n self.trans[u\"ડ\"] = \"dda\"\n self.trans[u\"ଠ\"] = \"ttha\"\n self.trans[u\"ത\"] = \"ta\"\n self.trans[u\"ਦ\"] = \"da\"\n self.trans[u\"ର\"] = \"ra\"\n self.trans[u\"ഴ\"] = \"llla\"\n self.trans[u\"ુ\"] = \"u\"\n self.trans[u\"ୀ\"] = \"ii\"\n self.trans[u\"ൄ\"] = \"rr\"\n self.trans[u\"ૡ\"] = \"ll\"\n self.trans[u\"ୠ\"] = \"rr\"\n self.trans[u\"੦\"] = \"0\"\n self.trans[u\"૱\"] = \"sign\"\n self.trans[u\"୰\"] = \"isshar\"\n self.trans[u\"൴\"] = \"1/2\"\n self.trans[u\"ਁ\"] = \"bindi\"\n self.trans[u\"આ\"] = \"aa\"\n self.trans[u\"ଋ\"] = \"r\"\n self.trans[u\"ഏ\"] = \"ee\"\n self.trans[u\"ખ\"] = \"kha\"\n self.trans[u\"ଛ\"] = \"cha\"\n self.trans[u\"ട\"] = \"tta\"\n self.trans[u\"ਡ\"] = \"dda\"\n self.trans[u\"દ\"] = \"da\"\n self.trans[u\"ଫ\"] = \"pha\"\n self.trans[u\"യ\"] = \"ya\"\n self.trans[u\"શ\"] = \"sha\"\n self.trans[u\"ി\"] = \"i\"\n self.trans[u\"ੁ\"] = \"u\"\n self.trans[u\"ୋ\"] = \"o\"\n self.trans[u\"ੑ\"] = \"udaat\"\n self.trans[u\"૦\"] = \"0\"\n self.trans[u\"୫\"] = \"5\"\n self.trans[u\"൯\"] = \"9\"\n self.trans[u\"ੱ\"] = \"addak\"\n self.trans[u\"ൿ\"] = \"k\"\n self.trans[u\"ആ\"] = \"aa\"\n self.trans[u\"ଊ\"] = \"uu\"\n self.trans[u\"એ\"] = \"e\"\n self.trans[u\"ਔ\"] = \"au\"\n self.trans[u\"ഖ\"] = \"kha\"\n self.trans[u\"ଚ\"] = \"ca\"\n self.trans[u\"ટ\"] = \"tta\"\n self.trans[u\"ਤ\"] = \"ta\"\n self.trans[u\"ദ\"] = \"da\"\n self.trans[u\"ପ\"] = \"pa\"\n self.trans[u\"ય\"] = \"ya\"\n self.trans[u\"ശ\"] = \"sha\"\n self.trans[u\"િ\"] = \"i\"\n self.trans[u\"െ\"] = \"e\"\n self.trans[u\"൦\"] = \"0\"\n self.trans[u\"୪\"] = \"4\"\n self.trans[u\"૯\"] = \"9\"\n self.trans[u\"ੴ\"] = \"onkar\"\n self.trans[u\"ଅ\"] = \"a\"\n self.trans[u\"ਏ\"] = \"ee\"\n self.trans[u\"କ\"] = \"ka\"\n self.trans[u\"ઔ\"] = \"au\"\n self.trans[u\"ਟ\"] = \"tta\"\n self.trans[u\"ഡ\"] = \"dda\"\n self.trans[u\"ଥ\"] = \"tha\"\n self.trans[u\"ત\"] = \"ta\"\n self.trans[u\"ਯ\"] = \"ya\"\n self.trans[u\"റ\"] = \"rra\"\n self.trans[u\"ଵ\"] = \"va\"\n self.trans[u\"ਿ\"] = \"i\"\n self.trans[u\"ു\"] = \"u\"\n self.trans[u\"ૄ\"] = \"rr\"\n self.trans[u\"ൡ\"] = \"ll\"\n self.trans[u\"੯\"] = \"9\"\n self.trans[u\"൱\"] = \"100\"\n self.trans[u\"୵\"] = \"sixteenth\"\n self.trans[u\"અ\"] = \"a\"\n self.trans[u\"ਊ\"] = \"uu\"\n self.trans[u\"ഐ\"] = \"ai\"\n self.trans[u\"ક\"] = \"ka\"\n self.trans[u\"ଔ\"] = \"au\"\n self.trans[u\"ਚ\"] = \"ca\"\n self.trans[u\"ഠ\"] = \"ttha\"\n self.trans[u\"થ\"] = \"tha\"\n self.trans[u\"ତ\"] = \"ta\"\n self.trans[u\"ਪ\"] = \"pa\"\n self.trans[u\"ര\"] = \"ra\"\n self.trans[u\"વ\"] = \"va\"\n self.trans[u\"ീ\"] = \"ii\"\n self.trans[u\"ૅ\"] = \"e\"\n self.trans[u\"ୄ\"] = \"rr\"\n self.trans[u\"ൠ\"] = \"rr\"\n self.trans[u\"ਜ਼\"] = \"za\"\n self.trans[u\"੪\"] = \"4\"\n self.trans[u\"൰\"] = \"10\"\n self.trans[u\"୴\"] = \"quarters\"\n self.trans[u\"ਅ\"] = \"a\"\n self.trans[u\"ഋ\"] = \"r\"\n self.trans[u\"ઊ\"] = \"uu\"\n self.trans[u\"ଏ\"] = \"e\"\n self.trans[u\"ਕ\"] = \"ka\"\n self.trans[u\"ഛ\"] = \"cha\"\n self.trans[u\"ચ\"] = \"ca\"\n self.trans[u\"ଟ\"] = \"tta\"\n self.trans[u\"ਥ\"] = \"tha\"\n self.trans[u\"ഫ\"] = \"pha\"\n self.trans[u\"પ\"] = \"pa\"\n self.trans[u\"ଯ\"] = \"ya\"\n self.trans[u\"ਵ\"] = \"va\"\n self.trans[u\"ି\"] = \"i\"\n self.trans[u\"ോ\"] = \"oo\"\n self.trans[u\"ୟ\"] = \"yya\"\n self.trans[u\"൫\"] = \"5\"\n self.trans[u\"૪\"] = \"4\"\n self.trans[u\"୯\"] = \"9\"\n self.trans[u\"ੵ\"] = \"yakash\"\n self.trans[u\"ൻ\"] = \"n\"\n self.trans[u\"ઃ\"] = \"visarga\"\n self.trans[u\"ം\"] = \"anusvara\"\n self.trans[u\"ਈ\"] = \"ii\"\n self.trans[u\"ઓ\"] = \"o\"\n self.trans[u\"ഒ\"] = \"o\"\n self.trans[u\"ਘ\"] = \"gha\"\n self.trans[u\"ଞ\"] = \"nya\"\n self.trans[u\"ણ\"] = \"nna\"\n self.trans[u\"ഢ\"] = \"ddha\"\n self.trans[u\"ਲ਼\"] = \"lla\"\n self.trans[u\"ਨ\"] = \"na\"\n self.trans[u\"ମ\"] = \"ma\"\n self.trans[u\"ળ\"] = \"lla\"\n self.trans[u\"ല\"] = \"la\"\n self.trans[u\"ਸ\"] = \"sa\"\n self.trans[u\"¿\"] = \"?\"\n self.trans[u\"ା\"] = \"aa\"\n self.trans[u\"ૃ\"] = \"r\"\n self.trans[u\"ൂ\"] = \"uu\"\n self.trans[u\"ੈ\"] = \"ai\"\n self.trans[u\"ૣ\"] = \"ll\"\n self.trans[u\"ൢ\"] = \"l\"\n self.trans[u\"੨\"] = \"2\"\n self.trans[u\"୮\"] = \"8\"\n self.trans[u\"൲\"] = \"1000\"\n self.trans[u\"ਃ\"] = \"visarga\"\n self.trans[u\"ଉ\"] = \"u\"\n self.trans[u\"ઈ\"] = \"ii\"\n self.trans[u\"ਓ\"] = \"oo\"\n self.trans[u\"ଙ\"] = \"nga\"\n self.trans[u\"ઘ\"] = \"gha\"\n self.trans[u\"ഝ\"] = \"jha\"\n self.trans[u\"ਣ\"] = \"nna\"\n self.trans[u\"ન\"] = \"na\"\n self.trans[u\"ഭ\"] = \"bha\"\n self.trans[u\"ଜ\"] = \"ja\"\n self.trans[u\"ହ\"] = \"ha\"\n self.trans[u\"સ\"] = \"sa\"\n self.trans[u\"ഽ\"] = \"avagraha\"\n self.trans[u\"ૈ\"] = \"ai\"\n self.trans[u\"്\"] = \"virama\"\n self.trans[u\"୩\"] = \"3\"\n self.trans[u\"૨\"] = \"2\"\n self.trans[u\"൭\"] = \"7\"\n self.trans[u\"ੳ\"] = \"ura\"\n self.trans[u\"ൽ\"] = \"l\"\n self.trans[u\"ઉ\"] = \"u\"\n self.trans[u\"ଈ\"] = \"ii\"\n self.trans[u\"ഌ\"] = \"l\"\n self.trans[u\"ઙ\"] = \"nga\"\n self.trans[u\"ଘ\"] = \"gha\"\n self.trans[u\"ജ\"] = \"ja\"\n self.trans[u\"ਞ\"] = \"nya\"\n self.trans[u\"ନ\"] = \"na\"\n self.trans[u\"ബ\"] = \"ba\"\n self.trans[u\"ਮ\"] = \"ma\"\n self.trans[u\"હ\"] = \"ha\"\n self.trans[u\"ସ\"] = \"sa\"\n self.trans[u\"ਾ\"] = \"aa\"\n self.trans[u\"ૉ\"] = \"o\"\n self.trans[u\"ୈ\"] = \"ai\"\n self.trans[u\"ൌ\"] = \"au\"\n self.trans[u\"૩\"] = \"3\"\n self.trans[u\"୨\"] = \"2\"\n self.trans[u\"൬\"] = \"6\"\n self.trans[u\"੮\"] = \"8\"\n self.trans[u\"ർ\"] = \"rr\"\n self.trans[u\"ଃ\"] = \"visarga\"\n self.trans[u\"ഇ\"] = \"i\"\n self.trans[u\"ਉ\"] = \"u\"\n self.trans[u\"ଓ\"] = \"o\"\n self.trans[u\"ഗ\"] = \"ga\"\n self.trans[u\"ਙ\"] = \"nga\"\n self.trans[u\"ઞ\"] = \"nya\"\n self.trans[u\"ଣ\"] = \"nna\"\n self.trans[u\"ധ\"] = \"dha\"\n self.trans[u\"મ\"] = \"ma\"\n self.trans[u\"ଳ\"] = \"lla\"\n self.trans[u\"ഷ\"] = \"ssa\"\n self.trans[u\"ਹ\"] = \"ha\"\n self.trans[u\"ਗ਼\"] = \"ghha\"\n self.trans[u\"ા\"] = \"aa\"\n self.trans[u\"ୃ\"] = \"r\"\n self.trans[u\"േ\"] = \"ee\"\n self.trans[u\"ൗ\"] = \"mark\"\n self.trans[u\"ଢ଼\"] = \"rha\"\n self.trans[u\"ୣ\"] = \"ll\"\n self.trans[u\"൧\"] = \"1\"\n self.trans[u\"੩\"] = \"3\"\n self.trans[u\"૮\"] = \"8\"\n self.trans[u\"୳\"] = \"half\"\n for char in self.trans:\n value = self.trans[char]\n if value == \"?\":\n continue\n while value.encode(encoding, 'replace').decode(encoding) == \"?\" and value in self.trans:\n assert value != self.trans[value], \"%r == self.trans[%r]!\" % (value, value)\n value = self.trans[value]\n self.trans[char] = value","def test_Encoder_encode_decode_content(self):\n c = Content(\"/data/test\", \"HelloWorld\")\n ec = self.encoder1.encode(c)\n dc = self.encoder1.decode(ec)\n self.assertTrue(c == dc)","def encoding(self) -> 'layout.Encoding':","def testEncodeDecodeEmpty(self):\n test_case = ''\n string_obj = ASCIITransportFormat(\n ASCIITransportFormat.SupportedTypes.STRING,\n test_case,\n )\n json_obj = string_obj.json()\n obj = ASCIITransportFormat(\n ASCIITransportFormat.SupportedTypes.JSON,\n json_obj,\n )\n self.assertEqual(obj.data, test_case)\n self.assertFalse(obj.encoded)\n self.assertFalse(obj.pseudo_encode)\n\n json_obj = obj.json()\n obj = ASCIITransportFormat(\n ASCIITransportFormat.SupportedTypes.JSON,\n json_obj,\n )\n obj.encode()\n self.assertEqual(obj.data, test_case)\n self.assertTrue(obj.encoded)\n self.assertTrue(obj.pseudo_encode)\n\n json_obj = obj.json()\n obj = ASCIITransportFormat(\n ASCIITransportFormat.SupportedTypes.JSON,\n json_obj,\n )\n obj.decode()\n self.assertEqual(obj.data, test_case)\n self.assertFalse(obj.encoded)\n self.assertFalse(obj.pseudo_encode)","def encode(self, seq):","def decode(data):\n raise NotImplementedError","def encode(self, text):","def recodeToUtf8(data):\n try:\n data = data.decode('utf8').encode('utf8')\n return data\n except UnicodeDecodeError:\n encoding = chardet.detect(data)['encoding']\n logging.log(5, 'encoding should be %s' % encoding)\n if encoding == None:\n encoding = 'latin1'\n try:\n data = data.decode(encoding).encode('utf8')\n except UnicodeDecodeError:\n logging.warn('Error when decoding as %s' % encoding)\n data = data\n except LookupError:\n logging.warn('Unknown encoding when decoding as %s' % encoding)\n data = data\n\n return data\n\n return","def _decode(self, input_dict):\n pass","def encoding(options):\n pass","def forward(self, x):\n x = self.Encoder(x)\n x = self.Decoder(x)\n return x","def forward(self, x):\n x = self.encoder(x)\n x = self.decoder(x)\n return x","def test_encoders_strings(encoder):\n assert \"\" == encoding.decode(b\"\", encoder)\n\n assert \"string\" == encoding.decode(\n encoding.encode(\n \"string\",\n encoder\n ),\n encoder\n )\n\n with pytest.raises(TypeError):\n encoding.encode(b\"string\", encoder)\n\n with pytest.raises(TypeError):\n encoding.decode(\"foobar\", encoder)","def encoder(self, inputs):\n pass","def test_encode_decode(self):\n self.assertEquals(self.txt_when,\n self.TDTT.encode_when(self.dt_when))\n self.assertEquals(None,\n self.TDTT.encode_when(self.NOT_DATE_AND_TIME))\n\n expected = {'when': self.txt_when}\n encoded_dict = self.TDTT.encode(when=self.dt_when)\n self.assertEquals(expected, encoded_dict)\n\n decoded_dtt = self.TDTT.decode(expected)\n self.assertEquals(self.dt_when, decoded_dtt.when)\n self.assertEquals(expected, decoded_dtt.encoded)\n\n constructed_dtt = self.TDTT(when=self.txt_when)\n self.assertEquals(expected, constructed_dtt.encoded)\n decoded_dict = constructed_dtt.decoded\n self.assertEquals(self.dt_when, decoded_dict.get('when'))","def test_decode(self):\n for (input, output) in self.tests:\n self.assertEqual(input, output.decode('imap4-utf-7'))","def decode(self, encoding=None, errors=None):\n return decode(self, encoding, errors)","def handle_decode(self, encoded_data):\n \n config.COD_PROMPT = config.DEC_PROMPT\n print config.DEC_PROMPT + \" decoding...\"\n \n # while there is another decoder, run each item through the next decoder\n data = encoded_data\n success = False\n for decoder in self.decoder_list:\n current_decoder = decoder()\n success, data = self.recursive_decoder(current_decoder.decode, data)\n if not success:\n break\n print config.DEC_PROMPT + \"%s decoded to '%s'\" % ( current_decoder.name(),data)\n return success, data","def convert(data):\n result = None\n if isinstance(data, bytes):\n try:\n result = json.loads(data.decode(CODING))\n except:\n print('wrong data format!')\n finally:\n return result\n elif isinstance(data, dict):\n try:\n result = json.dumps(data).encode(CODING)\n except:\n print('wrong data format!')\n finally:\n return result\n else:\n print('wrong data format!')\n return result","def getencdec():\n return (json.JSONEncoder(), json.JSONDecoder())","def compute_encoding(self):\n for input_quantizer in self._input_quantizers.values():\n input_quantizer.compute_encoding()\n\n for quantizer in self.param_quantizers.values():\n # NOTE: If quantizer.enabled is True but quantizer.encoding is None,\n # quantizer.compute_encoding() will set quantizer.enabled to False.\n # Otherwise, quantizer.compute_encodings() is equivalent to no-op.\n quantizer.compute_encoding()\n\n for output_quantizer in self._output_quantizers.values():\n output_quantizer.compute_encoding()","def encode(self): # pragma: no cover\n pass","def decode(self, value):\r\n pass","def _process_content_codings(self, chunk):\n content_codings = self.parsed_headers.get('content-encoding', [])\n content_codings.reverse()\n for coding in content_codings:\n # TODO: deflate support\n if coding in ['gzip', 'x-gzip'] and self._decode_ok:\n if not self._in_gzip_body:\n self._gzip_header_buffer += chunk\n try:\n chunk = self._read_gzip_header(\n self._gzip_header_buffer\n )\n self._in_gzip_body = True\n except IndexError:\n return '' # not a full header yet\n except IOError, gzip_error:\n self.add_note('header-content-encoding',\n rs.BAD_GZIP,\n gzip_error=str(gzip_error)\n )\n self._decode_ok = False\n return\n try:\n chunk = self._gzip_processor.decompress(chunk)\n except zlib.error, zlib_error:\n self.add_note(\n 'header-content-encoding', \n rs.BAD_ZLIB,\n zlib_error=str(zlib_error),\n ok_zlib_len=f_num(self.payload_sample[-1][0]),\n chunk_sample=chunk[:20].encode('string_escape')\n )\n self._decode_ok = False\n return\n else:\n # we can't handle other codecs, so punt on body processing.\n self._decode_ok = False\n return\n self._md5_post_processor.update(chunk)\n self.decoded_len += len(chunk)\n return chunk","def _pre_filter_decode(self):\n\n pass","def test_Encoder_encode_decode_nack(self):\n interest = Interest(\"/data/test\")\n n = Nack(\"/data/test\", NackReason.NO_CONTENT, interest=interest)\n en = self.encoder1.encode(n)\n dn = self.encoder1.decode(en)\n self.assertTrue(n == dn)","def easytest():\n (c,root) = huffman([1, 2, 3, 4])\n s = encode([1,2,3,0,3,2,1],c)\n print s\n ans = decode( s, root )\n print ans","def encode(self):\n \n assert False, \"Not implemented.\"","def test_encode():","def decode(self, crypto):","def Convert(*args, **kwargs):\n return _gdi_.EncodingConverter_Convert(*args, **kwargs)","def decode(decode_format):\n return output_from_decode","def _decode(self, rel_codes, anchors):\n pass","def convert( self, text ):\n if self.input_codec != self.output_codec:\n return unicode( text, self.input_codec, 'ignore' ).encode( self.output_codec, 'ignore' )\n else:\n return text","def _convertToOutputEncoding(export_text,\n input_encoding=None,\n output_encoding='utf-8'):\n if input_encoding:\n return _encode(_decode(export_text, input_encoding), output_encoding)\n return _encode(_decode(export_text), output_encoding)","def __encode(self):\n\n for i, char in enumerate(self.__chars):\n self.__char2idx[char] = i\n self.__idx2char[i] = char","def decode(self, probs, sizes=None):\n raise NotImplementedError","def _encode(self):\n with tf.variable_scope('encoding'):\n self.sep_p_encodes, _ = bilstm_layer(self.p_emb, self.p_length, self.hidden_size)\n tf.get_variable_scope().reuse_variables()\n self.sep_q_encodes, _ = bilstm_layer(self.q_emb, self.q_length, self.hidden_size)\n if self.use_dropout:\n self.sep_p_encodes = tf.nn.dropout(self.sep_p_encodes, 1-self.dropout)\n self.sep_q_encodes = tf.nn.dropout(self.sep_q_encodes, 1-self.dropout)","def testEncodeDecodeEmpty(self):\n test_case = ''\n obj = ASCIITransportFormat(\n ASCIITransportFormat.SupportedTypes.STRING,\n test_case,\n )\n self.assertEqual(obj.data, test_case)\n self.assertFalse(obj.encoded)\n self.assertFalse(obj.pseudo_encode)\n\n obj.encode()\n self.assertEqual(obj.data, test_case)\n self.assertTrue(obj.encoded)\n self.assertTrue(obj.pseudo_encode)\n\n obj.decode()\n self.assertEqual(obj.data, test_case)\n self.assertFalse(obj.encoded)\n self.assertFalse(obj.pseudo_encode)","def set_encodings(questions, candidates,\n question_encoder, response_encoder):\n for i, chunk in enumerate(chunks(questions, 100)):\n question_strs = [q.question for q in chunk]\n question_encodings = np.array(question_encoder(question_strs))\n\n for j in range(question_encodings.shape[0]):\n questions[i * 100 + j].encoding = question_encodings[j]\n if i % 50 == 0:\n print(\"Questions: encoded %s of %s...\" % (i * 100, len(questions)))\n\n for i, chunk in enumerate(chunks(candidates, 50)):\n candidate_strs = [(c.sentence, c.context) for c in chunk]\n candidate_encodings = np.array(response_encoder(candidate_strs))\n for j in range(candidate_encodings.shape[0]):\n candidates[i * 50 + j].encoding = candidate_encodings[j]\n if i % 100 == 0:\n print(\"Candidates: encoded %s of %s...\" % (i * 50, len(candidates)))","def encode_decode(self, img, img_metas):\n pass","def encode(path, tocodec, default_codec='windows-1251', unescape=False, force_encode=False):\n fromcodec = get_encoding_type(path)\n \n if fromcodec == tocodec:\n print(f\"Already {tocodec}: {path}\")\n if not force_encode and not unescape:\n return\n \n # fix error detections (RU region)\n if fromcodec not in ('windows-1251', 'ascii') and fromcodec.lower().find('utf') == -1:\n to_log('Possible Detect dismiss: ' + fromcodec + ' in ' + path)\n if not force_encode:\n to_log('Try default:', default_codec)\n fromcodec = default_codec\n else:\n to_log('Force encode:', fromcodec)\n \n print(f\"Convert from {fromcodec} to {tocodec}: {path}\")\n tmp_file = 'text.txt'\n try:\n with open(path, 'r', encoding=fromcodec) as f, open(tmp_file, 'w', encoding=tocodec) as e:\n text = f.read()\n if unescape:\n text = html.unescape(text)\n e.write(text)\n os.remove(path)\n os.rename(tmp_file, path) \n except UnicodeDecodeError:\n to_log(f'[{fromcodec}] → [{tocodec}] - Decoding Error: {path}')\n except UnicodeEncodeError:\n to_log(f'[{fromcodec}] → [{tocodec}] - Encoding Error: {path}')\n print(f'Converted!')","def encode_data(self, data):\n if self.unit == \"char\":\n data = self.char_encoding(data)\n elif self.unit == \"char-ngram\":\n data = self.ngram_encoding(data)\n elif self.unit == \"morpheme\" or self.unit == \"oracle\":\n data = self.morpheme_encoding(data)\n else:\n data = self.data_to_word_ids(data, False)\n return data","def _define_encoder(self):\n raise NotImplementedError","def forward(self, inputs_encoder, inputs_decoder):\n states_encoder = self.encoder(inputs_encoder)\n outputs_decoder, states_decoder = self.decoder(inputs_decoder, states_encoder)\n return outputs_decoder, states_decoder","def get_encoded(self):\n pass","def test_encode_decode():\n enig = Enigma(534, 10, 5, [3, 1, 3, 4, 1, 0, 2, 3, 4, 2])\n string = \"\"\"A semi-enigma-machine-like cypher that uses 'rotors' to cypher\nand decypher messages. It is more unlike the enigma machine than\nit is alike, the wheelsspin one way to encode, the other to decode,\nwhereas the enigma machine always went forward, and paired keys.\nThis doesn't pair keys, nor does the cypher travel throught the rotors\nand back after hitting a reflector. The wheel positions are setteable,\nand as many wheels as you want can be added for extra 'cypherage'. This\nalso lacks the letter pairings, instead using a seed for random numbers\nused to shift the charactersets passed through.\"\"\"\n newstr = enig.encode(string)\n assert_equal(string, enig.decode(newstr))","def testEncodeDecodeEmpty(self):\n empty_case = ''\n encoded_result = ASCIITransportFormat.encode_data(empty_case)\n decoded_result = ASCIITransportFormat.decode_data(encoded_result)\n self.assertEqual(decoded_result, empty_case)","def reset_encodings(self):\n group_quantizers = self._grouped_quantizers.values()\n for quantizer in group_quantizers:\n quantizer.reset_encoding_stats()\n for param_quantizer in self._param_quantizers.values():\n if param_quantizer not in group_quantizers:\n param_quantizer.reset_encoding_stats()\n for input_quantizer in self._input_quantizers.values():\n if input_quantizer not in group_quantizers:\n input_quantizer.reset_encoding_stats()\n for output_quantizer in self._output_quantizers.values():\n if output_quantizer not in group_quantizers:\n output_quantizer.reset_encoding_stats()","def apply_coder(text, coder):\n ### TODO.","def decodeUtf8(self, arrayBuffer):","def decodeUtf8(self, arrayBuffer):","def _encode_decode(self, cover, quantize=False):\n payload = self._random_data(cover)\n generated = self.encoder(cover, payload)\n if quantize:\n generated = (255.0 * (generated + 1.0) / 2.0).long()\n generated = 2.0 * generated.float() / 255.0 - 1.0\n\n decoded = self.decoder(generated)\n\n return generated, payload, decoded","def decode(self,m):\n raise NotImplementedError('subclasses must override decode()!')","def GetDataEncodings(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\n raise NotImplementedError()","def decode(self, data: bytes) -> bytes:\n ...","def GetDataEncodings(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","def test_file_gzip_utf8_readwrite_explicit_decode(self):\n if state.py2:\n FileWriter(self.unicode_path).gzip(self.unicode_string)\n gzip_contents = FileReader(self.unicode_path + \".gz\").read_gzip(\"utf-8\") # when read with explicit utf-8 decoding, strings should match\n self.assertEqual(gzip_contents, self.unicode_string)\n elif state.py3:\n FileWriter(self.unicode_path).gzip(bytes(self.unicode_string, 'utf-8'))\n gzip_contents = FileReader(self.unicode_path + \".gz\").read_gzip(\"utf-8\") # when read with explicit utf-8 decoding, strings should match\n self.assertEqual(gzip_contents, self.unicode_string)","def encode(self, input_):\n return self.encoder(input_)","def decode(self, code):\n raise NotImplementedError","def test_encoding_method_name(self):\n data = service_call.encode_call(\"foo\", [1, 2, 3])\n data = serial_datagram.decode(data)\n\n u = msgpack.Unpacker(encoding='ascii')\n u.feed(data)\n command = next(u)\n\n self.assertEqual(command, ['foo', [1, 2, 3]])","def encode_data(data, item_to_encoding):\n for line in data:\n for i, itemset in enumerate(line[1:]):\n encoded_itemset = set()\n for item in itemset:\n encoded_itemset.add(item_to_encoding[item])\n line[i + 1] = encoded_itemset\n\n return data","def _encode(self, data):\n raise NotImplementedError(\"_encode needs to be implemented in {} subclass\".format(type(self).__name__))","def _encode(self):\n with tf.variable_scope('passage_encoding'):\n self.sep_p_encodes, _ = rnn('bi-lstm', self.p_emb, self.p_length, self.hidden_size)\n with tf.variable_scope('question_encoding'):\n self.sep_q_encodes, _ = rnn('bi-lstm', self.q_emb, self.q_length, self.hidden_size)\n if self.use_dropout:\n self.sep_p_encodes = tf.nn.dropout(self.sep_p_encodes, self.dropout_keep_prob)\n self.sep_q_encodes = tf.nn.dropout(self.sep_q_encodes, self.dropout_keep_prob)","def test_encode(self):\n assert url_encoder.encode(1) == 'TheStakeOut'\n assert url_encoder.encode(800) == 'TheStockTip-TheSeven'\n assert url_encoder.encode(99999) == 'MaleUnbonding-TheConversion-TheAndreaDoria'","def _define_decoder(self):\n raise NotImplementedError","def video_encoding(self):\n self.output_file = outputs_filenames(self.input, self.output_file)\n\n if self.resume and (self.temp / 'done.json').exists():\n set_logging(self.logging, self.temp)\n else:\n setup(self.temp, self.resume)\n set_logging(self.logging, self.temp)\n print(self.queue)\n framenums = split_routine(self.input, self.scenes, self.split_method, self.temp, self.min_scene_len, self.queue, self.threshold)\n\n if self.extra_split:\n framenums = extra_splits(input, framenums, self.extra_split)\n\n segment(self.input, self.temp, framenums)\n extract_audio(input, self.temp, self.audio_params)\n\n chunk = get_video_queue(self.temp, self.resume)\n\n # Make encode queue\n commands, self.video_params = compose_encoding_queue(chunk, self.temp, self.encoder, self.video_params, self.ffmpeg_pipe, self.passes)\n log(f'Encoding Queue Composed\\n'\n f'Encoder: {self.encoder.upper()} Queue Size: {len(commands)} Passes: {self.passes}\\n'\n f'Params: {self.video_params}\\n\\n')\n\n self.workers = determine_resources(self.encoder, self.workers)\n\n self.encoding_loop(commands)\n\n try:\n concatenate_video(self.temp, self.output_file, keep=self.keep)\n\n except Exception as e:\n _, _, exc_tb = sys.exc_info()\n print(f'Concatenation failed, FFmpeg error\\nAt line: {exc_tb.tb_lineno}\\nError:{str(e)}')\n log(f'Concatenation failed, aborting, error: {e}\\n')\n terminate()\n\n if self.vmaf:\n plot_vmaf(self.input, self.output_file, model=self.vmaf_path)","def _encode_back(self):\n with tf.variable_scope('passage_encoding'):\n self.sep_p_encodes, _ = rnn('bi-lstm', self.p_emb, self.p_length, self.hidden_size)\n with tf.variable_scope('question_encoding'):\n self.sep_q_encodes, _ = rnn('bi-lstm', self.q_emb, self.q_length, self.hidden_size)\n if self.use_dropout:\n self.sep_p_encodes = tf.nn.dropout(self.sep_p_encodes, self.dropout_keep_prob)\n self.sep_q_encodes = tf.nn.dropout(self.sep_q_encodes, self.dropout_keep_prob)","def bencode(data, f):\n\tif isinstance(data, int):\n\t\t_encode_int(data, f)\n\telif isinstance(data, (str, bytes)):\n\t\t_encode_buffer(data, f)\n\telif isinstance(data, Mapping):\n\t\t_encode_mapping(data, f)\n\telif isinstance(data, Iterable):\n\t\t_encode_iterable(data, f)","def encode(self, commands):\n commands, counter = commands[0], commands[1]\n try:\n st_time = time.time()\n source, target = Path(commands[-1][0]), Path(commands[-1][1])\n frame_probe_source = frame_probe(source)\n\n # Target Vmaf Mode\n if self.vmaf_target:\n tg_cq = self.target_vmaf(source)\n cm1 = man_cq(commands[0], tg_cq)\n\n if self.passes == 2:\n cm2 = man_cq(commands[1], tg_cq)\n commands = (cm1, cm2) + commands[2:]\n else:\n commands = (cm1,) + commands[1:]\n\n # Boost\n if self.boost:\n commands, cq = boosting(self.boost_limit, self.boost_range, source, commands, self.passes)\n\n log(f'Enc: {source.name}, {frame_probe_source} fr\\n')\n\n # Queue execution\n for i in commands[:-1]:\n tqdm_bar(i, self.encoder, counter, frame_probe_source, self.passes)\n\n frame_check(source, target, self.temp, self.no_check)\n\n frame_probe_fr = frame_probe(target)\n\n enc_time = round(time.time() - st_time, 2)\n\n log(f'Done: {source.name} Fr: {frame_probe_fr}\\n'\n f'Fps: {round(frame_probe_fr / enc_time, 4)} Time: {enc_time} sec.\\n\\n')\n except Exception as e:\n _, _, exc_tb = sys.exc_info()\n print(f'Error in encoding loop {e}\\nAt line {exc_tb.tb_lineno}')","def _applyCipher(self, encode):\n pass","async def encode(self, ctx, value = None , from_type = None, *, to_type = None):\n\n if value == None or from_type == None or to_type == None:\n msg = 'Usage: `{}encode \"[value]\" [from_type] [to_type]`\\nTypes include ascii, hex, and base64.'.format(ctx.prefix)\n await ctx.send(msg)\n return\n\n types = [ \"base64\", \"hex\", \"ascii\" ]\n\n if not from_type.lower() in types:\n await ctx.send(\"Invalid *from* type!\")\n return\n\n if not to_type.lower() in types:\n await ctx.send(\"Invalid *to* type!\")\n return\n\n if from_type.lower() == to_type.lower():\n await ctx.send(\"*Poof!* Your encoding was done before it started!\")\n return\n\n try:\n if from_type.lower() == \"base64\":\n if to_type.lower() == \"hex\":\n await ctx.send(self.suppressed(ctx.guild, self._base64_to_hex(value)))\n return\n elif to_type.lower() == \"ascii\":\n await ctx.send(self.suppressed(ctx.guild, self._base64_to_ascii(value)))\n return\n elif from_type.lower() == \"hex\":\n if to_type.lower() == \"ascii\":\n await ctx.send(self.suppressed(ctx.guild, self._hex_to_ascii(value)))\n return\n elif to_type.lower() == \"base64\":\n await ctx.send(self.suppressed(ctx.guild, self._hex_to_base64(value)))\n return\n elif from_type.lower() == \"ascii\":\n if to_type.lower() == \"hex\":\n await ctx.send(self.suppressed(ctx.guild, self._ascii_to_hex(value)))\n return\n elif to_type.lower() == \"base64\":\n await ctx.send(self.suppressed(ctx.guild, self._ascii_to_base64(value)))\n return\n except Exception:\n await ctx.send(\"I couldn't make that conversion!\")\n return","def encode(self, obj):\n s = super(CustomEncoder, self).encode(obj)\n # If uncompressed, postprocess for formatting\n if len(s.splitlines()) > 1:\n s = self.postprocess(s)\n return s","def mostlikelydecode(self):\n\n # Add your code here\n most_likely_codeword = Cipher(None) # Replace None with a method\n return most_likely_codeword.decode(None) # Replace None. What does decode take again in the Cipher class? ","def __call__(self,\n encoder_input_tokens,\n decoder_input_tokens,\n decoder_target_tokens,\n encoder_segment_ids=None,\n decoder_segment_ids=None,\n encoder_positions=None,\n decoder_positions=None,\n *,\n enable_dropout: bool = True,\n decode: bool = False):\n encoded = self.encode(\n encoder_input_tokens,\n encoder_segment_ids=encoder_segment_ids,\n enable_dropout=enable_dropout)\n\n return self.decode(\n encoded,\n encoder_input_tokens, # only used for masks\n decoder_input_tokens,\n decoder_target_tokens,\n encoder_segment_ids=encoder_segment_ids,\n decoder_segment_ids=decoder_segment_ids,\n decoder_positions=decoder_positions,\n enable_dropout=enable_dropout,\n decode=decode)"],"string":"[\n \"def decode(self, encoded):\",\n \"def encode(self, decoded):\",\n \"def handle_encode(self, results):\\n \\n config.COD_PROMPT = config.ENC_PROMPT\\n print config.ENC_PROMPT + \\\" encoding results...\\\"\\n \\n # while there is another decoder, run each item through the next decoder\\n data = results\\n success = False\\n for encoder in self.encoder_list:\\n current_encoder = encoder()\\n full_body = getattr(current_encoder,'full_body_encode',False)\\n success, data = self.recursive_decoder(current_encoder.encode, data, full_body)\\n if not success:\\n break\\n print config.ENC_PROMPT + \\\"%s encoded to '%s'\\\" % ( current_encoder.name(),data)\\n return success, data\",\n \"def decode(data): #@NoSelf\",\n \"def decode(self): # pragma: no cover\\n pass\",\n \"def decoder(self):\\n pass\",\n \"def test_decode(self):\\n pass # TODO(tlarsen)\",\n \"def decode(self):\\n self.decoded_content = []\\n if self.encoded_content:\\n s = self.encoded_content\\n if self.encoding:\\n if self.encoding.lower() == u'base64':\\n s = decode_base64(s)\\n elif self.encoding.lower() == u'csv':\\n list_of_lines = s.split()\\n for line in list_of_lines:\\n self.decoded_content.extend(line.split(','))\\n self.decoded_content = map(int, [val for val in self.decoded_content if val])\\n s = \\\"\\\"\\n else:\\n raise Exception(u'unknown data encoding %s' % (self.encoding))\\n else:\\n # in the case of xml the encoded_content already contains a list of integers\\n self.decoded_content = map(int, self.encoded_content)\\n s = \\\"\\\"\\n if self.compression:\\n if self.compression == u'gzip':\\n s = decompress_gzip(s)\\n elif self.compression == u'zlib':\\n s = decompress_zlib(s)\\n else:\\n raise Exception(u'unknown data compression %s' %(self.compression))\\n else:\\n raise Exception(u'no encoded content to decode')\\n for idx in xrange(0, len(s), 4):\\n val = ord(str(s[idx])) | (ord(str(s[idx + 1])) << 8) | \\\\\\n (ord(str(s[idx + 2])) << 16) | (ord(str(s[idx + 3])) << 24)\\n self.decoded_content.append(val)\\n #print len(self.decoded_content)\\n # generate the 2D version\\n self._gen_2D()\",\n \"def decode(self, s):\",\n \"def decode(self, s):\",\n \"def testEncodeDecode(self):\\n test_cases = [\\n '\\\\n',\\n ' ',\\n 'aaabbb',\\n 'a b',\\n '\\\\n\\\\n\\\\n',\\n '122333',\\n 'aaaaaaaaaa',\\n 'aaaaaaaaaabbbbbbbbbbb',\\n 'a'*1001,\\n ''.join(['a'*1001, 'b'*909, 'c'*65, 'd'*2]),\\n ]\\n for data in test_cases:\\n encoded_result = ASCIITransportFormat.encode_data(data)\\n decoded_result = ASCIITransportFormat.decode_data(encoded_result)\\n self.assertEqual(decoded_result, data)\",\n \"def _dinamic_decode(self):\\n raise NotImplementedError\",\n \"def encode(self, strs):\",\n \"def encode(self, strs):\",\n \"def decode(self):\\n for layer in self.layers:\\n layer.decode()\",\n \"def decode(self):\\n for layer in self.layers:\\n layer.decode()\",\n \"def test_encode_decode(self):\\n assert self._test == pybinn.loads(pybinn.dumps(self._test))\",\n \"def decode(self, coded_set):\",\n \"def decode(self):\\n s = self.encoded_content\\n if self.encoded_content:\\n if self.encoding:\\n if self.encoding == u'base64':\\n s = decode_base64(s)\\n else:\\n raise Exception(u'unknown data encoding %s' % (self.encoding))\\n if self.compression:\\n if self.compression == u'gzip':\\n s = decompress_gzip(s)\\n else:\\n raise Exception(u'unknown data compression %s' %(self.compression))\\n else:\\n raise Exception(u'no encoded content to decode')\\n self.decoded_content = []\\n for idx in xrange(0, len(s), 4):\\n val = ord(str(s[idx])) | (ord(str(s[idx + 1])) << 8) | \\\\\\n (ord(str(s[idx + 2])) << 16) | (ord(str(s[idx + 3])) << 24)\\n self.decoded_content.append(val)\\n # generate the 2D version\\n self._gen_2D()\",\n \"def refresh(self):\\n\\n self._codec = {\\n 'datas': self._datas,\\n 'formats': create_encode_decode_formats(self._datas,\\n self._length)\\n }\",\n \"def test_decode():\",\n \"def runit():\\n print encode(\\\"no\\\")\\n\\n print encode(\\\"yes\\\")\\n\\n print encode(\\\"OMG\\\")\\n\\n print encode(\\\"O M G\\\")\\n\\n print encode(\\\"mindblowingly\\\")\\n\\n print encode(\\\"Testing, 1 2 3, testing.\\\")\\n\\n print encode(\\\"Truth is fiction.\\\")\\n\\n print encode(\\\"The quick brown fox jumps over the lazy dog.\\\")\\n\\n print decode(\\\"vcvix rhn\\\")\\n\\n print decode(\\\"zmlyh gzxov rhlug vmzhg vkkrm thglm v\\\")\",\n \"def test_encoders(encoder):\\n assert encoding.decode(None, encoder) is None\\n assert encoding.encode(None, encoder) is None\\n\\n assert b\\\"\\\" == encoding.decode(b\\\"\\\", encoder)\\n\\n assert b\\\"string\\\" == encoding.decode(\\n encoding.encode(\\n b\\\"string\\\",\\n encoder\\n ),\\n encoder\\n )\\n\\n with pytest.raises(TypeError):\\n encoding.encode(\\\"string\\\", encoder)\\n\\n with pytest.raises(TypeError):\\n encoding.decode(\\\"string\\\", encoder)\\n with pytest.raises(ValueError):\\n encoding.decode(b\\\"foobar\\\", encoder)\",\n \"def test_decode(self):\\n assert url_encoder.decode('TheStakeOut') == 1\\n assert url_encoder.decode('TheStockTip-TheSeven') == 800\\n assert url_encoder.decode('MaleUnbonding-TheConversion-TheAndreaDoria') == 99999\",\n \"def test_encode(self):\\n pass # TODO(tlarsen)\",\n \"def fit_transform(self):\\n if self.enc_types == \\\"label\\\":\\n return self._label_encoding()\\n elif self.enc_types == \\\"ohe\\\":\\n return self._one_hot_encoder()\\n elif self.enc_types == \\\"binary\\\":\\n return self._binarization()\\n else:\\n raise Exception(\\\"Encoding type not understood\\\")\",\n \"def __init__(self, encoding):\\n self.trans = {}\\n for char in u\\\"ÀÁÂẦẤẪẨẬÃĀĂẰẮẴẶẲȦǠẠḀȂĄǍẢ\\\":\\n self.trans[char] = u\\\"A\\\"\\n for char in u\\\"ȀǞ\\\":\\n self.trans[char] = u\\\"Ä\\\"\\n self.trans[u\\\"Ǻ\\\"] = u\\\"Å\\\"\\n self.trans[u\\\"Ä\\\"] = u\\\"Ae\\\"\\n self.trans[u\\\"Å\\\"] = u\\\"Aa\\\"\\n for char in u\\\"àáâầấẫẩậãāăằắẵặẳȧǡạḁȃąǎảẚ\\\":\\n self.trans[char] = u\\\"a\\\"\\n for char in u\\\"ȁǟ\\\":\\n self.trans[char] = u\\\"ä\\\"\\n self.trans[u\\\"ǻ\\\"] = u\\\"å\\\"\\n self.trans[u\\\"ä\\\"] = u\\\"ae\\\"\\n self.trans[u\\\"å\\\"] = u\\\"aa\\\"\\n for char in u\\\"ḂḄḆƁƂ\\\":\\n self.trans[char] = u\\\"B\\\"\\n for char in u\\\"ḃḅḇƀɓƃ\\\":\\n self.trans[char] = u\\\"b\\\"\\n for char in u\\\"ĆĈĊÇČƇ\\\":\\n self.trans[char] = u\\\"C\\\"\\n for char in u\\\"ćĉċçčƈȼ\\\":\\n self.trans[char] = u\\\"c\\\"\\n self.trans[u\\\"Ḉ\\\"] = u\\\"Ç\\\"\\n self.trans[u\\\"ḉ\\\"] = u\\\"ç\\\"\\n self.trans[u\\\"Ð\\\"] = u\\\"Dh\\\"\\n self.trans[u\\\"ð\\\"] = u\\\"dh\\\"\\n for char in u\\\"ĎḊḌḎḐḒĐƉƊƋ\\\":\\n self.trans[char] = u\\\"D\\\"\\n for char in u\\\"ďḋḍḏḑḓđɖɗƌ\\\":\\n self.trans[char] = u\\\"d\\\"\\n for char in u\\\"ÈȄÉÊḚËĒḔḖĔĖẸE̩ȆȨḜĘĚẼḘẺ\\\":\\n self.trans[char] = u\\\"E\\\"\\n for char in u\\\"ỀẾỄỆỂ\\\":\\n self.trans[char] = u\\\"Ê\\\"\\n for char in u\\\"èȅéêḛëēḕḗĕėẹe̩ȇȩḝęěẽḙẻ\\\":\\n self.trans[char] = u\\\"e\\\"\\n for char in u\\\"ềếễệể\\\":\\n self.trans[char] = u\\\"ê\\\"\\n for char in u\\\"ḞƑ\\\":\\n self.trans[char] = u\\\"F\\\"\\n for char in u\\\"ḟƒ\\\":\\n self.trans[char] = u\\\"f\\\"\\n for char in u\\\"ǴḠĞĠĢǦǤƓ\\\":\\n self.trans[char] = u\\\"G\\\"\\n for char in u\\\"ǵḡğġģǧǥɠ\\\":\\n self.trans[char] = u\\\"g\\\"\\n self.trans[u\\\"Ĝ\\\"] = u\\\"Gx\\\"\\n self.trans[u\\\"ĝ\\\"] = u\\\"gx\\\"\\n for char in u\\\"ḢḤḦȞḨḪH̱ĦǶ\\\":\\n self.trans[char] = u\\\"H\\\"\\n for char in u\\\"ḣḥḧȟḩḫ̱ẖħƕ\\\":\\n self.trans[char] = u\\\"h\\\"\\n for char in u\\\"IÌȈÍÎĨḬÏḮĪĬȊĮǏİỊỈƗ\\\":\\n self.trans[char] = u\\\"I\\\"\\n for char in u\\\"ıìȉíîĩḭïḯīĭȋįǐiịỉɨ\\\":\\n self.trans[char] = u\\\"i\\\"\\n for char in u\\\"ĴJ\\\":\\n self.trans[char] = u\\\"J\\\"\\n for char in u\\\"ɟĵ̌ǰ\\\":\\n self.trans[char] = u\\\"j\\\"\\n for char in u\\\"ḰǨĶḲḴƘ\\\":\\n self.trans[char] = u\\\"K\\\"\\n for char in u\\\"ḱǩķḳḵƙ\\\":\\n self.trans[char] = u\\\"k\\\"\\n for char in u\\\"ĹĻĽḶḸḺḼȽŁ\\\":\\n self.trans[char] = u\\\"L\\\"\\n for char in u\\\"ĺļľḷḹḻḽƚłɫ\\\":\\n self.trans[char] = u\\\"l\\\"\\n for char in u\\\"ḾṀṂ\\\":\\n self.trans[char] = u\\\"M\\\"\\n for char in u\\\"ḿṁṃɱ\\\":\\n self.trans[char] = u\\\"m\\\"\\n for char in u\\\"ǸŃÑŅŇṄṆṈṊŊƝɲȠ\\\":\\n self.trans[char] = u\\\"N\\\"\\n for char in u\\\"ǹńñņňṅṇṉṋŋɲƞ\\\":\\n self.trans[char] = u\\\"n\\\"\\n for char in u\\\"ÒÓÔÕṌṎȬÖŌṐṒŎǑȮȰỌǪǬƠỜỚỠỢỞỎƟØǾ\\\":\\n self.trans[char] = u\\\"O\\\"\\n for char in u\\\"òóôõṍṏȭöōṑṓŏǒȯȱọǫǭơờớỡợởỏɵøǿ\\\":\\n self.trans[char] = u\\\"o\\\"\\n for char in u\\\"ȌŐȪ\\\":\\n self.trans[char] = u\\\"Ö\\\"\\n for char in u\\\"ȍőȫ\\\":\\n self.trans[char] = u\\\"ö\\\"\\n for char in u\\\"ỒỐỖỘỔȎ\\\":\\n self.trans[char] = u\\\"Ô\\\"\\n for char in u\\\"ồốỗộổȏ\\\":\\n self.trans[char] = u\\\"ô\\\"\\n for char in u\\\"ṔṖƤ\\\":\\n self.trans[char] = u\\\"P\\\"\\n for char in u\\\"ṕṗƥ\\\":\\n self.trans[char] = u\\\"p\\\"\\n self.trans[u\\\"ᵽ\\\"] = u\\\"q\\\"\\n for char in u\\\"ȐŔŖŘȒṘṚṜṞ\\\":\\n self.trans[char] = u\\\"R\\\"\\n for char in u\\\"ȑŕŗřȓṙṛṝṟɽ\\\":\\n self.trans[char] = u\\\"r\\\"\\n for char in u\\\"ŚṤŞȘŠṦṠṢṨ\\\":\\n self.trans[char] = u\\\"S\\\"\\n for char in u\\\"śṥşșšṧṡṣṩȿ\\\":\\n self.trans[char] = u\\\"s\\\"\\n self.trans[u\\\"Ŝ\\\"] = u\\\"Sx\\\"\\n self.trans[u\\\"ŝ\\\"] = u\\\"sx\\\"\\n for char in u\\\"ŢȚŤṪṬṮṰŦƬƮ\\\":\\n self.trans[char] = u\\\"T\\\"\\n for char in u\\\"ţțťṫṭṯṱŧȾƭʈ\\\":\\n self.trans[char] = u\\\"t\\\"\\n for char in u\\\"ÙÚŨṸṴÜṲŪṺŬỤŮŲǓṶỦƯỮỰỬ\\\":\\n self.trans[char] = u\\\"U\\\"\\n for char in u\\\"ùúũṹṵüṳūṻŭụůųǔṷủưữựửʉ\\\":\\n self.trans[char] = u\\\"u\\\"\\n for char in u\\\"ȔŰǛǗǕǙ\\\":\\n self.trans[char] = u\\\"Ü\\\"\\n for char in u\\\"ȕűǜǘǖǚ\\\":\\n self.trans[char] = u\\\"ü\\\"\\n self.trans[u\\\"Û\\\"] = u\\\"Ux\\\"\\n self.trans[u\\\"û\\\"] = u\\\"ux\\\"\\n self.trans[u\\\"Ȗ\\\"] = u\\\"Û\\\"\\n self.trans[u\\\"ȗ\\\"] = u\\\"û\\\"\\n self.trans[u\\\"Ừ\\\"] = u\\\"Ù\\\"\\n self.trans[u\\\"ừ\\\"] = u\\\"ù\\\"\\n self.trans[u\\\"Ứ\\\"] = u\\\"Ú\\\"\\n self.trans[u\\\"ứ\\\"] = u\\\"ú\\\"\\n for char in u\\\"ṼṾ\\\":\\n self.trans[char] = u\\\"V\\\"\\n for char in u\\\"ṽṿ\\\":\\n self.trans[char] = u\\\"v\\\"\\n for char in u\\\"ẀẂŴẄẆẈ\\\":\\n self.trans[char] = u\\\"W\\\"\\n for char in u\\\"ẁẃŵẅẇẉ\\\":\\n self.trans[char] = u\\\"w\\\"\\n for char in u\\\"ẊẌ\\\":\\n self.trans[char] = u\\\"X\\\"\\n for char in u\\\"ẋẍ\\\":\\n self.trans[char] = u\\\"x\\\"\\n for char in u\\\"ỲÝŶŸỸȲẎỴỶƳ\\\":\\n self.trans[char] = u\\\"Y\\\"\\n for char in u\\\"ỳýŷÿỹȳẏỵỷƴ\\\":\\n self.trans[char] = u\\\"y\\\"\\n for char in u\\\"ŹẐŻẒŽẔƵȤ\\\":\\n self.trans[char] = u\\\"Z\\\"\\n for char in u\\\"źẑżẓžẕƶȥ\\\":\\n self.trans[char] = u\\\"z\\\"\\n self.trans[u\\\"ɀ\\\"] = u\\\"zv\\\"\\n\\n # Latin: extended Latin alphabet\\n self.trans[u\\\"ɑ\\\"] = u\\\"a\\\"\\n for char in u\\\"ÆǼǢ\\\":\\n self.trans[char] = u\\\"AE\\\"\\n for char in u\\\"æǽǣ\\\":\\n self.trans[char] = u\\\"ae\\\"\\n self.trans[u\\\"Ð\\\"] = u\\\"Dh\\\"\\n self.trans[u\\\"ð\\\"] = u\\\"dh\\\"\\n for char in u\\\"ƎƏƐ\\\":\\n self.trans[char] = u\\\"E\\\"\\n for char in u\\\"ǝəɛ\\\":\\n self.trans[char] = u\\\"e\\\"\\n for char in u\\\"ƔƢ\\\":\\n self.trans[char] = u\\\"G\\\"\\n for char in u\\\"ᵷɣƣᵹ\\\":\\n self.trans[char] = u\\\"g\\\"\\n self.trans[u\\\"Ƅ\\\"] = u\\\"H\\\"\\n self.trans[u\\\"ƅ\\\"] = u\\\"h\\\"\\n self.trans[u\\\"Ƕ\\\"] = u\\\"Wh\\\"\\n self.trans[u\\\"ƕ\\\"] = u\\\"wh\\\"\\n self.trans[u\\\"Ɩ\\\"] = u\\\"I\\\"\\n self.trans[u\\\"ɩ\\\"] = u\\\"i\\\"\\n self.trans[u\\\"Ŋ\\\"] = u\\\"Ng\\\"\\n self.trans[u\\\"ŋ\\\"] = u\\\"ng\\\"\\n self.trans[u\\\"Œ\\\"] = u\\\"OE\\\"\\n self.trans[u\\\"œ\\\"] = u\\\"oe\\\"\\n self.trans[u\\\"Ɔ\\\"] = u\\\"O\\\"\\n self.trans[u\\\"ɔ\\\"] = u\\\"o\\\"\\n self.trans[u\\\"Ȣ\\\"] = u\\\"Ou\\\"\\n self.trans[u\\\"ȣ\\\"] = u\\\"ou\\\"\\n self.trans[u\\\"Ƽ\\\"] = u\\\"Q\\\"\\n for char in u\\\"ĸƽ\\\":\\n self.trans[char] = u\\\"q\\\"\\n self.trans[u\\\"ȹ\\\"] = u\\\"qp\\\"\\n self.trans[u\\\"\\\"] = u\\\"r\\\"\\n self.trans[u\\\"ſ\\\"] = u\\\"s\\\"\\n self.trans[u\\\"ß\\\"] = u\\\"ss\\\"\\n self.trans[u\\\"Ʃ\\\"] = u\\\"Sh\\\"\\n for char in u\\\"ʃᶋ\\\":\\n self.trans[char] = u\\\"sh\\\"\\n self.trans[u\\\"Ʉ\\\"] = u\\\"U\\\"\\n self.trans[u\\\"ʉ\\\"] = u\\\"u\\\"\\n self.trans[u\\\"Ʌ\\\"] = u\\\"V\\\"\\n self.trans[u\\\"ʌ\\\"] = u\\\"v\\\"\\n for char in u\\\"ƜǷ\\\":\\n self.trans[char] = u\\\"W\\\"\\n for char in u\\\"ɯƿ\\\":\\n self.trans[char] = u\\\"w\\\"\\n self.trans[u\\\"Ȝ\\\"] = u\\\"Y\\\"\\n self.trans[u\\\"ȝ\\\"] = u\\\"y\\\"\\n self.trans[u\\\"IJ\\\"] = u\\\"IJ\\\"\\n self.trans[u\\\"ij\\\"] = u\\\"ij\\\"\\n self.trans[u\\\"Ƨ\\\"] = u\\\"Z\\\"\\n for char in u\\\"ʮƨ\\\":\\n self.trans[char] = u\\\"z\\\"\\n self.trans[u\\\"Ʒ\\\"] = u\\\"Zh\\\"\\n self.trans[u\\\"ʒ\\\"] = u\\\"zh\\\"\\n self.trans[u\\\"Ǯ\\\"] = u\\\"Dzh\\\"\\n self.trans[u\\\"ǯ\\\"] = u\\\"dzh\\\"\\n for char in u\\\"ƸƹʔˀɁɂ\\\":\\n self.trans[char] = u\\\"'\\\"\\n self.trans['Þ'] = 'Th'\\n self.trans['þ'] = 'th'\\n for char in u\\\"Cʗǃ\\\":\\n self.trans[char] = u\\\"!\\\"\\n\\n # Punctuation and typography\\n for char in u\\\"«»“”„¨\\\":\\n self.trans[char] = u'\\\"'\\n for char in u\\\"‘’′\\\":\\n self.trans[char] = u\\\"'\\\"\\n self.trans[u\\\"•\\\"] = u\\\"*\\\"\\n self.trans[u\\\"@\\\"] = u\\\"(at)\\\"\\n self.trans[u\\\"¤\\\"] = u\\\"$\\\"\\n self.trans[u\\\"¢\\\"] = u\\\"c\\\"\\n self.trans[u\\\"€\\\"] = u\\\"E\\\"\\n self.trans[u\\\"£\\\"] = u\\\"L\\\"\\n self.trans[u\\\"¥\\\"] = u\\\"yen\\\"\\n self.trans[u\\\"†\\\"] = u\\\"+\\\"\\n self.trans[u\\\"‡\\\"] = u\\\"++\\\"\\n self.trans[u\\\"°\\\"] = u\\\":\\\"\\n self.trans[u\\\"¡\\\"] = u\\\"!\\\"\\n self.trans[u\\\"¿\\\"] = u\\\"?\\\"\\n self.trans[u\\\"‰\\\"] = u\\\"o/oo\\\"\\n self.trans[u\\\"‱\\\"] = u\\\"o/ooo\\\"\\n for char in u\\\"¶§\\\":\\n self.trans[char] = u\\\">\\\"\\n self.trans['…'] = '...'\\n for char in u\\\"‒–—―\\\":\\n self.trans[char] = u\\\"-\\\"\\n self.trans['·'] = ' '\\n self.trans[u\\\"¦\\\"] = u\\\"|\\\"\\n self.trans[u\\\"⁂\\\"] = u\\\"***\\\"\\n self.trans[u\\\"◊\\\"] = u\\\"<>\\\"\\n self.trans[u\\\"‽\\\"] = u\\\"?!\\\"\\n self.trans[u\\\"؟\\\"] = u\\\";-)\\\"\\n self.trans[u\\\"¹\\\"] = u\\\"1\\\"\\n self.trans[u\\\"²\\\"] = u\\\"2\\\"\\n self.trans[u\\\"³\\\"] = u\\\"3\\\"\\n\\n # Cyrillic\\n self.trans.update({u\\\"А\\\": u\\\"A\\\", u\\\"а\\\": u\\\"a\\\", u\\\"Б\\\": u\\\"B\\\", u\\\"б\\\": u\\\"b\\\",\\n u\\\"В\\\": u\\\"V\\\", u\\\"в\\\": u\\\"v\\\", u\\\"Г\\\": u\\\"G\\\", u\\\"г\\\": u\\\"g\\\",\\n u\\\"Д\\\": u\\\"D\\\", u\\\"д\\\": u\\\"d\\\", u\\\"Е\\\": u\\\"E\\\", u\\\"е\\\": u\\\"e\\\",\\n u\\\"Ж\\\": u\\\"Zh\\\", u\\\"ж\\\": u\\\"zh\\\", u\\\"З\\\": u\\\"Z\\\", u\\\"з\\\": u\\\"z\\\",\\n u\\\"И\\\": u\\\"I\\\", u\\\"и\\\": u\\\"i\\\", u\\\"Й\\\": u\\\"J\\\", u\\\"й\\\": u\\\"j\\\",\\n u\\\"К\\\": u\\\"K\\\", u\\\"к\\\": u\\\"k\\\", u\\\"Л\\\": u\\\"L\\\", u\\\"л\\\": u\\\"l\\\",\\n u\\\"М\\\": u\\\"M\\\", u\\\"м\\\": u\\\"m\\\", u\\\"Н\\\": u\\\"N\\\", u\\\"н\\\": u\\\"n\\\",\\n u\\\"О\\\": u\\\"O\\\", u\\\"о\\\": u\\\"o\\\", u\\\"П\\\": u\\\"P\\\", u\\\"п\\\": u\\\"p\\\",\\n u\\\"Р\\\": u\\\"R\\\", u\\\"р\\\": u\\\"r\\\", u\\\"С\\\": u\\\"S\\\", u\\\"с\\\": u\\\"s\\\",\\n u\\\"Т\\\": u\\\"T\\\", u\\\"т\\\": u\\\"t\\\", u\\\"У\\\": u\\\"U\\\", u\\\"у\\\": u\\\"u\\\",\\n u\\\"Ф\\\": u\\\"F\\\", u\\\"ф\\\": u\\\"f\\\", u\\\"х\\\": u\\\"kh\\\", u\\\"Ц\\\": u\\\"C\\\",\\n u\\\"ц\\\": u\\\"c\\\", u\\\"Ч\\\": u\\\"Ch\\\", u\\\"ч\\\": u\\\"ch\\\", u\\\"Ш\\\": u\\\"Sh\\\",\\n u\\\"ш\\\": u\\\"sh\\\", u\\\"Щ\\\": u\\\"Shch\\\", u\\\"щ\\\": u\\\"shch\\\", u\\\"Ь\\\": u\\\"'\\\",\\n u\\\"ь\\\": \\\"'\\\", u\\\"Ъ\\\": u'\\\"', u\\\"ъ\\\": '\\\"', u\\\"Ю\\\": u\\\"Yu\\\",\\n u\\\"ю\\\": u\\\"yu\\\", u\\\"Я\\\": u\\\"Ya\\\", u\\\"я\\\": u\\\"ya\\\", u\\\"Х\\\": u\\\"Kh\\\",\\n u\\\"Χ\\\": u\\\"Kh\\\"})\\n\\n # Additional Cyrillic letters, most occuring in only one or a few languages\\n self.trans.update({u\\\"Ы\\\": u\\\"Y\\\", u\\\"ы\\\": u\\\"y\\\", u\\\"Ё\\\": u\\\"Ë\\\", u\\\"ё\\\": u\\\"ë\\\",\\n u\\\"Э\\\": u\\\"È\\\", u\\\"Ѐ\\\": u\\\"È\\\", u\\\"э\\\": u\\\"è\\\", u\\\"ѐ\\\": u\\\"è\\\",\\n u\\\"І\\\": u\\\"I\\\", u\\\"і\\\": u\\\"i\\\", u\\\"Ї\\\": u\\\"Ji\\\", u\\\"ї\\\": u\\\"ji\\\",\\n u\\\"Є\\\": u\\\"Je\\\", u\\\"є\\\": u\\\"je\\\", u\\\"Ґ\\\": u\\\"G\\\", u\\\"Ҝ\\\": u\\\"G\\\",\\n u\\\"ґ\\\": u\\\"g\\\", u\\\"ҝ\\\": u\\\"g\\\", u\\\"Ђ\\\": u\\\"Dj\\\", u\\\"ђ\\\": u\\\"dj\\\",\\n \\\"Љ\\\": \\\"Lj\\\", \\\"љ\\\": \\\"lj\\\",\\n u\\\"Њ\\\": u\\\"Nj\\\", u\\\"њ\\\": u\\\"nj\\\", u\\\"Ћ\\\": u\\\"Cj\\\", u\\\"ћ\\\": u\\\"cj\\\",\\n 'Җ': 'Zhj', 'Ѓ': 'Gj', 'ѓ': 'gj',\\n u\\\"Ќ\\\": u\\\"Kj\\\", u\\\"ќ\\\": u\\\"kj\\\", u\\\"Ӣ\\\": u\\\"Ii\\\", u\\\"ӣ\\\": u\\\"ii\\\",\\n \\\"Ҳ\\\": \\\"H\\\", \\\"ҳ\\\": \\\"h\\\",\\n u\\\"Ҷ\\\": u\\\"Dz\\\", u\\\"ҷ\\\": u\\\"dz\\\", u\\\"Ө\\\": u\\\"Ô\\\", u\\\"Ӫ\\\": u\\\"Ô\\\",\\n u\\\"ө\\\": u\\\"ô\\\", u\\\"ӫ\\\": u\\\"ô\\\", u\\\"Ү\\\": u\\\"Y\\\", u\\\"ү\\\": u\\\"y\\\", u\\\"Һ\\\": u\\\"H\\\",\\n u\\\"һ\\\": u\\\"h\\\", u\\\"Ә\\\": u\\\"AE\\\", u\\\"Ӕ\\\": u\\\"AE\\\", u\\\"ә\\\": u\\\"ae\\\",\\n 'Ӛ': 'Ë', 'Ӭ': 'Ë', 'ӛ': 'ë', 'ӭ': 'ë',\\n 'җ': 'zhj', 'Ұ': 'U', 'ў': 'ù', 'Ў': 'Ù',\\n u\\\"ѝ\\\": u\\\"ì\\\", u\\\"Ѝ\\\": u\\\"Ì\\\", u\\\"Ӑ\\\": u\\\"A\\\", u\\\"ă\\\": u\\\"a\\\", u\\\"Ӓ\\\": u\\\"Ä\\\",\\n \\\"Ҽ\\\": \\\"Ts\\\", \\\"Ҿ\\\": \\\"Ts\\\", \\\"ҽ\\\": \\\"ts\\\", \\\"ҿ\\\": \\\"ts\\\",\\n u\\\"Ҙ\\\": u\\\"Dh\\\", u\\\"ҙ\\\": u\\\"dh\\\", u\\\"Ӏ\\\": u\\\"\\\", u\\\"ӏ\\\": u\\\"\\\", u\\\"Ӆ\\\": u\\\"L\\\",\\n u\\\"ӆ\\\": u\\\"l\\\", u\\\"Ӎ\\\": u\\\"M\\\", u\\\"ӎ\\\": u\\\"m\\\", u\\\"Ӧ\\\": u\\\"Ö\\\", u\\\"ӧ\\\": u\\\"ö\\\",\\n u\\\"Ҩ\\\": u\\\"u\\\", u\\\"ҩ\\\": u\\\"u\\\", u\\\"Ҧ\\\": u\\\"Ph\\\", u\\\"ҧ\\\": u\\\"ph\\\", u\\\"Ҏ\\\": u\\\"R\\\",\\n u\\\"ҏ\\\": u\\\"r\\\", u\\\"Ҫ\\\": u\\\"Th\\\", u\\\"ҫ\\\": u\\\"th\\\", u\\\"Ҭ\\\": u\\\"T\\\", u\\\"ҭ\\\": u\\\"t\\\",\\n 'Ӯ': 'Û', 'ӯ': 'û', 'Ӹ': 'U', 'ұ': 'u',\\n u\\\"ӹ\\\": u\\\"u\\\", u\\\"Ҵ\\\": u\\\"Tts\\\", u\\\"ҵ\\\": u\\\"tts\\\", u\\\"Ӵ\\\": u\\\"Ch\\\", u\\\"ӵ\\\": u\\\"ch\\\"})\\n\\n for char in u\\\"ЈӤҊ\\\":\\n self.trans[char] = u\\\"J\\\"\\n for char in u\\\"јӥҋ\\\":\\n self.trans[char] = u\\\"j\\\"\\n for char in u\\\"ЏӁӜҶ\\\":\\n self.trans[char] = u\\\"Dzh\\\"\\n for char in u\\\"џӂӝҷ\\\":\\n self.trans[char] = u\\\"dzh\\\"\\n for char in u\\\"ЅӞӠӋҸ\\\":\\n self.trans[char] = u\\\"Dz\\\"\\n for char in u\\\"ѕӟӡӌҹ\\\":\\n self.trans[char] = u\\\"dz\\\"\\n for char in u\\\"ҒӶҔ\\\":\\n self.trans[char] = u\\\"G\\\"\\n for char in u\\\"ғӷҕ\\\":\\n self.trans[char] = u\\\"g\\\"\\n for char in u\\\"ҚҞҠӃ\\\":\\n self.trans[char] = u\\\"Q\\\"\\n for char in u\\\"қҟҡӄ\\\":\\n self.trans[char] = u\\\"q\\\"\\n for char in u\\\"ҢҤӉӇ\\\":\\n self.trans[char] = u\\\"Ng\\\"\\n for char in u\\\"ңҥӊӈ\\\":\\n self.trans[char] = u\\\"ng\\\"\\n for char in u\\\"ӖѢҌ\\\":\\n self.trans[char] = u\\\"E\\\"\\n for char in u\\\"ӗѣҍ\\\":\\n self.trans[char] = u\\\"e\\\"\\n for char in u\\\"ӲӰҮ\\\":\\n self.trans[char] = u\\\"Ü\\\"\\n for char in u\\\"ӳӱү\\\":\\n self.trans[char] = u\\\"ü\\\"\\n\\n # Archaic Cyrillic letters\\n self.trans.update({u\\\"Ѹ\\\": u\\\"Ou\\\", u\\\"ѹ\\\": u\\\"ou\\\", u\\\"Ѡ\\\": u\\\"O\\\", u\\\"Ѻ\\\": u\\\"O\\\", u\\\"ѡ\\\": u\\\"o\\\",\\n u\\\"ѻ\\\": u\\\"o\\\", u\\\"Ѿ\\\": u\\\"Ot\\\", u\\\"ѿ\\\": u\\\"ot\\\", u\\\"Ѣ\\\": u\\\"E\\\", u\\\"ѣ\\\": u\\\"e\\\",\\n u\\\"Ѥ\\\": u\\\"Ei\\\", u\\\"Ѧ\\\": u\\\"Ei\\\", u\\\"ѥ\\\": u\\\"ei\\\", u\\\"ѧ\\\": u\\\"ei\\\", u\\\"Ѫ\\\": u\\\"Ai\\\",\\n u\\\"ѫ\\\": u\\\"ai\\\", u\\\"Ѯ\\\": u\\\"X\\\", u\\\"ѯ\\\": u\\\"x\\\", u\\\"Ѱ\\\": u\\\"Ps\\\", u\\\"ѱ\\\": u\\\"ps\\\",\\n u\\\"Ѳ\\\": u\\\"Th\\\", u\\\"ѳ\\\": u\\\"th\\\", u\\\"Ѵ\\\": u\\\"Ü\\\", u\\\"Ѷ\\\": u\\\"Ü\\\", u\\\"ѵ\\\": u\\\"ü\\\"})\\n\\n # Hebrew alphabet\\n for char in u\\\"אע\\\":\\n self.trans[char] = u\\\"'\\\"\\n self.trans[u\\\"ב\\\"] = u\\\"b\\\"\\n self.trans[u\\\"ג\\\"] = u\\\"g\\\"\\n self.trans[u\\\"ד\\\"] = u\\\"d\\\"\\n self.trans[u\\\"ה\\\"] = u\\\"h\\\"\\n self.trans[u\\\"ו\\\"] = u\\\"v\\\"\\n self.trans[u\\\"ז\\\"] = u\\\"z\\\"\\n self.trans[u\\\"ח\\\"] = u\\\"kh\\\"\\n self.trans[u\\\"ט\\\"] = u\\\"t\\\"\\n self.trans[u\\\"י\\\"] = u\\\"y\\\"\\n for char in u\\\"ךכ\\\":\\n self.trans[char] = u\\\"k\\\"\\n self.trans[u\\\"ל\\\"] = u\\\"l\\\"\\n for char in u\\\"םמ\\\":\\n self.trans[char] = u\\\"m\\\"\\n for char in u\\\"ןנ\\\":\\n self.trans[char] = u\\\"n\\\"\\n self.trans[u\\\"ס\\\"] = u\\\"s\\\"\\n for char in u\\\"ףפ\\\":\\n self.trans[char] = u\\\"ph\\\"\\n for char in u\\\"ץצ\\\":\\n self.trans[char] = u\\\"ts\\\"\\n self.trans[u\\\"ק\\\"] = u\\\"q\\\"\\n self.trans[u\\\"ר\\\"] = u\\\"r\\\"\\n self.trans[u\\\"ש\\\"] = u\\\"sh\\\"\\n self.trans[u\\\"ת\\\"] = u\\\"th\\\"\\n\\n # Arab alphabet\\n for char in u\\\"اﺍﺎ\\\":\\n self.trans[char] = u\\\"a\\\"\\n for char in u\\\"بﺏﺐﺒﺑ\\\":\\n self.trans[char] = u\\\"b\\\"\\n for char in u\\\"تﺕﺖﺘﺗ\\\":\\n self.trans[char] = u\\\"t\\\"\\n for char in u\\\"ثﺙﺚﺜﺛ\\\":\\n self.trans[char] = u\\\"th\\\"\\n for char in u\\\"جﺝﺞﺠﺟ\\\":\\n self.trans[char] = u\\\"g\\\"\\n for char in u\\\"حﺡﺢﺤﺣ\\\":\\n self.trans[char] = u\\\"h\\\"\\n for char in u\\\"خﺥﺦﺨﺧ\\\":\\n self.trans[char] = u\\\"kh\\\"\\n for char in u\\\"دﺩﺪ\\\":\\n self.trans[char] = u\\\"d\\\"\\n for char in u\\\"ذﺫﺬ\\\":\\n self.trans[char] = u\\\"dh\\\"\\n for char in u\\\"رﺭﺮ\\\":\\n self.trans[char] = u\\\"r\\\"\\n for char in u\\\"زﺯﺰ\\\":\\n self.trans[char] = u\\\"z\\\"\\n for char in u\\\"سﺱﺲﺴﺳ\\\":\\n self.trans[char] = u\\\"s\\\"\\n for char in u\\\"شﺵﺶﺸﺷ\\\":\\n self.trans[char] = u\\\"sh\\\"\\n for char in u\\\"صﺹﺺﺼﺻ\\\":\\n self.trans[char] = u\\\"s\\\"\\n for char in u\\\"ضﺽﺾﻀﺿ\\\":\\n self.trans[char] = u\\\"d\\\"\\n for char in u\\\"طﻁﻂﻄﻃ\\\":\\n self.trans[char] = u\\\"t\\\"\\n for char in u\\\"ظﻅﻆﻈﻇ\\\":\\n self.trans[char] = u\\\"z\\\"\\n for char in u\\\"عﻉﻊﻌﻋ\\\":\\n self.trans[char] = u\\\"'\\\"\\n for char in u\\\"غﻍﻎﻐﻏ\\\":\\n self.trans[char] = u\\\"gh\\\"\\n for char in u\\\"فﻑﻒﻔﻓ\\\":\\n self.trans[char] = u\\\"f\\\"\\n for char in u\\\"قﻕﻖﻘﻗ\\\":\\n self.trans[char] = u\\\"q\\\"\\n for char in u\\\"كﻙﻚﻜﻛک\\\":\\n self.trans[char] = u\\\"k\\\"\\n for char in u\\\"لﻝﻞﻠﻟ\\\":\\n self.trans[char] = u\\\"l\\\"\\n for char in u\\\"مﻡﻢﻤﻣ\\\":\\n self.trans[char] = u\\\"m\\\"\\n for char in u\\\"نﻥﻦﻨﻧ\\\":\\n self.trans[char] = u\\\"n\\\"\\n for char in u\\\"هﻩﻪﻬﻫ\\\":\\n self.trans[char] = u\\\"h\\\"\\n for char in u\\\"وﻭﻮ\\\":\\n self.trans[char] = u\\\"w\\\"\\n for char in u\\\"یيﻱﻲﻴﻳ\\\":\\n self.trans[char] = u\\\"y\\\"\\n # Arabic - additional letters, modified letters and ligatures\\n self.trans[u\\\"ﺀ\\\"] = u\\\"'\\\"\\n for char in u\\\"آﺁﺂ\\\":\\n self.trans[char] = u\\\"'a\\\"\\n for char in u\\\"ةﺓﺔ\\\":\\n self.trans[char] = u\\\"th\\\"\\n for char in u\\\"ىﻯﻰ\\\":\\n self.trans[char] = u\\\"á\\\"\\n for char in u\\\"یﯼﯽﯿﯾ\\\":\\n self.trans[char] = u\\\"y\\\"\\n self.trans[u\\\"؟\\\"] = u\\\"?\\\"\\n # Arabic - ligatures\\n for char in u\\\"ﻻﻼ\\\":\\n self.trans[char] = u\\\"la\\\"\\n self.trans[u\\\"ﷲ\\\"] = u\\\"llah\\\"\\n for char in u\\\"إأ\\\":\\n self.trans[char] = u\\\"a'\\\"\\n self.trans[u\\\"ؤ\\\"] = u\\\"w'\\\"\\n self.trans[u\\\"ئ\\\"] = u\\\"y'\\\"\\n for char in u\\\"◌◌\\\":\\n self.trans[char] = u\\\"\\\" # indicates absence of vowels\\n # Arabic vowels\\n self.trans[u\\\"◌\\\"] = u\\\"a\\\"\\n self.trans[u\\\"◌\\\"] = u\\\"u\\\"\\n self.trans[u\\\"◌\\\"] = u\\\"i\\\"\\n self.trans[u\\\"◌\\\"] = u\\\"a\\\"\\n self.trans[u\\\"◌\\\"] = u\\\"ay\\\"\\n self.trans[u\\\"◌\\\"] = u\\\"ay\\\"\\n self.trans[u\\\"◌\\\"] = u\\\"u\\\"\\n self.trans[u\\\"◌\\\"] = u\\\"iy\\\"\\n # Arab numerals\\n for char in u\\\"٠۰\\\":\\n self.trans[char] = u\\\"0\\\"\\n for char in u\\\"١۱\\\":\\n self.trans[char] = u\\\"1\\\"\\n for char in u\\\"٢۲\\\":\\n self.trans[char] = u\\\"2\\\"\\n for char in u\\\"٣۳\\\":\\n self.trans[char] = u\\\"3\\\"\\n for char in u\\\"٤۴\\\":\\n self.trans[char] = u\\\"4\\\"\\n for char in u\\\"٥۵\\\":\\n self.trans[char] = u\\\"5\\\"\\n for char in u\\\"٦۶\\\":\\n self.trans[char] = u\\\"6\\\"\\n for char in u\\\"٧۷\\\":\\n self.trans[char] = u\\\"7\\\"\\n for char in u\\\"٨۸\\\":\\n self.trans[char] = u\\\"8\\\"\\n for char in u\\\"٩۹\\\":\\n self.trans[char] = u\\\"9\\\"\\n # Perso-Arabic\\n for char in u\\\"پﭙﭙپ\\\":\\n self.trans[char] = u\\\"p\\\"\\n for char in u\\\"چچچچ\\\":\\n self.trans[char] = u\\\"ch\\\"\\n for char in u\\\"ژژ\\\":\\n self.trans[char] = u\\\"zh\\\"\\n for char in u\\\"گﮔﮕﮓ\\\":\\n self.trans[char] = u\\\"g\\\"\\n\\n # Greek\\n self.trans.update({u\\\"Α\\\": u\\\"A\\\", u\\\"α\\\": u\\\"a\\\", u\\\"Β\\\": u\\\"B\\\", u\\\"β\\\": u\\\"b\\\", u\\\"Γ\\\": u\\\"G\\\",\\n u\\\"γ\\\": u\\\"g\\\", u\\\"Δ\\\": u\\\"D\\\", u\\\"δ\\\": u\\\"d\\\", u\\\"Ε\\\": u\\\"E\\\", u\\\"ε\\\": u\\\"e\\\",\\n u\\\"Ζ\\\": u\\\"Z\\\", u\\\"ζ\\\": u\\\"z\\\", u\\\"Η\\\": u\\\"I\\\", u\\\"η\\\": u\\\"i\\\", u\\\"θ\\\": u\\\"th\\\",\\n u\\\"Θ\\\": u\\\"Th\\\", u\\\"Ι\\\": u\\\"I\\\", u\\\"ι\\\": u\\\"i\\\", u\\\"Κ\\\": u\\\"K\\\", u\\\"κ\\\": u\\\"k\\\",\\n u\\\"Λ\\\": u\\\"L\\\", u\\\"λ\\\": u\\\"l\\\", u\\\"Μ\\\": u\\\"M\\\", u\\\"μ\\\": u\\\"m\\\", u\\\"Ν\\\": u\\\"N\\\",\\n u\\\"ν\\\": u\\\"n\\\", u\\\"Ξ\\\": u\\\"X\\\", u\\\"ξ\\\": u\\\"x\\\", u\\\"Ο\\\": u\\\"O\\\", u\\\"ο\\\": u\\\"o\\\",\\n u\\\"Π\\\": u\\\"P\\\", u\\\"π\\\": u\\\"p\\\", u\\\"Ρ\\\": u\\\"R\\\", u\\\"ρ\\\": u\\\"r\\\", u\\\"Σ\\\": u\\\"S\\\",\\n u\\\"σ\\\": u\\\"s\\\", u\\\"ς\\\": u\\\"s\\\", u\\\"Τ\\\": u\\\"T\\\", u\\\"τ\\\": u\\\"t\\\", u\\\"Υ\\\": u\\\"Y\\\",\\n u\\\"υ\\\": u\\\"y\\\", u\\\"Φ\\\": u\\\"F\\\", u\\\"φ\\\": u\\\"f\\\", u\\\"Ψ\\\": u\\\"Ps\\\", u\\\"ψ\\\": u\\\"ps\\\",\\n u\\\"Ω\\\": u\\\"O\\\", u\\\"ω\\\": u\\\"o\\\", u\\\"ϗ\\\": u\\\"&\\\", u\\\"Ϛ\\\": u\\\"St\\\", u\\\"ϛ\\\": u\\\"st\\\",\\n u\\\"Ϙ\\\": u\\\"Q\\\", u\\\"Ϟ\\\": u\\\"Q\\\", u\\\"ϙ\\\": u\\\"q\\\", u\\\"ϟ\\\": u\\\"q\\\", u\\\"Ϻ\\\": u\\\"S\\\",\\n u\\\"ϻ\\\": u\\\"s\\\", u\\\"Ϡ\\\": u\\\"Ss\\\", u\\\"ϡ\\\": u\\\"ss\\\", u\\\"Ϸ\\\": u\\\"Sh\\\", u\\\"ϸ\\\": u\\\"sh\\\",\\n u\\\"·\\\": u\\\":\\\", u\\\"Ά\\\": u\\\"Á\\\", u\\\"ά\\\": u\\\"á\\\", u\\\"Έ\\\": u\\\"É\\\", u\\\"Ή\\\": u\\\"É\\\",\\n u\\\"έ\\\": u\\\"é\\\", u\\\"ή\\\": u\\\"é\\\", u\\\"Ί\\\": u\\\"Í\\\", u\\\"ί\\\": u\\\"í\\\", u\\\"Ϊ\\\": u\\\"Ï\\\",\\n u\\\"ϊ\\\": u\\\"ï\\\", u\\\"ΐ\\\": u\\\"ï\\\", u\\\"Ό\\\": u\\\"Ó\\\", u\\\"ό\\\": u\\\"ó\\\", u\\\"Ύ\\\": u\\\"Ý\\\",\\n u\\\"ύ\\\": u\\\"ý\\\", u\\\"Ϋ\\\": u\\\"Y\\\", u\\\"ϋ\\\": u\\\"ÿ\\\", u\\\"ΰ\\\": u\\\"ÿ\\\", u\\\"Ώ\\\": u\\\"Ó\\\",\\n u\\\"ώ\\\": u\\\"ó\\\"})\\n\\n # Japanese (katakana and hiragana)\\n for char in u\\\"アァあ\\\":\\n self.trans[char] = u\\\"a\\\"\\n for char in u\\\"イィい\\\":\\n self.trans[char] = u\\\"i\\\"\\n for char in u\\\"ウう\\\":\\n self.trans[char] = u\\\"u\\\"\\n for char in u\\\"エェえ\\\":\\n self.trans[char] = u\\\"e\\\"\\n for char in u\\\"オォお\\\":\\n self.trans[char] = u\\\"o\\\"\\n for char in u\\\"ャや\\\":\\n self.trans[char] = u\\\"ya\\\"\\n for char in u\\\"ュゆ\\\":\\n self.trans[char] = u\\\"yu\\\"\\n for char in u\\\"ョよ\\\":\\n self.trans[char] = u\\\"yo\\\"\\n for char in u\\\"カか\\\":\\n self.trans[char] = u\\\"ka\\\"\\n for char in u\\\"キき\\\":\\n self.trans[char] = u\\\"ki\\\"\\n for char in u\\\"クく\\\":\\n self.trans[char] = u\\\"ku\\\"\\n for char in u\\\"ケけ\\\":\\n self.trans[char] = u\\\"ke\\\"\\n for char in u\\\"コこ\\\":\\n self.trans[char] = u\\\"ko\\\"\\n for char in u\\\"サさ\\\":\\n self.trans[char] = u\\\"sa\\\"\\n for char in u\\\"シし\\\":\\n self.trans[char] = u\\\"shi\\\"\\n for char in u\\\"スす\\\":\\n self.trans[char] = u\\\"su\\\"\\n for char in u\\\"セせ\\\":\\n self.trans[char] = u\\\"se\\\"\\n for char in u\\\"ソそ\\\":\\n self.trans[char] = u\\\"so\\\"\\n for char in u\\\"タた\\\":\\n self.trans[char] = u\\\"ta\\\"\\n for char in u\\\"チち\\\":\\n self.trans[char] = u\\\"chi\\\"\\n for char in u\\\"ツつ\\\":\\n self.trans[char] = u\\\"tsu\\\"\\n for char in u\\\"テて\\\":\\n self.trans[char] = u\\\"te\\\"\\n for char in u\\\"トと\\\":\\n self.trans[char] = u\\\"to\\\"\\n for char in u\\\"ナな\\\":\\n self.trans[char] = u\\\"na\\\"\\n for char in u\\\"ニに\\\":\\n self.trans[char] = u\\\"ni\\\"\\n for char in u\\\"ヌぬ\\\":\\n self.trans[char] = u\\\"nu\\\"\\n for char in u\\\"ネね\\\":\\n self.trans[char] = u\\\"ne\\\"\\n for char in u\\\"ノの\\\":\\n self.trans[char] = u\\\"no\\\"\\n for char in u\\\"ハは\\\":\\n self.trans[char] = u\\\"ha\\\"\\n for char in u\\\"ヒひ\\\":\\n self.trans[char] = u\\\"hi\\\"\\n for char in u\\\"フふ\\\":\\n self.trans[char] = u\\\"fu\\\"\\n for char in u\\\"ヘへ\\\":\\n self.trans[char] = u\\\"he\\\"\\n for char in u\\\"ホほ\\\":\\n self.trans[char] = u\\\"ho\\\"\\n for char in u\\\"マま\\\":\\n self.trans[char] = u\\\"ma\\\"\\n for char in u\\\"ミみ\\\":\\n self.trans[char] = u\\\"mi\\\"\\n for char in u\\\"ムむ\\\":\\n self.trans[char] = u\\\"mu\\\"\\n for char in u\\\"メめ\\\":\\n self.trans[char] = u\\\"me\\\"\\n for char in u\\\"モも\\\":\\n self.trans[char] = u\\\"mo\\\"\\n for char in u\\\"ラら\\\":\\n self.trans[char] = u\\\"ra\\\"\\n for char in u\\\"リり\\\":\\n self.trans[char] = u\\\"ri\\\"\\n for char in u\\\"ルる\\\":\\n self.trans[char] = u\\\"ru\\\"\\n for char in u\\\"レれ\\\":\\n self.trans[char] = u\\\"re\\\"\\n for char in u\\\"ロろ\\\":\\n self.trans[char] = u\\\"ro\\\"\\n for char in u\\\"ワわ\\\":\\n self.trans[char] = u\\\"wa\\\"\\n for char in u\\\"ヰゐ\\\":\\n self.trans[char] = u\\\"wi\\\"\\n for char in u\\\"ヱゑ\\\":\\n self.trans[char] = u\\\"we\\\"\\n for char in u\\\"ヲを\\\":\\n self.trans[char] = u\\\"wo\\\"\\n for char in u\\\"ンん\\\":\\n self.trans[char] = u\\\"n\\\"\\n for char in u\\\"ガが\\\":\\n self.trans[char] = u\\\"ga\\\"\\n for char in u\\\"ギぎ\\\":\\n self.trans[char] = u\\\"gi\\\"\\n for char in u\\\"グぐ\\\":\\n self.trans[char] = u\\\"gu\\\"\\n for char in u\\\"ゲげ\\\":\\n self.trans[char] = u\\\"ge\\\"\\n for char in u\\\"ゴご\\\":\\n self.trans[char] = u\\\"go\\\"\\n for char in u\\\"ザざ\\\":\\n self.trans[char] = u\\\"za\\\"\\n for char in u\\\"ジじ\\\":\\n self.trans[char] = u\\\"ji\\\"\\n for char in u\\\"ズず\\\":\\n self.trans[char] = u\\\"zu\\\"\\n for char in u\\\"ゼぜ\\\":\\n self.trans[char] = u\\\"ze\\\"\\n for char in u\\\"ゾぞ\\\":\\n self.trans[char] = u\\\"zo\\\"\\n for char in u\\\"ダだ\\\":\\n self.trans[char] = u\\\"da\\\"\\n for char in u\\\"ヂぢ\\\":\\n self.trans[char] = u\\\"dji\\\"\\n for char in u\\\"ヅづ\\\":\\n self.trans[char] = u\\\"dzu\\\"\\n for char in u\\\"デで\\\":\\n self.trans[char] = u\\\"de\\\"\\n for char in u\\\"ドど\\\":\\n self.trans[char] = u\\\"do\\\"\\n for char in u\\\"バば\\\":\\n self.trans[char] = u\\\"ba\\\"\\n for char in u\\\"ビび\\\":\\n self.trans[char] = u\\\"bi\\\"\\n for char in u\\\"ブぶ\\\":\\n self.trans[char] = u\\\"bu\\\"\\n for char in u\\\"ベべ\\\":\\n self.trans[char] = u\\\"be\\\"\\n for char in u\\\"ボぼ\\\":\\n self.trans[char] = u\\\"bo\\\"\\n for char in u\\\"パぱ\\\":\\n self.trans[char] = u\\\"pa\\\"\\n for char in u\\\"ピぴ\\\":\\n self.trans[char] = u\\\"pi\\\"\\n for char in u\\\"プぷ\\\":\\n self.trans[char] = u\\\"pu\\\"\\n for char in u\\\"ペぺ\\\":\\n self.trans[char] = u\\\"pe\\\"\\n for char in u\\\"ポぽ\\\":\\n self.trans[char] = u\\\"po\\\"\\n for char in u\\\"ヴゔ\\\":\\n self.trans[char] = u\\\"vu\\\"\\n self.trans[u\\\"ヷ\\\"] = u\\\"va\\\"\\n self.trans[u\\\"ヸ\\\"] = u\\\"vi\\\"\\n self.trans[u\\\"ヹ\\\"] = u\\\"ve\\\"\\n self.trans[u\\\"ヺ\\\"] = u\\\"vo\\\"\\n\\n # Japanese and Chinese punctuation and typography\\n for char in u\\\"・·\\\":\\n self.trans[char] = u\\\" \\\"\\n for char in u\\\"〃『』《》\\\":\\n self.trans[char] = u'\\\"'\\n for char in u\\\"「」〈〉〘〙〚〛\\\":\\n self.trans[char] = u\\\"'\\\"\\n for char in u\\\"（〔\\\":\\n self.trans[char] = u\\\"(\\\"\\n for char in u\\\"）〕\\\":\\n self.trans[char] = u\\\")\\\"\\n for char in u\\\"［【〖\\\":\\n self.trans[char] = u\\\"[\\\"\\n for char in u\\\"］】〗\\\":\\n self.trans[char] = u\\\"]\\\"\\n self.trans['｛'] = '{'\\n self.trans['｝'] = '}'\\n self.trans['っ'] = ':'\\n self.trans['ー'] = 'h'\\n self.trans['゛'] = \\\"'\\\"\\n self.trans['゜'] = 'p'\\n self.trans['。'] = '. '\\n self.trans['、'] = ', '\\n self.trans['・'] = ' '\\n self.trans['〆'] = 'shime'\\n self.trans['〜'] = '-'\\n self.trans['…'] = '...'\\n self.trans['‥'] = '..'\\n self.trans['ヶ'] = 'months'\\n for char in u\\\"•◦\\\":\\n self.trans[char] = u\\\"_\\\"\\n for char in u\\\"※＊\\\":\\n self.trans[char] = u\\\"*\\\"\\n self.trans['Ⓧ'] = '(X)'\\n self.trans['Ⓨ'] = '(Y)'\\n self.trans['！'] = '!'\\n self.trans['？'] = '?'\\n self.trans['；'] = ';'\\n self.trans['：'] = ':'\\n self.trans['。'] = '.'\\n for char in u\\\"，、\\\":\\n self.trans[char] = u\\\",\\\"\\n\\n # Georgian\\n self.trans['ა'] = 'a'\\n self.trans['ბ'] = 'b'\\n self.trans['გ'] = 'g'\\n self.trans['დ'] = 'd'\\n for char in u\\\"ეჱ\\\":\\n self.trans[char] = u\\\"e\\\"\\n self.trans['ვ'] = 'v'\\n self.trans['ზ'] = 'z'\\n self.trans['თ'] = 'th'\\n self.trans['ი'] = 'i'\\n self.trans['კ'] = 'k'\\n self.trans['ლ'] = 'l'\\n self.trans['მ'] = 'm'\\n self.trans['ნ'] = 'n'\\n self.trans['ო'] = 'o'\\n self.trans['პ'] = 'p'\\n self.trans['ჟ'] = 'zh'\\n self.trans['რ'] = 'r'\\n self.trans['ს'] = 's'\\n self.trans['ტ'] = 't'\\n self.trans['უ'] = 'u'\\n self.trans['ფ'] = 'ph'\\n self.trans['ქ'] = 'q'\\n self.trans['ღ'] = 'gh'\\n for char in u\\\"ყ\\\":\\n self.trans[char] = u\\\"q'\\\"\\n self.trans['შ'] = 'sh'\\n self.trans['ჩ'] = 'ch'\\n self.trans['ც'] = 'ts'\\n self.trans['ძ'] = 'dz'\\n for char in u\\\"წ\\\":\\n self.trans[char] = u\\\"ts'\\\"\\n for char in u\\\"ჭ\\\":\\n self.trans[char] = u\\\"ch'\\\"\\n self.trans['ხ'] = 'kh'\\n self.trans['ჯ'] = 'j'\\n self.trans['ჰ'] = 'h'\\n self.trans['ჳ'] = 'w'\\n self.trans['ჵ'] = 'o'\\n self.trans['ჶ'] = 'f'\\n\\n # Devanagari\\n for char in u\\\"पप\\\":\\n self.trans[char] = u\\\"p\\\"\\n self.trans['अ'] = 'a'\\n for char in u\\\"आा\\\":\\n self.trans[char] = u\\\"aa\\\"\\n self.trans['प'] = 'pa'\\n for char in u\\\"इि\\\":\\n self.trans[char] = u\\\"i\\\"\\n for char in u\\\"ईी\\\":\\n self.trans[char] = u\\\"ii\\\"\\n for char in u\\\"उु\\\":\\n self.trans[char] = u\\\"u\\\"\\n for char in u\\\"ऊू\\\":\\n self.trans[char] = u\\\"uu\\\"\\n for char in u\\\"एे\\\":\\n self.trans[char] = u\\\"e\\\"\\n for char in u\\\"ऐै\\\":\\n self.trans[char] = u\\\"ai\\\"\\n for char in u\\\"ओो\\\":\\n self.trans[char] = u\\\"o\\\"\\n for char in u\\\"औौ\\\":\\n self.trans[char] = u\\\"au\\\"\\n for char in u\\\"ऋृर\\\":\\n self.trans[char] = u\\\"r\\\"\\n for char in u\\\"ॠॄ\\\":\\n self.trans[char] = u\\\"rr\\\"\\n for char in u\\\"ऌॢल\\\":\\n self.trans[char] = u\\\"l\\\"\\n for char in u\\\"ॡॣ\\\":\\n self.trans[char] = u\\\"ll\\\"\\n self.trans['क'] = 'k'\\n self.trans['ख'] = 'kh'\\n self.trans['ग'] = 'g'\\n self.trans['घ'] = 'gh'\\n self.trans['ङ'] = 'ng'\\n self.trans['च'] = 'c'\\n self.trans['छ'] = 'ch'\\n self.trans['ज'] = 'j'\\n self.trans['झ'] = 'jh'\\n self.trans['ञ'] = 'ñ'\\n for char in u\\\"टत\\\":\\n self.trans[char] = u\\\"t\\\"\\n for char in u\\\"ठथ\\\":\\n self.trans[char] = u\\\"th\\\"\\n for char in u\\\"डद\\\":\\n self.trans[char] = u\\\"d\\\"\\n for char in u\\\"ढध\\\":\\n self.trans[char] = u\\\"dh\\\"\\n for char in u\\\"णन\\\":\\n self.trans[char] = u\\\"n\\\"\\n self.trans['फ'] = 'ph'\\n self.trans['ब'] = 'b'\\n self.trans['भ'] = 'bh'\\n self.trans['म'] = 'm'\\n self.trans['य'] = 'y'\\n self.trans['व'] = 'v'\\n self.trans['श'] = 'sh'\\n for char in u\\\"षस\\\":\\n self.trans[char] = u\\\"s\\\"\\n self.trans['ह'] = 'h'\\n self.trans['क'] = 'x'\\n self.trans['त'] = 'tr'\\n self.trans['ज'] = 'gj'\\n for char in u\\\"क़\\\":\\n self.trans[char] = u\\\"q\\\"\\n self.trans['फ'] = 'f'\\n self.trans['ख'] = 'hh'\\n self.trans['H'] = 'gh'\\n self.trans['ज'] = 'z'\\n for char in u\\\"डढ\\\":\\n self.trans[char] = u\\\"r\\\"\\n # Devanagari ligatures (possibly incomplete and/or incorrect)\\n for char in u\\\"ख्\\\":\\n self.trans[char] = u\\\"khn\\\"\\n self.trans['त'] = 'tn'\\n for char in u\\\"द्\\\":\\n self.trans[char] = u\\\"dn\\\"\\n self.trans['श'] = 'cn'\\n for char in u\\\"ह्\\\":\\n self.trans[char] = u\\\"fn\\\"\\n for char in u\\\"अँ\\\":\\n self.trans[char] = u\\\"m\\\"\\n for char in u\\\"॒॑\\\":\\n self.trans[char] = u\\\"\\\"\\n self.trans['०'] = '0'\\n self.trans['१'] = '1'\\n self.trans['२'] = '2'\\n self.trans['३'] = '3'\\n self.trans['४'] = '4'\\n self.trans['५'] = '5'\\n self.trans['६'] = '6'\\n self.trans['७'] = '7'\\n self.trans['८'] = '8'\\n self.trans['९'] = '9'\\n\\n # Armenian\\n self.trans['Ա'] = 'A'\\n self.trans['ա'] = 'a'\\n self.trans['Բ'] = 'B'\\n self.trans['բ'] = 'b'\\n self.trans['Գ'] = 'G'\\n self.trans['գ'] = 'g'\\n self.trans['Դ'] = 'D'\\n self.trans['դ'] = 'd'\\n self.trans['Ե'] = 'Je'\\n self.trans['ե'] = 'e'\\n self.trans['Զ'] = 'Z'\\n self.trans['զ'] = 'z'\\n self.trans['Է'] = 'É'\\n self.trans['է'] = 'é'\\n self.trans['Ը'] = 'Ë'\\n self.trans['ը'] = 'ë'\\n self.trans['Թ'] = 'Th'\\n self.trans['թ'] = 'th'\\n self.trans['Ժ'] = 'Zh'\\n self.trans['ժ'] = 'zh'\\n self.trans['Ի'] = 'I'\\n self.trans['ի'] = 'i'\\n self.trans['Լ'] = 'L'\\n self.trans['լ'] = 'l'\\n self.trans['Խ'] = 'Ch'\\n self.trans['խ'] = 'ch'\\n self.trans['Ծ'] = 'Ts'\\n self.trans['ծ'] = 'ts'\\n self.trans['Կ'] = 'K'\\n self.trans['կ'] = 'k'\\n self.trans['Հ'] = 'H'\\n self.trans['հ'] = 'h'\\n self.trans['Ձ'] = 'Dz'\\n self.trans['ձ'] = 'dz'\\n self.trans['Ղ'] = 'R'\\n self.trans['ղ'] = 'r'\\n self.trans['Ճ'] = 'Cz'\\n self.trans['ճ'] = 'cz'\\n self.trans['Մ'] = 'M'\\n self.trans['մ'] = 'm'\\n self.trans['Յ'] = 'J'\\n self.trans['յ'] = 'j'\\n self.trans['Ն'] = 'N'\\n self.trans['ն'] = 'n'\\n self.trans['Շ'] = 'S'\\n self.trans['շ'] = 's'\\n self.trans['Շ'] = 'Vo'\\n self.trans['շ'] = 'o'\\n self.trans['Չ'] = 'Tsh'\\n self.trans['չ'] = 'tsh'\\n self.trans['Պ'] = 'P'\\n self.trans['պ'] = 'p'\\n self.trans['Ջ'] = 'Dz'\\n self.trans['ջ'] = 'dz'\\n self.trans['Ռ'] = 'R'\\n self.trans['ռ'] = 'r'\\n self.trans['Ս'] = 'S'\\n self.trans['ս'] = 's'\\n self.trans['Վ'] = 'V'\\n self.trans['վ'] = 'v'\\n for char in u\\\"Տ\\\":\\n self.trans[char] = u\\\"T'\\\"\\n for char in u\\\"տ\\\":\\n self.trans[char] = u\\\"t'\\\"\\n self.trans['Ր'] = 'R'\\n self.trans['ր'] = 'r'\\n self.trans['Ց'] = 'Tsh'\\n self.trans['ց'] = 'tsh'\\n self.trans['Ւ'] = 'V'\\n self.trans['ւ'] = 'v'\\n self.trans['Փ'] = 'Ph'\\n self.trans['փ'] = 'ph'\\n self.trans['Ք'] = 'Kh'\\n self.trans['ք'] = 'kh'\\n self.trans['Օ'] = 'O'\\n self.trans['օ'] = 'o'\\n self.trans['Ֆ'] = 'F'\\n self.trans['ֆ'] = 'f'\\n self.trans['և'] = '&'\\n self.trans['՟'] = '.'\\n self.trans['՞'] = '?'\\n self.trans['՝'] = ';'\\n self.trans['՛'] = ''\\n\\n # Tamil\\n for char in u\\\"க்\\\":\\n self.trans[char] = u\\\"k\\\"\\n for char in u\\\"ஙண்ந்ன்\\\":\\n self.trans[char] = u\\\"n\\\"\\n self.trans['ச'] = 'c'\\n for char in u\\\"ஞ்\\\":\\n self.trans[char] = u\\\"ñ\\\"\\n for char in u\\\"ட்\\\":\\n self.trans[char] = u\\\"th\\\"\\n self.trans['த'] = 't'\\n self.trans['ப'] = 'p'\\n for char in u\\\"ம்\\\":\\n self.trans[char] = u\\\"m\\\"\\n for char in u\\\"ய்\\\":\\n self.trans[char] = u\\\"y\\\"\\n for char in u\\\"ர்ழ்ற\\\":\\n self.trans[char] = u\\\"r\\\"\\n for char in u\\\"ல்ள\\\":\\n self.trans[char] = u\\\"l\\\"\\n for char in u\\\"வ்\\\":\\n self.trans[char] = u\\\"v\\\"\\n self.trans['ஜ'] = 'j'\\n self.trans['ஷ'] = 'sh'\\n self.trans['ஸ'] = 's'\\n self.trans['ஹ'] = 'h'\\n for char in u\\\"க்ஷ\\\":\\n self.trans[char] = u\\\"x\\\"\\n self.trans['அ'] = 'a'\\n self.trans['ஆ'] = 'aa'\\n self.trans['இ'] = 'i'\\n self.trans['ஈ'] = 'ii'\\n self.trans['உ'] = 'u'\\n self.trans['ஊ'] = 'uu'\\n self.trans['எ'] = 'e'\\n self.trans['ஏ'] = 'ee'\\n self.trans['ஐ'] = 'ai'\\n self.trans['ஒ'] = 'o'\\n self.trans['ஓ'] = 'oo'\\n self.trans['ஔ'] = 'au'\\n self.trans['ஃ'] = ''\\n\\n # Bengali\\n self.trans['অ'] = 'ô'\\n for char in u\\\"আা\\\":\\n self.trans[char] = u\\\"a\\\"\\n for char in u\\\"ইিঈী\\\":\\n self.trans[char] = u\\\"i\\\"\\n for char in u\\\"উুঊূ\\\":\\n self.trans[char] = u\\\"u\\\"\\n for char in u\\\"ঋৃ\\\":\\n self.trans[char] = u\\\"ri\\\"\\n for char in u\\\"এেয়\\\":\\n self.trans[char] = u\\\"e\\\"\\n for char in u\\\"ঐৈ\\\":\\n self.trans[char] = u\\\"oi\\\"\\n for char in u\\\"ওো\\\":\\n self.trans[char] = u\\\"o\\\"\\n for char in u\\\"ঔৌ\\\":\\n self.trans[char] = \\\"ou\\\"\\n self.trans['্'] = ''\\n self.trans['ৎ'] = 't'\\n self.trans['ং'] = 'n'\\n self.trans['ঃ'] = 'h'\\n self.trans['ঁ'] = 'ñ'\\n self.trans['ক'] = 'k'\\n self.trans['খ'] = 'kh'\\n self.trans['গ'] = 'g'\\n self.trans['ঘ'] = 'gh'\\n self.trans['ঙ'] = 'ng'\\n self.trans['চ'] = 'ch'\\n self.trans['ছ'] = 'chh'\\n self.trans['জ'] = 'j'\\n self.trans['ঝ'] = 'jh'\\n self.trans['ঞ'] = 'n'\\n for char in u\\\"টত\\\":\\n self.trans[char] = u\\\"t\\\"\\n for char in u\\\"ঠথ\\\":\\n self.trans[char] = u\\\"th\\\"\\n for char in u\\\"ডদ\\\":\\n self.trans[char] = u\\\"d\\\"\\n for char in u\\\"ঢধ\\\":\\n self.trans[char] = u\\\"dh\\\"\\n for char in u\\\"ণন\\\":\\n self.trans[char] = u\\\"n\\\"\\n self.trans['প'] = 'p'\\n self.trans['ফ'] = 'ph'\\n self.trans['ব'] = 'b'\\n self.trans['ভ'] = 'bh'\\n self.trans['ম'] = 'm'\\n self.trans['য'] = 'dzh'\\n self.trans['র'] = 'r'\\n self.trans['ল'] = 'l'\\n self.trans['শ'] = 's'\\n self.trans['হ'] = 'h'\\n for char in u\\\"য়\\\":\\n self.trans[char] = u\\\"-\\\"\\n for char in u\\\"ড়\\\":\\n self.trans[char] = u\\\"r\\\"\\n self.trans['ঢ'] = 'rh'\\n self.trans['০'] = '0'\\n self.trans['১'] = '1'\\n self.trans['২'] = '2'\\n self.trans['৩'] = '3'\\n self.trans['৪'] = '4'\\n self.trans['৫'] = '5'\\n self.trans['৬'] = '6'\\n self.trans['৭'] = '7'\\n self.trans['৮'] = '8'\\n self.trans['৯'] = '9'\\n\\n # Thai (because of complications of the alphabet, self.transliterations\\n # are very imprecise here)\\n self.trans['ก'] = 'k'\\n for char in u\\\"ขฃคฅฆ\\\":\\n self.trans[char] = u\\\"kh\\\"\\n self.trans['ง'] = 'ng'\\n for char in u\\\"จฉชฌ\\\":\\n self.trans[char] = u\\\"ch\\\"\\n for char in u\\\"ซศษส\\\":\\n self.trans[char] = u\\\"s\\\"\\n for char in u\\\"ญย\\\":\\n self.trans[char] = u\\\"y\\\"\\n for char in u\\\"ฎด\\\":\\n self.trans[char] = u\\\"d\\\"\\n for char in u\\\"ฏต\\\":\\n self.trans[char] = u\\\"t\\\"\\n for char in u\\\"ฐฑฒถทธ\\\":\\n self.trans[char] = u\\\"th\\\"\\n for char in u\\\"ณน\\\":\\n self.trans[char] = u\\\"n\\\"\\n self.trans['บ'] = 'b'\\n self.trans['ป'] = 'p'\\n for char in u\\\"ผพภ\\\":\\n self.trans[char] = u\\\"ph\\\"\\n for char in u\\\"ฝฟ\\\":\\n self.trans[char] = u\\\"f\\\"\\n self.trans['ม'] = 'm'\\n self.trans['ร'] = 'r'\\n self.trans['ฤ'] = 'rue'\\n self.trans['ๅ'] = ':'\\n for char in u\\\"ลฬ\\\":\\n self.trans[char] = u\\\"l\\\"\\n self.trans['ฦ'] = 'lue'\\n self.trans['ว'] = 'w'\\n for char in u\\\"หฮ\\\":\\n self.trans[char] = u\\\"h\\\"\\n self.trans['อ'] = ''\\n self.trans['ร'] = 'ü'\\n self.trans['ว'] = 'ua'\\n for char in u\\\"อวโิ\\\":\\n self.trans[char] = u\\\"o\\\"\\n for char in u\\\"ะัา\\\":\\n self.trans[char] = u\\\"a\\\"\\n self.trans['ว'] = 'u'\\n self.trans['ำ'] = 'am'\\n self.trans['ิ'] = 'i'\\n self.trans['ี'] = 'i:'\\n self.trans['ึ'] = 'ue'\\n self.trans['ื'] = 'ue:'\\n self.trans['ุ'] = 'u'\\n self.trans['ู'] = 'u:'\\n for char in u\\\"เ็\\\":\\n self.trans[char] = u\\\"e\\\"\\n self.trans['แ'] = 'ae'\\n for char in u\\\"ใไ\\\":\\n self.trans[char] = u\\\"ai\\\"\\n for char in u\\\"่้๊๋็์\\\":\\n self.trans[char] = u\\\"\\\"\\n self.trans['ฯ'] = '.'\\n self.trans['ๆ'] = '(2)'\\n\\n # Korean (Revised Romanization system within possible, incomplete)\\n self.trans['국'] = 'guk'\\n self.trans['명'] = 'myeong'\\n self.trans['검'] = 'geom'\\n self.trans['타'] = 'ta'\\n self.trans['분'] = 'bun'\\n self.trans['사'] = 'sa'\\n self.trans['류'] = 'ryu'\\n self.trans['포'] = 'po'\\n self.trans['르'] = 'reu'\\n self.trans['투'] = 'tu'\\n self.trans['갈'] = 'gal'\\n self.trans['어'] = 'eo'\\n self.trans['노'] = 'no'\\n self.trans['웨'] = 'we'\\n self.trans['이'] = 'i'\\n self.trans['라'] = 'ra'\\n self.trans['틴'] = 'tin'\\n self.trans['루'] = 'ru'\\n self.trans['마'] = 'ma'\\n self.trans['니'] = 'ni'\\n self.trans['아'] = 'a'\\n self.trans['독'] = 'dok'\\n self.trans['일'] = 'il'\\n self.trans['모'] = 'mo'\\n self.trans['크'] = 'keu'\\n self.trans['샤'] = 'sya'\\n self.trans['영'] = 'yeong'\\n self.trans['불'] = 'bul'\\n self.trans['가'] = 'ga'\\n self.trans['리'] = 'ri'\\n self.trans['그'] = 'geu'\\n self.trans['지'] = 'ji'\\n self.trans['야'] = 'ya'\\n self.trans['바'] = 'ba'\\n self.trans['슈'] = 'syu'\\n self.trans['키'] = 'ki'\\n self.trans['프'] = 'peu'\\n self.trans['랑'] = 'rang'\\n self.trans['스'] = 'seu'\\n self.trans['로'] = 'ro'\\n self.trans['메'] = 'me'\\n self.trans['역'] = 'yeok'\\n self.trans['도'] = 'do'\\n\\n # Kannada\\n self.trans[u\\\"ಅ\\\"] = u\\\"a\\\"\\n for char in u\\\"ಆಾ\\\":\\n self.trans[char] = u\\\"aa\\\"\\n for char in u\\\"ಇಿ\\\":\\n self.trans[char] = u\\\"i\\\"\\n for char in u\\\"ಈೀ\\\":\\n self.trans[char] = u\\\"ii\\\"\\n for char in u\\\"ಉು\\\":\\n self.trans[char] = u\\\"u\\\"\\n for char in u\\\"ಊೂ\\\":\\n self.trans[char] = u\\\"uu\\\"\\n for char in u\\\"ಋೂ\\\":\\n self.trans[char] = u\\\"r'\\\"\\n for char in u\\\"ಎೆ\\\":\\n self.trans[char] = u\\\"e\\\"\\n for char in u\\\"ಏೇ\\\":\\n self.trans[char] = u\\\"ee\\\"\\n for char in u\\\"ಐೈ\\\":\\n self.trans[char] = u\\\"ai\\\"\\n for char in u\\\"ಒೊ\\\":\\n self.trans[char] = u\\\"o\\\"\\n for char in u\\\"ಓೋ\\\":\\n self.trans[char] = u\\\"oo\\\"\\n for char in u\\\"ಔೌ\\\":\\n self.trans[char] = u\\\"au\\\"\\n self.trans[u\\\"ಂ\\\"] = u\\\"m'\\\"\\n self.trans[u\\\"ಃ\\\"] = u\\\"h'\\\"\\n self.trans[u\\\"ಕ\\\"] = u\\\"k\\\"\\n self.trans[u\\\"ಖ\\\"] = u\\\"kh\\\"\\n self.trans[u\\\"ಗ\\\"] = u\\\"g\\\"\\n self.trans[u\\\"ಘ\\\"] = u\\\"gh\\\"\\n self.trans[u\\\"ಙ\\\"] = u\\\"ng\\\"\\n self.trans[u\\\"ಚ\\\"] = u\\\"c\\\"\\n self.trans[u\\\"ಛ\\\"] = u\\\"ch\\\"\\n self.trans[u\\\"ಜ\\\"] = u\\\"j\\\"\\n self.trans[u\\\"ಝ\\\"] = u\\\"ny\\\"\\n self.trans[u\\\"ಟ\\\"] = u\\\"tt\\\"\\n self.trans[u\\\"ಠ\\\"] = u\\\"tth\\\"\\n self.trans[u\\\"ಡ\\\"] = u\\\"dd\\\"\\n self.trans[u\\\"ಢ\\\"] = u\\\"ddh\\\"\\n self.trans[u\\\"ಣ\\\"] = u\\\"nn\\\"\\n self.trans[u\\\"ತ\\\"] = u\\\"t\\\"\\n self.trans[u\\\"ಥ\\\"] = u\\\"th\\\"\\n self.trans[u\\\"ದ\\\"] = u\\\"d\\\"\\n self.trans[u\\\"ಧ\\\"] = u\\\"dh\\\"\\n self.trans[u\\\"ನ\\\"] = u\\\"n\\\"\\n self.trans[u\\\"ಪ\\\"] = u\\\"p\\\"\\n self.trans[u\\\"ಫ\\\"] = u\\\"ph\\\"\\n self.trans[u\\\"ಬ\\\"] = u\\\"b\\\"\\n self.trans[u\\\"ಭ\\\"] = u\\\"bh\\\"\\n self.trans[u\\\"ಮ\\\"] = u\\\"m\\\"\\n self.trans[u\\\"ಯ\\\"] = u\\\"y\\\"\\n self.trans[u\\\"ರ\\\"] = u\\\"r\\\"\\n self.trans[u\\\"ಲ\\\"] = u\\\"l\\\"\\n self.trans[u\\\"ವ\\\"] = u\\\"v\\\"\\n self.trans[u\\\"ಶ\\\"] = u\\\"sh\\\"\\n self.trans[u\\\"ಷ\\\"] = u\\\"ss\\\"\\n self.trans[u\\\"ಸ\\\"] = u\\\"s\\\"\\n self.trans[u\\\"ಹ\\\"] = u\\\"h\\\"\\n self.trans[u\\\"ಳ\\\"] = u\\\"ll\\\"\\n self.trans[u\\\"೦\\\"] = u\\\"0\\\"\\n self.trans[u\\\"೧\\\"] = u\\\"1\\\"\\n self.trans[u\\\"೨\\\"] = u\\\"2\\\"\\n self.trans[u\\\"೩\\\"] = u\\\"3\\\"\\n self.trans[u\\\"೪\\\"] = u\\\"4\\\"\\n self.trans[u\\\"೫\\\"] = u\\\"5\\\"\\n self.trans[u\\\"೬\\\"] = u\\\"6\\\"\\n self.trans[u\\\"೭\\\"] = u\\\"7\\\"\\n self.trans[u\\\"೮\\\"] = u\\\"8\\\"\\n self.trans[u\\\"೯\\\"] = u\\\"9\\\"\\n # Telugu\\n self.trans['అ'] = 'a'\\n for char in u\\\"ఆా\\\":\\n self.trans[char] = u\\\"aa\\\"\\n for char in u\\\"ఇి\\\":\\n self.trans[char] = u\\\"i\\\"\\n for char in u\\\"ఈీ\\\":\\n self.trans[char] = u\\\"ii\\\"\\n for char in u\\\"ఉు\\\":\\n self.trans[char] = u\\\"u\\\"\\n for char in u\\\"ఊూ\\\":\\n self.trans[char] = u\\\"uu\\\"\\n for char in u\\\"ఋృ\\\":\\n self.trans[char] = u\\\"r'\\\"\\n for char in u\\\"ౠౄ\\\":\\n self.trans[char] = u'r\\\"'\\n self.trans[u\\\"ఌ\\\"] = u\\\"l'\\\"\\n self.trans[u\\\"ౡ\\\"] = u'l\\\"'\\n for char in u\\\"ఎె\\\":\\n self.trans[char] = u\\\"e\\\"\\n for char in u\\\"ఏే\\\":\\n self.trans[char] = u\\\"ee\\\"\\n for char in u\\\"ఐై\\\":\\n self.trans[char] = u\\\"ai\\\"\\n for char in u\\\"ఒొ\\\":\\n self.trans[char] = u\\\"o\\\"\\n for char in u\\\"ఓో\\\":\\n self.trans[char] = u\\\"oo\\\"\\n for char in u\\\"ఔౌ\\\":\\n self.trans[char] = u\\\"au\\\"\\n self.trans[u\\\"ం\\\"] = u\\\"'\\\"\\n self.trans[u\\\"ః\\\"] = u'\\\"'\\n self.trans[u\\\"క\\\"] = u\\\"k\\\"\\n self.trans[u\\\"ఖ\\\"] = u\\\"kh\\\"\\n self.trans[u\\\"గ\\\"] = u\\\"g\\\"\\n self.trans[u\\\"ఘ\\\"] = u\\\"gh\\\"\\n self.trans[u\\\"ఙ\\\"] = u\\\"ng\\\"\\n self.trans[u\\\"చ\\\"] = u\\\"ts\\\"\\n self.trans[u\\\"ఛ\\\"] = u\\\"tsh\\\"\\n self.trans[u\\\"జ\\\"] = u\\\"j\\\"\\n self.trans[u\\\"ఝ\\\"] = u\\\"jh\\\"\\n self.trans[u\\\"ఞ\\\"] = u\\\"ñ\\\"\\n for char in u\\\"టత\\\":\\n self.trans[char] = u\\\"t\\\"\\n for char in u\\\"ఠథ\\\":\\n self.trans[char] = u\\\"th\\\"\\n for char in u\\\"డద\\\":\\n self.trans[char] = u\\\"d\\\"\\n for char in u\\\"ఢధ\\\":\\n self.trans[char] = u\\\"dh\\\"\\n for char in u\\\"ణన\\\":\\n self.trans[char] = u\\\"n\\\"\\n self.trans[u\\\"ప\\\"] = u\\\"p\\\"\\n self.trans[u\\\"ఫ\\\"] = u\\\"ph\\\"\\n self.trans[u\\\"బ\\\"] = u\\\"b\\\"\\n self.trans[u\\\"భ\\\"] = u\\\"bh\\\"\\n self.trans[u\\\"మ\\\"] = u\\\"m\\\"\\n self.trans[u\\\"య\\\"] = u\\\"y\\\"\\n for char in u\\\"రఱ\\\":\\n self.trans[char] = u\\\"r\\\"\\n for char in u\\\"లళ\\\":\\n self.trans[char] = u\\\"l\\\"\\n self.trans[u\\\"వ\\\"] = u\\\"v\\\"\\n self.trans[u\\\"శ\\\"] = u\\\"sh\\\"\\n for char in u\\\"షస\\\":\\n self.trans[char] = u\\\"s\\\"\\n self.trans[u\\\"హ\\\"] = u\\\"h\\\"\\n self.trans[u\\\"్\\\"] = \\\"\\\"\\n for char in u\\\"ంఁ\\\":\\n self.trans[char] = u\\\"^\\\"\\n self.trans[u\\\"ః\\\"] = u\\\"-\\\"\\n self.trans[u\\\"౦\\\"] = u\\\"0\\\"\\n self.trans[u\\\"౧\\\"] = u\\\"1\\\"\\n self.trans[u\\\"౨\\\"] = u\\\"2\\\"\\n self.trans[u\\\"౩\\\"] = u\\\"3\\\"\\n self.trans[u\\\"౪\\\"] = u\\\"4\\\"\\n self.trans[u\\\"౫\\\"] = u\\\"5\\\"\\n self.trans[u\\\"౬\\\"] = u\\\"6\\\"\\n self.trans[u\\\"౭\\\"] = u\\\"7\\\"\\n self.trans[u\\\"౮\\\"] = u\\\"8\\\"\\n self.trans[u\\\"౯\\\"] = u\\\"9\\\"\\n self.trans[u\\\"౹\\\"] = u\\\"1/4\\\"\\n self.trans[u\\\"౺\\\"] = u\\\"1/2\\\"\\n self.trans[u\\\"౻\\\"] = u\\\"3/4\\\"\\n self.trans[u\\\"౼\\\"] = u\\\"1/16\\\"\\n self.trans[u\\\"౽\\\"] = u\\\"1/8\\\"\\n self.trans[u\\\"౾\\\"] = u\\\"3/16\\\"\\n # Lao - note: pronounciation in initial position is used;\\n # different pronounciation in final position is ignored\\n self.trans[u\\\"ກ\\\"] = \\\"k\\\"\\n for char in u\\\"ຂຄ\\\":\\n self.trans[char] = \\\"kh\\\"\\n self.trans[u\\\"ງ\\\"] = \\\"ng\\\"\\n self.trans[u\\\"ຈ\\\"] = \\\"ch\\\"\\n for char in u\\\"ສຊ\\\":\\n self.trans[char] = \\\"s\\\"\\n self.trans[u\\\"ຍ\\\"] = \\\"ny\\\"\\n self.trans[u\\\"ດ\\\"] = \\\"d\\\"\\n self.trans[u\\\"ຕ\\\"] = \\\"t\\\"\\n for char in u\\\"ຖທ\\\":\\n self.trans[char] = \\\"th\\\"\\n self.trans[u\\\"ນ\\\"] = \\\"n\\\"\\n self.trans[u\\\"ບ\\\"] = \\\"b\\\"\\n self.trans[u\\\"ປ\\\"] = \\\"p\\\"\\n for char in u\\\"ຜພ\\\":\\n self.trans[char] = \\\"ph\\\"\\n for char in u\\\"ຝຟ\\\":\\n self.trans[char] = \\\"f\\\"\\n for char in u\\\"ມໝ\\\":\\n self.trans[char] = \\\"m\\\"\\n self.trans[u\\\"ຢ\\\"] = \\\"y\\\"\\n for char in u\\\"ຣຼ\\\":\\n self.trans[char] = \\\"r\\\"\\n for char in u\\\"ລຼ\\\":\\n self.trans[char] = \\\"l\\\"\\n self.trans[u\\\"ວ\\\"] = \\\"v\\\"\\n self.trans['ຮ'] = 'h'\\n self.trans[u\\\"ອ\\\"] = \\\"'\\\"\\n for char in u\\\"ະັ\\\":\\n self.trans[char] = \\\"a\\\"\\n self.trans[u\\\"ິ\\\"] = \\\"i\\\"\\n self.trans[u\\\"ຶ\\\"] = \\\"ue\\\"\\n self.trans[u\\\"ຸ\\\"] = \\\"u\\\"\\n self.trans[u\\\"ເ\\\"] = u\\\"é\\\"\\n self.trans[u\\\"ແ\\\"] = u\\\"è\\\"\\n for char in u\\\"ໂົາໍ\\\":\\n self.trans[char] = \\\"o\\\"\\n self.trans[u\\\"ຽ\\\"] = \\\"ia\\\"\\n self.trans[u\\\"ເຶ\\\"] = \\\"uea\\\"\\n self.trans[u\\\"ຍ\\\"] = \\\"i\\\"\\n for char in u\\\"ໄໃ\\\":\\n self.trans[char] = \\\"ai\\\"\\n self.trans[u\\\"ຳ\\\"] = \\\"am\\\"\\n self.trans[u\\\"າ\\\"] = \\\"aa\\\"\\n self.trans[u\\\"ີ\\\"] = \\\"ii\\\"\\n self.trans[u\\\"ື\\\"] = \\\"yy\\\"\\n self.trans[u\\\"ູ\\\"] = \\\"uu\\\"\\n self.trans[u\\\"ເ\\\"] = \\\"e\\\"\\n self.trans[u\\\"ແ\\\"] = \\\"ei\\\"\\n self.trans[u\\\"໐\\\"] = \\\"0\\\"\\n self.trans[u\\\"໑\\\"] = \\\"1\\\"\\n self.trans[u\\\"໒\\\"] = \\\"2\\\"\\n self.trans[u\\\"໓\\\"] = \\\"3\\\"\\n self.trans[u\\\"໔\\\"] = \\\"4\\\"\\n self.trans[u\\\"໕\\\"] = \\\"5\\\"\\n self.trans[u\\\"໖\\\"] = \\\"6\\\"\\n self.trans[u\\\"໗\\\"] = \\\"7\\\"\\n self.trans[u\\\"໘\\\"] = \\\"8\\\"\\n self.trans[u\\\"໙\\\"] = \\\"9\\\"\\n # Chinese -- note: incomplete\\n for char in u\\\"埃挨哎唉哀皑癌蔼矮艾碍爱隘\\\":\\n self.trans[char] = u\\\"ai\\\"\\n for char in u\\\"鞍氨安俺按暗岸胺案\\\":\\n self.trans[char] = u\\\"an\\\"\\n for char in u\\\"肮昂盎\\\":\\n self.trans[char] = u\\\"ang\\\"\\n for char in u\\\"凹敖熬翱袄傲奥懊澳\\\":\\n self.trans[char] = u\\\"ao\\\"\\n for char in u\\\"芭捌扒叭吧笆八疤巴拔跋靶把耙坝霸罢爸\\\":\\n self.trans[char] = u\\\"ba\\\"\\n for char in u\\\"白柏百摆佰败拜稗\\\":\\n self.trans[char] = u\\\"bai\\\"\\n for char in u\\\"斑班搬扳般颁板版扮拌伴瓣半办绊\\\":\\n self.trans[char] = u\\\"ban\\\"\\n for char in u\\\"邦帮梆榜膀绑棒磅蚌镑傍谤\\\":\\n self.trans[char] = u\\\"bang\\\"\\n for char in u\\\"苞胞包褒剥薄雹保堡饱宝抱报暴豹鲍爆\\\":\\n self.trans[char] = u\\\"bao\\\"\\n for char in u\\\"杯碑悲卑北辈背贝钡倍狈备惫焙被\\\":\\n self.trans[char] = u\\\"bei\\\"\\n for char in u\\\"奔苯本笨\\\":\\n self.trans[char] = u\\\"ben\\\"\\n for char in u\\\"崩绷甭泵蹦迸\\\":\\n self.trans[char] = u\\\"beng\\\"\\n for char in u\\\"逼鼻比鄙笔彼碧蓖蔽毕毙毖币庇痹闭敝弊必辟壁臂避陛\\\":\\n self.trans[char] = u\\\"bi\\\"\\n for char in u\\\"鞭边编贬扁便变卞辨辩辫遍\\\":\\n self.trans[char] = u\\\"bian\\\"\\n for char in u\\\"标彪膘表\\\":\\n self.trans[char] = u\\\"biao\\\"\\n for char in u\\\"鳖憋别瘪\\\":\\n self.trans[char] = u\\\"bie\\\"\\n for char in u\\\"彬斌濒滨宾摈\\\":\\n self.trans[char] = u\\\"bin\\\"\\n for char in u\\\"兵冰柄丙秉饼炳病并\\\":\\n self.trans[char] = u\\\"bing\\\"\\n for char in u\\\"玻菠播拨钵波博勃搏铂箔伯帛舶脖膊渤泊驳捕卜亳\\\":\\n self.trans[char] = u\\\"bo\\\"\\n for char in u\\\"哺补埠不布步簿部怖\\\":\\n self.trans[char] = u\\\"bu\\\"\\n for char in u\\\"猜裁材才财睬踩采彩菜蔡\\\":\\n self.trans[char] = u\\\"cai\\\"\\n for char in u\\\"餐参蚕残惭惨灿\\\":\\n self.trans[char] = u\\\"can\\\"\\n for char in u\\\"苍舱仓沧藏\\\":\\n self.trans[char] = u\\\"cang\\\"\\n for char in u\\\"操糙槽曹草\\\":\\n self.trans[char] = u\\\"cao\\\"\\n for char in u\\\"厕策侧册测\\\":\\n self.trans[char] = u\\\"ce\\\"\\n for char in u\\\"层蹭\\\":\\n self.trans[char] = u\\\"ceng\\\"\\n for char in u\\\"插叉茬茶查碴搽察岔差诧\\\":\\n self.trans[char] = u\\\"cha\\\"\\n for char in u\\\"拆柴豺\\\":\\n self.trans[char] = u\\\"chai\\\"\\n for char in u\\\"搀掺蝉馋谗缠铲产阐颤\\\":\\n self.trans[char] = u\\\"chan\\\"\\n for char in u\\\"昌猖场尝常长偿肠厂敞畅唱倡\\\":\\n self.trans[char] = u\\\"chang\\\"\\n for char in u\\\"超抄钞朝嘲潮巢吵炒\\\":\\n self.trans[char] = u\\\"chao\\\"\\n for char in u\\\"车扯撤掣彻澈\\\":\\n self.trans[char] = u\\\"che\\\"\\n for char in u\\\"郴臣辰尘晨忱沉陈趁衬\\\":\\n self.trans[char] = u\\\"chen\\\"\\n for char in u\\\"撑称城橙成呈乘程惩澄诚承逞骋秤\\\":\\n self.trans[char] = u\\\"cheng\\\"\\n for char in u\\\"吃痴持匙池迟弛驰耻齿侈尺赤翅斥炽\\\":\\n self.trans[char] = u\\\"chi\\\"\\n for char in u\\\"充冲虫崇宠\\\":\\n self.trans[char] = u\\\"chong\\\"\\n for char in u\\\"抽酬畴踌稠愁筹仇绸瞅丑臭\\\":\\n self.trans[char] = u\\\"chou\\\"\\n for char in u\\\"初出橱厨躇锄雏滁除楚储矗搐触处\\\":\\n self.trans[char] = u\\\"chu\\\"\\n self.trans['揣'] = 'chuai'\\n for char in u\\\"川穿椽传船喘串\\\":\\n self.trans[char] = u\\\"chuan\\\"\\n for char in u\\\"疮窗幢床闯创\\\":\\n self.trans[char] = u\\\"chuang\\\"\\n for char in u\\\"吹炊捶锤垂\\\":\\n self.trans[char] = u\\\"chui\\\"\\n for char in u\\\"春椿醇唇淳纯蠢\\\":\\n self.trans[char] = u\\\"chun\\\"\\n for char in u\\\"戳绰\\\":\\n self.trans[char] = u\\\"chuo\\\"\\n for char in u\\\"疵茨磁雌辞慈瓷词此刺赐次\\\":\\n self.trans[char] = u\\\"ci\\\"\\n for char in u\\\"聪葱囱匆从丛\\\":\\n self.trans[char] = u\\\"cong\\\"\\n self.trans['凑'] = 'cou'\\n for char in u\\\"粗醋簇促\\\":\\n self.trans[char] = u\\\"cu\\\"\\n for char in u\\\"蹿篡窜\\\":\\n self.trans[char] = u\\\"cuan\\\"\\n for char in u\\\"摧崔催脆瘁粹淬翠\\\":\\n self.trans[char] = u\\\"cui\\\"\\n for char in u\\\"村存寸\\\":\\n self.trans[char] = u\\\"cun\\\"\\n for char in u\\\"磋撮搓措挫错\\\":\\n self.trans[char] = u\\\"cuo\\\"\\n for char in u\\\"搭达答瘩打大\\\":\\n self.trans[char] = u\\\"da\\\"\\n for char in u\\\"呆歹傣戴带殆代贷袋待逮怠\\\":\\n self.trans[char] = u\\\"dai\\\"\\n for char in u\\\"耽担丹单郸掸胆旦氮但惮淡诞弹蛋儋\\\":\\n self.trans[char] = u\\\"dan\\\"\\n for char in u\\\"当挡党荡档\\\":\\n self.trans[char] = u\\\"dang\\\"\\n for char in u\\\"刀捣蹈倒岛祷导到稻悼道盗\\\":\\n self.trans[char] = u\\\"dao\\\"\\n for char in u\\\"德得的\\\":\\n self.trans[char] = u\\\"de\\\"\\n for char in u\\\"蹬灯登等瞪凳邓\\\":\\n self.trans[char] = u\\\"deng\\\"\\n for char in u\\\"堤低滴迪敌笛狄涤翟嫡抵底地蒂第帝弟递缔\\\":\\n self.trans[char] = u\\\"di\\\"\\n for char in u\\\"颠掂滇碘点典靛垫电佃甸店惦奠淀殿\\\":\\n self.trans[char] = u\\\"dian\\\"\\n for char in u\\\"碉叼雕凋刁掉吊钓调\\\":\\n self.trans[char] = u\\\"diao\\\"\\n for char in u\\\"跌爹碟蝶迭谍叠\\\":\\n self.trans[char] = u\\\"die\\\"\\n for char in u\\\"丁盯叮钉顶鼎锭定订\\\":\\n self.trans[char] = u\\\"ding\\\"\\n self.trans['丢'] = 'diu'\\n for char in u\\\"东冬董懂动栋侗恫冻洞\\\":\\n self.trans[char] = u\\\"dong\\\"\\n for char in u\\\"兜抖斗陡豆逗痘\\\":\\n self.trans[char] = u\\\"dou\\\"\\n for char in u\\\"都督毒犊独读堵睹赌杜镀肚度渡妒\\\":\\n self.trans[char] = u\\\"du\\\"\\n for char in u\\\"端短锻段断缎\\\":\\n self.trans[char] = u\\\"duan\\\"\\n for char in u\\\"堆兑队对\\\":\\n self.trans[char] = u\\\"dui\\\"\\n for char in u\\\"墩吨蹲敦顿囤钝盾遁\\\":\\n self.trans[char] = u\\\"dun\\\"\\n for char in u\\\"掇哆多夺垛躲朵跺舵剁惰堕\\\":\\n self.trans[char] = u\\\"duo\\\"\\n for char in u\\\"蛾峨鹅俄额讹娥恶厄扼遏鄂饿\\\":\\n self.trans[char] = u\\\"e\\\"\\n for char in u\\\"恩嗯\\\":\\n self.trans[char] = u\\\"en\\\"\\n for char in u\\\"而儿耳尔饵洱二贰\\\":\\n self.trans[char] = u\\\"er\\\"\\n for char in u\\\"发罚筏伐乏阀法珐\\\":\\n self.trans[char] = u\\\"fa\\\"\\n for char in u\\\"藩帆番翻樊矾钒繁凡烦反返范贩犯饭泛\\\":\\n self.trans[char] = u\\\"fan\\\"\\n for char in u\\\"坊芳方肪房防妨仿访纺放\\\":\\n self.trans[char] = u\\\"fang\\\"\\n for char in u\\\"菲非啡飞肥匪诽吠肺废沸费\\\":\\n self.trans[char] = u\\\"fei\\\"\\n for char in u\\\"芬酚吩氛分纷坟焚汾粉奋份忿愤粪\\\":\\n self.trans[char] = u\\\"fen\\\"\\n for char in u\\\"丰封枫蜂峰锋风疯烽逢冯缝讽奉凤\\\":\\n self.trans[char] = u\\\"feng\\\"\\n self.trans['佛'] = 'fo'\\n self.trans['否'] = 'fou'\\n for char in u\\\"夫敷肤孵扶拂辐幅氟符伏俘服浮涪福袱弗甫抚辅俯釜斧脯腑府腐赴副覆赋复傅付阜父腹负富讣附妇缚咐\\\":\\n self.trans[char] = u\\\"fu\\\"\\n for char in u\\\"噶嘎\\\":\\n self.trans[char] = u\\\"ga\\\"\\n for char in u\\\"该改概钙盖溉\\\":\\n self.trans[char] = u\\\"gai\\\"\\n for char in u\\\"干甘杆柑竿肝赶感秆敢赣\\\":\\n self.trans[char] = u\\\"gan\\\"\\n for char in u\\\"冈刚钢缸肛纲岗港杠\\\":\\n self.trans[char] = u\\\"gang\\\"\\n for char in u\\\"篙皋高膏羔糕搞镐稿告\\\":\\n self.trans[char] = u\\\"gao\\\"\\n for char in u\\\"哥歌搁戈鸽胳疙割革葛格蛤阁隔铬个各\\\":\\n self.trans[char] = u\\\"ge\\\"\\n self.trans['给'] = 'gei'\\n for char in u\\\"根跟\\\":\\n self.trans[char] = u\\\"gen\\\"\\n for char in u\\\"耕更庚羹埂耿梗\\\":\\n self.trans[char] = u\\\"geng\\\"\\n for char in u\\\"工攻功恭龚供躬公宫弓巩汞拱贡共\\\":\\n self.trans[char] = u\\\"gong\\\"\\n for char in u\\\"钩勾沟苟狗垢构购够\\\":\\n self.trans[char] = u\\\"gou\\\"\\n for char in u\\\"辜菇咕箍估沽孤姑鼓古蛊骨谷股故顾固雇\\\":\\n self.trans[char] = u\\\"gu\\\"\\n for char in u\\\"刮瓜剐寡挂褂\\\":\\n self.trans[char] = u\\\"gua\\\"\\n for char in u\\\"乖拐怪\\\":\\n self.trans[char] = u\\\"guai\\\"\\n for char in u\\\"棺关官冠观管馆罐惯灌贯\\\":\\n self.trans[char] = u\\\"guan\\\"\\n for char in u\\\"光广逛\\\":\\n self.trans[char] = u\\\"guang\\\"\\n for char in u\\\"瑰规圭硅归龟闺轨鬼诡癸桂柜跪贵刽\\\":\\n self.trans[char] = u\\\"gui\\\"\\n for char in u\\\"辊滚棍\\\":\\n self.trans[char] = u\\\"gun\\\"\\n for char in u\\\"锅郭国果裹过\\\":\\n self.trans[char] = u\\\"guo\\\"\\n self.trans['哈'] = 'ha'\\n for char in u\\\"骸孩海氦亥害骇\\\":\\n self.trans[char] = u\\\"hai\\\"\\n for char in u\\\"酣憨邯韩含涵寒函喊罕翰撼捍旱憾悍焊汗汉\\\":\\n self.trans[char] = u\\\"han\\\"\\n for char in u\\\"夯杭航\\\":\\n self.trans[char] = u\\\"hang\\\"\\n for char in u\\\"壕嚎豪毫郝好耗号浩\\\":\\n self.trans[char] = u\\\"hao\\\"\\n for char in u\\\"呵喝荷菏核禾和何合盒貉阂河涸赫褐鹤贺\\\":\\n self.trans[char] = u\\\"he\\\"\\n for char in u\\\"嘿黑\\\":\\n self.trans[char] = u\\\"hei\\\"\\n for char in u\\\"痕很狠恨\\\":\\n self.trans[char] = u\\\"hen\\\"\\n for char in u\\\"哼亨横衡恒\\\":\\n self.trans[char] = u\\\"heng\\\"\\n for char in u\\\"轰哄烘虹鸿洪宏弘红\\\":\\n self.trans[char] = u\\\"hong\\\"\\n for char in u\\\"喉侯猴吼厚候后\\\":\\n self.trans[char] = u\\\"hou\\\"\\n for char in u\\\"呼乎忽瑚壶葫胡蝴狐糊湖弧虎唬护互沪户\\\":\\n self.trans[char] = u\\\"hu\\\"\\n for char in u\\\"花哗华猾滑画划化话\\\":\\n self.trans[char] = u\\\"hua\\\"\\n for char in u\\\"槐徊怀淮坏\\\":\\n self.trans[char] = u\\\"huai\\\"\\n for char in u\\\"欢环桓还缓换患唤痪豢焕涣宦幻\\\":\\n self.trans[char] = u\\\"huan\\\"\\n for char in u\\\"荒慌黄磺蝗簧皇凰惶煌晃幌恍谎\\\":\\n self.trans[char] = u\\\"huang\\\"\\n for char in u\\\"灰挥辉徽恢蛔回毁悔慧卉惠晦贿秽会烩汇讳诲绘\\\":\\n self.trans[char] = u\\\"hui\\\"\\n for char in u\\\"荤昏婚魂浑混\\\":\\n self.trans[char] = u\\\"hun\\\"\\n for char in u\\\"豁活伙火获或惑霍货祸\\\":\\n self.trans[char] = u\\\"huo\\\"\\n for char in u\\\"击圾基机畸稽积箕肌饥迹激讥鸡姬绩缉吉极棘辑籍集及急疾汲即嫉级挤几脊己蓟技冀季伎祭剂悸济寄寂计记既忌际妓继纪\\\":\\n self.trans[char] = u\\\"ji\\\"\\n for char in u\\\"嘉枷夹佳家加荚颊贾甲钾假稼价架驾嫁\\\":\\n self.trans[char] = u\\\"jia\\\"\\n for char in u\\\"歼监坚尖笺间煎兼肩艰奸缄茧检柬碱硷拣捡简俭剪减荐槛鉴践贱见键箭件健舰剑饯渐溅涧建\\\":\\n self.trans[char] = u\\\"jian\\\"\\n for char in u\\\"僵姜将浆江疆蒋桨奖讲匠酱降\\\":\\n self.trans[char] = u\\\"jiang\\\"\\n for char in u\\\"蕉椒礁焦胶交郊浇骄娇嚼搅铰矫侥脚狡角饺缴绞剿教酵轿较叫窖\\\":\\n self.trans[char] = u\\\"jiao\\\"\\n for char in u\\\"揭接皆秸街阶截劫节桔杰捷睫竭洁结解姐戒藉芥界借介疥诫届\\\":\\n self.trans[char] = u\\\"jie\\\"\\n for char in u\\\"巾筋斤金今津襟紧锦仅谨进靳晋禁近烬浸尽劲\\\":\\n self.trans[char] = u\\\"jin\\\"\\n for char in u\\\"荆兢茎睛晶鲸京惊精粳经井警景颈静境敬镜径痉靖竟竞净\\\":\\n self.trans[char] = u\\\"jing\\\"\\n for char in u\\\"囧炯窘\\\":\\n self.trans[char] = u\\\"jiong\\\"\\n for char in u\\\"揪究纠玖韭久灸九酒厩救旧臼舅咎就疚\\\":\\n self.trans[char] = u\\\"jiu\\\"\\n for char in u\\\"鞠拘狙疽居驹菊局咀矩举沮聚拒据巨具距踞锯俱句惧炬剧\\\":\\n self.trans[char] = u\\\"ju\\\"\\n for char in u\\\"捐鹃娟倦眷卷绢\\\":\\n self.trans[char] = u\\\"juan\\\"\\n for char in u\\\"撅攫抉掘倔爵觉决诀绝\\\":\\n self.trans[char] = u\\\"jue\\\"\\n for char in u\\\"均菌钧军君峻俊竣浚郡骏\\\":\\n self.trans[char] = u\\\"jun\\\"\\n for char in u\\\"喀咖卡咯\\\":\\n self.trans[char] = u\\\"ka\\\"\\n for char in u\\\"开揩楷凯慨\\\":\\n self.trans[char] = u\\\"kai\\\"\\n for char in u\\\"刊堪勘坎砍看\\\":\\n self.trans[char] = u\\\"kan\\\"\\n for char in u\\\"康慷糠扛抗亢炕\\\":\\n self.trans[char] = u\\\"kang\\\"\\n for char in u\\\"考拷烤靠\\\":\\n self.trans[char] = u\\\"kao\\\"\\n for char in u\\\"坷苛柯棵磕颗科壳咳可渴克刻客课\\\":\\n self.trans[char] = u\\\"ke\\\"\\n for char in u\\\"肯啃垦恳\\\":\\n self.trans[char] = u\\\"ken\\\"\\n for char in u\\\"坑吭\\\":\\n self.trans[char] = u\\\"keng\\\"\\n for char in u\\\"空恐孔控\\\":\\n self.trans[char] = u\\\"kong\\\"\\n for char in u\\\"抠口扣寇\\\":\\n self.trans[char] = u\\\"kou\\\"\\n for char in u\\\"枯哭窟苦酷库裤\\\":\\n self.trans[char] = u\\\"ku\\\"\\n for char in u\\\"夸垮挎跨胯\\\":\\n self.trans[char] = u\\\"kua\\\"\\n for char in u\\\"块筷侩快\\\":\\n self.trans[char] = u\\\"kuai\\\"\\n for char in u\\\"宽款\\\":\\n self.trans[char] = u\\\"kuan\\\"\\n for char in u\\\"匡筐狂框矿眶旷况\\\":\\n self.trans[char] = u\\\"kuang\\\"\\n for char in u\\\"亏盔岿窥葵奎魁傀馈愧溃\\\":\\n self.trans[char] = u\\\"kui\\\"\\n for char in u\\\"坤昆捆困\\\":\\n self.trans[char] = u\\\"kun\\\"\\n for char in u\\\"括扩廓阔\\\":\\n self.trans[char] = u\\\"kuo\\\"\\n for char in u\\\"垃拉喇蜡腊辣啦\\\":\\n self.trans[char] = u\\\"la\\\"\\n for char in u\\\"莱来赖\\\":\\n self.trans[char] = u\\\"lai\\\"\\n for char in u\\\"蓝婪栏拦篮阑兰澜谰揽览懒缆烂滥\\\":\\n self.trans[char] = u\\\"lan\\\"\\n for char in u\\\"琅榔狼廊郎朗浪\\\":\\n self.trans[char] = u\\\"lang\\\"\\n for char in u\\\"捞劳牢老佬姥酪烙涝\\\":\\n self.trans[char] = u\\\"lao\\\"\\n for char in u\\\"勒乐\\\":\\n self.trans[char] = u\\\"le\\\"\\n for char in u\\\"雷镭蕾磊累儡垒擂肋类泪\\\":\\n self.trans[char] = u\\\"lei\\\"\\n for char in u\\\"棱楞冷\\\":\\n self.trans[char] = u\\\"leng\\\"\\n for char in u\\\"厘梨犁黎篱狸离漓理李里鲤礼莉荔吏栗丽厉励砾历利傈例俐痢立粒沥隶力璃哩\\\":\\n self.trans[char] = u\\\"li\\\"\\n self.trans['俩'] = 'lia'\\n for char in u\\\"联莲连镰廉怜涟帘敛脸链恋炼练\\\":\\n self.trans[char] = u\\\"lian\\\"\\n for char in u\\\"粮凉梁粱良两辆量晾亮谅\\\":\\n self.trans[char] = u\\\"liang\\\"\\n for char in u\\\"撩聊僚疗燎寥辽潦了撂镣廖料\\\":\\n self.trans[char] = u\\\"liao\\\"\\n for char in u\\\"列裂烈劣猎\\\":\\n self.trans[char] = u\\\"lie\\\"\\n for char in u\\\"琳林磷霖临邻鳞淋凛赁吝拎\\\":\\n self.trans[char] = u\\\"lin\\\"\\n for char in u\\\"玲菱零龄铃伶羚凌灵陵岭领另令\\\":\\n self.trans[char] = u\\\"ling\\\"\\n for char in u\\\"溜琉榴硫馏留刘瘤流柳六\\\":\\n self.trans[char] = u\\\"liu\\\"\\n for char in u\\\"龙聋咙笼窿隆垄拢陇\\\":\\n self.trans[char] = u\\\"long\\\"\\n for char in u\\\"楼娄搂篓漏陋\\\":\\n self.trans[char] = u\\\"lou\\\"\\n for char in u\\\"芦卢颅庐炉掳卤虏鲁麓碌露路赂鹿潞禄录陆戮泸\\\":\\n self.trans[char] = u\\\"lu\\\"\\n for char in u\\\"峦挛孪滦卵乱\\\":\\n self.trans[char] = u\\\"luan\\\"\\n for char in u\\\"掠略\\\":\\n self.trans[char] = u\\\"lue\\\"\\n for char in u\\\"抡轮伦仑沦纶论\\\":\\n self.trans[char] = u\\\"lun\\\"\\n for char in u\\\"萝螺罗逻锣箩骡裸落洛骆络漯\\\":\\n self.trans[char] = u\\\"luo\\\"\\n for char in u\\\"驴吕铝侣旅履屡缕虑氯律率滤绿\\\":\\n self.trans[char] = u\\\"lv\\\"\\n for char in u\\\"妈麻玛码蚂马骂嘛吗\\\":\\n self.trans[char] = u\\\"ma\\\"\\n for char in u\\\"埋买麦卖迈脉\\\":\\n self.trans[char] = u\\\"mai\\\"\\n for char in u\\\"瞒馒蛮满蔓曼慢漫谩\\\":\\n self.trans[char] = u\\\"man\\\"\\n for char in u\\\"芒茫盲氓忙莽\\\":\\n self.trans[char] = u\\\"mang\\\"\\n for char in u\\\"猫茅锚毛矛铆卯茂冒帽貌贸\\\":\\n self.trans[char] = u\\\"mao\\\"\\n self.trans['么'] = 'me'\\n for char in u\\\"玫枚梅酶霉煤没眉媒镁每美昧寐妹媚\\\":\\n self.trans[char] = u\\\"mei\\\"\\n for char in u\\\"门闷们\\\":\\n self.trans[char] = u\\\"men\\\"\\n for char in u\\\"萌蒙檬盟锰猛梦孟\\\":\\n self.trans[char] = u\\\"meng\\\"\\n for char in u\\\"眯醚靡糜迷谜弥米秘觅泌蜜密幂\\\":\\n self.trans[char] = u\\\"mi\\\"\\n for char in u\\\"棉眠绵冕免勉娩缅面\\\":\\n self.trans[char] = u\\\"mian\\\"\\n for char in u\\\"苗描瞄藐秒渺庙妙\\\":\\n self.trans[char] = u\\\"miao\\\"\\n for char in u\\\"蔑灭\\\":\\n self.trans[char] = u\\\"mie\\\"\\n for char in u\\\"民抿皿敏悯闽\\\":\\n self.trans[char] = u\\\"min\\\"\\n for char in u\\\"明螟鸣铭名命\\\":\\n self.trans[char] = u\\\"ming\\\"\\n self.trans['谬'] = 'miu'\\n for char in u\\\"摸摹蘑模膜磨摩魔抹末莫墨默沫漠寞陌\\\":\\n self.trans[char] = u\\\"mo\\\"\\n for char in u\\\"谋牟某\\\":\\n self.trans[char] = u\\\"mou\\\"\\n for char in u\\\"拇牡亩姆母墓暮幕募慕木目睦牧穆\\\":\\n self.trans[char] = u\\\"mu\\\"\\n for char in u\\\"拿哪呐钠那娜纳\\\":\\n self.trans[char] = u\\\"na\\\"\\n for char in u\\\"氖乃奶耐奈\\\":\\n self.trans[char] = u\\\"nai\\\"\\n for char in u\\\"南男难\\\":\\n self.trans[char] = u\\\"nan\\\"\\n self.trans['囊'] = 'nang'\\n for char in u\\\"挠脑恼闹淖\\\":\\n self.trans[char] = u\\\"nao\\\"\\n self.trans['呢'] = 'ne'\\n for char in u\\\"馁内\\\":\\n self.trans[char] = u\\\"nei\\\"\\n self.trans['嫩'] = 'nen'\\n self.trans['能'] = 'neng'\\n for char in u\\\"妮霓倪泥尼拟你匿腻逆溺\\\":\\n self.trans[char] = u\\\"ni\\\"\\n for char in u\\\"蔫拈年碾撵捻念\\\":\\n self.trans[char] = u\\\"nian\\\"\\n for char in u\\\"娘酿\\\":\\n self.trans[char] = u\\\"niang\\\"\\n for char in u\\\"鸟尿\\\":\\n self.trans[char] = u\\\"niao\\\"\\n for char in u\\\"捏聂孽啮镊镍涅\\\":\\n self.trans[char] = u\\\"nie\\\"\\n self.trans['您'] = 'nin'\\n for char in u\\\"柠狞凝宁拧泞\\\":\\n self.trans[char] = u\\\"ning\\\"\\n for char in u\\\"牛扭钮纽\\\":\\n self.trans[char] = u\\\"niu\\\"\\n for char in u\\\"脓浓农弄\\\":\\n self.trans[char] = u\\\"nong\\\"\\n for char in u\\\"奴努怒\\\":\\n self.trans[char] = u\\\"nu\\\"\\n self.trans['暖'] = 'nuan'\\n for char in u\\\"虐疟\\\":\\n self.trans[char] = u\\\"nue\\\"\\n for char in u\\\"挪懦糯诺\\\":\\n self.trans[char] = u\\\"nuo\\\"\\n self.trans['女'] = 'nv'\\n self.trans['哦'] = 'o'\\n for char in u\\\"欧鸥殴藕呕偶沤\\\":\\n self.trans[char] = u\\\"ou\\\"\\n for char in u\\\"啪趴爬帕怕琶\\\":\\n self.trans[char] = u\\\"pa\\\"\\n for char in u\\\"拍排牌徘湃派\\\":\\n self.trans[char] = u\\\"pai\\\"\\n for char in u\\\"攀潘盘磐盼畔判叛\\\":\\n self.trans[char] = u\\\"pan\\\"\\n for char in u\\\"乓庞旁耪胖\\\":\\n self.trans[char] = u\\\"pang\\\"\\n for char in u\\\"抛咆刨炮袍跑泡\\\":\\n self.trans[char] = u\\\"pao\\\"\\n for char in u\\\"呸胚培裴赔陪配佩沛\\\":\\n self.trans[char] = u\\\"pei\\\"\\n for char in u\\\"喷盆\\\":\\n self.trans[char] = u\\\"pen\\\"\\n for char in u\\\"砰抨烹澎彭蓬棚硼篷膨朋鹏捧碰\\\":\\n self.trans[char] = u\\\"peng\\\"\\n for char in u\\\"坯砒霹批披劈琵毗啤脾疲皮匹痞僻屁譬\\\":\\n self.trans[char] = u\\\"pi\\\"\\n for char in u\\\"篇偏片骗\\\":\\n self.trans[char] = u\\\"pian\\\"\\n for char in u\\\"飘漂瓢票\\\":\\n self.trans[char] = u\\\"piao\\\"\\n for char in u\\\"撇瞥\\\":\\n self.trans[char] = u\\\"pie\\\"\\n for char in u\\\"拼频贫品聘\\\":\\n self.trans[char] = u\\\"pin\\\"\\n for char in u\\\"乒坪苹萍平凭瓶评屏\\\":\\n self.trans[char] = u\\\"ping\\\"\\n for char in u\\\"坡泼颇婆破魄迫粕剖\\\":\\n self.trans[char] = u\\\"po\\\"\\n for char in u\\\"扑铺仆莆葡菩蒲埔朴圃普浦谱曝瀑濮\\\":\\n self.trans[char] = u\\\"pu\\\"\\n for char in u\\\"期欺栖戚妻七凄漆柒沏其棋奇歧畦崎脐齐旗祈祁骑起岂乞企启契砌器气迄弃汽泣讫\\\":\\n self.trans[char] = u\\\"qi\\\"\\n for char in u\\\"掐恰洽\\\":\\n self.trans[char] = u\\\"qia\\\"\\n for char in u\\\"牵扦钎铅千迁签仟谦乾黔钱钳前潜遣浅谴堑嵌欠歉\\\":\\n self.trans[char] = u\\\"qian\\\"\\n for char in u\\\"枪呛腔羌墙蔷强抢\\\":\\n self.trans[char] = u\\\"qiang\\\"\\n for char in u\\\"橇锹敲悄桥瞧乔侨巧鞘撬翘峭俏窍\\\":\\n self.trans[char] = u\\\"qiao\\\"\\n for char in u\\\"切茄且怯窃\\\":\\n self.trans[char] = u\\\"qie\\\"\\n for char in u\\\"钦侵亲秦琴勤芹擒禽寝沁\\\":\\n self.trans[char] = u\\\"qin\\\"\\n for char in u\\\"青轻氢倾卿清擎晴氰情顷请庆\\\":\\n self.trans[char] = u\\\"qing\\\"\\n for char in u\\\"琼穷\\\":\\n self.trans[char] = u\\\"qiong\\\"\\n for char in u\\\"秋丘邱球求囚酋泅\\\":\\n self.trans[char] = u\\\"qiu\\\"\\n for char in u\\\"趋区蛆曲躯屈驱渠取娶龋趣去\\\":\\n self.trans[char] = u\\\"qu\\\"\\n for char in u\\\"圈颧权醛泉全痊拳犬券劝\\\":\\n self.trans[char] = u\\\"quan\\\"\\n for char in u\\\"缺炔瘸却鹊榷确雀\\\":\\n self.trans[char] = u\\\"que\\\"\\n for char in u\\\"裙群\\\":\\n self.trans[char] = u\\\"qun\\\"\\n for char in u\\\"然燃冉染\\\":\\n self.trans[char] = u\\\"ran\\\"\\n for char in u\\\"瓤壤攘嚷让\\\":\\n self.trans[char] = u\\\"rang\\\"\\n for char in u\\\"饶扰绕\\\":\\n self.trans[char] = u\\\"rao\\\"\\n for char in u\\\"惹热\\\":\\n self.trans[char] = u\\\"re\\\"\\n for char in u\\\"壬仁人忍韧任认刃妊纫\\\":\\n self.trans[char] = u\\\"ren\\\"\\n for char in u\\\"扔仍\\\":\\n self.trans[char] = u\\\"reng\\\"\\n self.trans['日'] = 'ri'\\n for char in u\\\"戎茸蓉荣融熔溶容绒冗\\\":\\n self.trans[char] = u\\\"rong\\\"\\n for char in u\\\"揉柔肉\\\":\\n self.trans[char] = u\\\"rou\\\"\\n for char in u\\\"茹蠕儒孺如辱乳汝入褥\\\":\\n self.trans[char] = u\\\"ru\\\"\\n for char in u\\\"软阮\\\":\\n self.trans[char] = u\\\"ruan\\\"\\n for char in u\\\"蕊瑞锐\\\":\\n self.trans[char] = u\\\"rui\\\"\\n for char in u\\\"闰润\\\":\\n self.trans[char] = u\\\"run\\\"\\n for char in u\\\"若弱\\\":\\n self.trans[char] = u\\\"ruo\\\"\\n for char in u\\\"撒洒萨\\\":\\n self.trans[char] = u\\\"sa\\\"\\n for char in u\\\"腮鳃塞赛\\\":\\n self.trans[char] = u\\\"sai\\\"\\n for char in u\\\"三叁伞散\\\":\\n self.trans[char] = u\\\"san\\\"\\n for char in u\\\"桑嗓丧\\\":\\n self.trans[char] = u\\\"sang\\\"\\n for char in u\\\"搔骚扫嫂\\\":\\n self.trans[char] = u\\\"sao\\\"\\n for char in u\\\"瑟色涩\\\":\\n self.trans[char] = u\\\"se\\\"\\n self.trans['森'] = 'sen'\\n self.trans['僧'] = 'seng'\\n for char in u\\\"莎砂杀刹沙纱傻啥煞\\\":\\n self.trans[char] = u\\\"sha\\\"\\n for char in u\\\"筛晒\\\":\\n self.trans[char] = u\\\"shai\\\"\\n for char in u\\\"珊苫杉山删煽衫闪陕擅赡膳善汕扇缮\\\":\\n self.trans[char] = u\\\"shan\\\"\\n for char in u\\\"墒伤商赏晌上尚裳\\\":\\n self.trans[char] = u\\\"shang\\\"\\n for char in u\\\"梢捎稍烧芍勺韶少哨邵绍\\\":\\n self.trans[char] = u\\\"shao\\\"\\n for char in u\\\"奢赊蛇舌舍赦摄射慑涉社设\\\":\\n self.trans[char] = u\\\"she\\\"\\n for char in u\\\"砷申呻伸身深娠绅神沈审婶甚肾慎渗\\\":\\n self.trans[char] = u\\\"shen\\\"\\n for char in u\\\"声生甥牲升绳省盛剩胜圣\\\":\\n self.trans[char] = u\\\"sheng\\\"\\n for char in u\\\"师失狮施湿诗尸虱十石拾时什食蚀实识史矢使屎驶始式示士世柿事拭誓逝势是嗜噬适仕侍释饰氏市恃室视试\\\":\\n self.trans[char] = u\\\"shi\\\"\\n for char in u\\\"收手首守寿授售受瘦兽\\\":\\n self.trans[char] = u\\\"shou\\\"\\n for char in u\\\"蔬枢梳殊抒输叔舒淑疏书赎孰熟薯暑曙署蜀黍鼠属术述树束戍竖墅庶数漱恕\\\":\\n self.trans[char] = u\\\"shu\\\"\\n for char in u\\\"刷耍\\\":\\n self.trans[char] = u\\\"shua\\\"\\n for char in u\\\"摔衰甩帅\\\":\\n self.trans[char] = u\\\"shuai\\\"\\n for char in u\\\"栓拴\\\":\\n self.trans[char] = u\\\"shuan\\\"\\n for char in u\\\"霜双爽\\\":\\n self.trans[char] = u\\\"shuang\\\"\\n for char in u\\\"谁水睡税\\\":\\n self.trans[char] = u\\\"shui\\\"\\n for char in u\\\"吮瞬顺舜\\\":\\n self.trans[char] = u\\\"shun\\\"\\n for char in u\\\"说硕朔烁\\\":\\n self.trans[char] = u\\\"shuo\\\"\\n for char in u\\\"斯撕嘶思私司丝死肆寺嗣四伺似饲巳\\\":\\n self.trans[char] = u\\\"si\\\"\\n for char in u\\\"松耸怂颂送宋讼诵\\\":\\n self.trans[char] = u\\\"song\\\"\\n for char in u\\\"搜艘擞\\\":\\n self.trans[char] = u\\\"sou\\\"\\n for char in u\\\"嗽苏酥俗素速粟僳塑溯宿诉肃\\\":\\n self.trans[char] = u\\\"su\\\"\\n for char in u\\\"酸蒜算\\\":\\n self.trans[char] = u\\\"suan\\\"\\n for char in u\\\"虽隋随绥髓碎岁穗遂隧祟\\\":\\n self.trans[char] = u\\\"sui\\\"\\n for char in u\\\"孙损笋\\\":\\n self.trans[char] = u\\\"sun\\\"\\n for char in u\\\"蓑梭唆缩琐索锁所\\\":\\n self.trans[char] = u\\\"suo\\\"\\n for char in u\\\"塌他它她塔獭挞蹋踏\\\":\\n self.trans[char] = u\\\"ta\\\"\\n for char in u\\\"胎苔抬台泰酞太态汰\\\":\\n self.trans[char] = u\\\"tai\\\"\\n for char in u\\\"坍摊贪瘫滩坛檀痰潭谭谈坦毯袒碳探叹炭\\\":\\n self.trans[char] = u\\\"tan\\\"\\n for char in u\\\"汤塘搪堂棠膛唐糖倘躺淌趟烫\\\":\\n self.trans[char] = u\\\"tang\\\"\\n for char in u\\\"掏涛滔绦萄桃逃淘陶讨套\\\":\\n self.trans[char] = u\\\"tao\\\"\\n self.trans['特'] = 'te'\\n for char in u\\\"藤腾疼誊\\\":\\n self.trans[char] = u\\\"teng\\\"\\n for char in u\\\"梯剔踢锑提题蹄啼体替嚏惕涕剃屉\\\":\\n self.trans[char] = u\\\"ti\\\"\\n for char in u\\\"兲天添填田甜恬舔腆\\\":\\n self.trans[char] = u\\\"tian\\\"\\n for char in u\\\"挑条迢眺跳\\\":\\n self.trans[char] = u\\\"tiao\\\"\\n for char in u\\\"贴铁帖\\\":\\n self.trans[char] = u\\\"tie\\\"\\n for char in u\\\"厅听烃汀廷停亭庭挺艇\\\":\\n self.trans[char] = u\\\"ting\\\"\\n for char in u\\\"通桐酮瞳同铜彤童桶捅筒统痛\\\":\\n self.trans[char] = u\\\"tong\\\"\\n for char in u\\\"偷投头透\\\":\\n self.trans[char] = u\\\"tou\\\"\\n for char in u\\\"凸秃突图徒途涂屠土吐兔\\\":\\n self.trans[char] = u\\\"tu\\\"\\n for char in u\\\"湍团\\\":\\n self.trans[char] = u\\\"tuan\\\"\\n for char in u\\\"推颓腿蜕褪退\\\":\\n self.trans[char] = u\\\"tui\\\"\\n for char in u\\\"吞屯臀\\\":\\n self.trans[char] = u\\\"tun\\\"\\n for char in u\\\"拖托脱鸵陀驮驼椭妥拓唾\\\":\\n self.trans[char] = u\\\"tuo\\\"\\n for char in u\\\"挖哇蛙洼娃瓦袜\\\":\\n self.trans[char] = u\\\"wa\\\"\\n for char in u\\\"歪外\\\":\\n self.trans[char] = u\\\"wai\\\"\\n for char in u\\\"豌弯湾玩顽丸烷完碗挽晚皖惋宛婉万腕莞\\\":\\n self.trans[char] = u\\\"wan\\\"\\n for char in u\\\"汪王亡枉网往旺望忘妄\\\":\\n self.trans[char] = u\\\"wang\\\"\\n for char in u\\\"威巍微危韦违桅围唯惟为潍维苇萎委伟伪尾纬未蔚味畏胃喂魏位渭谓尉慰卫\\\":\\n self.trans[char] = u\\\"wei\\\"\\n for char in u\\\"瘟温蚊文闻纹吻稳紊问\\\":\\n self.trans[char] = u\\\"wen\\\"\\n for char in u\\\"嗡翁瓮\\\":\\n self.trans[char] = u\\\"weng\\\"\\n for char in u\\\"挝蜗涡窝我斡卧握沃\\\":\\n self.trans[char] = u\\\"wo\\\"\\n for char in u\\\"巫呜钨乌污诬屋无芜梧吾吴毋武五捂午舞伍侮坞戊雾晤物勿务悟误\\\":\\n self.trans[char] = u\\\"wu\\\"\\n for char in u\\\"昔熙析西硒矽晰嘻吸锡牺稀息希悉膝夕惜熄烯溪汐犀檄袭席习媳喜铣洗系隙戏细\\\":\\n self.trans[char] = u\\\"xi\\\"\\n for char in u\\\"瞎虾匣霞辖暇峡侠狭下厦夏吓\\\":\\n self.trans[char] = u\\\"xia\\\"\\n for char in u\\\"掀锨先仙鲜纤咸贤衔舷闲涎弦嫌显险现献县腺馅羡宪陷限线\\\":\\n self.trans[char] = u\\\"xian\\\"\\n for char in u\\\"相厢镶香箱襄湘乡翔祥详想响享项巷橡像向象\\\":\\n self.trans[char] = u\\\"xiang\\\"\\n for char in u\\\"萧硝霄削哮嚣销消宵淆晓小孝校肖啸笑效\\\":\\n self.trans[char] = u\\\"xiao\\\"\\n for char in u\\\"楔些歇蝎鞋协挟携邪斜胁谐写械卸蟹懈泄泻谢屑\\\":\\n self.trans[char] = u\\\"xie\\\"\\n for char in u\\\"薪芯锌欣辛新忻心信衅\\\":\\n self.trans[char] = u\\\"xin\\\"\\n for char in u\\\"星腥猩惺兴刑型形邢行醒幸杏性姓\\\":\\n self.trans[char] = u\\\"xing\\\"\\n for char in u\\\"兄凶胸匈汹雄熊\\\":\\n self.trans[char] = u\\\"xiong\\\"\\n for char in u\\\"休修羞朽嗅锈秀袖绣\\\":\\n self.trans[char] = u\\\"xiu\\\"\\n for char in u\\\"墟戌需虚嘘须徐许蓄酗叙旭序畜恤絮婿绪续\\\":\\n self.trans[char] = u\\\"xu\\\"\\n for char in u\\\"轩喧宣悬旋玄选癣眩绚\\\":\\n self.trans[char] = u\\\"xuan\\\"\\n for char in u\\\"靴薛学穴雪血\\\":\\n self.trans[char] = u\\\"xue\\\"\\n for char in u\\\"勋熏循旬询寻驯巡殉汛训讯逊迅\\\":\\n self.trans[char] = u\\\"xun\\\"\\n for char in u\\\"压押鸦鸭呀丫芽牙蚜崖衙涯雅哑亚讶\\\":\\n self.trans[char] = u\\\"ya\\\"\\n for char in u\\\"焉咽阉烟淹盐严研蜒岩延言颜阎炎沿奄掩眼衍演艳堰燕厌砚雁唁彦焰宴谚验\\\":\\n self.trans[char] = u\\\"yan\\\"\\n for char in u\\\"殃央鸯秧杨扬佯疡羊洋阳氧仰痒养样漾\\\":\\n self.trans[char] = u\\\"yang\\\"\\n for char in u\\\"邀腰妖瑶摇尧遥窑谣姚咬舀药要耀\\\":\\n self.trans[char] = u\\\"yao\\\"\\n for char in u\\\"椰噎耶爷野冶也页掖业叶曳腋夜液\\\":\\n self.trans[char] = u\\\"ye\\\"\\n for char in u\\\"一壹医揖铱依伊衣颐夷遗移仪胰疑沂宜姨彝椅蚁倚已乙矣以艺抑易邑屹亿役臆逸肄疫亦裔意毅忆义益溢诣议谊译异翼翌绎\\\":\\n self.trans[char] = u\\\"yi\\\"\\n for char in u\\\"茵荫因殷音阴姻吟银淫寅饮尹引隐印\\\":\\n self.trans[char] = u\\\"yin\\\"\\n for char in u\\\"英樱婴鹰应缨莹萤营荧蝇迎赢盈影颖硬映\\\":\\n self.trans[char] = u\\\"ying\\\"\\n self.trans['哟'] = 'yo'\\n for char in u\\\"拥佣臃痈庸雍踊蛹咏泳涌永恿勇用\\\":\\n self.trans[char] = u\\\"yong\\\"\\n for char in u\\\"幽优悠忧尤由邮铀犹油游酉有友右佑釉诱又幼迂\\\":\\n self.trans[char] = u\\\"you\\\"\\n for char in u\\\"淤于盂榆虞愚舆余俞逾鱼愉渝渔隅予娱雨与屿禹宇语羽玉域芋郁吁遇喻峪御愈欲狱育誉浴寓裕预豫驭\\\":\\n self.trans[char] = u\\\"yu\\\"\\n for char in u\\\"鸳渊冤元垣袁原援辕园员圆猿源缘远苑愿怨院\\\":\\n self.trans[char] = u\\\"yuan\\\"\\n for char in u\\\"曰约越跃钥岳粤月悦阅\\\":\\n self.trans[char] = u\\\"yue\\\"\\n for char in u\\\"耘云郧匀陨允运蕴酝晕韵孕\\\":\\n self.trans[char] = u\\\"yun\\\"\\n for char in u\\\"匝砸杂\\\":\\n self.trans[char] = u\\\"za\\\"\\n for char in u\\\"栽哉灾宰载再在\\\":\\n self.trans[char] = u\\\"zai\\\"\\n for char in u\\\"咱攒暂赞\\\":\\n self.trans[char] = u\\\"zan\\\"\\n for char in u\\\"赃脏葬\\\":\\n self.trans[char] = u\\\"zang\\\"\\n for char in u\\\"遭糟凿藻枣早澡蚤躁噪造皂灶燥\\\":\\n self.trans[char] = u\\\"zao\\\"\\n for char in u\\\"责择则泽\\\":\\n self.trans[char] = u\\\"ze\\\"\\n self.trans['贼'] = 'zei'\\n self.trans['怎'] = 'zen'\\n for char in u\\\"增憎曾赠\\\":\\n self.trans[char] = u\\\"zeng\\\"\\n for char in u\\\"扎喳渣札轧铡闸眨栅榨咋乍炸诈\\\":\\n self.trans[char] = u\\\"zha\\\"\\n for char in u\\\"摘斋宅窄债寨\\\":\\n self.trans[char] = u\\\"zhai\\\"\\n for char in u\\\"瞻毡詹粘沾盏斩辗崭展蘸栈占战站湛绽\\\":\\n self.trans[char] = u\\\"zhan\\\"\\n for char in u\\\"樟章彰漳张掌涨杖丈帐账仗胀瘴障\\\":\\n self.trans[char] = u\\\"zhang\\\"\\n for char in u\\\"招昭找沼赵照罩兆肇召\\\":\\n self.trans[char] = u\\\"zhao\\\"\\n for char in u\\\"遮折哲蛰辙者锗蔗这浙\\\":\\n self.trans[char] = u\\\"zhe\\\"\\n for char in u\\\"珍斟真甄砧臻贞针侦枕疹诊震振镇阵圳\\\":\\n self.trans[char] = u\\\"zhen\\\"\\n for char in u\\\"蒸挣睁征狰争怔整拯正政帧症郑证\\\":\\n self.trans[char] = u\\\"zheng\\\"\\n for char in u\\\"芝枝支吱蜘知肢脂汁之织职直植殖执值侄址指止趾只旨纸志挚掷至致置帜峙制智秩稚质炙痔滞治窒\\\":\\n self.trans[char] = u\\\"zhi\\\"\\n for char in u\\\"中盅忠钟衷终种肿重仲众\\\":\\n self.trans[char] = u\\\"zhong\\\"\\n for char in u\\\"舟周州洲诌粥轴肘帚咒皱宙昼骤\\\":\\n self.trans[char] = u\\\"zhou\\\"\\n for char in u\\\"珠株蛛朱猪诸诛逐竹烛煮拄瞩嘱主著柱助蛀贮铸筑住注祝驻\\\":\\n self.trans[char] = u\\\"zhu\\\"\\n for char in u\\\"抓爪\\\":\\n self.trans[char] = u\\\"zhua\\\"\\n self.trans['拽'] = 'zhuai'\\n for char in u\\\"专砖转撰赚篆\\\":\\n self.trans[char] = u\\\"zhuan\\\"\\n for char in u\\\"桩庄装妆撞壮状\\\":\\n self.trans[char] = u\\\"zhuang\\\"\\n for char in u\\\"椎锥追赘坠缀\\\":\\n self.trans[char] = u\\\"zhui\\\"\\n for char in u\\\"谆准\\\":\\n self.trans[char] = u\\\"zhun\\\"\\n for char in u\\\"捉拙卓桌琢茁酌啄着灼浊\\\":\\n self.trans[char] = u\\\"zhuo\\\"\\n for char in u\\\"兹咨资姿滋淄孜紫仔籽滓子自渍字\\\":\\n self.trans[char] = u\\\"zi\\\"\\n for char in u\\\"鬃棕踪宗综总纵\\\":\\n self.trans[char] = u\\\"zong\\\"\\n for char in u\\\"邹走奏揍\\\":\\n self.trans[char] = u\\\"zou\\\"\\n for char in u\\\"租足卒族祖诅阻组\\\":\\n self.trans[char] = u\\\"zu\\\"\\n for char in u\\\"钻纂\\\":\\n self.trans[char] = u\\\"zuan\\\"\\n for char in u\\\"嘴醉最罪\\\":\\n self.trans[char] = u\\\"zui\\\"\\n for char in u\\\"尊遵\\\":\\n self.trans[char] = u\\\"zun\\\"\\n for char in u\\\"昨左佐柞做作坐座\\\":\\n self.trans[char] = u\\\"zuo\\\"\\n # from: https://www.wikidata.org/wiki/MediaWiki:Gadget-SimpleTransliterate.js\\n self.trans[u\\\"ଂ\\\"] = \\\"anusvara\\\"\\n self.trans[u\\\"ઇ\\\"] = \\\"i\\\"\\n self.trans[u\\\"എ\\\"] = \\\"e\\\"\\n self.trans[u\\\"ગ\\\"] = \\\"ga\\\"\\n self.trans[u\\\"ਜ\\\"] = \\\"ja\\\"\\n self.trans[u\\\"ഞ\\\"] = \\\"nya\\\"\\n self.trans[u\\\"ଢ\\\"] = \\\"ddha\\\"\\n self.trans[u\\\"ધ\\\"] = \\\"dha\\\"\\n self.trans[u\\\"ਬ\\\"] = \\\"ba\\\"\\n self.trans[u\\\"മ\\\"] = \\\"ma\\\"\\n self.trans[u\\\"ଲ\\\"] = \\\"la\\\"\\n self.trans[u\\\"ષ\\\"] = \\\"ssa\\\"\\n self.trans[u\\\"਼\\\"] = \\\"nukta\\\"\\n self.trans[u\\\"ാ\\\"] = \\\"aa\\\"\\n self.trans[u\\\"ୂ\\\"] = \\\"uu\\\"\\n self.trans[u\\\"ે\\\"] = \\\"e\\\"\\n self.trans[u\\\"ੌ\\\"] = \\\"au\\\"\\n self.trans[u\\\"ൎ\\\"] = \\\"reph\\\"\\n self.trans[u\\\"ੜ\\\"] = \\\"rra\\\"\\n self.trans[u\\\"՞\\\"] = \\\"?\\\"\\n self.trans[u\\\"ୢ\\\"] = \\\"l\\\"\\n self.trans[u\\\"૧\\\"] = \\\"1\\\"\\n self.trans[u\\\"੬\\\"] = \\\"6\\\"\\n self.trans[u\\\"൮\\\"] = \\\"8\\\"\\n self.trans[u\\\"୲\\\"] = \\\"quarter\\\"\\n self.trans[u\\\"ൾ\\\"] = \\\"ll\\\"\\n self.trans[u\\\"ਇ\\\"] = \\\"i\\\"\\n self.trans[u\\\"ഉ\\\"] = \\\"u\\\"\\n self.trans[u\\\"ઌ\\\"] = \\\"l\\\"\\n self.trans[u\\\"ਗ\\\"] = \\\"ga\\\"\\n self.trans[u\\\"ങ\\\"] = \\\"nga\\\"\\n self.trans[u\\\"ଝ\\\"] = \\\"jha\\\"\\n self.trans[u\\\"જ\\\"] = \\\"ja\\\"\\n self.trans[u\\\"؟\\\"] = \\\"?\\\"\\n self.trans[u\\\"ਧ\\\"] = \\\"dha\\\"\\n self.trans[u\\\"ഩ\\\"] = \\\"nnna\\\"\\n self.trans[u\\\"ଭ\\\"] = \\\"bha\\\"\\n self.trans[u\\\"બ\\\"] = \\\"ba\\\"\\n self.trans[u\\\"ഹ\\\"] = \\\"ha\\\"\\n self.trans[u\\\"ଽ\\\"] = \\\"avagraha\\\"\\n self.trans[u\\\"઼\\\"] = \\\"nukta\\\"\\n self.trans[u\\\"ੇ\\\"] = \\\"ee\\\"\\n self.trans[u\\\"୍\\\"] = \\\"virama\\\"\\n self.trans[u\\\"ૌ\\\"] = \\\"au\\\"\\n self.trans[u\\\"੧\\\"] = \\\"1\\\"\\n self.trans[u\\\"൩\\\"] = \\\"3\\\"\\n self.trans[u\\\"୭\\\"] = \\\"7\\\"\\n self.trans[u\\\"૬\\\"] = \\\"6\\\"\\n self.trans[u\\\"൹\\\"] = \\\"mark\\\"\\n self.trans[u\\\"ਖ਼\\\"] = \\\"khha\\\"\\n self.trans[u\\\"ਂ\\\"] = \\\"bindi\\\"\\n self.trans[u\\\"ഈ\\\"] = \\\"ii\\\"\\n self.trans[u\\\"ઍ\\\"] = \\\"e\\\"\\n self.trans[u\\\"ଌ\\\"] = \\\"l\\\"\\n self.trans[u\\\"ഘ\\\"] = \\\"gha\\\"\\n self.trans[u\\\"ઝ\\\"] = \\\"jha\\\"\\n self.trans[u\\\"ଡ଼\\\"] = \\\"rra\\\"\\n self.trans[u\\\"ਢ\\\"] = \\\"ddha\\\"\\n self.trans[u\\\"ന\\\"] = \\\"na\\\"\\n self.trans[u\\\"ભ\\\"] = \\\"bha\\\"\\n self.trans[u\\\"ବ\\\"] = \\\"ba\\\"\\n self.trans[u\\\"ਲ\\\"] = \\\"la\\\"\\n self.trans[u\\\"സ\\\"] = \\\"sa\\\"\\n self.trans[u\\\"ઽ\\\"] = \\\"avagraha\\\"\\n self.trans[u\\\"଼\\\"] = \\\"nukta\\\"\\n self.trans[u\\\"ੂ\\\"] = \\\"uu\\\"\\n self.trans[u\\\"ൈ\\\"] = \\\"ai\\\"\\n self.trans[u\\\"્\\\"] = \\\"virama\\\"\\n self.trans[u\\\"ୌ\\\"] = \\\"au\\\"\\n self.trans[u\\\"൨\\\"] = \\\"2\\\"\\n self.trans[u\\\"૭\\\"] = \\\"7\\\"\\n self.trans[u\\\"୬\\\"] = \\\"6\\\"\\n self.trans[u\\\"ੲ\\\"] = \\\"iri\\\"\\n self.trans[u\\\"ഃ\\\"] = \\\"visarga\\\"\\n self.trans[u\\\"ં\\\"] = \\\"anusvara\\\"\\n self.trans[u\\\"ଇ\\\"] = \\\"i\\\"\\n self.trans[u\\\"ഓ\\\"] = \\\"oo\\\"\\n self.trans[u\\\"ଗ\\\"] = \\\"ga\\\"\\n self.trans[u\\\"ਝ\\\"] = \\\"jha\\\"\\n self.trans[u\\\"？\\\"] = \\\"?\\\"\\n self.trans[u\\\"ണ\\\"] = \\\"nna\\\"\\n self.trans[u\\\"ઢ\\\"] = \\\"ddha\\\"\\n self.trans[u\\\"ଧ\\\"] = \\\"dha\\\"\\n self.trans[u\\\"ਭ\\\"] = \\\"bha\\\"\\n self.trans[u\\\"ള\\\"] = \\\"lla\\\"\\n self.trans[u\\\"લ\\\"] = \\\"la\\\"\\n self.trans[u\\\"ଷ\\\"] = \\\"ssa\\\"\\n self.trans[u\\\"ൃ\\\"] = \\\"r\\\"\\n self.trans[u\\\"ૂ\\\"] = \\\"uu\\\"\\n self.trans[u\\\"େ\\\"] = \\\"e\\\"\\n self.trans[u\\\"੍\\\"] = \\\"virama\\\"\\n self.trans[u\\\"ୗ\\\"] = \\\"mark\\\"\\n self.trans[u\\\"ൣ\\\"] = \\\"ll\\\"\\n self.trans[u\\\"ૢ\\\"] = \\\"l\\\"\\n self.trans[u\\\"୧\\\"] = \\\"1\\\"\\n self.trans[u\\\"੭\\\"] = \\\"7\\\"\\n self.trans[u\\\"൳\\\"] = \\\"1/4\\\"\\n self.trans[u\\\"୷\\\"] = \\\"sixteenths\\\"\\n self.trans[u\\\"ଆ\\\"] = \\\"aa\\\"\\n self.trans[u\\\"ઋ\\\"] = \\\"r\\\"\\n self.trans[u\\\"ഊ\\\"] = \\\"uu\\\"\\n self.trans[u\\\"ਐ\\\"] = \\\"ai\\\"\\n self.trans[u\\\"ଖ\\\"] = \\\"kha\\\"\\n self.trans[u\\\"છ\\\"] = \\\"cha\\\"\\n self.trans[u\\\"ച\\\"] = \\\"ca\\\"\\n self.trans[u\\\"ਠ\\\"] = \\\"ttha\\\"\\n self.trans[u\\\"ଦ\\\"] = \\\"da\\\"\\n self.trans[u\\\"ફ\\\"] = \\\"pha\\\"\\n self.trans[u\\\"പ\\\"] = \\\"pa\\\"\\n self.trans[u\\\"ਰ\\\"] = \\\"ra\\\"\\n self.trans[u\\\"ଶ\\\"] = \\\"sha\\\"\\n self.trans[u\\\"ഺ\\\"] = \\\"ttta\\\"\\n self.trans[u\\\"ੀ\\\"] = \\\"ii\\\"\\n self.trans[u\\\"ો\\\"] = \\\"o\\\"\\n self.trans[u\\\"ൊ\\\"] = \\\"o\\\"\\n self.trans[u\\\"ୖ\\\"] = \\\"mark\\\"\\n self.trans[u\\\"୦\\\"] = \\\"0\\\"\\n self.trans[u\\\"૫\\\"] = \\\"5\\\"\\n self.trans[u\\\"൪\\\"] = \\\"4\\\"\\n self.trans[u\\\"ੰ\\\"] = \\\"tippi\\\"\\n self.trans[u\\\"୶\\\"] = \\\"eighth\\\"\\n self.trans[u\\\"ൺ\\\"] = \\\"nn\\\"\\n self.trans[u\\\"ଁ\\\"] = \\\"candrabindu\\\"\\n self.trans[u\\\"അ\\\"] = \\\"a\\\"\\n self.trans[u\\\"ઐ\\\"] = \\\"ai\\\"\\n self.trans[u\\\"ക\\\"] = \\\"ka\\\"\\n self.trans[u\\\"ਸ਼\\\"] = \\\"sha\\\"\\n self.trans[u\\\"ਛ\\\"] = \\\"cha\\\"\\n self.trans[u\\\"ଡ\\\"] = \\\"dda\\\"\\n self.trans[u\\\"ઠ\\\"] = \\\"ttha\\\"\\n self.trans[u\\\"ഥ\\\"] = \\\"tha\\\"\\n self.trans[u\\\"ਫ\\\"] = \\\"pha\\\"\\n self.trans[u\\\"ર\\\"] = \\\"ra\\\"\\n self.trans[u\\\"വ\\\"] = \\\"va\\\"\\n self.trans[u\\\"ୁ\\\"] = \\\"u\\\"\\n self.trans[u\\\"ી\\\"] = \\\"ii\\\"\\n self.trans[u\\\"ੋ\\\"] = \\\"oo\\\"\\n self.trans[u\\\"ૐ\\\"] = \\\"om\\\"\\n self.trans[u\\\"ୡ\\\"] = \\\"ll\\\"\\n self.trans[u\\\"ૠ\\\"] = \\\"rr\\\"\\n self.trans[u\\\"੫\\\"] = \\\"5\\\"\\n self.trans[u\\\"ୱ\\\"] = \\\"wa\\\"\\n self.trans[u\\\"૰\\\"] = \\\"sign\\\"\\n self.trans[u\\\"൵\\\"] = \\\"quarters\\\"\\n self.trans[u\\\"ਫ਼\\\"] = \\\"fa\\\"\\n self.trans[u\\\"ઁ\\\"] = \\\"candrabindu\\\"\\n self.trans[u\\\"ਆ\\\"] = \\\"aa\\\"\\n self.trans[u\\\"ઑ\\\"] = \\\"o\\\"\\n self.trans[u\\\"ଐ\\\"] = \\\"ai\\\"\\n self.trans[u\\\"ഔ\\\"] = \\\"au\\\"\\n self.trans[u\\\"ਖ\\\"] = \\\"kha\\\"\\n self.trans[u\\\"ડ\\\"] = \\\"dda\\\"\\n self.trans[u\\\"ଠ\\\"] = \\\"ttha\\\"\\n self.trans[u\\\"ത\\\"] = \\\"ta\\\"\\n self.trans[u\\\"ਦ\\\"] = \\\"da\\\"\\n self.trans[u\\\"ର\\\"] = \\\"ra\\\"\\n self.trans[u\\\"ഴ\\\"] = \\\"llla\\\"\\n self.trans[u\\\"ુ\\\"] = \\\"u\\\"\\n self.trans[u\\\"ୀ\\\"] = \\\"ii\\\"\\n self.trans[u\\\"ൄ\\\"] = \\\"rr\\\"\\n self.trans[u\\\"ૡ\\\"] = \\\"ll\\\"\\n self.trans[u\\\"ୠ\\\"] = \\\"rr\\\"\\n self.trans[u\\\"੦\\\"] = \\\"0\\\"\\n self.trans[u\\\"૱\\\"] = \\\"sign\\\"\\n self.trans[u\\\"୰\\\"] = \\\"isshar\\\"\\n self.trans[u\\\"൴\\\"] = \\\"1/2\\\"\\n self.trans[u\\\"ਁ\\\"] = \\\"bindi\\\"\\n self.trans[u\\\"આ\\\"] = \\\"aa\\\"\\n self.trans[u\\\"ଋ\\\"] = \\\"r\\\"\\n self.trans[u\\\"ഏ\\\"] = \\\"ee\\\"\\n self.trans[u\\\"ખ\\\"] = \\\"kha\\\"\\n self.trans[u\\\"ଛ\\\"] = \\\"cha\\\"\\n self.trans[u\\\"ട\\\"] = \\\"tta\\\"\\n self.trans[u\\\"ਡ\\\"] = \\\"dda\\\"\\n self.trans[u\\\"દ\\\"] = \\\"da\\\"\\n self.trans[u\\\"ଫ\\\"] = \\\"pha\\\"\\n self.trans[u\\\"യ\\\"] = \\\"ya\\\"\\n self.trans[u\\\"શ\\\"] = \\\"sha\\\"\\n self.trans[u\\\"ി\\\"] = \\\"i\\\"\\n self.trans[u\\\"ੁ\\\"] = \\\"u\\\"\\n self.trans[u\\\"ୋ\\\"] = \\\"o\\\"\\n self.trans[u\\\"ੑ\\\"] = \\\"udaat\\\"\\n self.trans[u\\\"૦\\\"] = \\\"0\\\"\\n self.trans[u\\\"୫\\\"] = \\\"5\\\"\\n self.trans[u\\\"൯\\\"] = \\\"9\\\"\\n self.trans[u\\\"ੱ\\\"] = \\\"addak\\\"\\n self.trans[u\\\"ൿ\\\"] = \\\"k\\\"\\n self.trans[u\\\"ആ\\\"] = \\\"aa\\\"\\n self.trans[u\\\"ଊ\\\"] = \\\"uu\\\"\\n self.trans[u\\\"એ\\\"] = \\\"e\\\"\\n self.trans[u\\\"ਔ\\\"] = \\\"au\\\"\\n self.trans[u\\\"ഖ\\\"] = \\\"kha\\\"\\n self.trans[u\\\"ଚ\\\"] = \\\"ca\\\"\\n self.trans[u\\\"ટ\\\"] = \\\"tta\\\"\\n self.trans[u\\\"ਤ\\\"] = \\\"ta\\\"\\n self.trans[u\\\"ദ\\\"] = \\\"da\\\"\\n self.trans[u\\\"ପ\\\"] = \\\"pa\\\"\\n self.trans[u\\\"ય\\\"] = \\\"ya\\\"\\n self.trans[u\\\"ശ\\\"] = \\\"sha\\\"\\n self.trans[u\\\"િ\\\"] = \\\"i\\\"\\n self.trans[u\\\"െ\\\"] = \\\"e\\\"\\n self.trans[u\\\"൦\\\"] = \\\"0\\\"\\n self.trans[u\\\"୪\\\"] = \\\"4\\\"\\n self.trans[u\\\"૯\\\"] = \\\"9\\\"\\n self.trans[u\\\"ੴ\\\"] = \\\"onkar\\\"\\n self.trans[u\\\"ଅ\\\"] = \\\"a\\\"\\n self.trans[u\\\"ਏ\\\"] = \\\"ee\\\"\\n self.trans[u\\\"କ\\\"] = \\\"ka\\\"\\n self.trans[u\\\"ઔ\\\"] = \\\"au\\\"\\n self.trans[u\\\"ਟ\\\"] = \\\"tta\\\"\\n self.trans[u\\\"ഡ\\\"] = \\\"dda\\\"\\n self.trans[u\\\"ଥ\\\"] = \\\"tha\\\"\\n self.trans[u\\\"ત\\\"] = \\\"ta\\\"\\n self.trans[u\\\"ਯ\\\"] = \\\"ya\\\"\\n self.trans[u\\\"റ\\\"] = \\\"rra\\\"\\n self.trans[u\\\"ଵ\\\"] = \\\"va\\\"\\n self.trans[u\\\"ਿ\\\"] = \\\"i\\\"\\n self.trans[u\\\"ു\\\"] = \\\"u\\\"\\n self.trans[u\\\"ૄ\\\"] = \\\"rr\\\"\\n self.trans[u\\\"ൡ\\\"] = \\\"ll\\\"\\n self.trans[u\\\"੯\\\"] = \\\"9\\\"\\n self.trans[u\\\"൱\\\"] = \\\"100\\\"\\n self.trans[u\\\"୵\\\"] = \\\"sixteenth\\\"\\n self.trans[u\\\"અ\\\"] = \\\"a\\\"\\n self.trans[u\\\"ਊ\\\"] = \\\"uu\\\"\\n self.trans[u\\\"ഐ\\\"] = \\\"ai\\\"\\n self.trans[u\\\"ક\\\"] = \\\"ka\\\"\\n self.trans[u\\\"ଔ\\\"] = \\\"au\\\"\\n self.trans[u\\\"ਚ\\\"] = \\\"ca\\\"\\n self.trans[u\\\"ഠ\\\"] = \\\"ttha\\\"\\n self.trans[u\\\"થ\\\"] = \\\"tha\\\"\\n self.trans[u\\\"ତ\\\"] = \\\"ta\\\"\\n self.trans[u\\\"ਪ\\\"] = \\\"pa\\\"\\n self.trans[u\\\"ര\\\"] = \\\"ra\\\"\\n self.trans[u\\\"વ\\\"] = \\\"va\\\"\\n self.trans[u\\\"ീ\\\"] = \\\"ii\\\"\\n self.trans[u\\\"ૅ\\\"] = \\\"e\\\"\\n self.trans[u\\\"ୄ\\\"] = \\\"rr\\\"\\n self.trans[u\\\"ൠ\\\"] = \\\"rr\\\"\\n self.trans[u\\\"ਜ਼\\\"] = \\\"za\\\"\\n self.trans[u\\\"੪\\\"] = \\\"4\\\"\\n self.trans[u\\\"൰\\\"] = \\\"10\\\"\\n self.trans[u\\\"୴\\\"] = \\\"quarters\\\"\\n self.trans[u\\\"ਅ\\\"] = \\\"a\\\"\\n self.trans[u\\\"ഋ\\\"] = \\\"r\\\"\\n self.trans[u\\\"ઊ\\\"] = \\\"uu\\\"\\n self.trans[u\\\"ଏ\\\"] = \\\"e\\\"\\n self.trans[u\\\"ਕ\\\"] = \\\"ka\\\"\\n self.trans[u\\\"ഛ\\\"] = \\\"cha\\\"\\n self.trans[u\\\"ચ\\\"] = \\\"ca\\\"\\n self.trans[u\\\"ଟ\\\"] = \\\"tta\\\"\\n self.trans[u\\\"ਥ\\\"] = \\\"tha\\\"\\n self.trans[u\\\"ഫ\\\"] = \\\"pha\\\"\\n self.trans[u\\\"પ\\\"] = \\\"pa\\\"\\n self.trans[u\\\"ଯ\\\"] = \\\"ya\\\"\\n self.trans[u\\\"ਵ\\\"] = \\\"va\\\"\\n self.trans[u\\\"ି\\\"] = \\\"i\\\"\\n self.trans[u\\\"ോ\\\"] = \\\"oo\\\"\\n self.trans[u\\\"ୟ\\\"] = \\\"yya\\\"\\n self.trans[u\\\"൫\\\"] = \\\"5\\\"\\n self.trans[u\\\"૪\\\"] = \\\"4\\\"\\n self.trans[u\\\"୯\\\"] = \\\"9\\\"\\n self.trans[u\\\"ੵ\\\"] = \\\"yakash\\\"\\n self.trans[u\\\"ൻ\\\"] = \\\"n\\\"\\n self.trans[u\\\"ઃ\\\"] = \\\"visarga\\\"\\n self.trans[u\\\"ം\\\"] = \\\"anusvara\\\"\\n self.trans[u\\\"ਈ\\\"] = \\\"ii\\\"\\n self.trans[u\\\"ઓ\\\"] = \\\"o\\\"\\n self.trans[u\\\"ഒ\\\"] = \\\"o\\\"\\n self.trans[u\\\"ਘ\\\"] = \\\"gha\\\"\\n self.trans[u\\\"ଞ\\\"] = \\\"nya\\\"\\n self.trans[u\\\"ણ\\\"] = \\\"nna\\\"\\n self.trans[u\\\"ഢ\\\"] = \\\"ddha\\\"\\n self.trans[u\\\"ਲ਼\\\"] = \\\"lla\\\"\\n self.trans[u\\\"ਨ\\\"] = \\\"na\\\"\\n self.trans[u\\\"ମ\\\"] = \\\"ma\\\"\\n self.trans[u\\\"ળ\\\"] = \\\"lla\\\"\\n self.trans[u\\\"ല\\\"] = \\\"la\\\"\\n self.trans[u\\\"ਸ\\\"] = \\\"sa\\\"\\n self.trans[u\\\"¿\\\"] = \\\"?\\\"\\n self.trans[u\\\"ା\\\"] = \\\"aa\\\"\\n self.trans[u\\\"ૃ\\\"] = \\\"r\\\"\\n self.trans[u\\\"ൂ\\\"] = \\\"uu\\\"\\n self.trans[u\\\"ੈ\\\"] = \\\"ai\\\"\\n self.trans[u\\\"ૣ\\\"] = \\\"ll\\\"\\n self.trans[u\\\"ൢ\\\"] = \\\"l\\\"\\n self.trans[u\\\"੨\\\"] = \\\"2\\\"\\n self.trans[u\\\"୮\\\"] = \\\"8\\\"\\n self.trans[u\\\"൲\\\"] = \\\"1000\\\"\\n self.trans[u\\\"ਃ\\\"] = \\\"visarga\\\"\\n self.trans[u\\\"ଉ\\\"] = \\\"u\\\"\\n self.trans[u\\\"ઈ\\\"] = \\\"ii\\\"\\n self.trans[u\\\"ਓ\\\"] = \\\"oo\\\"\\n self.trans[u\\\"ଙ\\\"] = \\\"nga\\\"\\n self.trans[u\\\"ઘ\\\"] = \\\"gha\\\"\\n self.trans[u\\\"ഝ\\\"] = \\\"jha\\\"\\n self.trans[u\\\"ਣ\\\"] = \\\"nna\\\"\\n self.trans[u\\\"ન\\\"] = \\\"na\\\"\\n self.trans[u\\\"ഭ\\\"] = \\\"bha\\\"\\n self.trans[u\\\"ଜ\\\"] = \\\"ja\\\"\\n self.trans[u\\\"ହ\\\"] = \\\"ha\\\"\\n self.trans[u\\\"સ\\\"] = \\\"sa\\\"\\n self.trans[u\\\"ഽ\\\"] = \\\"avagraha\\\"\\n self.trans[u\\\"ૈ\\\"] = \\\"ai\\\"\\n self.trans[u\\\"്\\\"] = \\\"virama\\\"\\n self.trans[u\\\"୩\\\"] = \\\"3\\\"\\n self.trans[u\\\"૨\\\"] = \\\"2\\\"\\n self.trans[u\\\"൭\\\"] = \\\"7\\\"\\n self.trans[u\\\"ੳ\\\"] = \\\"ura\\\"\\n self.trans[u\\\"ൽ\\\"] = \\\"l\\\"\\n self.trans[u\\\"ઉ\\\"] = \\\"u\\\"\\n self.trans[u\\\"ଈ\\\"] = \\\"ii\\\"\\n self.trans[u\\\"ഌ\\\"] = \\\"l\\\"\\n self.trans[u\\\"ઙ\\\"] = \\\"nga\\\"\\n self.trans[u\\\"ଘ\\\"] = \\\"gha\\\"\\n self.trans[u\\\"ജ\\\"] = \\\"ja\\\"\\n self.trans[u\\\"ਞ\\\"] = \\\"nya\\\"\\n self.trans[u\\\"ନ\\\"] = \\\"na\\\"\\n self.trans[u\\\"ബ\\\"] = \\\"ba\\\"\\n self.trans[u\\\"ਮ\\\"] = \\\"ma\\\"\\n self.trans[u\\\"હ\\\"] = \\\"ha\\\"\\n self.trans[u\\\"ସ\\\"] = \\\"sa\\\"\\n self.trans[u\\\"ਾ\\\"] = \\\"aa\\\"\\n self.trans[u\\\"ૉ\\\"] = \\\"o\\\"\\n self.trans[u\\\"ୈ\\\"] = \\\"ai\\\"\\n self.trans[u\\\"ൌ\\\"] = \\\"au\\\"\\n self.trans[u\\\"૩\\\"] = \\\"3\\\"\\n self.trans[u\\\"୨\\\"] = \\\"2\\\"\\n self.trans[u\\\"൬\\\"] = \\\"6\\\"\\n self.trans[u\\\"੮\\\"] = \\\"8\\\"\\n self.trans[u\\\"ർ\\\"] = \\\"rr\\\"\\n self.trans[u\\\"ଃ\\\"] = \\\"visarga\\\"\\n self.trans[u\\\"ഇ\\\"] = \\\"i\\\"\\n self.trans[u\\\"ਉ\\\"] = \\\"u\\\"\\n self.trans[u\\\"ଓ\\\"] = \\\"o\\\"\\n self.trans[u\\\"ഗ\\\"] = \\\"ga\\\"\\n self.trans[u\\\"ਙ\\\"] = \\\"nga\\\"\\n self.trans[u\\\"ઞ\\\"] = \\\"nya\\\"\\n self.trans[u\\\"ଣ\\\"] = \\\"nna\\\"\\n self.trans[u\\\"ധ\\\"] = \\\"dha\\\"\\n self.trans[u\\\"મ\\\"] = \\\"ma\\\"\\n self.trans[u\\\"ଳ\\\"] = \\\"lla\\\"\\n self.trans[u\\\"ഷ\\\"] = \\\"ssa\\\"\\n self.trans[u\\\"ਹ\\\"] = \\\"ha\\\"\\n self.trans[u\\\"ਗ਼\\\"] = \\\"ghha\\\"\\n self.trans[u\\\"ા\\\"] = \\\"aa\\\"\\n self.trans[u\\\"ୃ\\\"] = \\\"r\\\"\\n self.trans[u\\\"േ\\\"] = \\\"ee\\\"\\n self.trans[u\\\"ൗ\\\"] = \\\"mark\\\"\\n self.trans[u\\\"ଢ଼\\\"] = \\\"rha\\\"\\n self.trans[u\\\"ୣ\\\"] = \\\"ll\\\"\\n self.trans[u\\\"൧\\\"] = \\\"1\\\"\\n self.trans[u\\\"੩\\\"] = \\\"3\\\"\\n self.trans[u\\\"૮\\\"] = \\\"8\\\"\\n self.trans[u\\\"୳\\\"] = \\\"half\\\"\\n for char in self.trans:\\n value = self.trans[char]\\n if value == \\\"?\\\":\\n continue\\n while value.encode(encoding, 'replace').decode(encoding) == \\\"?\\\" and value in self.trans:\\n assert value != self.trans[value], \\\"%r == self.trans[%r]!\\\" % (value, value)\\n value = self.trans[value]\\n self.trans[char] = value\",\n \"def test_Encoder_encode_decode_content(self):\\n c = Content(\\\"/data/test\\\", \\\"HelloWorld\\\")\\n ec = self.encoder1.encode(c)\\n dc = self.encoder1.decode(ec)\\n self.assertTrue(c == dc)\",\n \"def encoding(self) -> 'layout.Encoding':\",\n \"def testEncodeDecodeEmpty(self):\\n test_case = ''\\n string_obj = ASCIITransportFormat(\\n ASCIITransportFormat.SupportedTypes.STRING,\\n test_case,\\n )\\n json_obj = string_obj.json()\\n obj = ASCIITransportFormat(\\n ASCIITransportFormat.SupportedTypes.JSON,\\n json_obj,\\n )\\n self.assertEqual(obj.data, test_case)\\n self.assertFalse(obj.encoded)\\n self.assertFalse(obj.pseudo_encode)\\n\\n json_obj = obj.json()\\n obj = ASCIITransportFormat(\\n ASCIITransportFormat.SupportedTypes.JSON,\\n json_obj,\\n )\\n obj.encode()\\n self.assertEqual(obj.data, test_case)\\n self.assertTrue(obj.encoded)\\n self.assertTrue(obj.pseudo_encode)\\n\\n json_obj = obj.json()\\n obj = ASCIITransportFormat(\\n ASCIITransportFormat.SupportedTypes.JSON,\\n json_obj,\\n )\\n obj.decode()\\n self.assertEqual(obj.data, test_case)\\n self.assertFalse(obj.encoded)\\n self.assertFalse(obj.pseudo_encode)\",\n \"def encode(self, seq):\",\n \"def decode(data):\\n raise NotImplementedError\",\n \"def encode(self, text):\",\n \"def recodeToUtf8(data):\\n try:\\n data = data.decode('utf8').encode('utf8')\\n return data\\n except UnicodeDecodeError:\\n encoding = chardet.detect(data)['encoding']\\n logging.log(5, 'encoding should be %s' % encoding)\\n if encoding == None:\\n encoding = 'latin1'\\n try:\\n data = data.decode(encoding).encode('utf8')\\n except UnicodeDecodeError:\\n logging.warn('Error when decoding as %s' % encoding)\\n data = data\\n except LookupError:\\n logging.warn('Unknown encoding when decoding as %s' % encoding)\\n data = data\\n\\n return data\\n\\n return\",\n \"def _decode(self, input_dict):\\n pass\",\n \"def encoding(options):\\n pass\",\n \"def forward(self, x):\\n x = self.Encoder(x)\\n x = self.Decoder(x)\\n return x\",\n \"def forward(self, x):\\n x = self.encoder(x)\\n x = self.decoder(x)\\n return x\",\n \"def test_encoders_strings(encoder):\\n assert \\\"\\\" == encoding.decode(b\\\"\\\", encoder)\\n\\n assert \\\"string\\\" == encoding.decode(\\n encoding.encode(\\n \\\"string\\\",\\n encoder\\n ),\\n encoder\\n )\\n\\n with pytest.raises(TypeError):\\n encoding.encode(b\\\"string\\\", encoder)\\n\\n with pytest.raises(TypeError):\\n encoding.decode(\\\"foobar\\\", encoder)\",\n \"def encoder(self, inputs):\\n pass\",\n \"def test_encode_decode(self):\\n self.assertEquals(self.txt_when,\\n self.TDTT.encode_when(self.dt_when))\\n self.assertEquals(None,\\n self.TDTT.encode_when(self.NOT_DATE_AND_TIME))\\n\\n expected = {'when': self.txt_when}\\n encoded_dict = self.TDTT.encode(when=self.dt_when)\\n self.assertEquals(expected, encoded_dict)\\n\\n decoded_dtt = self.TDTT.decode(expected)\\n self.assertEquals(self.dt_when, decoded_dtt.when)\\n self.assertEquals(expected, decoded_dtt.encoded)\\n\\n constructed_dtt = self.TDTT(when=self.txt_when)\\n self.assertEquals(expected, constructed_dtt.encoded)\\n decoded_dict = constructed_dtt.decoded\\n self.assertEquals(self.dt_when, decoded_dict.get('when'))\",\n \"def test_decode(self):\\n for (input, output) in self.tests:\\n self.assertEqual(input, output.decode('imap4-utf-7'))\",\n \"def decode(self, encoding=None, errors=None):\\n return decode(self, encoding, errors)\",\n \"def handle_decode(self, encoded_data):\\n \\n config.COD_PROMPT = config.DEC_PROMPT\\n print config.DEC_PROMPT + \\\" decoding...\\\"\\n \\n # while there is another decoder, run each item through the next decoder\\n data = encoded_data\\n success = False\\n for decoder in self.decoder_list:\\n current_decoder = decoder()\\n success, data = self.recursive_decoder(current_decoder.decode, data)\\n if not success:\\n break\\n print config.DEC_PROMPT + \\\"%s decoded to '%s'\\\" % ( current_decoder.name(),data)\\n return success, data\",\n \"def convert(data):\\n result = None\\n if isinstance(data, bytes):\\n try:\\n result = json.loads(data.decode(CODING))\\n except:\\n print('wrong data format!')\\n finally:\\n return result\\n elif isinstance(data, dict):\\n try:\\n result = json.dumps(data).encode(CODING)\\n except:\\n print('wrong data format!')\\n finally:\\n return result\\n else:\\n print('wrong data format!')\\n return result\",\n \"def getencdec():\\n return (json.JSONEncoder(), json.JSONDecoder())\",\n \"def compute_encoding(self):\\n for input_quantizer in self._input_quantizers.values():\\n input_quantizer.compute_encoding()\\n\\n for quantizer in self.param_quantizers.values():\\n # NOTE: If quantizer.enabled is True but quantizer.encoding is None,\\n # quantizer.compute_encoding() will set quantizer.enabled to False.\\n # Otherwise, quantizer.compute_encodings() is equivalent to no-op.\\n quantizer.compute_encoding()\\n\\n for output_quantizer in self._output_quantizers.values():\\n output_quantizer.compute_encoding()\",\n \"def encode(self): # pragma: no cover\\n pass\",\n \"def decode(self, value):\\r\\n pass\",\n \"def _process_content_codings(self, chunk):\\n content_codings = self.parsed_headers.get('content-encoding', [])\\n content_codings.reverse()\\n for coding in content_codings:\\n # TODO: deflate support\\n if coding in ['gzip', 'x-gzip'] and self._decode_ok:\\n if not self._in_gzip_body:\\n self._gzip_header_buffer += chunk\\n try:\\n chunk = self._read_gzip_header(\\n self._gzip_header_buffer\\n )\\n self._in_gzip_body = True\\n except IndexError:\\n return '' # not a full header yet\\n except IOError, gzip_error:\\n self.add_note('header-content-encoding',\\n rs.BAD_GZIP,\\n gzip_error=str(gzip_error)\\n )\\n self._decode_ok = False\\n return\\n try:\\n chunk = self._gzip_processor.decompress(chunk)\\n except zlib.error, zlib_error:\\n self.add_note(\\n 'header-content-encoding', \\n rs.BAD_ZLIB,\\n zlib_error=str(zlib_error),\\n ok_zlib_len=f_num(self.payload_sample[-1][0]),\\n chunk_sample=chunk[:20].encode('string_escape')\\n )\\n self._decode_ok = False\\n return\\n else:\\n # we can't handle other codecs, so punt on body processing.\\n self._decode_ok = False\\n return\\n self._md5_post_processor.update(chunk)\\n self.decoded_len += len(chunk)\\n return chunk\",\n \"def _pre_filter_decode(self):\\n\\n pass\",\n \"def test_Encoder_encode_decode_nack(self):\\n interest = Interest(\\\"/data/test\\\")\\n n = Nack(\\\"/data/test\\\", NackReason.NO_CONTENT, interest=interest)\\n en = self.encoder1.encode(n)\\n dn = self.encoder1.decode(en)\\n self.assertTrue(n == dn)\",\n \"def easytest():\\n (c,root) = huffman([1, 2, 3, 4])\\n s = encode([1,2,3,0,3,2,1],c)\\n print s\\n ans = decode( s, root )\\n print ans\",\n \"def encode(self):\\n \\n assert False, \\\"Not implemented.\\\"\",\n \"def test_encode():\",\n \"def decode(self, crypto):\",\n \"def Convert(*args, **kwargs):\\n return _gdi_.EncodingConverter_Convert(*args, **kwargs)\",\n \"def decode(decode_format):\\n return output_from_decode\",\n \"def _decode(self, rel_codes, anchors):\\n pass\",\n \"def convert( self, text ):\\n if self.input_codec != self.output_codec:\\n return unicode( text, self.input_codec, 'ignore' ).encode( self.output_codec, 'ignore' )\\n else:\\n return text\",\n \"def _convertToOutputEncoding(export_text,\\n input_encoding=None,\\n output_encoding='utf-8'):\\n if input_encoding:\\n return _encode(_decode(export_text, input_encoding), output_encoding)\\n return _encode(_decode(export_text), output_encoding)\",\n \"def __encode(self):\\n\\n for i, char in enumerate(self.__chars):\\n self.__char2idx[char] = i\\n self.__idx2char[i] = char\",\n \"def decode(self, probs, sizes=None):\\n raise NotImplementedError\",\n \"def _encode(self):\\n with tf.variable_scope('encoding'):\\n self.sep_p_encodes, _ = bilstm_layer(self.p_emb, self.p_length, self.hidden_size)\\n tf.get_variable_scope().reuse_variables()\\n self.sep_q_encodes, _ = bilstm_layer(self.q_emb, self.q_length, self.hidden_size)\\n if self.use_dropout:\\n self.sep_p_encodes = tf.nn.dropout(self.sep_p_encodes, 1-self.dropout)\\n self.sep_q_encodes = tf.nn.dropout(self.sep_q_encodes, 1-self.dropout)\",\n \"def testEncodeDecodeEmpty(self):\\n test_case = ''\\n obj = ASCIITransportFormat(\\n ASCIITransportFormat.SupportedTypes.STRING,\\n test_case,\\n )\\n self.assertEqual(obj.data, test_case)\\n self.assertFalse(obj.encoded)\\n self.assertFalse(obj.pseudo_encode)\\n\\n obj.encode()\\n self.assertEqual(obj.data, test_case)\\n self.assertTrue(obj.encoded)\\n self.assertTrue(obj.pseudo_encode)\\n\\n obj.decode()\\n self.assertEqual(obj.data, test_case)\\n self.assertFalse(obj.encoded)\\n self.assertFalse(obj.pseudo_encode)\",\n \"def set_encodings(questions, candidates,\\n question_encoder, response_encoder):\\n for i, chunk in enumerate(chunks(questions, 100)):\\n question_strs = [q.question for q in chunk]\\n question_encodings = np.array(question_encoder(question_strs))\\n\\n for j in range(question_encodings.shape[0]):\\n questions[i * 100 + j].encoding = question_encodings[j]\\n if i % 50 == 0:\\n print(\\\"Questions: encoded %s of %s...\\\" % (i * 100, len(questions)))\\n\\n for i, chunk in enumerate(chunks(candidates, 50)):\\n candidate_strs = [(c.sentence, c.context) for c in chunk]\\n candidate_encodings = np.array(response_encoder(candidate_strs))\\n for j in range(candidate_encodings.shape[0]):\\n candidates[i * 50 + j].encoding = candidate_encodings[j]\\n if i % 100 == 0:\\n print(\\\"Candidates: encoded %s of %s...\\\" % (i * 50, len(candidates)))\",\n \"def encode_decode(self, img, img_metas):\\n pass\",\n \"def encode(path, tocodec, default_codec='windows-1251', unescape=False, force_encode=False):\\n fromcodec = get_encoding_type(path)\\n \\n if fromcodec == tocodec:\\n print(f\\\"Already {tocodec}: {path}\\\")\\n if not force_encode and not unescape:\\n return\\n \\n # fix error detections (RU region)\\n if fromcodec not in ('windows-1251', 'ascii') and fromcodec.lower().find('utf') == -1:\\n to_log('Possible Detect dismiss: ' + fromcodec + ' in ' + path)\\n if not force_encode:\\n to_log('Try default:', default_codec)\\n fromcodec = default_codec\\n else:\\n to_log('Force encode:', fromcodec)\\n \\n print(f\\\"Convert from {fromcodec} to {tocodec}: {path}\\\")\\n tmp_file = 'text.txt'\\n try:\\n with open(path, 'r', encoding=fromcodec) as f, open(tmp_file, 'w', encoding=tocodec) as e:\\n text = f.read()\\n if unescape:\\n text = html.unescape(text)\\n e.write(text)\\n os.remove(path)\\n os.rename(tmp_file, path) \\n except UnicodeDecodeError:\\n to_log(f'[{fromcodec}] → [{tocodec}] - Decoding Error: {path}')\\n except UnicodeEncodeError:\\n to_log(f'[{fromcodec}] → [{tocodec}] - Encoding Error: {path}')\\n print(f'Converted!')\",\n \"def encode_data(self, data):\\n if self.unit == \\\"char\\\":\\n data = self.char_encoding(data)\\n elif self.unit == \\\"char-ngram\\\":\\n data = self.ngram_encoding(data)\\n elif self.unit == \\\"morpheme\\\" or self.unit == \\\"oracle\\\":\\n data = self.morpheme_encoding(data)\\n else:\\n data = self.data_to_word_ids(data, False)\\n return data\",\n \"def _define_encoder(self):\\n raise NotImplementedError\",\n \"def forward(self, inputs_encoder, inputs_decoder):\\n states_encoder = self.encoder(inputs_encoder)\\n outputs_decoder, states_decoder = self.decoder(inputs_decoder, states_encoder)\\n return outputs_decoder, states_decoder\",\n \"def get_encoded(self):\\n pass\",\n \"def test_encode_decode():\\n enig = Enigma(534, 10, 5, [3, 1, 3, 4, 1, 0, 2, 3, 4, 2])\\n string = \\\"\\\"\\\"A semi-enigma-machine-like cypher that uses 'rotors' to cypher\\nand decypher messages. It is more unlike the enigma machine than\\nit is alike, the wheelsspin one way to encode, the other to decode,\\nwhereas the enigma machine always went forward, and paired keys.\\nThis doesn't pair keys, nor does the cypher travel throught the rotors\\nand back after hitting a reflector. The wheel positions are setteable,\\nand as many wheels as you want can be added for extra 'cypherage'. This\\nalso lacks the letter pairings, instead using a seed for random numbers\\nused to shift the charactersets passed through.\\\"\\\"\\\"\\n newstr = enig.encode(string)\\n assert_equal(string, enig.decode(newstr))\",\n \"def testEncodeDecodeEmpty(self):\\n empty_case = ''\\n encoded_result = ASCIITransportFormat.encode_data(empty_case)\\n decoded_result = ASCIITransportFormat.decode_data(encoded_result)\\n self.assertEqual(decoded_result, empty_case)\",\n \"def reset_encodings(self):\\n group_quantizers = self._grouped_quantizers.values()\\n for quantizer in group_quantizers:\\n quantizer.reset_encoding_stats()\\n for param_quantizer in self._param_quantizers.values():\\n if param_quantizer not in group_quantizers:\\n param_quantizer.reset_encoding_stats()\\n for input_quantizer in self._input_quantizers.values():\\n if input_quantizer not in group_quantizers:\\n input_quantizer.reset_encoding_stats()\\n for output_quantizer in self._output_quantizers.values():\\n if output_quantizer not in group_quantizers:\\n output_quantizer.reset_encoding_stats()\",\n \"def apply_coder(text, coder):\\n ### TODO.\",\n \"def decodeUtf8(self, arrayBuffer):\",\n \"def decodeUtf8(self, arrayBuffer):\",\n \"def _encode_decode(self, cover, quantize=False):\\n payload = self._random_data(cover)\\n generated = self.encoder(cover, payload)\\n if quantize:\\n generated = (255.0 * (generated + 1.0) / 2.0).long()\\n generated = 2.0 * generated.float() / 255.0 - 1.0\\n\\n decoded = self.decoder(generated)\\n\\n return generated, payload, decoded\",\n \"def decode(self,m):\\n raise NotImplementedError('subclasses must override decode()!')\",\n \"def GetDataEncodings(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\\n raise NotImplementedError()\",\n \"def decode(self, data: bytes) -> bytes:\\n ...\",\n \"def GetDataEncodings(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"def test_file_gzip_utf8_readwrite_explicit_decode(self):\\n if state.py2:\\n FileWriter(self.unicode_path).gzip(self.unicode_string)\\n gzip_contents = FileReader(self.unicode_path + \\\".gz\\\").read_gzip(\\\"utf-8\\\") # when read with explicit utf-8 decoding, strings should match\\n self.assertEqual(gzip_contents, self.unicode_string)\\n elif state.py3:\\n FileWriter(self.unicode_path).gzip(bytes(self.unicode_string, 'utf-8'))\\n gzip_contents = FileReader(self.unicode_path + \\\".gz\\\").read_gzip(\\\"utf-8\\\") # when read with explicit utf-8 decoding, strings should match\\n self.assertEqual(gzip_contents, self.unicode_string)\",\n \"def encode(self, input_):\\n return self.encoder(input_)\",\n \"def decode(self, code):\\n raise NotImplementedError\",\n \"def test_encoding_method_name(self):\\n data = service_call.encode_call(\\\"foo\\\", [1, 2, 3])\\n data = serial_datagram.decode(data)\\n\\n u = msgpack.Unpacker(encoding='ascii')\\n u.feed(data)\\n command = next(u)\\n\\n self.assertEqual(command, ['foo', [1, 2, 3]])\",\n \"def encode_data(data, item_to_encoding):\\n for line in data:\\n for i, itemset in enumerate(line[1:]):\\n encoded_itemset = set()\\n for item in itemset:\\n encoded_itemset.add(item_to_encoding[item])\\n line[i + 1] = encoded_itemset\\n\\n return data\",\n \"def _encode(self, data):\\n raise NotImplementedError(\\\"_encode needs to be implemented in {} subclass\\\".format(type(self).__name__))\",\n \"def _encode(self):\\n with tf.variable_scope('passage_encoding'):\\n self.sep_p_encodes, _ = rnn('bi-lstm', self.p_emb, self.p_length, self.hidden_size)\\n with tf.variable_scope('question_encoding'):\\n self.sep_q_encodes, _ = rnn('bi-lstm', self.q_emb, self.q_length, self.hidden_size)\\n if self.use_dropout:\\n self.sep_p_encodes = tf.nn.dropout(self.sep_p_encodes, self.dropout_keep_prob)\\n self.sep_q_encodes = tf.nn.dropout(self.sep_q_encodes, self.dropout_keep_prob)\",\n \"def test_encode(self):\\n assert url_encoder.encode(1) == 'TheStakeOut'\\n assert url_encoder.encode(800) == 'TheStockTip-TheSeven'\\n assert url_encoder.encode(99999) == 'MaleUnbonding-TheConversion-TheAndreaDoria'\",\n \"def _define_decoder(self):\\n raise NotImplementedError\",\n \"def video_encoding(self):\\n self.output_file = outputs_filenames(self.input, self.output_file)\\n\\n if self.resume and (self.temp / 'done.json').exists():\\n set_logging(self.logging, self.temp)\\n else:\\n setup(self.temp, self.resume)\\n set_logging(self.logging, self.temp)\\n print(self.queue)\\n framenums = split_routine(self.input, self.scenes, self.split_method, self.temp, self.min_scene_len, self.queue, self.threshold)\\n\\n if self.extra_split:\\n framenums = extra_splits(input, framenums, self.extra_split)\\n\\n segment(self.input, self.temp, framenums)\\n extract_audio(input, self.temp, self.audio_params)\\n\\n chunk = get_video_queue(self.temp, self.resume)\\n\\n # Make encode queue\\n commands, self.video_params = compose_encoding_queue(chunk, self.temp, self.encoder, self.video_params, self.ffmpeg_pipe, self.passes)\\n log(f'Encoding Queue Composed\\\\n'\\n f'Encoder: {self.encoder.upper()} Queue Size: {len(commands)} Passes: {self.passes}\\\\n'\\n f'Params: {self.video_params}\\\\n\\\\n')\\n\\n self.workers = determine_resources(self.encoder, self.workers)\\n\\n self.encoding_loop(commands)\\n\\n try:\\n concatenate_video(self.temp, self.output_file, keep=self.keep)\\n\\n except Exception as e:\\n _, _, exc_tb = sys.exc_info()\\n print(f'Concatenation failed, FFmpeg error\\\\nAt line: {exc_tb.tb_lineno}\\\\nError:{str(e)}')\\n log(f'Concatenation failed, aborting, error: {e}\\\\n')\\n terminate()\\n\\n if self.vmaf:\\n plot_vmaf(self.input, self.output_file, model=self.vmaf_path)\",\n \"def _encode_back(self):\\n with tf.variable_scope('passage_encoding'):\\n self.sep_p_encodes, _ = rnn('bi-lstm', self.p_emb, self.p_length, self.hidden_size)\\n with tf.variable_scope('question_encoding'):\\n self.sep_q_encodes, _ = rnn('bi-lstm', self.q_emb, self.q_length, self.hidden_size)\\n if self.use_dropout:\\n self.sep_p_encodes = tf.nn.dropout(self.sep_p_encodes, self.dropout_keep_prob)\\n self.sep_q_encodes = tf.nn.dropout(self.sep_q_encodes, self.dropout_keep_prob)\",\n \"def bencode(data, f):\\n\\tif isinstance(data, int):\\n\\t\\t_encode_int(data, f)\\n\\telif isinstance(data, (str, bytes)):\\n\\t\\t_encode_buffer(data, f)\\n\\telif isinstance(data, Mapping):\\n\\t\\t_encode_mapping(data, f)\\n\\telif isinstance(data, Iterable):\\n\\t\\t_encode_iterable(data, f)\",\n \"def encode(self, commands):\\n commands, counter = commands[0], commands[1]\\n try:\\n st_time = time.time()\\n source, target = Path(commands[-1][0]), Path(commands[-1][1])\\n frame_probe_source = frame_probe(source)\\n\\n # Target Vmaf Mode\\n if self.vmaf_target:\\n tg_cq = self.target_vmaf(source)\\n cm1 = man_cq(commands[0], tg_cq)\\n\\n if self.passes == 2:\\n cm2 = man_cq(commands[1], tg_cq)\\n commands = (cm1, cm2) + commands[2:]\\n else:\\n commands = (cm1,) + commands[1:]\\n\\n # Boost\\n if self.boost:\\n commands, cq = boosting(self.boost_limit, self.boost_range, source, commands, self.passes)\\n\\n log(f'Enc: {source.name}, {frame_probe_source} fr\\\\n')\\n\\n # Queue execution\\n for i in commands[:-1]:\\n tqdm_bar(i, self.encoder, counter, frame_probe_source, self.passes)\\n\\n frame_check(source, target, self.temp, self.no_check)\\n\\n frame_probe_fr = frame_probe(target)\\n\\n enc_time = round(time.time() - st_time, 2)\\n\\n log(f'Done: {source.name} Fr: {frame_probe_fr}\\\\n'\\n f'Fps: {round(frame_probe_fr / enc_time, 4)} Time: {enc_time} sec.\\\\n\\\\n')\\n except Exception as e:\\n _, _, exc_tb = sys.exc_info()\\n print(f'Error in encoding loop {e}\\\\nAt line {exc_tb.tb_lineno}')\",\n \"def _applyCipher(self, encode):\\n pass\",\n \"async def encode(self, ctx, value = None , from_type = None, *, to_type = None):\\n\\n if value == None or from_type == None or to_type == None:\\n msg = 'Usage: `{}encode \\\"[value]\\\" [from_type] [to_type]`\\\\nTypes include ascii, hex, and base64.'.format(ctx.prefix)\\n await ctx.send(msg)\\n return\\n\\n types = [ \\\"base64\\\", \\\"hex\\\", \\\"ascii\\\" ]\\n\\n if not from_type.lower() in types:\\n await ctx.send(\\\"Invalid *from* type!\\\")\\n return\\n\\n if not to_type.lower() in types:\\n await ctx.send(\\\"Invalid *to* type!\\\")\\n return\\n\\n if from_type.lower() == to_type.lower():\\n await ctx.send(\\\"*Poof!* Your encoding was done before it started!\\\")\\n return\\n\\n try:\\n if from_type.lower() == \\\"base64\\\":\\n if to_type.lower() == \\\"hex\\\":\\n await ctx.send(self.suppressed(ctx.guild, self._base64_to_hex(value)))\\n return\\n elif to_type.lower() == \\\"ascii\\\":\\n await ctx.send(self.suppressed(ctx.guild, self._base64_to_ascii(value)))\\n return\\n elif from_type.lower() == \\\"hex\\\":\\n if to_type.lower() == \\\"ascii\\\":\\n await ctx.send(self.suppressed(ctx.guild, self._hex_to_ascii(value)))\\n return\\n elif to_type.lower() == \\\"base64\\\":\\n await ctx.send(self.suppressed(ctx.guild, self._hex_to_base64(value)))\\n return\\n elif from_type.lower() == \\\"ascii\\\":\\n if to_type.lower() == \\\"hex\\\":\\n await ctx.send(self.suppressed(ctx.guild, self._ascii_to_hex(value)))\\n return\\n elif to_type.lower() == \\\"base64\\\":\\n await ctx.send(self.suppressed(ctx.guild, self._ascii_to_base64(value)))\\n return\\n except Exception:\\n await ctx.send(\\\"I couldn't make that conversion!\\\")\\n return\",\n \"def encode(self, obj):\\n s = super(CustomEncoder, self).encode(obj)\\n # If uncompressed, postprocess for formatting\\n if len(s.splitlines()) > 1:\\n s = self.postprocess(s)\\n return s\",\n \"def mostlikelydecode(self):\\n\\n # Add your code here\\n most_likely_codeword = Cipher(None) # Replace None with a method\\n return most_likely_codeword.decode(None) # Replace None. What does decode take again in the Cipher class? \",\n \"def __call__(self,\\n encoder_input_tokens,\\n decoder_input_tokens,\\n decoder_target_tokens,\\n encoder_segment_ids=None,\\n decoder_segment_ids=None,\\n encoder_positions=None,\\n decoder_positions=None,\\n *,\\n enable_dropout: bool = True,\\n decode: bool = False):\\n encoded = self.encode(\\n encoder_input_tokens,\\n encoder_segment_ids=encoder_segment_ids,\\n enable_dropout=enable_dropout)\\n\\n return self.decode(\\n encoded,\\n encoder_input_tokens, # only used for masks\\n decoder_input_tokens,\\n decoder_target_tokens,\\n encoder_segment_ids=encoder_segment_ids,\\n decoder_segment_ids=decoder_segment_ids,\\n decoder_positions=decoder_positions,\\n enable_dropout=enable_dropout,\\n decode=decode)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70250314","0.692117","0.6498695","0.636545","0.61599547","0.6076334","0.60151535","0.60063004","0.5958467","0.5958467","0.5940094","0.59077394","0.58827055","0.58827055","0.58725035","0.58725035","0.5870088","0.583431","0.58002776","0.5792148","0.5766689","0.5761179","0.5741968","0.5738231","0.573799","0.57309824","0.5722498","0.5712032","0.5680842","0.5653592","0.5648149","0.56337017","0.5601507","0.5579451","0.55780464","0.5547228","0.55344003","0.5515989","0.55074596","0.5502466","0.547937","0.5456383","0.5453925","0.5452868","0.54519993","0.54437566","0.5437951","0.5426136","0.5399482","0.539658","0.53963923","0.5395836","0.5393738","0.5378306","0.53775483","0.5371093","0.53688306","0.5360342","0.53556156","0.5351317","0.5346278","0.5332957","0.5325238","0.53136545","0.530448","0.52992254","0.5275231","0.52636707","0.52533597","0.52365065","0.5218035","0.5216397","0.52135193","0.5213368","0.51858675","0.5172653","0.51652503","0.51652503","0.51627827","0.5160369","0.51567215","0.51534307","0.5150613","0.5147881","0.5135026","0.5125607","0.51175004","0.51146674","0.511202","0.5111192","0.5105576","0.5104564","0.5103285","0.5097878","0.509781","0.50972664","0.5094644","0.50927174","0.50896317","0.5089315","0.5088509"],"string":"[\n \"0.70250314\",\n \"0.692117\",\n \"0.6498695\",\n \"0.636545\",\n \"0.61599547\",\n \"0.6076334\",\n \"0.60151535\",\n \"0.60063004\",\n \"0.5958467\",\n \"0.5958467\",\n \"0.5940094\",\n \"0.59077394\",\n \"0.58827055\",\n \"0.58827055\",\n \"0.58725035\",\n \"0.58725035\",\n \"0.5870088\",\n \"0.583431\",\n \"0.58002776\",\n \"0.5792148\",\n \"0.5766689\",\n \"0.5761179\",\n \"0.5741968\",\n \"0.5738231\",\n \"0.573799\",\n \"0.57309824\",\n \"0.5722498\",\n \"0.5712032\",\n \"0.5680842\",\n \"0.5653592\",\n \"0.5648149\",\n \"0.56337017\",\n \"0.5601507\",\n \"0.5579451\",\n \"0.55780464\",\n \"0.5547228\",\n \"0.55344003\",\n \"0.5515989\",\n \"0.55074596\",\n \"0.5502466\",\n \"0.547937\",\n \"0.5456383\",\n \"0.5453925\",\n \"0.5452868\",\n \"0.54519993\",\n \"0.54437566\",\n \"0.5437951\",\n \"0.5426136\",\n \"0.5399482\",\n \"0.539658\",\n \"0.53963923\",\n \"0.5395836\",\n \"0.5393738\",\n \"0.5378306\",\n \"0.53775483\",\n \"0.5371093\",\n \"0.53688306\",\n \"0.5360342\",\n \"0.53556156\",\n \"0.5351317\",\n \"0.5346278\",\n \"0.5332957\",\n \"0.5325238\",\n \"0.53136545\",\n \"0.530448\",\n \"0.52992254\",\n \"0.5275231\",\n \"0.52636707\",\n \"0.52533597\",\n \"0.52365065\",\n \"0.5218035\",\n \"0.5216397\",\n \"0.52135193\",\n \"0.5213368\",\n \"0.51858675\",\n \"0.5172653\",\n \"0.51652503\",\n \"0.51652503\",\n \"0.51627827\",\n \"0.5160369\",\n \"0.51567215\",\n \"0.51534307\",\n \"0.5150613\",\n \"0.5147881\",\n \"0.5135026\",\n \"0.5125607\",\n \"0.51175004\",\n \"0.51146674\",\n \"0.511202\",\n \"0.5111192\",\n \"0.5105576\",\n \"0.5104564\",\n \"0.5103285\",\n \"0.5097878\",\n \"0.509781\",\n \"0.50972664\",\n \"0.5094644\",\n \"0.50927174\",\n \"0.50896317\",\n \"0.5089315\",\n \"0.5088509\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76692,"cells":{"query":{"kind":"string","value":"Generates a sample transition probability matrix given the count matrix C using dirichlet distributions"},"document":{"kind":"string","value":"def sample_nonrev(C, nsample=1):\n # copy C\n C = np.array(C)\n if nsample==1:\n return _sample_nonrev_single(C)\n elif nsample > 1:\n res = np.empty((nsample), dtype=object)\n for i in range(nsample):\n res[i] = _sample_nonrev_single(C)\n return res\n else:\n raise ValueError('nsample must be a positive integer')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def entropy_permutation_test(ordered_pitch_types, single_pitch_pdf, conditional_joint_probabilities, total_transitions,\n n=1000):\n pitch_types, pitch_probabilities = zip(*single_pitch_pdf.items())\n permutation_entropies = []\n progress = progressbar.ProgressBar()\n\n for test_number in progress(xrange(n)):\n # create the new matrix\n permutation_counts = {}\n for first_pitch_type in ordered_pitch_types:\n permutation_counts[first_pitch_type] = {}\n for second_pitch_type in ordered_pitch_types:\n permutation_counts[first_pitch_type][second_pitch_type] = 0\n\n pitch_permutation = numpy.random.choice(pitch_types, total_transitions, p=pitch_probabilities)\n current_pitch = numpy.random.choice(pitch_types, p=pitch_probabilities)\n for next_pitch in pitch_permutation:\n permutation_counts[current_pitch][next_pitch] += 1\n current_pitch = next_pitch\n\n joint_probabilities, _, _ = joint_probabilities_from_transitions(ordered_pitch_types, permutation_counts)\n permutation_entropies.append(entropy_from_probability_matrix(joint_probabilities))\n\n joint_entropy = entropy_from_probability_matrix(conditional_joint_probabilities)\n # print 'Mean', numpy.mean(permutation_entropies)\n # print 'Standard deviation', numpy.std(permutation_entropies)\n # tdof, tloc, tscale = stats.t.fit(permutation_entropies)\n # print 'DF', tdof, 'Loc (mean)', tloc, 'Scale (SD)', tscale\n # t_score = (joint_entropy - tloc) / tscale\n # print stats.t.cdf(joint_entropy, df=tdof, loc=tloc, scale=tscale)\n\n mean, stddev = stats.norm.fit(permutation_entropies)\n print 'Mean = {mean}\\t StdDev = {stddev}'.format(mean=mean, stddev=stddev)\n z_score = (joint_entropy - mean) / stddev\n p_value = stats.norm.cdf(joint_entropy, mean, stddev)\n print 'The joint entropy has a Z-score of {z_score} which gives a P-value of {p_value}'.format(z_score=z_score,\n p_value=p_value)\n return z_score, p_value","def marcovNuc (i = random.choice(stateSpace), step = 100):\n # matrix of transition probabilities\n #matrix = [[0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25]] \n matrix = [[0.4, 0.3, 0.2, 0.1], [0.4, 0.3, 0.2, 0.1], [0.4, 0.3, 0.2, 0.1], [0.4, 0.3, 0.2, 0.1]] \n step += 1 # add one to the range because we remove it at the end\n sims = [] # List to hold the results of the Marcov chain\n sims.append(i) # append the seed value to the sims list\n for x in range(step):\n \n if sims[-1] == 'A':\n w = np.random.random() # Random number generator\n # the next set of if statements determine where the random number \n # sits on the number line of probabilities\n if matrix[0][0] > w:\n sims.append('A')\n elif matrix[0][1] + matrix[0][0] > w:\n sims.append('C')\n elif matrix[0][2] + matrix[0][1] + matrix[0][0] > w:\n sims.append('G')\n else:\n sims.append('T')\n elif sims[-1] == 'C':\n x = np.random.random()\n if matrix[1][0] > x:\n sims.append('A')\n elif matrix[1][1] + matrix[1][0] > x:\n sims.append('C')\n elif matrix[1][2] + matrix[1][1] + matrix[1][0] > x:\n sims.append('G')\n else:\n sims.append('T')\n \n elif sims[-1] == 'G':\n y = np.random.random()\n if matrix[2][0] > y:\n sims.append('A')\n elif matrix[2][1] + matrix[2][0] > y:\n sims.append('C')\n elif matrix[2][2] + matrix[2][1] + matrix[2][0] > y:\n sims.append('G')\n else:\n sims.append('T')\n\n else:\n z = np.random.random()\n if matrix[3][0] > z:\n sims.append('A')\n elif matrix[3][1] + matrix[3][0] > z:\n sims.append('C')\n elif matrix[3][2] + matrix[3][1] + matrix[3][0] > z:\n sims.append('G')\n else:\n sims.append('T')\n\n return sims[1:-1] # remove the initial value (the seed)","def make_transition_probs(self):\n n = len(self.speakers) # TODO why this line ???\n transitions = np.random.randint(5, size=(n, n)) + 1\n transitions += transitions.transpose()\n for i in range(0, math.floor(n / 2)):\n s1 = np.random.randint(n)\n s2 = np.random.randint(n)\n transitions[s1][s2] += 10\n transitions[s2][s1] += 8\n return(transitions)","def sample_pagerank(corpus, damping_factor, n):\n probabilities = dict()\n samples = []\n\n # Random first sample\n page = random.choice(list(corpus.keys()))\n samples.append(page)\n \n # Remaining samples after first\n for i in range(n-1):\n p = transition_model(corpus, page, damping_factor)\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\n samples.append(page)\n\n # Count\n for p in corpus.keys():\n probabilities[p] = samples.count(p) / n\n\n return probabilities","def prob_distr(input_matrix):\n size = len(input_matrix)\n prob_matrix = np.zeros((size, size), dtype=complex)\n for m in range(size):\n for n in range(size):\n prob_matrix[m, n] = input_matrix[m, n, m, n]\n\n return prob_matrix","def sample_pagerank(corpus, damping_factor, n):\n all_pages = []\n first_sample_prob = random.randint(0, len(corpus) - 1)\n distribution_count = dict()\n\n for u in corpus:\n distribution_count[u] = 0\n all_pages.append(u)\n\n sample = all_pages[first_sample_prob]\n for i in range(n - 1): # n - 1 because first sample was already calculated\n selection_bucket = dict()\n selection_start = 0.0\n sample_distribution = transition_model(corpus, sample, damping_factor)\n sample_prob = random.random()\n for u in sample_distribution:\n floor = selection_start\n ceiling = selection_start + sample_distribution[u]\n selection_start = ceiling\n selection_bucket[u] = [floor, ceiling]\n for u in selection_bucket:\n v = selection_bucket[u]\n if v[0] < sample_prob < v[1]:\n sample = u\n distribution_count[u] += 1\n distribution = dict()\n for u in distribution_count:\n distribution[u] = float(distribution_count[u]) / n\n\n return distribution","def random_start_probs(self) -> np.ndarray:\n return self.random_state.dirichlet(np.ones(self.n_states), size=1).flatten()","def get_uniformization_sample(initial_state, terminal_state, states, path_length, rate_matrix):\n # map states to indices\n state_to_index = dict((state, i) for i, state in enumerate(states))\n # find the maximum rate away from a state\n max_rate = max(-rate_matrix[(a, a)] for a in states)\n # create a uniformized discrete transition matrix in convenient dictionary form\n discrete_transition_matrix = {}\n for (a, b), r in rate_matrix.items():\n discrete_transition_matrix[(a, b)] = r / max_rate\n if a == b:\n discrete_transition_matrix[(a, b)] += 1.0\n # create a discrete transition matrix in the numpy format,\n # and create the rate matrix in the numpy format\n R = np.zeros((len(states), len(states)))\n numpy_rate_matrix = np.zeros((len(states), len(states)))\n for (a, b), r in rate_matrix.items():\n ia = state_to_index[a]\n ib = state_to_index[b]\n numpy_rate_matrix[ia, ib] = r\n R[ia, ib] = discrete_transition_matrix[(a, b)]\n # convert initial and terminal states to indices\n initial_index = state_to_index[initial_state]\n terminal_index = state_to_index[terminal_state]\n # get the probability of the terminal state given the initial state and the path length\n rate_matrix_exponential = scipy.linalg.matfuncs.expm(numpy_rate_matrix * path_length)\n Pab = rate_matrix_exponential[initial_index, terminal_index]\n # draw the number of state changes\n cumulative_probability = 0\n n = 0\n matrix_powers = MatrixPowerCache(R)\n cutoff = random.uniform(0, Pab)\n #print 'cutoff =', cutoff\n #print 'initial_index =', initial_index\n #print 'terminal_index =', terminal_index\n #print matrix_powers.get_power(0)\n while 1:\n poisson_factor = scipy.stats.poisson.pmf(n, max_rate * path_length)\n discrete_transition_factor = matrix_powers.get_power(n)[initial_index, terminal_index]\n cumulative_probability += poisson_factor * discrete_transition_factor\n #print 'cumulative probability =', cumulative_probability\n if cutoff < cumulative_probability:\n break\n n += 1\n #print 'n =', n\n # deal with degenerate cases\n if n == 0:\n return []\n elif n == 1:\n if initial_state == terminal_state:\n return []\n else:\n return [(random.uniform(0, path_length), terminal_state)]\n # Simulate a discrete path given the number of changes and the initial and terminal states.\n # The path is called virtual because some changes may be from a state to itself.\n virtual_path = get_discrete_path_sample(initial_state, terminal_state, states, n+1, discrete_transition_matrix)[1:]\n virtual_times = list(sorted(random.uniform(0, path_length) for i in range(n)))\n events = []\n last_state = initial_state\n last_time = 0\n for current_state, current_time in zip(virtual_path, virtual_times):\n if current_state == last_state:\n continue\n events.append((current_state, current_time))\n last_state = current_state\n last_time = current_time\n return events","def training_by_counting(K, D, X, Z):\n init_probs = [ 0 for i in range(K) ]\n trans_probs = [ [ 0 for i in range(K) ] for j in range(K) ]\n emi_probs = [ [ 0 for i in range(D) ] for j in range(K) ] \n occurrences_z = [0 for i in range(K)]\n occurrences_x = [0 for i in range(D)]\n \n for x,z in zip(X,Z):\n z_indices = translate_path_to_indices(z)\n x_indices = translate_observations_to_indices(x)\n\n num_sequences = len(z_indices)/3\n\n for i in range(0,len(z_indices),3):\n init_probs[z_indices[i]] += 1\n\n for j in range(K):\n init_probs[j] = init_probs[j] / num_sequences\n\n trans, emi = count_transitions_and_emissions(K,D,x_indices,z_indices)\n \n for i in range(K):\n for j in range(K):\n trans_probs[i][j] += trans[i][j]\n \n for i in range(K):\n for j in range(D):\n emi_probs[i][j] += emi[i][j]\n \n for i in range(len(z_indices)):\n occurrences_z[z_indices[i]] +=1\n\n for i in range(len(x_indices)):\n occurrences_x[x_indices[i]] += 1\n \n for i in range(K):\n for j in range(K):\n trans_probs[i][j] /= occurrences_z[j]\n\n for i in range(K):\n for j in range(D):\n emi_probs[i][j] /= occurrences_x[j]\n\n my_hmm = hmm(init_probs,trans_probs,emi_probs)\n print(init_probs)\n print(\"\\n\",trans_probs)\n print(\"\\n\",emi_probs)\n return my_hmm","def sample_from_ibp(K, alpha, sigma, c):\n pp = poissonparams(K, alpha, sigma, c)\n new_nodes = np.random.poisson(pp)\n Ncols = new_nodes.sum()\n node_count = np.zeros(Ncols)\n\n # used to build sparse matrix, entries of each Zij=1\n colidx = [] \n rowidx = []\n rightmost_node = 0\n\n # for each clique\n for n in range(K):\n # revisit each previously seen node\n for k in range(rightmost_node):\n prob_repeat = (node_count[k] - sigma) / (n + c)\n r = np.random.rand()\n if r < prob_repeat:\n rowidx.append(n)\n colidx.append(k)\n node_count[k] += 1\n\n for k in range(rightmost_node, rightmost_node + new_nodes[n]):\n rowidx.append(n)\n colidx.append(k)\n node_count[k] += 1\n \n rightmost_node += new_nodes[n]\n\n # build sparse matrix\n data = np.ones(len(rowidx), int)\n shape = (K, Ncols)\n Z = csr_matrix((data, (rowidx, colidx)), shape)\n\n return Z","def get_matrix(self):\n matrix = np.zeros([len(self.states), len(self.states)])\n starting_states = []\n transitions = []\n\n for chords in self.training_data:\n states = []\n is_starting_state = True\n chunks = [chords[x:x+self.order] for x in range(0,\n len(chords), self.order)]\n for chunk in chunks:\n chunk_string = \" \".join(chunk)\n if is_starting_state:\n starting_states.append(chunk_string)\n is_starting_state = False\n states.append(chunk_string)\n\n for i in range(0, len(states)):\n if i < (len(states)) - 1:\n transitions.append([states[i], states[i + 1]])\n else:\n transitions.append([states[i]])\n\n self.starting_probabilities = np.zeros([len(self.states)])\n\n for transition in transitions:\n for row, row_contents in enumerate(self.transitions):\n for col, _ in enumerate(row_contents):\n if transition == self.transitions[row][col]:\n matrix[row][col] += 1\n\n for i, state in enumerate(self.states):\n for j, possible_state in enumerate(starting_states):\n if state == possible_state:\n self.starting_probabilities[j] += 1\n\n num = sum(self.starting_probabilities)\n for i, prob in enumerate(self.starting_probabilities):\n self.starting_probabilities[i] = prob / num\n\n for m in range(len(matrix)):\n num = sum(matrix[m])\n if int(num) is not 0:\n for i in range(len(matrix[m])):\n matrix[m][i] = (matrix[m][i] / num)\n else:\n matrix[m] = self.starting_probabilities\n return matrix","def test_dc():\n signal = np.zeros(100)\n pe = permutation_entropy(signal, 2)\n\n assert pe == 0","def lv_sampler(weights):\n indeces = []\n NUM = np.float(len(weights))\n r = np.random.uniform(0, 1 / NUM)\n c = weights[0]\n i = 0\n for m in np.arange(NUM):\n u = r + (m - 1) * (1 / NUM)\n while(u > c):\n i += 1\n c += weights[i]\n indeces.append(i)\n return np.array(indeces, dtype='int')","def get_C100():\n m = 100\n random.seed(1111*m)\n A = random.randn(m, m) + 1j*random.randn(m, m)\n A = 0.5*(A + np.conj(A).T)\n return A","def sample_entropy(data, m, r):\n\n n = len(data)\n lastrun = np.zeros((1,n))\n run = np.zeros((1,n))\n\n A = np.zeros((m,1), dtype=int)\n B = np.zeros((m,1), dtype=int)\n p = np.zeros((m,1))\n entropy = np.zeros((m,1))\n\n for idx_py, idx_mat in enumerate(np.arange(1,n, dtype=int)):\n nj = n - idx_mat\n y1 = data[idx_mat-1]\n\n for jj_py, jj_mat in enumerate(np.arange(1,nj+1, dtype=int)):\n j_py = jj_py + idx_py\n j_mat = jj_mat + idx_mat\n\n if np.abs(data[j_mat-1]-y1) < r:\n run[:,(jj_mat-1)] = lastrun[:,(jj_mat-1)] + 1\n M1 = np.min(np.concatenate(([m],run[:,(jj_mat-1)])))\n\n for mi_py, mi_mat in enumerate(np.arange(1,M1+1,dtype=int)):\n A[(mi_mat-1)] = A[(mi_mat-1)] + 1\n\n if j_mat<n:\n B[(mi_mat-1)] = B[(mi_mat-1)] + 1\n\n else:\n run[:,(jj_mat-1)] = 0\n\n for j1_py, j1_mat in enumerate(np.arange(1,nj+1, dtype=int)):\n lastrun[:,(j1_mat-1)] = run[:,(j1_mat-1)]\n\n N = np.zeros((1,1))\n N[0] = (n*(n-1))/2\n B = np.concatenate((N,B[0:(m-1)])).reshape(A.shape)\n p = np.divide(A,B)\n entropy = -np.log(p)\n\n return(entropy,A,B)","def prob4():\n\n\n N = 500000\n random_draws = np.random.multivariate_normal(mean = [-1,1], cov =[[1,0],[0,1]], size = N)\n\n h = lambda x: x[0] < -1 and x[1] > 1\n f = lambda x: stats.multivariate_normal(mean = [ 0, 0]).pdf(x)\n g = lambda x: stats.multivariate_normal(mean = [-1, 1]).pdf(x)\n\n probability = [h(random_draws[i]) * f(random_draws[i]) / g(random_draws[i]) for i in range(N)]\n\n return 1./N * np.sum(probability)","def collapsed_gibbs_sample(self, X):\n K = self.K # number of topics\n M, V = X.shape\n alpha = self.alpha\n lmda = self.lmda\n topics = np.arange(0, K)\n\n #initialize everything uniformly\n # KxV dense matrix (used like beta)\n\n C = np.zeros(shape=(K, V), dtype=float)\n props = np.zeros(shape=(M, K), dtype=float)\n\n #Current state\n Ns = np.array(range(M), dtype=object)\n #Running sum\n MC_z = np.array(range(M), dtype=object)\n MC_c = np.zeros(shape=(K, V), dtype=float)\n\n for d in range(M):\n #allocate topics from prior\n word_indices = X[d, :].nonzero()[1]\n p = np.random.dirichlet(np.ones(K) * alpha)\n #TODO: Remove the p argument to get completely random topic assignments\n random_ks = np.random.choice(topics, size=len(word_indices), p=p)\n N_d = sp.coo_matrix((np.ones(len(word_indices)),\n (word_indices, random_ks)), shape=(V, K)).tolil()\n C = C + N_d.A.T\n Ns[d] = N_d\n MC_z[d] = sp.coo_matrix((V, K)).tolil()\n\n log_Xs = []\n perplexities = []\n for epoch in xrange(self.nr_em_epochs):\n print \"Epoch\", epoch\n for d in np.random.permutation(np.arange(M)):\n x = X[d]\n N_d = Ns[d]\n for v in np.nonzero(x)[1]:\n old_z_n = N_d[v, :].nonzero()[1][0]\n\n except_v = [v_prime for v_prime in np.nonzero(x)[1] if v_prime != v]\n N_d_except_v = N_d[except_v, :]\n C_except_v = np.copy(C[:, v])\n C_except_v[old_z_n] = C_except_v[old_z_n] - 1\n\n p = (np.sum(N_d_except_v.A, axis=0) + alpha) *\\\n ((C_except_v + lmda) / (np.sum(C, axis=1) -1 + V*lmda))\n p = p/np.sum(p)\n z_n = np.random.choice(topics, p = p)\n N_d[v, old_z_n] = 0\n N_d[v, z_n] = 1\n C[old_z_n, v] -= 1\n C[z_n, v] += 1\n Ns[d] = N_d\n MC_z[d] += N_d\n\n MC_c += C\n word_props = (MC_c.T / np.sum(MC_c, axis=1)).T\n log_X = 0\n for d in range(M):\n props_d = np.sum(MC_z[d].A, axis=0)\n props_d /= float(np.sum(props_d))\n props[d] = props_d\n ixw = np.nonzero(X[d, :])[1]\n log_X += np.sum(_doc_probability_from_p_of_z(props_d, word_props[:, ixw]))\n\n log_Xs.append(log_X)\n print log_X\n perplexities.append(self._perplexity(X, log_X))\n\n\n return props, word_props, log_Xs, perplexities","def inital_probabilities(self,fc,ec):\n matrix = np.empty(ec*fc)\n matrix = matrix.reshape(ec,fc)\n matrix.fill(1/fc)\n return matrix # repreents probability - p(e|f)","def basis(u, s, p = 0.5):\n # build cumulative distribution\n d = 0\n s_cm = 0\n p = max(p, 1e-8) # ensure d is at least 1\n while s_cm / sum(s) < p and d < len(s):\n s_cm += s[d]\n d += 1\n \n return u[:, :d]","def generate_distorted_t(t, C, sigma):\n S_u = generate_S_u(t)\n\n m= np.random.multivariate_normal(mean= np.zeros(len(C)), cov=C)\n noise= generate_noisy_window(len(t), sigma)\n return np.dot(S_u, m) + noise","def sample_discrete_pmf(X, PM, N):\n\n assert np.isclose(np.sum(PM), 1.0)\n assert all(0.0 <= p <= 1.0 for p in PM)\n \n y = np.zeros(N)\n cumulativePM = np.cumsum(PM) # build CDF based on PMF\n offsetRand = np.random.uniform(0, 1) * (1 / N) # offset to circumvent numerical issues with cumulativePM\n comb = np.arange(offsetRand, 1 + offsetRand, 1 / N) # new axis with N values in the range ]0,1[\n \n j = 0\n for i in range(0, N):\n while comb[i] >= cumulativePM[j]: # map the linear distributed values comb according to the CDF\n j += 1\t\n y[i] = X[j]\n \n return rd.permutation(y) # permutation of all samples","def state_ensemble(m, C, N=10):\n\n M = np.random.multivariate_normal(m.real, C.real, size=N)\\\n + 1.0j * np.random.multivariate_normal(m.imag, C.real, size=N)\n\n return M.T","def outputGenerationProbability(self):\n self.b = zeros((self.noOfEmmittingStates, self.T))\n for row in range(self.noOfEmmittingStates):\n for col in range(self.T):\n self.b[row, col] = self.gaussianDist(self.observationSequence[0, col],\n self.outputProbabilities[row, 0],\n self.outputProbabilities[row, 1])","def sample_categorical(distribution):\n sample = random.random()\n for event, prob in distribution.items():\n if sample < prob:\n return event\n sample -= prob\n raise ValueError('sum of distribution less than one')","def sample_pagerank(corpus, damping_factor, n):\n data = []\n\n #Choosing a random page from the corpus and adding to data\n sample = random.choice(list(corpus.keys()))\n data.append(sample)\n\n for _ in range(n-1):\n prob_distrib = transition_model(corpus, sample, damping_factor)\n\n #Choosing a page from the corpus based on transition model and adding to data\n sample = np.random.choice(list(prob_distrib.keys()), p=list(prob_distrib.values()))\n data.append(sample)\n\n #Dividing the number of times each page was visited by numebr of samples \n pagerank = {k : v/n for k, v in Counter(data).items()}\n\n return pagerank","def sample_pagerank(corpus, damping_factor, n):\n # All pages in entire corpus\n all_pages = [key for key in corpus.keys()]\n\n # Dictionary which will map each page to the number of generated samples that were the page\n counts = dict()\n for page in all_pages:\n counts[page] = 0\n\n # Choosing first sample randomly and updating counts dict\n first_sample = random.choice(all_pages)\n counts[first_sample] += 1\n\n for i in range(1, n):\n # If we are on the second sample, do the calculation based on the first\n if i == 1:\n prev_sample = first_sample\n\n distributions = transition_model(corpus, prev_sample, damping_factor)\n\n keys = [key for key in distributions.keys()]\n values = [distributions[key] for key in distributions.keys()]\n\n # Using random.choices to select an option given a list of weights (distribution)\n current_sample = random.choices(keys, weights=values, k=1)[0]\n\n # Updating counts dict given the current sample\n counts[current_sample] += 1\n\n prev_sample = current_sample\n\n # Modifying counts dict to represent the proportion of samples that corresponded to that page instead of the raw amount\n for page in all_pages:\n counts[page] /= n\n\n return counts","def build_transition_matrix(k_counter, q, p):\r\n dim = k_counter + NOT_MOTIF_STATES\r\n transition_mat = np.zeros([dim, dim])\r\n # B start\r\n transition_mat[0, 1] = q\r\n transition_mat[0, -2] = 1 - q\r\n # B_1\r\n transition_mat[1, 1:3] = [1 - p, p]\r\n # B_2\r\n transition_mat[-2, -2:] = [1 - p, p]\r\n # S_last\r\n transition_mat[-3, -2] = 1\r\n # all S\r\n transition_mat[2:-3, 3:-2] = np.eye(k_counter - 1)\r\n return wrap_log(transition_mat)","def initialization(n, D):\n\n samples = []\n while len(samples) < n:\n # X = np.random.randint(0, D.shape[1], 10*n)\n # Y = np.random.randint(0, D.shape[0], 10*n)\n X = np.random.uniform(0, D.shape[1], 10*n)\n Y = np.random.uniform(0, D.shape[0], 10*n)\n P = np.random.uniform(0, 1, 10*n)\n index = 0\n while index < len(X) and len(samples) < n:\n x, y = X[index], Y[index]\n x_, y_ = int(np.floor(x)), int(np.floor(y))\n if P[index] < D[y_, x_]:\n samples.append([x, y])\n index += 1\n return np.array(samples)","def crp_gen(N, alpha):\n assert N > 0\n assert alpha > 0.0\n alpha = float(alpha)\n\n partition = np.zeros(N, dtype=int)\n Nk = [1]\n for i in range(1, N):\n K = len(Nk)\n ps = np.zeros(K+1)\n for k in range(K):\n # get the number of people sitting at table k\n ps[k] = float(Nk[k])\n\n ps[K] = alpha\n\n ps /= (float(i)-1+alpha)\n\n assignment = pflip(ps)\n\n if assignment == K:\n Nk.append(1)\n elif assignment < K:\n Nk[assignment] += 1\n else:\n raise ValueError(\"invalid assignment: %i, max=%i\" %\n (assignment, K))\n\n partition[i] = assignment\n\n assert max(partition)+1 == len(Nk)\n assert len(partition) == N\n assert sum(Nk) == N\n\n K = len(Nk)\n\n if K > 1:\n shuffle(partition)\n\n return np.array(partition), Nk, K","def binary_dec(A,n_iter = 1000):\n\n\t### Initialization ###\n\n\tp, q = np.shape(A)\n\t### B : to be changed\n\tB = np.eye(p)\n \t###\n\tC = bin_random_mat(p,q)\n\tlist_dist = []\n\tB_argmin = B\n\tC_argmin = C\n\n\n\n\n\t## temperature ##\n\tT_n = np.log(np.arange(2,n_iter+2,1))\n\t#T_n = np.arange(2,n_iter+2,1)\n\tfor i in range(n_iter):\n\t## update ##\n\t\tC_0 = np.matrix(C)\n\t\tlist_dist =np.append( list_dist, V_potential(np.dot(B,C_0),A) )\n\t\tif V_potential(np.dot(B_argmin,C_argmin),A) == 0:\n\t\t\tbreak\n\t########## transition #############\n\t# Here we take 2 steps independent(for B and for C respectively)\n\t# We could also use metropolis hasting kernel.\n\n\t\tC_iter = np.matrix(Metropolis_transition_C(C))\n\t\n\n\t\tB_iter = B[np.random.permutation(np.arange(p))]\n\t\t\n\t\tif np.random.uniform(0,1,1) < \\\n\t\t\t\tnp.exp(-1./T_n[i]*( V_potential(np.dot(B_iter,C_iter), A)\\\n\t\t\t\t - V_potential(np.dot(B,C_0),A) ) ):\n\t\t\tC = C_iter\n\t\t\tB = B_iter\n\t######### end of transition ##############\n\n\t\t\tif V_potential(np.dot(B,C),A) < np.min(list_dist):\n\t\t\t\t\n\t\t\t\tB_argmin = B\n\t\t\t\tC_argmin = np.matrix(C)\n\t\t\t# print i+1\n\t\t\t# print V_potential(np.dot(B_argmin,C_argmin),A)\n\t\t\t# print C_argmin\n\t\t\t# print '\\n'\n\n\treturn list_dist,B_argmin, C_argmin","def sample_discrete(probs):\r\n q = np.random.rand()\r\n i = 0\r\n p_sum = 0.0\r\n while p_sum < q:\r\n p_sum += probs[i]\r\n i += 1\r\n return i - 1","def sample_transitions(self, val) -> None:\n\n # get values\n states = val.states\n pi_conc = val.transitions_conc\n num_states = val.num_states\n\n # count the number of each transition that occurs\n counts = np.zeros((num_states + 1, num_states))\n for i in range(num_states):\n counts[-1, i] = np.sum(states[:, :, 0] == i)\n for j in range(num_states):\n counts[i, j] = np.sum((states[:, :, :-1] == i) * (states[:, :, 1:] == j))\n counts[-1, -1] = 0 # fluorophores starting photobleached are interpretted as load off only\n\n # sample from dirichlet distribution\n val.transitions = Dirichlet.sample(counts + pi_conc)\n val.P = self.posterior(val)\n\n return","def simulate_counts(self, C, seed=None, reverse=False, **contract_opts):\n p_dense = self.to_dense(reverse=reverse, **contract_opts)\n return qu.simulate_counts(p_dense, C=C, seed=seed)","def _generate_sample_from_state(self, state):\n\n res = []\n for e in range(self.n_emissions):\n cdf = np.cumsum(self.B[e][state, :])\n res.append((cdf > np.random.rand()).argmax())\n return np.asarray(res)","def Gaussian_conv(n):\n\n\t# Choose sigma and mu\n\tsigma = 1\n\tmu = 0\n\n\t# Create table\n\tb = sigma * np.random.randn(n) + mu\n\tsum = b.sum()\n\t#b = b/sum\n\tprint(b)\n\treturn b","def appearance_space(state_data, C):\n return np.dot(C, state_data)","def conditionalDistribution(self, d, v):\n probabilities_ts = np.ones((self.n_topic_components, self.n_sentiment_components))\n firstFactor = (self.n_ds[d] + self.alphaVec) / \\\n (self.n_d[d] + np.sum(self.alphaVec))\n secondFactor = np.zeros((self.n_topic_components,self.n_sentiment_components))\n for s in range(self.n_sentiment_components):\n \n secondFactor[:,s] = ((self.n_dst[d, s, :] + self.gammaVec) / \\\n (self.n_ds[d, s] + np.sum(self.gammaVec)))\n\n thirdFactor = (self.n_vts[v,:, :] + self.beta) / \\\n (self.n_ts + self.n_vts.shape[0] * self.beta)\n\n #forthFactor = np.zeros((self.n_topic_components, self.n_sentiment_components))\n #for k in range(self.n_topic_components):\n # forthFactor[k,:] = np.exp(np.dot(self.topic_embeddings[k,:],self.word_embeddings[v,:]))/np.sum(np.exp(np.dot(self.topic_embeddings[k,:],self.word_embeddings.T)))\n \n forthFactor = np.exp(np.dot(self.topic_embeddings,self.word_embeddings[v,:]))/np.sum(np.exp(np.dot(self.topic_embeddings,self.word_embeddings.T)),-1)\n probabilities_ts *= firstFactor[:, np.newaxis]\n #probabilities_ts *= secondFactor * thirdFactor\n probabilities_ts *= secondFactor * ((1-self.lambda_)*thirdFactor + self.lambda_*forthFactor)\n probabilities_ts /= np.sum(probabilities_ts)\n \n return probabilities_ts","def random_chain(n):\n transition_matrix = np.random.random((n,n))\n return transition_matrix / transition_matrix.sum(axis=0)","def build_transition_matrix(state_space, markov_process, normalize=True):\n\n P = pd.DataFrame(0, index=state_space, columns=state_space)\n for trans in markov_process:\n P.loc[trans[0], trans[1]] = P.loc[trans[0], trans[1]] + 1\n\n if normalize:\n P = P.div(P.sum(axis=1), axis=0)\n\n return P","def generate_probabilities(self):\n k = 1\n v= 10\n for g in self.class_probabilities:\n curr_list = self.class_probabilities[g]\n for l in range(0,28):\n for w in range(0,28):\n total = float(curr_list[l][w][0] + curr_list[l][w][1] + curr_list[l][w][2])\n curr_list[l][w][0] = (float(curr_list[l][w][0])+k)/(total + k*v) \n curr_list[l][w][1] = (float(curr_list[l][w][1])+k)/(total + k*v)\n curr_list[l][w][2] = (float(curr_list[l][w][2])+k)/(total + k*v)\n curr_list[l][w][3] = curr_list[l][w][0] + curr_list[l][w][1] + curr_list[l][w][2]","def compCGP_C(M):\n # we store the coedd as a lower triangular matrix\n # random polynomial coefficients\n c = 0.5 * np.random.uniform(-1.0, -0.45, size=(M + 1, M + 1)) +\\\n 0.5 * np.random.uniform(0.45, 1.0, size=(M + 1, M + 1))\n for i in np.arange(M + 1):\n c[i, :] /= 2**(np.arange(M + 1) + i)\n c /= 1.5\n c = np.tril(c)\n c[0, 0] = 0\n c[1, 0] = 0\n c[1, 1] = 1\n\n return c","def sample_indices_from_coupling(c, num_samples=None, return_all = False, thr = 10**(-6)):\n\n if return_all:\n return [[i, j] for i in range(c.shape[0]) for j in range(c.shape[1]) if c[i,j] > thr]\n else:\n linear_samples = np.random.choice(range(c.size), size = num_samples, p = c.flat)\n \n if num_samples in [1, None]:\n linear_samples = [linear_samples]\n \n n_cols = c.shape[1]\n return np.array([[s//n_cols , s%n_cols ] for s in linear_samples])","def createTransMatrix(pages):\n links = getLinks(pages)\n urls = getUrls(pages)\n nPages = len(pages)\n m = np.matrix([[weightLink(nPages, u, l) for u in urls] for l in links])\n return dampingFactor(nPages, m)","def build_fisher_contingency_table(overlap_count, user_count, gene_count, count):\n table = np.zeros(shape=(2, 2))\n table[0, 0] = overlap_count\n table[0, 1] = user_count - overlap_count\n table[1, 0] = gene_count - overlap_count\n table[1, 1] = count - user_count - gene_count + overlap_count\n\n return table","def random_transitions(self) -> np.ndarray:\n transitions = np.zeros((self.n_states, self.n_states))\n for i, row in enumerate(transitions):\n size = min(2, self.n_states - i)\n row[i:(i + size)] = self.random_state.dirichlet(np.ones(size))\n return transitions","def random_transitions(self) -> np.ndarray:\n return self.random_state.dirichlet(np.ones(self.n_states), size=self.n_states)","def generate(markov_process):\n sequence = []\n for index, matrix in enumerate(markov_process):\n prefix = tuple(sequence[-min(index, matrix.order):])\n probabilities = matrix[prefix]\n value = np.random.choice(probabilities.keys(), p=probabilities.values())\n sequence.append(value)\n return sequence","def sample_pagerank(corpus, damping_factor, n):\n first_page = random.choice(list(corpus))\n model = transition_model(corpus, first_page, DAMPING)\n\n for i in range(n):\n\n choosen = random.random()\n total = 0\n\n for k, v in model.items():\n total += v\n\n if choosen <= total:\n page = k\n break\n \n model = transition_model(corpus, page, DAMPING)\n \n return model","def multinomial_prob(counts, probs):\n return nCkarray(*counts.values) * (probs ** counts).prod()","def sdd(events,probs):\n \n import random\n nprobs=[x*1000 for x in probs] #so, here i multiply each float in 'probs' by 1000 and store the products in 'nprobs'\n newlist=[]\n for a in range(len(events)) : #then, in this loop, i create a list (newlist), in which each event appears 1000*its probability times\n b=nprobs[a]\n b=int(b)\n for c in range(b) :\n newlist.append(events[a]) \n return (random.choice(newlist)) #and finally, i ramdonly sample ","def _make_random_matrix(self, n_components, n_features):","def bsc_output(a, x):\n y = []\n transitions = list(np.random.binomial(size=len(x), n=1, p= a))\n for i in range(len(x)):\n if(transitions[i]==1): y.append(1-x[i])\n else: y.append(x[i])\n return y","def Metropolis_transition_C(X, p_0 = 0.5, shake_times = 1):\n\tp,q = np.shape(X)\n\tX_iter = X\n\tfor i in range(shake_times):\n\t\tm,n = np.random.choice(range(p)),np.random.choice(range(q))\n\t\t\n\t\tX_iter[m,n] = np.float(X_iter[m,n] == 0)\n\treturn X_iter","def _good_turing_new_c(self, count: int) -> float:\n next_count_index = count + 1\n next_count: Optional[float] = None\n if next_count_index not in self.count_map:\n # this happens when N_{c+1} is 0\n # this can make the total probability not equal to 1\n next_count = 0.\n else:\n next_count = float(self.count_map[next_count_index])\n\n new_count: Optional[float] = None\n new_count = (count + 1) * next_count / self.count_map[count]\n return new_count","def cond_gen(self, context):\n dist = {}\n for event in self.alphabet:\n dist[event] = self.cond_prob(event, context)\n return sample_categorical(dist)","def mh(N, disttype):\n xs = np.array([])\n ys = np.array([])\n pos_now = (0,0)\n accept = 0\n for i in range(N):\n pos_cand = proposal_pdf(pos_now)\n prob_stay = target_pdf(pos_now, disttype)\n prob_move = target_pdf(pos_cand, disttype)\n if prob_move / prob_stay > np.random.uniform(0,1,1):\n pos_now = pos_cand\n xs = np.append(xs, pos_now[0])\n ys = np.append(ys, pos_now[1])\n accept += 1\n return xs, ys, accept/N","def get_transition_probs(x, classes=None):\n ### ==============================================================================================================\n ### define list_ind as the list of unique ID if not defined\n if not classes:\n classes = np.unique(x.values)\n ### ==============================================================================================================\n transitions_matrix = []\n for cl in classes:\n list_mat = []\n list_freq = []\n for yi in np.unique(x.index.year):\n ### select the year\n mat = x.ix[str(yi),].values.flatten()\n ### select the class cl index + 1\n i = np.where(mat == cl)[0] + 1\n ### make sure we clip\n i = np.delete(i, np.where(i >= len(mat)))\n ### count the total number of days following the cluster \"cl\"\n list_freq.append(len(i))\n mat = mat[i,].tolist()\n ### count the number of occurences of each of the N clusters\n list_mat.append([mat.count(c) for c in classes])\n list_mat = np.array(list_mat)\n list_mat = list_mat.sum(0) * 1.0\n list_mat = list_mat / np.array(list_freq).sum()\n transitions_matrix.append(list_mat)\n transitions_matrix = np.array(transitions_matrix)\n return classes, transitions_matrix","def sample_stable_net(alpha, sigma_w, sigma_b, phi, x, M, M_last, L):\n k = x.shape[1]\n weight_b, point_b = gamma_bias(alpha, sigma_b, k)\n for i in range(L):\n if i > 0:\n x = phi(x)\n weights_w, points_w = gamma_weight(alpha, sigma_w, x)\n weights = np.concatenate([weight_b, weights_w])\n points = np.concatenate([point_b[None, ...], points_w])\n weights = 0.5 * np.concatenate([weights, weights])\n points = np.concatenate([points, -points])\n m = M if (i < L - 1) else M_last\n x = sample_mv_stable_discrete(alpha, weights, points, m)\n return x","def sample(probs):\n\n probs = probs / probs.sum()\n return np.random.choice(np.arange(len(probs)), p=probs.flatten())","def sample_prior(size):\n return torch.randn(size)","def data_gen(size, p):\n #print(np.random.get_state()[1][0])\n random_table = np.random.binomial(size = size, p = p, n = 1)\n test_array = np.zeros((size, 2), dtype = int)\n for i in range(size):\n test_array[i,0] = i\n test_array[i,1] = random_table[i]\n return test_array","def compute_probability_weights(indexing, \n counts, \n image, \n binary_set_mappings): \n S_w_cardinalities = np.zeros_like(indexing)\n\n countsgeq2 = sum(c>=2 for c in counts) # compute amount of indices that have count>=2\n countseq1 = [v for v in range(indexing.shape[0]) if counts[indexing[v]]==1]\n K_cardinalities = np.zeros_like(indexing)\n for card,w in enumerate(countseq1[::-1]):\n K_cardinalities[w] = card\n\n for w,index in enumerate(indexing):\n if counts[index] >= 3:\n S_w_cardinalities[w] = len(image)\n elif counts[index] == 2:\n offset = 1 if w==binary_set_mappings[index] else 0\n S_w_cardinalities[w] = len(image) - 1 + offset\n elif counts[index] == 1:\n S_w_cardinalities[w] = countsgeq2 + K_cardinalities[w]\n\n return S_w_cardinalities/np.sum(S_w_cardinalities)","def discrete_sampling(N, probs, states=None):\n\n p = probs.squeeze()/np.sum(probs)\n\n bins = np.digitize(\n np.random.uniform(0., 1., (N, 1)), np.hstack((0, np.cumsum(p))))-1\n\n if states is None:\n x = bins\n else:\n assert(states.shape[0] == probs.shape[0])\n x = states[bins]\n\n return x.squeeze()","def agg_tran_prob_mat(g, step):\n g = my_scale_sim_mat(g)\n g = csc_matrix.toarray(g)\n a_k = g\n a = g\n for k in np.arange(2, step+1):\n a_k = np.matmul(a_k, g)\n a = a+a_k/k\n return a","def initialize_record_distribution(states, actions, empty_atoms):\n dist = {}\n\n atom_shape = empty_atoms.shape\n\n # initial values, equal probability distribution\n val = np.full(shape=atom_shape, fill_value=1.0 / float(atom_shape[0]))\n\n for i in states:\n dist[i] = {}\n for j in actions:\n dist[i][j] = val.copy()\n\n return dist","def initialize_distribution(states, actions):\n dist = {}\n\n for i in states:\n dist[i] = {}\n for j in actions:\n dist[i][j] = [0.0]\n\n return dist","def random_transition_matrix(n: int) -> np.ndarray:\n\n x = np.abs(np.random.normal(size=(n, n)))\n rsum = x.sum(axis=1)\n return x / rsum[:, np.newaxis]","def logprob_dc(counts, prior, axis=None):\n # Note that this excludes the factorial(counts) term, since we explicitly\n # track permutations in assignments.\n return gammaln(np.add(counts, prior, dtype=np.float32)).sum(axis)","def calculate_cd_transpose(P,C):\n conf_deg_t = C.T.dot(P)\n a1 = P.sum(axis=0)\n B = np.tile(a1, (C.shape[1], 1))\n CD_t = 1. * conf_deg_t / (B + np.spacing(0))\n return CD_t","def generate_transition_bigram_probabilities(transition_unigram_counts, transition_bigram_counts):\r\n\ttransition_bigram_probabilities = dict()\r\n\tfor tag_bigram in transition_bigram_counts:\r\n\t\ttransition_bigram_probabilities[tag_bigram] = float(transition_bigram_counts[tag_bigram])/transition_unigram_counts[tag_bigram[0]]\r\n\treturn transition_bigram_probabilities","def get_random_smoothing_matrix(counts, width=3):\n st = time.time()\n num_obs = len(counts)\n s_matrix = []\n lns = []\n if type(width) != list:\n width = [(width, 2*width)]\n for ni in range(num_obs):\n for _w, nrs in width:\n p = np.ones(len(counts))\n p[ni] = 0\n if counts[ni] >= _w:\n region = get_region(ni, counts, 1, p)\n s_matrix.append(region)\n for nr in range(nrs):\n region = get_region(ni, counts, _w, p)\n s_matrix.append(region)\n lns.append(len(np.nonzero(region)[0]))\n for nr in range(nrs):\n region = get_region(ni, counts, _w, p)\n s_matrix.append(region)\n lns.append(len(np.nonzero(region)[0]))\n S = np.stack(s_matrix, axis=0).astype(np.float32)\n assert np.alltrue(S.sum(axis=-1)>0) \n return torch.from_numpy(S)","def cmc(distmat, glabels=None, plabels=None, ds=None, repeat=None):\n m, n = distmat.shape\n if glabels is None and plabels is None:\n glabels = np.arange(0, m)\n plabels = np.arange(0, n)\n if isinstance(glabels, list):\n glabels = np.asarray(glabels)\n if isinstance(plabels, list):\n plabels = np.asarray(plabels)\n ug = np.unique(glabels)\n if ds is None:\n ds = ug.size\n if repeat is None:\n if ds == ug.size and ug.size == len(glabels):\n repeat = 1\n else:\n repeat = 100\n\n ret = 0\n for __ in xrange(repeat):\n # Randomly select gallery labels\n G = np.random.choice(ug, ds, replace=False)\n # Select corresponding probe samples\n p_inds = [i for i in xrange(len(plabels)) if plabels[i] in G]\n P = plabels[p_inds]\n # Randomly select one gallery sample per label selected\n D = np.zeros((ds, P.size))\n for i, g in enumerate(G):\n samples = np.where(glabels == g)[0]\n j = np.random.choice(samples)\n D[i, :] = distmat[j, p_inds]\n # Compute CMC\n ret += _cmc_core(D, G, P)\n return ret / repeat","def estimateProbabilities(X, C):\n p = []\n q = []\n r = []\n n = len(X)\n d = 9 # Dimension of the elements\n c = 2 # Number of classes\n for i in range(d):\n p_i = []\n q_i = []\n r_i = []\n for j in range(c):\n p_tmp = []\n q_tmp = []\n r_tmp = []\n elements = C[j] # Elements on the training set for class w_j\n nj = len(C[j])\n for k in elements:\n # p_i_j\n res = X[k][i]*(X[k][i] + 1)*0.5\n p_tmp.append(res)\n # q_i_j\n res = 1 - X[k][i]*X[k][i]\n q_tmp.append(res)\n # r_i_j\n res = X[k][i]*(X[k][i] - 1)*0.5\n r_tmp.append(res)\n p_i.append(sum(p_tmp) / float(nj))\n q_i.append(sum(q_tmp) / float(nj))\n r_i.append(sum(r_tmp) / float(nj))\n p.append(p_i)\n q.append(q_i)\n r.append(r_i)\n return (p, q, r)","def gen_samp(self, N, M):\n Sm = np.empty((M, 2))\n count = 0\n for i in range(1, M+1, 1):\n Sm[count, 0] = (i-1)/M\n Sm[count, 1] = i/M\n count = count + 1\n \n Ns = int(N/M)\n \n uni_rand = np.empty((M, Ns))\n for i in range(0, M):\n uni_rand[i,:] = np.random.uniform(Sm[i,0], Sm[i,1], Ns)\n \n uni_rand = uni_rand.reshape((1, -1))\n \n sample = self.dist.ppf(uni_rand)\n \n return(sample)","def random_transitions(self) -> np.ndarray:\n transitions = np.zeros((self.n_states, self.n_states))\n for i, row in enumerate(transitions):\n row[i:] = self.random_state.dirichlet(np.ones(self.n_states - i))\n return transitions","def call(self, states):\n dist, mode = self.get_dist_and_mode(states)\n samples = dist.sample()\n log_probs = dist.log_prob(samples)\n log_probs = tf.expand_dims(log_probs, -1) # To avoid broadcasting\n return mode, samples, log_probs","def collapsed_theta_gibbs_sample(self, X):\n\n K = self.K # number of topics\n M, V = X.shape\n alpha = self.alpha\n lmda = self.lmda\n topics = np.arange(0, K)\n\n #initialize everything uniformly\n Beta = np.ones(shape=(K, V), dtype=float) / V\n for k in topics:\n Beta[k] = np.random.dirichlet(np.ones(V)*lmda)\n\n props = np.zeros(shape=(M, K), dtype=float)\n #Current state\n Ns = np.array(range(M), dtype=object)\n\n #Running sum\n MC_z = np.array(range(M), dtype=object)\n MC_beta = np.zeros(shape=(K, V), dtype=float)\n\n for d in range(M):\n #allocate topics randomly\n word_indices = X[d, :].nonzero()[1]\n p = np.random.dirichlet(np.ones(K) * alpha)\n #TODO: Remove the p argument to get completely random topic assignments\n random_ks = np.random.choice(topics, size=len(word_indices), p=p)\n Ns[d] = sp.coo_matrix((np.ones(len(word_indices)),\n (word_indices, random_ks)), shape=(V, K)).tolil()\n MC_z[d] = sp.coo_matrix((V, K)).tolil()\n\n log_Xs = []\n perplexities =[]\n t = 0\n for epoch in xrange(self.nr_em_epochs):\n t+=1\n C = np.zeros((K, V))\n print \"Epoch\", epoch\n for d in np.random.permutation(np.arange(M)):\n x = X[d]\n N_d = Ns[d]\n\n for v in np.nonzero(x)[1]:\n except_v = [v_prime for v_prime in np.nonzero(x)[1] if v_prime != v]\n N_d_except_v = N_d[except_v, :]\n old_z_n = N_d[v, :].nonzero()[1][0]\n p = (np.sum(N_d_except_v.A, axis=0) + alpha) * Beta[:, v]\n p = p.clip(min=0)\n p = p/np.sum(p)\n z_n = np.random.choice(topics, p = p)\n N_d[v, old_z_n] = 0\n N_d[v, z_n] = 1\n Ns[d] = N_d\n MC_z[d] += N_d\n C = C + N_d.A.T\n\n # Sample beta given all z and thetas\n for k in topics:\n c_beta = C[k, :]\n Beta[k, :] = np.random.dirichlet(c_beta + lmda)\n MC_beta += Beta\n\n log_X = 0\n Beta_hat = MC_beta / t\n for d in range(M):\n props_d = np.sum(MC_z[d].A, axis=0)\n props_d /= float(np.sum(props_d))\n props[d] = props_d\n ixw = np.nonzero(X[d, :])[1]\n log_X += np.sum(_doc_probability_from_p_of_z(props_d, Beta_hat[:, ixw]))\n\n log_Xs.append(log_X)\n print log_X\n perplexities.append(self._perplexity(X, log_X))\n\n return props, Beta_hat, log_Xs, perplexities","def caculate_prob(self):\n t_H = self.tree.depth()\n t_h = 1\n while(t_h <= t_H):\n t_hnodes = self.get_h(t_h)\n t_sum = 0\n t_hpro = []\n t_cpro = []\n for t_n in t_hnodes:\n t_sum = self.tree.get_node(t_n).data[0] + t_sum\n t_node = self.tree.get_node(t_n)\n if t_node.is_leaf():\n t_node.data.append(0)\n continue\n t_childrens = self.tree.children(t_n)\n t_shang = 0\n for child in t_childrens:\n t_shang = t_shang + (child.data[0]/t_node.data[0])*np.log(child.data[0]/t_node.data[0])\n t_node.data.append(-t_shang)\n for t_n in t_hnodes:\n t_node = self.tree.get_node(t_n)\n t_parentnode = self.tree.parent(t_n)\n if t_h > 1:\n t_node.data.append((t_node.data[0] / t_sum) * (t_node.data[0]/t_parentnode.data[0]))\n t_hpro.append((t_node.data[0]/t_sum) * (t_node.data[0]/t_parentnode.data[0]))\n else:\n t_node.data.append((t_node.data[0] / t_sum))\n t_hpro.append((t_node.data[0] / t_sum))\n\n t_cpro.append(t_node.data[1])\n t_ndata = np.array(t_hpro)\n mean = np.mean(t_ndata)\n std = np.std(t_ndata,ddof=1)\n t_sdata = np.array(t_cpro)\n mean_s = np.mean(t_sdata)\n std_s = np.std(t_sdata,ddof=1)\n for t_n in t_hnodes:\n t_node = self.tree.get_node(t_n)\n if(std != 0):\n t_node.data[2] = (t_node.data[2] - mean)/std\n else:\n t_node.data[2] = (t_node.data[2] - mean)\n if(mean_s == 0 and std_s ==0):\n t_node.data[1] = -100.0\n continue\n t_node.data[1] = (t_node.data[1] - mean_s)/std_s\n t_h = t_h + 1","def sample_propensities(mutated_params: torch.Tensor) -> torch.Tensor:\n return torch.softmax(mutated_params, -1)","def transition_model(corpus, page, damping_factor):\n\n probability_distribution = dict()\n\n #if page has outgoing links\n if corpus[page]:\n\n for link in corpus:\n probability_distribution[link] = (1 - damping_factor) / len(corpus)\n\n if link in corpus[page]:\n probability_distribution[link] = probability_distribution[link] + (damping_factor / len(corpus[page]))\n\n else:\n\n for link in corpus:\n probability_distribution[link] = 1 / len(corpus)\n\n return probability_distribution","def compute_transition_matrix(M, triang_distr):\n prob_self = 0.99\n \n A = np.zeros((2*M, 2*M))\n max_step = len(triang_distr) // 2\n\n for i in range(M):\n if i < max_step:\n A[i, 0:i+max_step] = prob_self * triang_distr[max_step - i:-1] / np.sum(triang_distr[max_step - i:-1])\n A[i+M, M:i+M+max_step] = prob_self * triang_distr[max_step - i:-1] / np.sum(triang_distr[max_step - i:-1])\n\n if i >= max_step and i < M-max_step:\n A[i, i-max_step:i+max_step+1] = prob_self * triang_distr\n A[i+M, (i+M)-max_step:(i+M)+max_step+1] = prob_self * triang_distr\n\n if i >= M-max_step:\n A[i, i-max_step:M] = prob_self * triang_distr[0:max_step - (i-M)] / np.sum(triang_distr[0:max_step - (i-M)])\n A[i+M, i+M-max_step:2*M] = prob_self * triang_distr[0:max_step - (i - M)] / \\\n np.sum(triang_distr[0:max_step - (i - M)])\n\n A[i, i+M] = 1 - prob_self\n A[i+M, i] = 1 - prob_self\n \n return A","def wsimenv(pts, dist, pcc=True):\n\tN = 100\n\tx = np.array(pts)\n\tn = len(x)\n\n\trgen = get_random()\n\t\n\tfor e in xrange(N):\n\t\tr = []\n\t\tprev_dtup = None\n\t\tfor dtup in dist:\n\t\t\tif prev_dtup == None:\n\t\t\t\tlb = 0.0\n\t\t\telse:\t\n\t\t\t\tlb = prev_dtup[0]\n\t\t\tub = dtup[0] + lb\n\t\t\tn1 = int(math.ceil(dtup[0]*n))\n\t\n\t\t\tfor i in xrange(n1):\n\t\t\t\trvar = rgen.uniform(lb,ub)\n\t\t\t\tr.append(dtup[1].cdf_inv(rvar))\n\t\t\t\n\t\t\tprev_dtup = dtup\n\t\t\n\t\tif pcc == True:\n\t\t\tncc = ccdf(r, issorted=False)\n\t\t\tplt.loglog(ncc[:,0], ncc[:,1], color='#A0A0A0' , linestyle='steps', linewidth=0.5)\n\t\telse:\n\t\t\tncc = ecdf(r, issorted=False)\n\t\t\tplt.plot(ncc[:,0], ncc[:,1], color='#A0A0A0', linestyle='steps', linewidth=0.5)\n\t\t\n\tif pcc == True:\n\t\tcc = ccdf(x, issorted=True)\n\t\tplt.loglog(cc[:,0],cc[:,1],'k-',linestyle='steps',linewidth=3)\n\telse:\n\t\tcc = ecdf(x, issorted=True)\n\t\tplt.plot(cc[:,0],cc[:,1],'k-',linestyle='steps',linewidth=3)","def prob4():\n#raise NotImplementedError(\"Problem 4 Incomplete\")\n h = lambda x : x[0] < -1 and x[1] > 1\n f = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([0,0]),cov=np.eye(2))\n g = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([-1,1]),cov=np.eye(2))\n X = np.random.multivariate_normal(mean=np.array([-1,1]),cov=np.eye(2),size=10000)\n return 1./10000*np.sum(np.apply_along_axis(h,1,X)*np.apply_along_axis(f,1,X)/np.apply_along_axis(g,1,X))","def random_distribution(m):\n urls = []\n for i in range(m):\n p = 'https://en.wikipedia.org/wiki/Special:Random'\n urls.append(p)\n return Counter(distribution(urls))","def cdf(weights):\r\n\treturn np.cumsum(weights) / sum(weights)","def sample_mv_stable_discrete(alpha, weights, points, n):\n assert weights.shape[0] == points.shape[0], str(weights.shape) + str(points.shape)\n assert len(weights.shape) == 1\n assert len(points.shape) == 2\n for point in points:\n assert np.abs(norm(point) - 1) < 1e-8\n m = weights.shape[0]\n k = points.shape[1]\n if alpha == 1:\n X = []\n for _ in range(n):\n xs = (\n weights\n * (\n sample_stable(alpha=alpha, beta=1, size=[m])\n + 2 / np.pi * np.log(weights)\n )\n )[..., None] * points\n X.append(np.sum(xs, axis=0))\n X = np.array(X)\n else:\n X = []\n for _ in range(n):\n xs = (\n weights ** (1 / alpha) * sample_stable(alpha=alpha, beta=1, size=[m])\n )[..., None] * points\n X.append(np.sum(xs, axis=0))\n X = np.array(X)\n assert X.shape == (n, k)\n return X","def sample_chain(T, dc, pi, ttr):\n c = np.zeros((T, dc), 'd')\n # t=0\n c[0,:] = np.argmax(np.random.multinomial(1, pi, size=(dc)))\n # t>0\n for t in range(1,T):\n for i in range(dc):\n p = ttr[int(c[t-1,i]), :]\n tmp = np.random.multinomial(1, p, size=(1))\n c[t, i] = np.argmax(tmp[0])\n return c","def uniform_start_probs(self) -> np.ndarray:\n return np.ones(self.n_states) / self.n_states","def gen_interpolation_coeff(self):\n\n L_counts = [0 for x in range(self.N)]\n ngram_freqs = [self.get_ngram_freq(n) for n in range(0,self.N + 1)]\n\n for ngram in self.freq_dist:\n\n c_at_n = [0 for x in range(self.N)]\n p_at_n = [0 for x in range(self.N)]\n \n for n_len in range(1, self.N + 1):\n\n nom_ngram = ngram[len(ngram) - n_len:]\n denom_ngram = nom_ngram[:-1]\n\n nom_freq = ngram_freqs[n_len]\n denom_freq = ngram_freqs[n_len - 1 ]\n\n nom_count = nom_freq[nom_ngram] - 1\n denom_count = denom_freq[denom_ngram] - 1\n\n if n_len == 1:\n denom_count = sum(nom_freq.values()) - 1 \n \n p_at_n_len = 0\n if denom_count:\n p_at_n_len = nom_count / denom_count\n\n c_at_n[n_len - 1] = nom_count + 1\n p_at_n[n_len - 1] = p_at_n_len \n\n p_max = max(p_at_n)\n for i, p_item in enumerate(p_at_n):\n if p_max == p_item:\n L_counts[i] += c_at_n[i]\n\n total_L = sum(L_counts)\n return [L_count_item / total_L for L_count_item in L_counts]","def random_table(self):\n rule_set = []\n for i in range(self.k ** (2 * self.r + 1) - 1):\n g = np.random.rand()\n if g > self.lambda_param:\n g = 0\n else:\n g = np.random.randint(1, self.k)\n rule_set.append(g)\n rule_set.append(0)\n return rule_set","def Nmatrix(init_par, alpha, delta, obs, sigma_obs, ccoef, N):\n\tparallax, v, sigma_v = init_par[:-4], init_par[-4:-1], init_par[-1] \n\tplx_obs, mualpha_obs, mudelta_obs = obs[:, 0], obs[:, 1], obs[:, 2]\n\n\tp, q, r = normalTriad(alpha, delta)\n\tmualpha_mod = np.dot(np.transpose(p),v)*parallax/_A\n\tmudelta_mod = np.dot(np.transpose(q),v)*parallax/_A\n\t\n\tplx_mod, mualpha_mod, mudelta_mod = parallax, mualpha_mod, mudelta_mod\n\tsigma_plx, sigma_mualpha, sigma_mudelta = np.transpose(sigma_obs)\n\ta,like, expo, detD = np.ones(N),np.ones(N),np.ones(N), np.ones(N) \n\tC = np.zeros((3,3,N),dtype=np.float64)\n\tC[0,0,:],C[1,1,:],C[2,2,:] = sigma_plx**2.,sigma_mualpha**2., sigma_mudelta**2.\n\tcorr_coefficient_plx_mualpha, corr_coefficient_plx_mudelta, corr_coefficient_mualpha_mudelta = np.zeros(N), np.zeros(N), np.zeros(N)\n\tcorr_coefficient_plx_mualpha[:], corr_coefficient_plx_mudelta[:], corr_coefficient_mualpha_mudelta[:] = ccoef[:, 0], ccoef[:, 1], ccoef[:, 2] \n\t\n\tC[0,1,:], C[0,2,:] = corr_coefficient_plx_mualpha*sigma_plx*sigma_mualpha, corr_coefficient_plx_mudelta*sigma_plx*sigma_mudelta\n\tC[1,0,:], C[1,2,:] = corr_coefficient_plx_mualpha*sigma_plx*sigma_mualpha, corr_coefficient_mualpha_mudelta*sigma_mualpha*sigma_mudelta\n\tC[2,0,:], C[2,1,:] = corr_coefficient_plx_mudelta*sigma_plx*sigma_mudelta, corr_coefficient_mualpha_mudelta*sigma_mualpha*sigma_mudelta\n\tE = np.zeros((3,3,N),dtype=np.float64)\n\tE[1,1,:],E[2,2,:] = (sigma_v**2.)*(parallax/_A)**2., (sigma_v**2.)*(parallax/_A)**2.\n\tD,invD = np.zeros((3,3,N),dtype=np.float64),np.zeros((3,3,N),dtype=np.float64)\n\tD = np.add(E,C)\n\tfor i in range(N):\n\t\tdetD[i] = matrix_det(D[:,:,i]) \n\t\tinvD[:,:,i] = matrix_inv(D[:,:,i])\n\t\t\n\ta_c = np.ones((3,N))\n\ta_c = [plx_obs - plx_mod, mualpha_obs - mualpha_mod, mudelta_obs-mudelta_mod]\n\t\n\t\n\n\t\n\tcprime_pi, cprime_vx, cprime_vy, cprime_vz, = np.ones((3,N)), np.ones((3,N)), \\\n\t\t\t\t\t\t\tnp.ones((3,N)), np.ones((3,N)), \n\tcprime_pi[0,:] = 1.\n\tcprime_pi[1,:] = np.dot(np.transpose(p),v)/_A\n\tcprime_pi[2,:] = np.dot(np.transpose(q),v)/_A\n\t\n\tcprime_vx[0,:] = 0.\n\tcprime_vx[1,:] = -np.sin(alpha)*plx_mod/_A \n\tcprime_vx[2,:] = -np.sin(delta)*np.cos(alpha)*plx_mod/_A\n\n\t\n\tcprime_vy[0,:] = 0.\n\tcprime_vy[1,:] = np.cos(alpha)*plx_mod/_A \n\tcprime_vy[2,:] = -np.sin(delta)*np.sin(alpha)*plx_mod/_A\n\n\tcprime_vz[0,:] = 0.\n\tcprime_vz[1,:] = 0. \n\tcprime_vz[2,:] = np.cos(delta)*plx_mod/_A\n\n\tdlnd_dpi, dlnd_dsigmav = np.zeros(N), np.zeros(N)\n\tde_dpi, de_dsigmav = np.zeros(N), np.zeros(N)\n\t\n\n\t### See formula A.5 \n\tde_dpi[:] = ((sigma_v/_A)**2.)*2.*plx_mod[:]\n\tde_dsigmav[:] = ((plx_mod[:]/_A)**2.)*2.*sigma_v\n\t\n\tdlnd_dpi[:] = (invD[1,1,:] + invD[2,2,:])*de_dpi[:] \n\tdlnd_dsigmav[:] = (invD[1,1,:] + invD[2,2,:])*de_dsigmav[:]\n\t\n\t\n\t\n\t### See formula A.7\n\thess = np.zeros((N+4, N+4))\n\n\thess_diag_pi, hess_diag_pi_1, hess_diag_pi_2 = np.zeros(N), np.zeros(N), np.zeros(N)\n\thess_diag_pi_1[:] = invD[0, 0, :]*cprime_pi[0, :]*cprime_pi[0, :] + invD[0, 1, :]*cprime_pi[0, :]*cprime_pi[1, :] + invD[0, 2, :]*cprime_pi[0, :]*cprime_pi[2, :] + \\\n\t\t\t invD[1, 0, :]*cprime_pi[1, :]*cprime_pi[0, :] + invD[1, 1, :]*cprime_pi[1, :]*cprime_pi[1, :] + invD[1, 2, :]*cprime_pi[1, :]*cprime_pi[2, :] + \\\n\t\t \t invD[2, 0, :]*cprime_pi[2, :]*cprime_pi[0, :] + invD[2, 1, :]*cprime_pi[2, :]*cprime_pi[1, :] + invD[2, 2, :]*cprime_pi[2, :]*cprime_pi[2, :]\t\n\t\n\t\n\t#hess_diag_pi_2[:] = np.sum(0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dpi[:]*de_dpi[:]) ### Check if it's with or without sum: without!\n\t# So correct formula is below.\n\thess_diag_pi_2[:] = (0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dpi[:]*de_dpi[:])\n\thess_diag_pi[:] = hess_diag_pi_1[:] + hess_diag_pi_2[:]\t\n\n\t\n\thess_diag_vx, hess_diag_vy, hess_diag_vz, hess_diag_sigmav = np.zeros(N), np.zeros(N), np.zeros(N), np.zeros(N)\n\thess_pi_vx, hess_pi_vy, hess_pi_vz, hess_pi_sigmav = np.zeros(N), np.zeros(N), np.zeros(N), np.zeros(N)\n\thess_diag_vxi, hess_diag_vyi, hess_diag_vzi = np.zeros(N), np.zeros(N), np.zeros(N)\n\t\n\thess_diag_vxi[:] = invD[0, 0, :]*cprime_vx[0, :]*cprime_vx[0, :] + invD[0, 1, :]*cprime_vx[0, :]*cprime_vx[1, :] + invD[0, 2, :]*cprime_vx[0, :]*cprime_vx[2, :] + \\\n\t\t\t invD[1, 0, :]*cprime_vx[1, :]*cprime_vx[0, :] + invD[1, 1, :]*cprime_vx[1, :]*cprime_vx[1, :] + invD[1, 2, :]*cprime_vx[1, :]*cprime_vx[2, :] + \\\n\t\t\t invD[2, 0, :]*cprime_vx[2, :]*cprime_vx[0, :] + invD[2, 1, :]*cprime_vx[2, :]*cprime_vx[1, :] + invD[2, 2, :]*cprime_vx[2, :]*cprime_vx[2, :] \t\t\n\t\n\thess_diag_vyi[:] = invD[0, 0, :]*cprime_vy[0, :]*cprime_vy[0, :] + invD[0, 1, :]*cprime_vy[0, :]*cprime_vy[1, :] + invD[0, 2, :]*cprime_vy[0, :]*cprime_vy[2, :] +\\\n\t\t\t invD[1, 0, :]*cprime_vy[1, :]*cprime_vy[0, :] + invD[1, 1, :]*cprime_vy[1, :]*cprime_vy[1, :] + invD[1, 2, :]*cprime_vy[1, :]*cprime_vy[2, :] +\\\n\t\t\t invD[2, 0, :]*cprime_vy[2, :]*cprime_vy[0, :] + invD[2, 1, :]*cprime_vy[2, :]*cprime_vy[1, :] + invD[2, 2, :]*cprime_vy[2, :]*cprime_vy[2, :] \t\n\n\n\thess_diag_vzi[:] = invD[0, 0, :]*cprime_vz[0, :]*cprime_vz[0, :] + invD[0, 1, :]*cprime_vz[0, :]*cprime_vz[1, :] + invD[0, 2, :]*cprime_vz[0, :]*cprime_vz[2, :] +\\\n\t\t\t invD[1, 0, :]*cprime_vz[1, :]*cprime_vz[0, :] + invD[1, 1, :]*cprime_vz[1, :]*cprime_vz[1, :] + invD[1, 2, :]*cprime_vz[1, :]*cprime_vz[2, :] +\\\n\t\t\t invD[2, 0, :]*cprime_vz[2, :]*cprime_vz[0, :] + invD[2, 1, :]*cprime_vz[2, :]*cprime_vz[1, :] + invD[2, 2, :]*cprime_vz[2, :]*cprime_vz[2, :] \t\t\n\t\n\n\thess_pi_vx[:] = invD[0, 0, :]*cprime_pi[0,:]*cprime_vx[0, :] + invD[0, 1, :]*cprime_pi[0,:]*cprime_vx[1, :] + invD[0, 2, :]*cprime_pi[0,:]*cprime_vx[2, :] +\\\n\t\t\tinvD[1, 0, :]*cprime_pi[1,:]*cprime_vx[0, :] + invD[1, 1, :]*cprime_pi[1,:]*cprime_vx[1, :] + invD[1, 2, :]*cprime_pi[1,:]*cprime_vx[2, :] +\\\n\t\t\tinvD[2, 0, :]*cprime_pi[2,:]*cprime_vx[0, :] + invD[2, 1, :]*cprime_pi[2,:]*cprime_vx[1, :] + invD[2, 2, :]*cprime_pi[2,:]*cprime_vx[2, :] \n\n\thess_pi_vy[:] = invD[0, 0, :]*cprime_pi[0,:]*cprime_vy[0, :] + invD[0, 1, :]*cprime_pi[0,:]*cprime_vy[1, :] + invD[0, 2, :]*cprime_pi[0,:]*cprime_vy[2, :] +\\\n\t\t\tinvD[1, 0, :]*cprime_pi[1,:]*cprime_vy[0, :] + invD[1, 1, :]*cprime_pi[1,:]*cprime_vy[1, :] + invD[1, 2, :]*cprime_pi[1,:]*cprime_vy[2, :] +\\\n\t\t\tinvD[2, 0, :]*cprime_pi[2,:]*cprime_vy[0, :] + invD[2, 1, :]*cprime_pi[2,:]*cprime_vy[1, :] + invD[2, 2, :]*cprime_pi[2,:]*cprime_vy[2, :] \n\n\thess_pi_vz[:] = invD[0, 0, :]*cprime_pi[0,:]*cprime_vz[0, :] + invD[0, 1, :]*cprime_pi[0,:]*cprime_vz[1, :] + invD[0, 2, :]*cprime_pi[0,:]*cprime_vz[2, :] +\\\n\t\t\tinvD[1, 0, :]*cprime_pi[1,:]*cprime_vz[0, :] + invD[1, 1, :]*cprime_pi[1,:]*cprime_vz[1, :] + invD[1, 2, :]*cprime_pi[1,:]*cprime_vz[2, :] +\\\n\t\t\tinvD[2, 0, :]*cprime_pi[2,:]*cprime_vz[0, :] + invD[2, 1, :]*cprime_pi[2,:]*cprime_vz[1, :] + invD[2, 2, :]*cprime_pi[2,:]*cprime_vz[2, :] \n\n\t\t\t\t\t\t\n\thess_diag_vx = np.sum(hess_diag_vxi)\n\thess_diag_vy = np.sum(hess_diag_vyi)\n\thess_diag_vz = np.sum(hess_diag_vzi)\t\n\t\n\thess_diag_sigmav = np.sum(0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dsigmav[:]*de_dsigmav[:])\n\thess_pi_sigmav[:] = 0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dpi[:]*de_dsigmav[:] \n\n\thess_diag = np.concatenate((hess_diag_pi, np.array([hess_diag_vx, hess_diag_vy, hess_diag_vz, hess_diag_sigmav])))\n\t\n\tfor i in range(N+4):\n\t\thess[i, i] = hess_diag[i]\n\t\t\n\t\n\tfor j in range(N):\n\t\t\thess[j, -4] = hess_pi_vx[j]\n\t\t\thess[j, -3] = hess_pi_vy[j]\n\t\t\thess[j, -2] = hess_pi_vz[j]\n\t\t\thess[j, -1] = hess_pi_sigmav[j]\n\t\t\thess[-4, j] = hess_pi_vx[j]\n\t\t\thess[-3, j] = hess_pi_vy[j] \n\t\t\thess[-2, j] = hess_pi_vz[j]\n\t\t\thess[-1, j] = hess_pi_sigmav[j]\n\t\t\t\n\n\t\n\t\n\tpart_12, part_13, part_23 = np.zeros(N),np.zeros(N),np.zeros(N)\n\tfor ia in range(3):\n\t\tfor ib in range(3):\n\t\t\tpart_12[:] += invD[ia, ib, :]*cprime_vx[ia, :]*cprime_vy[ib, :] \n\t\t\tpart_13[:] += invD[ia, ib, :]*cprime_vx[ia, :]*cprime_vz[ib, :] \n\t\t\tpart_23[:] += invD[ia, ib, :]*cprime_vy[ia, :]*cprime_vz[ib, :] \t\t\t\t\n\n\thess[-4, -3] = np.sum(part_12)\n\thess[-3, -4] = hess[-4, -3]\n\t\n\thess[-4, -2] = np.sum(part_13)\n\thess[-2, -4] = hess[-4, -2]\n\n\thess[-3, -2] = np.sum(part_23)\n\thess[-2, -3] = hess[-3, -2]\n\n\t#### I am returning here the matrix Njk, which is defined as -E(H),\n\t#### where H is the hessian of the likelihood: therefore to obtain the real hessian, one\n\t#### should multiply this by '-1' (see function below.)\n\treturn hess ### See eq. 18","def sample_pdf(bins,\n weights,\n N_importance,\n det=False,\n eps=1e-5):\n N_rays, N_samples_ = weights.shape\n weights = weights + eps # prevent division by zero (don't do inplace op!)\n pdf = weights / torch.sum(weights, -1, keepdim=True) # (N_rays, N_samples_)\n cdf = torch.cumsum(pdf, -1) # (N_rays, N_samples), cumulative distribution function\n cdf = torch.cat([torch.zeros_like(cdf[:, :1]), cdf], -1) # (N_rays, N_samples_+1)\n # padded to 0~1 inclusive\n\n if det:\n u = torch.linspace(0, 1, N_importance, device=bins.device)\n u = u.expand(N_rays, N_importance)\n else:\n u = torch.rand(N_rays, N_importance, device=bins.device)\n u = u.contiguous()\n\n inds = torch.searchsorted(cdf, u)\n below = torch.clamp_min(inds - 1, 0)\n above = torch.clamp_max(inds, N_samples_)\n\n inds_sampled = torch.stack([below, above], -1).view(N_rays, 2 * N_importance)\n cdf_g = torch.gather(cdf, 1, inds_sampled)\n cdf_g = cdf_g.view(N_rays, N_importance, 2)\n bins_g = torch.gather(bins, 1, inds_sampled).view(N_rays, N_importance, 2)\n\n denom = cdf_g[..., 1] - cdf_g[..., 0]\n denom[denom < eps] = 1 # denom equals 0 means a bin has weight 0, in which case it will not be sampled\n # anyway, therefore any value for it is fine (set to 1 here)\n\n samples = bins_g[..., 0] + (u - cdf_g[..., 0]) / denom * (bins_g[..., 1] - bins_g[..., 0])\n return samples","def discrete_rv(p):\n u = np.random.uniform()\n cdf = np.cumsum(p)\n j = np.searchsorted(cdf, u)\n return j","def transition_model(corpus, page, damping_factor):\n prob_distrib = dict()\n\n #Initialising probability distribution to 0 for each page\n for pg in corpus.keys():\n prob_distrib[pg] = 0\n\n if len(corpus[page]) > 0:\n #With probability damping_factor, assigning links from the page with equal probability\n for pg in corpus[page]:\n prob_distrib[pg] += damping_factor/len(corpus[page])\n\n #With probability (1-damping_factor), assigning all pages of the corpus with equal probability\n for pg in corpus.keys():\n prob_distrib[pg] += (1-damping_factor)/len(corpus.keys())\n else:\n #Assigning all pages of the corpus with equal probability\n for pg in corpus.keys():\n prob_distrib[pg] += 1/len(corpus.keys())\n\n return prob_distrib","def dctmtx(n):\n x,y = np.meshgrid(range(n), range(n))\n D = np.sqrt(2.0/n) * np.cos(np.pi * (2*x+1) * y / (2*n))\n D[0] /= np.sqrt(2)\n return D","def generate_transition_trigram_probabilities(transition_bigram_counts, transition_trigram_counts):\r\n\ttransition_trigram_probabilities = dict()\r\n\tfor tag_trigram in transition_trigram_counts:\r\n\t\ttransition_trigram_probabilities[tag_trigram] = float(transition_trigram_counts[tag_trigram])/transition_bigram_counts[(tag_trigram[0], tag_trigram[1])]\r\n\treturn transition_trigram_probabilities","def test_bp_example():\n signal = np.array([4, 7, 9, 10, 6, 11, 3])\n\n pe = permutation_entropy(signal, 2)\n\n assert 0.91 < pe < 0.92 # Should be approx 0.918.\n\n pe = permutation_entropy(signal, 3)\n\n assert 1.52 < pe < 1.53 # Should be approx 1.522.","def make_prog(self):\r\n\r\n self.cnv.clear()\r\n cdf = self.df[self.df.L != 0]\r\n c0 = cdf['C0'].value_counts().idxmax()\r\n c1 = cdf['C1'].value_counts().idxmax()\r\n c2 = cdf['C2'].value_counts().idxmax()\r\n c3 = cdf['C3'].value_counts().idxmax()\r\n self.cnv.extend([c0, c1, c2, c3])","def random_times(p):\n while True:\n if sum(p.values()) != 1:\n raise ValueError('Probabilities must sum to unity')\n r = random.random()\n remaining = 1\n for category, probability in p.items():\n remaining -= probability\n if remaining <= r:\n yield category\n break","def generateMatrix_CN(N, sigma):\n\n # Setup the diagonal\n d = 2*numpy.diag(numpy.ones(N-2)*(1+1./sigma))\n\n # Consider Neumann BC\n d[-1,-1] = 1+2./sigma\n\n # Setup upper diagonal\n ud = numpy.diag(numpy.ones(N-3)*-1, 1)\n\n # Setup lower diagonal\n ld = numpy.diag(numpy.ones(N-3)*-1, -1)\n\n A = d + ud + ld\n\n return A","def stateOccupationProbabilityGeneration(self):\n self.L = zeros((self.noOfEmmittingStates, self.T))\n\n for j in range(self.noOfEmmittingStates):\n for t in range(self.T):\n self.L[j,t] = (self.alpha[j+1, t+1] * self.beta[j+1, t+1]) / self.observationLikelihood"],"string":"[\n \"def entropy_permutation_test(ordered_pitch_types, single_pitch_pdf, conditional_joint_probabilities, total_transitions,\\n n=1000):\\n pitch_types, pitch_probabilities = zip(*single_pitch_pdf.items())\\n permutation_entropies = []\\n progress = progressbar.ProgressBar()\\n\\n for test_number in progress(xrange(n)):\\n # create the new matrix\\n permutation_counts = {}\\n for first_pitch_type in ordered_pitch_types:\\n permutation_counts[first_pitch_type] = {}\\n for second_pitch_type in ordered_pitch_types:\\n permutation_counts[first_pitch_type][second_pitch_type] = 0\\n\\n pitch_permutation = numpy.random.choice(pitch_types, total_transitions, p=pitch_probabilities)\\n current_pitch = numpy.random.choice(pitch_types, p=pitch_probabilities)\\n for next_pitch in pitch_permutation:\\n permutation_counts[current_pitch][next_pitch] += 1\\n current_pitch = next_pitch\\n\\n joint_probabilities, _, _ = joint_probabilities_from_transitions(ordered_pitch_types, permutation_counts)\\n permutation_entropies.append(entropy_from_probability_matrix(joint_probabilities))\\n\\n joint_entropy = entropy_from_probability_matrix(conditional_joint_probabilities)\\n # print 'Mean', numpy.mean(permutation_entropies)\\n # print 'Standard deviation', numpy.std(permutation_entropies)\\n # tdof, tloc, tscale = stats.t.fit(permutation_entropies)\\n # print 'DF', tdof, 'Loc (mean)', tloc, 'Scale (SD)', tscale\\n # t_score = (joint_entropy - tloc) / tscale\\n # print stats.t.cdf(joint_entropy, df=tdof, loc=tloc, scale=tscale)\\n\\n mean, stddev = stats.norm.fit(permutation_entropies)\\n print 'Mean = {mean}\\\\t StdDev = {stddev}'.format(mean=mean, stddev=stddev)\\n z_score = (joint_entropy - mean) / stddev\\n p_value = stats.norm.cdf(joint_entropy, mean, stddev)\\n print 'The joint entropy has a Z-score of {z_score} which gives a P-value of {p_value}'.format(z_score=z_score,\\n p_value=p_value)\\n return z_score, p_value\",\n \"def marcovNuc (i = random.choice(stateSpace), step = 100):\\n # matrix of transition probabilities\\n #matrix = [[0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25]] \\n matrix = [[0.4, 0.3, 0.2, 0.1], [0.4, 0.3, 0.2, 0.1], [0.4, 0.3, 0.2, 0.1], [0.4, 0.3, 0.2, 0.1]] \\n step += 1 # add one to the range because we remove it at the end\\n sims = [] # List to hold the results of the Marcov chain\\n sims.append(i) # append the seed value to the sims list\\n for x in range(step):\\n \\n if sims[-1] == 'A':\\n w = np.random.random() # Random number generator\\n # the next set of if statements determine where the random number \\n # sits on the number line of probabilities\\n if matrix[0][0] > w:\\n sims.append('A')\\n elif matrix[0][1] + matrix[0][0] > w:\\n sims.append('C')\\n elif matrix[0][2] + matrix[0][1] + matrix[0][0] > w:\\n sims.append('G')\\n else:\\n sims.append('T')\\n elif sims[-1] == 'C':\\n x = np.random.random()\\n if matrix[1][0] > x:\\n sims.append('A')\\n elif matrix[1][1] + matrix[1][0] > x:\\n sims.append('C')\\n elif matrix[1][2] + matrix[1][1] + matrix[1][0] > x:\\n sims.append('G')\\n else:\\n sims.append('T')\\n \\n elif sims[-1] == 'G':\\n y = np.random.random()\\n if matrix[2][0] > y:\\n sims.append('A')\\n elif matrix[2][1] + matrix[2][0] > y:\\n sims.append('C')\\n elif matrix[2][2] + matrix[2][1] + matrix[2][0] > y:\\n sims.append('G')\\n else:\\n sims.append('T')\\n\\n else:\\n z = np.random.random()\\n if matrix[3][0] > z:\\n sims.append('A')\\n elif matrix[3][1] + matrix[3][0] > z:\\n sims.append('C')\\n elif matrix[3][2] + matrix[3][1] + matrix[3][0] > z:\\n sims.append('G')\\n else:\\n sims.append('T')\\n\\n return sims[1:-1] # remove the initial value (the seed)\",\n \"def make_transition_probs(self):\\n n = len(self.speakers) # TODO why this line ???\\n transitions = np.random.randint(5, size=(n, n)) + 1\\n transitions += transitions.transpose()\\n for i in range(0, math.floor(n / 2)):\\n s1 = np.random.randint(n)\\n s2 = np.random.randint(n)\\n transitions[s1][s2] += 10\\n transitions[s2][s1] += 8\\n return(transitions)\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n probabilities = dict()\\n samples = []\\n\\n # Random first sample\\n page = random.choice(list(corpus.keys()))\\n samples.append(page)\\n \\n # Remaining samples after first\\n for i in range(n-1):\\n p = transition_model(corpus, page, damping_factor)\\n page = random.choices(list(p.keys()), weights=list(p.values()), k=1)[0]\\n samples.append(page)\\n\\n # Count\\n for p in corpus.keys():\\n probabilities[p] = samples.count(p) / n\\n\\n return probabilities\",\n \"def prob_distr(input_matrix):\\n size = len(input_matrix)\\n prob_matrix = np.zeros((size, size), dtype=complex)\\n for m in range(size):\\n for n in range(size):\\n prob_matrix[m, n] = input_matrix[m, n, m, n]\\n\\n return prob_matrix\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n all_pages = []\\n first_sample_prob = random.randint(0, len(corpus) - 1)\\n distribution_count = dict()\\n\\n for u in corpus:\\n distribution_count[u] = 0\\n all_pages.append(u)\\n\\n sample = all_pages[first_sample_prob]\\n for i in range(n - 1): # n - 1 because first sample was already calculated\\n selection_bucket = dict()\\n selection_start = 0.0\\n sample_distribution = transition_model(corpus, sample, damping_factor)\\n sample_prob = random.random()\\n for u in sample_distribution:\\n floor = selection_start\\n ceiling = selection_start + sample_distribution[u]\\n selection_start = ceiling\\n selection_bucket[u] = [floor, ceiling]\\n for u in selection_bucket:\\n v = selection_bucket[u]\\n if v[0] < sample_prob < v[1]:\\n sample = u\\n distribution_count[u] += 1\\n distribution = dict()\\n for u in distribution_count:\\n distribution[u] = float(distribution_count[u]) / n\\n\\n return distribution\",\n \"def random_start_probs(self) -> np.ndarray:\\n return self.random_state.dirichlet(np.ones(self.n_states), size=1).flatten()\",\n \"def get_uniformization_sample(initial_state, terminal_state, states, path_length, rate_matrix):\\n # map states to indices\\n state_to_index = dict((state, i) for i, state in enumerate(states))\\n # find the maximum rate away from a state\\n max_rate = max(-rate_matrix[(a, a)] for a in states)\\n # create a uniformized discrete transition matrix in convenient dictionary form\\n discrete_transition_matrix = {}\\n for (a, b), r in rate_matrix.items():\\n discrete_transition_matrix[(a, b)] = r / max_rate\\n if a == b:\\n discrete_transition_matrix[(a, b)] += 1.0\\n # create a discrete transition matrix in the numpy format,\\n # and create the rate matrix in the numpy format\\n R = np.zeros((len(states), len(states)))\\n numpy_rate_matrix = np.zeros((len(states), len(states)))\\n for (a, b), r in rate_matrix.items():\\n ia = state_to_index[a]\\n ib = state_to_index[b]\\n numpy_rate_matrix[ia, ib] = r\\n R[ia, ib] = discrete_transition_matrix[(a, b)]\\n # convert initial and terminal states to indices\\n initial_index = state_to_index[initial_state]\\n terminal_index = state_to_index[terminal_state]\\n # get the probability of the terminal state given the initial state and the path length\\n rate_matrix_exponential = scipy.linalg.matfuncs.expm(numpy_rate_matrix * path_length)\\n Pab = rate_matrix_exponential[initial_index, terminal_index]\\n # draw the number of state changes\\n cumulative_probability = 0\\n n = 0\\n matrix_powers = MatrixPowerCache(R)\\n cutoff = random.uniform(0, Pab)\\n #print 'cutoff =', cutoff\\n #print 'initial_index =', initial_index\\n #print 'terminal_index =', terminal_index\\n #print matrix_powers.get_power(0)\\n while 1:\\n poisson_factor = scipy.stats.poisson.pmf(n, max_rate * path_length)\\n discrete_transition_factor = matrix_powers.get_power(n)[initial_index, terminal_index]\\n cumulative_probability += poisson_factor * discrete_transition_factor\\n #print 'cumulative probability =', cumulative_probability\\n if cutoff < cumulative_probability:\\n break\\n n += 1\\n #print 'n =', n\\n # deal with degenerate cases\\n if n == 0:\\n return []\\n elif n == 1:\\n if initial_state == terminal_state:\\n return []\\n else:\\n return [(random.uniform(0, path_length), terminal_state)]\\n # Simulate a discrete path given the number of changes and the initial and terminal states.\\n # The path is called virtual because some changes may be from a state to itself.\\n virtual_path = get_discrete_path_sample(initial_state, terminal_state, states, n+1, discrete_transition_matrix)[1:]\\n virtual_times = list(sorted(random.uniform(0, path_length) for i in range(n)))\\n events = []\\n last_state = initial_state\\n last_time = 0\\n for current_state, current_time in zip(virtual_path, virtual_times):\\n if current_state == last_state:\\n continue\\n events.append((current_state, current_time))\\n last_state = current_state\\n last_time = current_time\\n return events\",\n \"def training_by_counting(K, D, X, Z):\\n init_probs = [ 0 for i in range(K) ]\\n trans_probs = [ [ 0 for i in range(K) ] for j in range(K) ]\\n emi_probs = [ [ 0 for i in range(D) ] for j in range(K) ] \\n occurrences_z = [0 for i in range(K)]\\n occurrences_x = [0 for i in range(D)]\\n \\n for x,z in zip(X,Z):\\n z_indices = translate_path_to_indices(z)\\n x_indices = translate_observations_to_indices(x)\\n\\n num_sequences = len(z_indices)/3\\n\\n for i in range(0,len(z_indices),3):\\n init_probs[z_indices[i]] += 1\\n\\n for j in range(K):\\n init_probs[j] = init_probs[j] / num_sequences\\n\\n trans, emi = count_transitions_and_emissions(K,D,x_indices,z_indices)\\n \\n for i in range(K):\\n for j in range(K):\\n trans_probs[i][j] += trans[i][j]\\n \\n for i in range(K):\\n for j in range(D):\\n emi_probs[i][j] += emi[i][j]\\n \\n for i in range(len(z_indices)):\\n occurrences_z[z_indices[i]] +=1\\n\\n for i in range(len(x_indices)):\\n occurrences_x[x_indices[i]] += 1\\n \\n for i in range(K):\\n for j in range(K):\\n trans_probs[i][j] /= occurrences_z[j]\\n\\n for i in range(K):\\n for j in range(D):\\n emi_probs[i][j] /= occurrences_x[j]\\n\\n my_hmm = hmm(init_probs,trans_probs,emi_probs)\\n print(init_probs)\\n print(\\\"\\\\n\\\",trans_probs)\\n print(\\\"\\\\n\\\",emi_probs)\\n return my_hmm\",\n \"def sample_from_ibp(K, alpha, sigma, c):\\n pp = poissonparams(K, alpha, sigma, c)\\n new_nodes = np.random.poisson(pp)\\n Ncols = new_nodes.sum()\\n node_count = np.zeros(Ncols)\\n\\n # used to build sparse matrix, entries of each Zij=1\\n colidx = [] \\n rowidx = []\\n rightmost_node = 0\\n\\n # for each clique\\n for n in range(K):\\n # revisit each previously seen node\\n for k in range(rightmost_node):\\n prob_repeat = (node_count[k] - sigma) / (n + c)\\n r = np.random.rand()\\n if r < prob_repeat:\\n rowidx.append(n)\\n colidx.append(k)\\n node_count[k] += 1\\n\\n for k in range(rightmost_node, rightmost_node + new_nodes[n]):\\n rowidx.append(n)\\n colidx.append(k)\\n node_count[k] += 1\\n \\n rightmost_node += new_nodes[n]\\n\\n # build sparse matrix\\n data = np.ones(len(rowidx), int)\\n shape = (K, Ncols)\\n Z = csr_matrix((data, (rowidx, colidx)), shape)\\n\\n return Z\",\n \"def get_matrix(self):\\n matrix = np.zeros([len(self.states), len(self.states)])\\n starting_states = []\\n transitions = []\\n\\n for chords in self.training_data:\\n states = []\\n is_starting_state = True\\n chunks = [chords[x:x+self.order] for x in range(0,\\n len(chords), self.order)]\\n for chunk in chunks:\\n chunk_string = \\\" \\\".join(chunk)\\n if is_starting_state:\\n starting_states.append(chunk_string)\\n is_starting_state = False\\n states.append(chunk_string)\\n\\n for i in range(0, len(states)):\\n if i < (len(states)) - 1:\\n transitions.append([states[i], states[i + 1]])\\n else:\\n transitions.append([states[i]])\\n\\n self.starting_probabilities = np.zeros([len(self.states)])\\n\\n for transition in transitions:\\n for row, row_contents in enumerate(self.transitions):\\n for col, _ in enumerate(row_contents):\\n if transition == self.transitions[row][col]:\\n matrix[row][col] += 1\\n\\n for i, state in enumerate(self.states):\\n for j, possible_state in enumerate(starting_states):\\n if state == possible_state:\\n self.starting_probabilities[j] += 1\\n\\n num = sum(self.starting_probabilities)\\n for i, prob in enumerate(self.starting_probabilities):\\n self.starting_probabilities[i] = prob / num\\n\\n for m in range(len(matrix)):\\n num = sum(matrix[m])\\n if int(num) is not 0:\\n for i in range(len(matrix[m])):\\n matrix[m][i] = (matrix[m][i] / num)\\n else:\\n matrix[m] = self.starting_probabilities\\n return matrix\",\n \"def test_dc():\\n signal = np.zeros(100)\\n pe = permutation_entropy(signal, 2)\\n\\n assert pe == 0\",\n \"def lv_sampler(weights):\\n indeces = []\\n NUM = np.float(len(weights))\\n r = np.random.uniform(0, 1 / NUM)\\n c = weights[0]\\n i = 0\\n for m in np.arange(NUM):\\n u = r + (m - 1) * (1 / NUM)\\n while(u > c):\\n i += 1\\n c += weights[i]\\n indeces.append(i)\\n return np.array(indeces, dtype='int')\",\n \"def get_C100():\\n m = 100\\n random.seed(1111*m)\\n A = random.randn(m, m) + 1j*random.randn(m, m)\\n A = 0.5*(A + np.conj(A).T)\\n return A\",\n \"def sample_entropy(data, m, r):\\n\\n n = len(data)\\n lastrun = np.zeros((1,n))\\n run = np.zeros((1,n))\\n\\n A = np.zeros((m,1), dtype=int)\\n B = np.zeros((m,1), dtype=int)\\n p = np.zeros((m,1))\\n entropy = np.zeros((m,1))\\n\\n for idx_py, idx_mat in enumerate(np.arange(1,n, dtype=int)):\\n nj = n - idx_mat\\n y1 = data[idx_mat-1]\\n\\n for jj_py, jj_mat in enumerate(np.arange(1,nj+1, dtype=int)):\\n j_py = jj_py + idx_py\\n j_mat = jj_mat + idx_mat\\n\\n if np.abs(data[j_mat-1]-y1) < r:\\n run[:,(jj_mat-1)] = lastrun[:,(jj_mat-1)] + 1\\n M1 = np.min(np.concatenate(([m],run[:,(jj_mat-1)])))\\n\\n for mi_py, mi_mat in enumerate(np.arange(1,M1+1,dtype=int)):\\n A[(mi_mat-1)] = A[(mi_mat-1)] + 1\\n\\n if j_mat<n:\\n B[(mi_mat-1)] = B[(mi_mat-1)] + 1\\n\\n else:\\n run[:,(jj_mat-1)] = 0\\n\\n for j1_py, j1_mat in enumerate(np.arange(1,nj+1, dtype=int)):\\n lastrun[:,(j1_mat-1)] = run[:,(j1_mat-1)]\\n\\n N = np.zeros((1,1))\\n N[0] = (n*(n-1))/2\\n B = np.concatenate((N,B[0:(m-1)])).reshape(A.shape)\\n p = np.divide(A,B)\\n entropy = -np.log(p)\\n\\n return(entropy,A,B)\",\n \"def prob4():\\n\\n\\n N = 500000\\n random_draws = np.random.multivariate_normal(mean = [-1,1], cov =[[1,0],[0,1]], size = N)\\n\\n h = lambda x: x[0] < -1 and x[1] > 1\\n f = lambda x: stats.multivariate_normal(mean = [ 0, 0]).pdf(x)\\n g = lambda x: stats.multivariate_normal(mean = [-1, 1]).pdf(x)\\n\\n probability = [h(random_draws[i]) * f(random_draws[i]) / g(random_draws[i]) for i in range(N)]\\n\\n return 1./N * np.sum(probability)\",\n \"def collapsed_gibbs_sample(self, X):\\n K = self.K # number of topics\\n M, V = X.shape\\n alpha = self.alpha\\n lmda = self.lmda\\n topics = np.arange(0, K)\\n\\n #initialize everything uniformly\\n # KxV dense matrix (used like beta)\\n\\n C = np.zeros(shape=(K, V), dtype=float)\\n props = np.zeros(shape=(M, K), dtype=float)\\n\\n #Current state\\n Ns = np.array(range(M), dtype=object)\\n #Running sum\\n MC_z = np.array(range(M), dtype=object)\\n MC_c = np.zeros(shape=(K, V), dtype=float)\\n\\n for d in range(M):\\n #allocate topics from prior\\n word_indices = X[d, :].nonzero()[1]\\n p = np.random.dirichlet(np.ones(K) * alpha)\\n #TODO: Remove the p argument to get completely random topic assignments\\n random_ks = np.random.choice(topics, size=len(word_indices), p=p)\\n N_d = sp.coo_matrix((np.ones(len(word_indices)),\\n (word_indices, random_ks)), shape=(V, K)).tolil()\\n C = C + N_d.A.T\\n Ns[d] = N_d\\n MC_z[d] = sp.coo_matrix((V, K)).tolil()\\n\\n log_Xs = []\\n perplexities = []\\n for epoch in xrange(self.nr_em_epochs):\\n print \\\"Epoch\\\", epoch\\n for d in np.random.permutation(np.arange(M)):\\n x = X[d]\\n N_d = Ns[d]\\n for v in np.nonzero(x)[1]:\\n old_z_n = N_d[v, :].nonzero()[1][0]\\n\\n except_v = [v_prime for v_prime in np.nonzero(x)[1] if v_prime != v]\\n N_d_except_v = N_d[except_v, :]\\n C_except_v = np.copy(C[:, v])\\n C_except_v[old_z_n] = C_except_v[old_z_n] - 1\\n\\n p = (np.sum(N_d_except_v.A, axis=0) + alpha) *\\\\\\n ((C_except_v + lmda) / (np.sum(C, axis=1) -1 + V*lmda))\\n p = p/np.sum(p)\\n z_n = np.random.choice(topics, p = p)\\n N_d[v, old_z_n] = 0\\n N_d[v, z_n] = 1\\n C[old_z_n, v] -= 1\\n C[z_n, v] += 1\\n Ns[d] = N_d\\n MC_z[d] += N_d\\n\\n MC_c += C\\n word_props = (MC_c.T / np.sum(MC_c, axis=1)).T\\n log_X = 0\\n for d in range(M):\\n props_d = np.sum(MC_z[d].A, axis=0)\\n props_d /= float(np.sum(props_d))\\n props[d] = props_d\\n ixw = np.nonzero(X[d, :])[1]\\n log_X += np.sum(_doc_probability_from_p_of_z(props_d, word_props[:, ixw]))\\n\\n log_Xs.append(log_X)\\n print log_X\\n perplexities.append(self._perplexity(X, log_X))\\n\\n\\n return props, word_props, log_Xs, perplexities\",\n \"def inital_probabilities(self,fc,ec):\\n matrix = np.empty(ec*fc)\\n matrix = matrix.reshape(ec,fc)\\n matrix.fill(1/fc)\\n return matrix # repreents probability - p(e|f)\",\n \"def basis(u, s, p = 0.5):\\n # build cumulative distribution\\n d = 0\\n s_cm = 0\\n p = max(p, 1e-8) # ensure d is at least 1\\n while s_cm / sum(s) < p and d < len(s):\\n s_cm += s[d]\\n d += 1\\n \\n return u[:, :d]\",\n \"def generate_distorted_t(t, C, sigma):\\n S_u = generate_S_u(t)\\n\\n m= np.random.multivariate_normal(mean= np.zeros(len(C)), cov=C)\\n noise= generate_noisy_window(len(t), sigma)\\n return np.dot(S_u, m) + noise\",\n \"def sample_discrete_pmf(X, PM, N):\\n\\n assert np.isclose(np.sum(PM), 1.0)\\n assert all(0.0 <= p <= 1.0 for p in PM)\\n \\n y = np.zeros(N)\\n cumulativePM = np.cumsum(PM) # build CDF based on PMF\\n offsetRand = np.random.uniform(0, 1) * (1 / N) # offset to circumvent numerical issues with cumulativePM\\n comb = np.arange(offsetRand, 1 + offsetRand, 1 / N) # new axis with N values in the range ]0,1[\\n \\n j = 0\\n for i in range(0, N):\\n while comb[i] >= cumulativePM[j]: # map the linear distributed values comb according to the CDF\\n j += 1\\t\\n y[i] = X[j]\\n \\n return rd.permutation(y) # permutation of all samples\",\n \"def state_ensemble(m, C, N=10):\\n\\n M = np.random.multivariate_normal(m.real, C.real, size=N)\\\\\\n + 1.0j * np.random.multivariate_normal(m.imag, C.real, size=N)\\n\\n return M.T\",\n \"def outputGenerationProbability(self):\\n self.b = zeros((self.noOfEmmittingStates, self.T))\\n for row in range(self.noOfEmmittingStates):\\n for col in range(self.T):\\n self.b[row, col] = self.gaussianDist(self.observationSequence[0, col],\\n self.outputProbabilities[row, 0],\\n self.outputProbabilities[row, 1])\",\n \"def sample_categorical(distribution):\\n sample = random.random()\\n for event, prob in distribution.items():\\n if sample < prob:\\n return event\\n sample -= prob\\n raise ValueError('sum of distribution less than one')\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n data = []\\n\\n #Choosing a random page from the corpus and adding to data\\n sample = random.choice(list(corpus.keys()))\\n data.append(sample)\\n\\n for _ in range(n-1):\\n prob_distrib = transition_model(corpus, sample, damping_factor)\\n\\n #Choosing a page from the corpus based on transition model and adding to data\\n sample = np.random.choice(list(prob_distrib.keys()), p=list(prob_distrib.values()))\\n data.append(sample)\\n\\n #Dividing the number of times each page was visited by numebr of samples \\n pagerank = {k : v/n for k, v in Counter(data).items()}\\n\\n return pagerank\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n # All pages in entire corpus\\n all_pages = [key for key in corpus.keys()]\\n\\n # Dictionary which will map each page to the number of generated samples that were the page\\n counts = dict()\\n for page in all_pages:\\n counts[page] = 0\\n\\n # Choosing first sample randomly and updating counts dict\\n first_sample = random.choice(all_pages)\\n counts[first_sample] += 1\\n\\n for i in range(1, n):\\n # If we are on the second sample, do the calculation based on the first\\n if i == 1:\\n prev_sample = first_sample\\n\\n distributions = transition_model(corpus, prev_sample, damping_factor)\\n\\n keys = [key for key in distributions.keys()]\\n values = [distributions[key] for key in distributions.keys()]\\n\\n # Using random.choices to select an option given a list of weights (distribution)\\n current_sample = random.choices(keys, weights=values, k=1)[0]\\n\\n # Updating counts dict given the current sample\\n counts[current_sample] += 1\\n\\n prev_sample = current_sample\\n\\n # Modifying counts dict to represent the proportion of samples that corresponded to that page instead of the raw amount\\n for page in all_pages:\\n counts[page] /= n\\n\\n return counts\",\n \"def build_transition_matrix(k_counter, q, p):\\r\\n dim = k_counter + NOT_MOTIF_STATES\\r\\n transition_mat = np.zeros([dim, dim])\\r\\n # B start\\r\\n transition_mat[0, 1] = q\\r\\n transition_mat[0, -2] = 1 - q\\r\\n # B_1\\r\\n transition_mat[1, 1:3] = [1 - p, p]\\r\\n # B_2\\r\\n transition_mat[-2, -2:] = [1 - p, p]\\r\\n # S_last\\r\\n transition_mat[-3, -2] = 1\\r\\n # all S\\r\\n transition_mat[2:-3, 3:-2] = np.eye(k_counter - 1)\\r\\n return wrap_log(transition_mat)\",\n \"def initialization(n, D):\\n\\n samples = []\\n while len(samples) < n:\\n # X = np.random.randint(0, D.shape[1], 10*n)\\n # Y = np.random.randint(0, D.shape[0], 10*n)\\n X = np.random.uniform(0, D.shape[1], 10*n)\\n Y = np.random.uniform(0, D.shape[0], 10*n)\\n P = np.random.uniform(0, 1, 10*n)\\n index = 0\\n while index < len(X) and len(samples) < n:\\n x, y = X[index], Y[index]\\n x_, y_ = int(np.floor(x)), int(np.floor(y))\\n if P[index] < D[y_, x_]:\\n samples.append([x, y])\\n index += 1\\n return np.array(samples)\",\n \"def crp_gen(N, alpha):\\n assert N > 0\\n assert alpha > 0.0\\n alpha = float(alpha)\\n\\n partition = np.zeros(N, dtype=int)\\n Nk = [1]\\n for i in range(1, N):\\n K = len(Nk)\\n ps = np.zeros(K+1)\\n for k in range(K):\\n # get the number of people sitting at table k\\n ps[k] = float(Nk[k])\\n\\n ps[K] = alpha\\n\\n ps /= (float(i)-1+alpha)\\n\\n assignment = pflip(ps)\\n\\n if assignment == K:\\n Nk.append(1)\\n elif assignment < K:\\n Nk[assignment] += 1\\n else:\\n raise ValueError(\\\"invalid assignment: %i, max=%i\\\" %\\n (assignment, K))\\n\\n partition[i] = assignment\\n\\n assert max(partition)+1 == len(Nk)\\n assert len(partition) == N\\n assert sum(Nk) == N\\n\\n K = len(Nk)\\n\\n if K > 1:\\n shuffle(partition)\\n\\n return np.array(partition), Nk, K\",\n \"def binary_dec(A,n_iter = 1000):\\n\\n\\t### Initialization ###\\n\\n\\tp, q = np.shape(A)\\n\\t### B : to be changed\\n\\tB = np.eye(p)\\n \\t###\\n\\tC = bin_random_mat(p,q)\\n\\tlist_dist = []\\n\\tB_argmin = B\\n\\tC_argmin = C\\n\\n\\n\\n\\n\\t## temperature ##\\n\\tT_n = np.log(np.arange(2,n_iter+2,1))\\n\\t#T_n = np.arange(2,n_iter+2,1)\\n\\tfor i in range(n_iter):\\n\\t## update ##\\n\\t\\tC_0 = np.matrix(C)\\n\\t\\tlist_dist =np.append( list_dist, V_potential(np.dot(B,C_0),A) )\\n\\t\\tif V_potential(np.dot(B_argmin,C_argmin),A) == 0:\\n\\t\\t\\tbreak\\n\\t########## transition #############\\n\\t# Here we take 2 steps independent(for B and for C respectively)\\n\\t# We could also use metropolis hasting kernel.\\n\\n\\t\\tC_iter = np.matrix(Metropolis_transition_C(C))\\n\\t\\n\\n\\t\\tB_iter = B[np.random.permutation(np.arange(p))]\\n\\t\\t\\n\\t\\tif np.random.uniform(0,1,1) < \\\\\\n\\t\\t\\t\\tnp.exp(-1./T_n[i]*( V_potential(np.dot(B_iter,C_iter), A)\\\\\\n\\t\\t\\t\\t - V_potential(np.dot(B,C_0),A) ) ):\\n\\t\\t\\tC = C_iter\\n\\t\\t\\tB = B_iter\\n\\t######### end of transition ##############\\n\\n\\t\\t\\tif V_potential(np.dot(B,C),A) < np.min(list_dist):\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tB_argmin = B\\n\\t\\t\\t\\tC_argmin = np.matrix(C)\\n\\t\\t\\t# print i+1\\n\\t\\t\\t# print V_potential(np.dot(B_argmin,C_argmin),A)\\n\\t\\t\\t# print C_argmin\\n\\t\\t\\t# print '\\\\n'\\n\\n\\treturn list_dist,B_argmin, C_argmin\",\n \"def sample_discrete(probs):\\r\\n q = np.random.rand()\\r\\n i = 0\\r\\n p_sum = 0.0\\r\\n while p_sum < q:\\r\\n p_sum += probs[i]\\r\\n i += 1\\r\\n return i - 1\",\n \"def sample_transitions(self, val) -> None:\\n\\n # get values\\n states = val.states\\n pi_conc = val.transitions_conc\\n num_states = val.num_states\\n\\n # count the number of each transition that occurs\\n counts = np.zeros((num_states + 1, num_states))\\n for i in range(num_states):\\n counts[-1, i] = np.sum(states[:, :, 0] == i)\\n for j in range(num_states):\\n counts[i, j] = np.sum((states[:, :, :-1] == i) * (states[:, :, 1:] == j))\\n counts[-1, -1] = 0 # fluorophores starting photobleached are interpretted as load off only\\n\\n # sample from dirichlet distribution\\n val.transitions = Dirichlet.sample(counts + pi_conc)\\n val.P = self.posterior(val)\\n\\n return\",\n \"def simulate_counts(self, C, seed=None, reverse=False, **contract_opts):\\n p_dense = self.to_dense(reverse=reverse, **contract_opts)\\n return qu.simulate_counts(p_dense, C=C, seed=seed)\",\n \"def _generate_sample_from_state(self, state):\\n\\n res = []\\n for e in range(self.n_emissions):\\n cdf = np.cumsum(self.B[e][state, :])\\n res.append((cdf > np.random.rand()).argmax())\\n return np.asarray(res)\",\n \"def Gaussian_conv(n):\\n\\n\\t# Choose sigma and mu\\n\\tsigma = 1\\n\\tmu = 0\\n\\n\\t# Create table\\n\\tb = sigma * np.random.randn(n) + mu\\n\\tsum = b.sum()\\n\\t#b = b/sum\\n\\tprint(b)\\n\\treturn b\",\n \"def appearance_space(state_data, C):\\n return np.dot(C, state_data)\",\n \"def conditionalDistribution(self, d, v):\\n probabilities_ts = np.ones((self.n_topic_components, self.n_sentiment_components))\\n firstFactor = (self.n_ds[d] + self.alphaVec) / \\\\\\n (self.n_d[d] + np.sum(self.alphaVec))\\n secondFactor = np.zeros((self.n_topic_components,self.n_sentiment_components))\\n for s in range(self.n_sentiment_components):\\n \\n secondFactor[:,s] = ((self.n_dst[d, s, :] + self.gammaVec) / \\\\\\n (self.n_ds[d, s] + np.sum(self.gammaVec)))\\n\\n thirdFactor = (self.n_vts[v,:, :] + self.beta) / \\\\\\n (self.n_ts + self.n_vts.shape[0] * self.beta)\\n\\n #forthFactor = np.zeros((self.n_topic_components, self.n_sentiment_components))\\n #for k in range(self.n_topic_components):\\n # forthFactor[k,:] = np.exp(np.dot(self.topic_embeddings[k,:],self.word_embeddings[v,:]))/np.sum(np.exp(np.dot(self.topic_embeddings[k,:],self.word_embeddings.T)))\\n \\n forthFactor = np.exp(np.dot(self.topic_embeddings,self.word_embeddings[v,:]))/np.sum(np.exp(np.dot(self.topic_embeddings,self.word_embeddings.T)),-1)\\n probabilities_ts *= firstFactor[:, np.newaxis]\\n #probabilities_ts *= secondFactor * thirdFactor\\n probabilities_ts *= secondFactor * ((1-self.lambda_)*thirdFactor + self.lambda_*forthFactor)\\n probabilities_ts /= np.sum(probabilities_ts)\\n \\n return probabilities_ts\",\n \"def random_chain(n):\\n transition_matrix = np.random.random((n,n))\\n return transition_matrix / transition_matrix.sum(axis=0)\",\n \"def build_transition_matrix(state_space, markov_process, normalize=True):\\n\\n P = pd.DataFrame(0, index=state_space, columns=state_space)\\n for trans in markov_process:\\n P.loc[trans[0], trans[1]] = P.loc[trans[0], trans[1]] + 1\\n\\n if normalize:\\n P = P.div(P.sum(axis=1), axis=0)\\n\\n return P\",\n \"def generate_probabilities(self):\\n k = 1\\n v= 10\\n for g in self.class_probabilities:\\n curr_list = self.class_probabilities[g]\\n for l in range(0,28):\\n for w in range(0,28):\\n total = float(curr_list[l][w][0] + curr_list[l][w][1] + curr_list[l][w][2])\\n curr_list[l][w][0] = (float(curr_list[l][w][0])+k)/(total + k*v) \\n curr_list[l][w][1] = (float(curr_list[l][w][1])+k)/(total + k*v)\\n curr_list[l][w][2] = (float(curr_list[l][w][2])+k)/(total + k*v)\\n curr_list[l][w][3] = curr_list[l][w][0] + curr_list[l][w][1] + curr_list[l][w][2]\",\n \"def compCGP_C(M):\\n # we store the coedd as a lower triangular matrix\\n # random polynomial coefficients\\n c = 0.5 * np.random.uniform(-1.0, -0.45, size=(M + 1, M + 1)) +\\\\\\n 0.5 * np.random.uniform(0.45, 1.0, size=(M + 1, M + 1))\\n for i in np.arange(M + 1):\\n c[i, :] /= 2**(np.arange(M + 1) + i)\\n c /= 1.5\\n c = np.tril(c)\\n c[0, 0] = 0\\n c[1, 0] = 0\\n c[1, 1] = 1\\n\\n return c\",\n \"def sample_indices_from_coupling(c, num_samples=None, return_all = False, thr = 10**(-6)):\\n\\n if return_all:\\n return [[i, j] for i in range(c.shape[0]) for j in range(c.shape[1]) if c[i,j] > thr]\\n else:\\n linear_samples = np.random.choice(range(c.size), size = num_samples, p = c.flat)\\n \\n if num_samples in [1, None]:\\n linear_samples = [linear_samples]\\n \\n n_cols = c.shape[1]\\n return np.array([[s//n_cols , s%n_cols ] for s in linear_samples])\",\n \"def createTransMatrix(pages):\\n links = getLinks(pages)\\n urls = getUrls(pages)\\n nPages = len(pages)\\n m = np.matrix([[weightLink(nPages, u, l) for u in urls] for l in links])\\n return dampingFactor(nPages, m)\",\n \"def build_fisher_contingency_table(overlap_count, user_count, gene_count, count):\\n table = np.zeros(shape=(2, 2))\\n table[0, 0] = overlap_count\\n table[0, 1] = user_count - overlap_count\\n table[1, 0] = gene_count - overlap_count\\n table[1, 1] = count - user_count - gene_count + overlap_count\\n\\n return table\",\n \"def random_transitions(self) -> np.ndarray:\\n transitions = np.zeros((self.n_states, self.n_states))\\n for i, row in enumerate(transitions):\\n size = min(2, self.n_states - i)\\n row[i:(i + size)] = self.random_state.dirichlet(np.ones(size))\\n return transitions\",\n \"def random_transitions(self) -> np.ndarray:\\n return self.random_state.dirichlet(np.ones(self.n_states), size=self.n_states)\",\n \"def generate(markov_process):\\n sequence = []\\n for index, matrix in enumerate(markov_process):\\n prefix = tuple(sequence[-min(index, matrix.order):])\\n probabilities = matrix[prefix]\\n value = np.random.choice(probabilities.keys(), p=probabilities.values())\\n sequence.append(value)\\n return sequence\",\n \"def sample_pagerank(corpus, damping_factor, n):\\n first_page = random.choice(list(corpus))\\n model = transition_model(corpus, first_page, DAMPING)\\n\\n for i in range(n):\\n\\n choosen = random.random()\\n total = 0\\n\\n for k, v in model.items():\\n total += v\\n\\n if choosen <= total:\\n page = k\\n break\\n \\n model = transition_model(corpus, page, DAMPING)\\n \\n return model\",\n \"def multinomial_prob(counts, probs):\\n return nCkarray(*counts.values) * (probs ** counts).prod()\",\n \"def sdd(events,probs):\\n \\n import random\\n nprobs=[x*1000 for x in probs] #so, here i multiply each float in 'probs' by 1000 and store the products in 'nprobs'\\n newlist=[]\\n for a in range(len(events)) : #then, in this loop, i create a list (newlist), in which each event appears 1000*its probability times\\n b=nprobs[a]\\n b=int(b)\\n for c in range(b) :\\n newlist.append(events[a]) \\n return (random.choice(newlist)) #and finally, i ramdonly sample \",\n \"def _make_random_matrix(self, n_components, n_features):\",\n \"def bsc_output(a, x):\\n y = []\\n transitions = list(np.random.binomial(size=len(x), n=1, p= a))\\n for i in range(len(x)):\\n if(transitions[i]==1): y.append(1-x[i])\\n else: y.append(x[i])\\n return y\",\n \"def Metropolis_transition_C(X, p_0 = 0.5, shake_times = 1):\\n\\tp,q = np.shape(X)\\n\\tX_iter = X\\n\\tfor i in range(shake_times):\\n\\t\\tm,n = np.random.choice(range(p)),np.random.choice(range(q))\\n\\t\\t\\n\\t\\tX_iter[m,n] = np.float(X_iter[m,n] == 0)\\n\\treturn X_iter\",\n \"def _good_turing_new_c(self, count: int) -> float:\\n next_count_index = count + 1\\n next_count: Optional[float] = None\\n if next_count_index not in self.count_map:\\n # this happens when N_{c+1} is 0\\n # this can make the total probability not equal to 1\\n next_count = 0.\\n else:\\n next_count = float(self.count_map[next_count_index])\\n\\n new_count: Optional[float] = None\\n new_count = (count + 1) * next_count / self.count_map[count]\\n return new_count\",\n \"def cond_gen(self, context):\\n dist = {}\\n for event in self.alphabet:\\n dist[event] = self.cond_prob(event, context)\\n return sample_categorical(dist)\",\n \"def mh(N, disttype):\\n xs = np.array([])\\n ys = np.array([])\\n pos_now = (0,0)\\n accept = 0\\n for i in range(N):\\n pos_cand = proposal_pdf(pos_now)\\n prob_stay = target_pdf(pos_now, disttype)\\n prob_move = target_pdf(pos_cand, disttype)\\n if prob_move / prob_stay > np.random.uniform(0,1,1):\\n pos_now = pos_cand\\n xs = np.append(xs, pos_now[0])\\n ys = np.append(ys, pos_now[1])\\n accept += 1\\n return xs, ys, accept/N\",\n \"def get_transition_probs(x, classes=None):\\n ### ==============================================================================================================\\n ### define list_ind as the list of unique ID if not defined\\n if not classes:\\n classes = np.unique(x.values)\\n ### ==============================================================================================================\\n transitions_matrix = []\\n for cl in classes:\\n list_mat = []\\n list_freq = []\\n for yi in np.unique(x.index.year):\\n ### select the year\\n mat = x.ix[str(yi),].values.flatten()\\n ### select the class cl index + 1\\n i = np.where(mat == cl)[0] + 1\\n ### make sure we clip\\n i = np.delete(i, np.where(i >= len(mat)))\\n ### count the total number of days following the cluster \\\"cl\\\"\\n list_freq.append(len(i))\\n mat = mat[i,].tolist()\\n ### count the number of occurences of each of the N clusters\\n list_mat.append([mat.count(c) for c in classes])\\n list_mat = np.array(list_mat)\\n list_mat = list_mat.sum(0) * 1.0\\n list_mat = list_mat / np.array(list_freq).sum()\\n transitions_matrix.append(list_mat)\\n transitions_matrix = np.array(transitions_matrix)\\n return classes, transitions_matrix\",\n \"def sample_stable_net(alpha, sigma_w, sigma_b, phi, x, M, M_last, L):\\n k = x.shape[1]\\n weight_b, point_b = gamma_bias(alpha, sigma_b, k)\\n for i in range(L):\\n if i > 0:\\n x = phi(x)\\n weights_w, points_w = gamma_weight(alpha, sigma_w, x)\\n weights = np.concatenate([weight_b, weights_w])\\n points = np.concatenate([point_b[None, ...], points_w])\\n weights = 0.5 * np.concatenate([weights, weights])\\n points = np.concatenate([points, -points])\\n m = M if (i < L - 1) else M_last\\n x = sample_mv_stable_discrete(alpha, weights, points, m)\\n return x\",\n \"def sample(probs):\\n\\n probs = probs / probs.sum()\\n return np.random.choice(np.arange(len(probs)), p=probs.flatten())\",\n \"def sample_prior(size):\\n return torch.randn(size)\",\n \"def data_gen(size, p):\\n #print(np.random.get_state()[1][0])\\n random_table = np.random.binomial(size = size, p = p, n = 1)\\n test_array = np.zeros((size, 2), dtype = int)\\n for i in range(size):\\n test_array[i,0] = i\\n test_array[i,1] = random_table[i]\\n return test_array\",\n \"def compute_probability_weights(indexing, \\n counts, \\n image, \\n binary_set_mappings): \\n S_w_cardinalities = np.zeros_like(indexing)\\n\\n countsgeq2 = sum(c>=2 for c in counts) # compute amount of indices that have count>=2\\n countseq1 = [v for v in range(indexing.shape[0]) if counts[indexing[v]]==1]\\n K_cardinalities = np.zeros_like(indexing)\\n for card,w in enumerate(countseq1[::-1]):\\n K_cardinalities[w] = card\\n\\n for w,index in enumerate(indexing):\\n if counts[index] >= 3:\\n S_w_cardinalities[w] = len(image)\\n elif counts[index] == 2:\\n offset = 1 if w==binary_set_mappings[index] else 0\\n S_w_cardinalities[w] = len(image) - 1 + offset\\n elif counts[index] == 1:\\n S_w_cardinalities[w] = countsgeq2 + K_cardinalities[w]\\n\\n return S_w_cardinalities/np.sum(S_w_cardinalities)\",\n \"def discrete_sampling(N, probs, states=None):\\n\\n p = probs.squeeze()/np.sum(probs)\\n\\n bins = np.digitize(\\n np.random.uniform(0., 1., (N, 1)), np.hstack((0, np.cumsum(p))))-1\\n\\n if states is None:\\n x = bins\\n else:\\n assert(states.shape[0] == probs.shape[0])\\n x = states[bins]\\n\\n return x.squeeze()\",\n \"def agg_tran_prob_mat(g, step):\\n g = my_scale_sim_mat(g)\\n g = csc_matrix.toarray(g)\\n a_k = g\\n a = g\\n for k in np.arange(2, step+1):\\n a_k = np.matmul(a_k, g)\\n a = a+a_k/k\\n return a\",\n \"def initialize_record_distribution(states, actions, empty_atoms):\\n dist = {}\\n\\n atom_shape = empty_atoms.shape\\n\\n # initial values, equal probability distribution\\n val = np.full(shape=atom_shape, fill_value=1.0 / float(atom_shape[0]))\\n\\n for i in states:\\n dist[i] = {}\\n for j in actions:\\n dist[i][j] = val.copy()\\n\\n return dist\",\n \"def initialize_distribution(states, actions):\\n dist = {}\\n\\n for i in states:\\n dist[i] = {}\\n for j in actions:\\n dist[i][j] = [0.0]\\n\\n return dist\",\n \"def random_transition_matrix(n: int) -> np.ndarray:\\n\\n x = np.abs(np.random.normal(size=(n, n)))\\n rsum = x.sum(axis=1)\\n return x / rsum[:, np.newaxis]\",\n \"def logprob_dc(counts, prior, axis=None):\\n # Note that this excludes the factorial(counts) term, since we explicitly\\n # track permutations in assignments.\\n return gammaln(np.add(counts, prior, dtype=np.float32)).sum(axis)\",\n \"def calculate_cd_transpose(P,C):\\n conf_deg_t = C.T.dot(P)\\n a1 = P.sum(axis=0)\\n B = np.tile(a1, (C.shape[1], 1))\\n CD_t = 1. * conf_deg_t / (B + np.spacing(0))\\n return CD_t\",\n \"def generate_transition_bigram_probabilities(transition_unigram_counts, transition_bigram_counts):\\r\\n\\ttransition_bigram_probabilities = dict()\\r\\n\\tfor tag_bigram in transition_bigram_counts:\\r\\n\\t\\ttransition_bigram_probabilities[tag_bigram] = float(transition_bigram_counts[tag_bigram])/transition_unigram_counts[tag_bigram[0]]\\r\\n\\treturn transition_bigram_probabilities\",\n \"def get_random_smoothing_matrix(counts, width=3):\\n st = time.time()\\n num_obs = len(counts)\\n s_matrix = []\\n lns = []\\n if type(width) != list:\\n width = [(width, 2*width)]\\n for ni in range(num_obs):\\n for _w, nrs in width:\\n p = np.ones(len(counts))\\n p[ni] = 0\\n if counts[ni] >= _w:\\n region = get_region(ni, counts, 1, p)\\n s_matrix.append(region)\\n for nr in range(nrs):\\n region = get_region(ni, counts, _w, p)\\n s_matrix.append(region)\\n lns.append(len(np.nonzero(region)[0]))\\n for nr in range(nrs):\\n region = get_region(ni, counts, _w, p)\\n s_matrix.append(region)\\n lns.append(len(np.nonzero(region)[0]))\\n S = np.stack(s_matrix, axis=0).astype(np.float32)\\n assert np.alltrue(S.sum(axis=-1)>0) \\n return torch.from_numpy(S)\",\n \"def cmc(distmat, glabels=None, plabels=None, ds=None, repeat=None):\\n m, n = distmat.shape\\n if glabels is None and plabels is None:\\n glabels = np.arange(0, m)\\n plabels = np.arange(0, n)\\n if isinstance(glabels, list):\\n glabels = np.asarray(glabels)\\n if isinstance(plabels, list):\\n plabels = np.asarray(plabels)\\n ug = np.unique(glabels)\\n if ds is None:\\n ds = ug.size\\n if repeat is None:\\n if ds == ug.size and ug.size == len(glabels):\\n repeat = 1\\n else:\\n repeat = 100\\n\\n ret = 0\\n for __ in xrange(repeat):\\n # Randomly select gallery labels\\n G = np.random.choice(ug, ds, replace=False)\\n # Select corresponding probe samples\\n p_inds = [i for i in xrange(len(plabels)) if plabels[i] in G]\\n P = plabels[p_inds]\\n # Randomly select one gallery sample per label selected\\n D = np.zeros((ds, P.size))\\n for i, g in enumerate(G):\\n samples = np.where(glabels == g)[0]\\n j = np.random.choice(samples)\\n D[i, :] = distmat[j, p_inds]\\n # Compute CMC\\n ret += _cmc_core(D, G, P)\\n return ret / repeat\",\n \"def estimateProbabilities(X, C):\\n p = []\\n q = []\\n r = []\\n n = len(X)\\n d = 9 # Dimension of the elements\\n c = 2 # Number of classes\\n for i in range(d):\\n p_i = []\\n q_i = []\\n r_i = []\\n for j in range(c):\\n p_tmp = []\\n q_tmp = []\\n r_tmp = []\\n elements = C[j] # Elements on the training set for class w_j\\n nj = len(C[j])\\n for k in elements:\\n # p_i_j\\n res = X[k][i]*(X[k][i] + 1)*0.5\\n p_tmp.append(res)\\n # q_i_j\\n res = 1 - X[k][i]*X[k][i]\\n q_tmp.append(res)\\n # r_i_j\\n res = X[k][i]*(X[k][i] - 1)*0.5\\n r_tmp.append(res)\\n p_i.append(sum(p_tmp) / float(nj))\\n q_i.append(sum(q_tmp) / float(nj))\\n r_i.append(sum(r_tmp) / float(nj))\\n p.append(p_i)\\n q.append(q_i)\\n r.append(r_i)\\n return (p, q, r)\",\n \"def gen_samp(self, N, M):\\n Sm = np.empty((M, 2))\\n count = 0\\n for i in range(1, M+1, 1):\\n Sm[count, 0] = (i-1)/M\\n Sm[count, 1] = i/M\\n count = count + 1\\n \\n Ns = int(N/M)\\n \\n uni_rand = np.empty((M, Ns))\\n for i in range(0, M):\\n uni_rand[i,:] = np.random.uniform(Sm[i,0], Sm[i,1], Ns)\\n \\n uni_rand = uni_rand.reshape((1, -1))\\n \\n sample = self.dist.ppf(uni_rand)\\n \\n return(sample)\",\n \"def random_transitions(self) -> np.ndarray:\\n transitions = np.zeros((self.n_states, self.n_states))\\n for i, row in enumerate(transitions):\\n row[i:] = self.random_state.dirichlet(np.ones(self.n_states - i))\\n return transitions\",\n \"def call(self, states):\\n dist, mode = self.get_dist_and_mode(states)\\n samples = dist.sample()\\n log_probs = dist.log_prob(samples)\\n log_probs = tf.expand_dims(log_probs, -1) # To avoid broadcasting\\n return mode, samples, log_probs\",\n \"def collapsed_theta_gibbs_sample(self, X):\\n\\n K = self.K # number of topics\\n M, V = X.shape\\n alpha = self.alpha\\n lmda = self.lmda\\n topics = np.arange(0, K)\\n\\n #initialize everything uniformly\\n Beta = np.ones(shape=(K, V), dtype=float) / V\\n for k in topics:\\n Beta[k] = np.random.dirichlet(np.ones(V)*lmda)\\n\\n props = np.zeros(shape=(M, K), dtype=float)\\n #Current state\\n Ns = np.array(range(M), dtype=object)\\n\\n #Running sum\\n MC_z = np.array(range(M), dtype=object)\\n MC_beta = np.zeros(shape=(K, V), dtype=float)\\n\\n for d in range(M):\\n #allocate topics randomly\\n word_indices = X[d, :].nonzero()[1]\\n p = np.random.dirichlet(np.ones(K) * alpha)\\n #TODO: Remove the p argument to get completely random topic assignments\\n random_ks = np.random.choice(topics, size=len(word_indices), p=p)\\n Ns[d] = sp.coo_matrix((np.ones(len(word_indices)),\\n (word_indices, random_ks)), shape=(V, K)).tolil()\\n MC_z[d] = sp.coo_matrix((V, K)).tolil()\\n\\n log_Xs = []\\n perplexities =[]\\n t = 0\\n for epoch in xrange(self.nr_em_epochs):\\n t+=1\\n C = np.zeros((K, V))\\n print \\\"Epoch\\\", epoch\\n for d in np.random.permutation(np.arange(M)):\\n x = X[d]\\n N_d = Ns[d]\\n\\n for v in np.nonzero(x)[1]:\\n except_v = [v_prime for v_prime in np.nonzero(x)[1] if v_prime != v]\\n N_d_except_v = N_d[except_v, :]\\n old_z_n = N_d[v, :].nonzero()[1][0]\\n p = (np.sum(N_d_except_v.A, axis=0) + alpha) * Beta[:, v]\\n p = p.clip(min=0)\\n p = p/np.sum(p)\\n z_n = np.random.choice(topics, p = p)\\n N_d[v, old_z_n] = 0\\n N_d[v, z_n] = 1\\n Ns[d] = N_d\\n MC_z[d] += N_d\\n C = C + N_d.A.T\\n\\n # Sample beta given all z and thetas\\n for k in topics:\\n c_beta = C[k, :]\\n Beta[k, :] = np.random.dirichlet(c_beta + lmda)\\n MC_beta += Beta\\n\\n log_X = 0\\n Beta_hat = MC_beta / t\\n for d in range(M):\\n props_d = np.sum(MC_z[d].A, axis=0)\\n props_d /= float(np.sum(props_d))\\n props[d] = props_d\\n ixw = np.nonzero(X[d, :])[1]\\n log_X += np.sum(_doc_probability_from_p_of_z(props_d, Beta_hat[:, ixw]))\\n\\n log_Xs.append(log_X)\\n print log_X\\n perplexities.append(self._perplexity(X, log_X))\\n\\n return props, Beta_hat, log_Xs, perplexities\",\n \"def caculate_prob(self):\\n t_H = self.tree.depth()\\n t_h = 1\\n while(t_h <= t_H):\\n t_hnodes = self.get_h(t_h)\\n t_sum = 0\\n t_hpro = []\\n t_cpro = []\\n for t_n in t_hnodes:\\n t_sum = self.tree.get_node(t_n).data[0] + t_sum\\n t_node = self.tree.get_node(t_n)\\n if t_node.is_leaf():\\n t_node.data.append(0)\\n continue\\n t_childrens = self.tree.children(t_n)\\n t_shang = 0\\n for child in t_childrens:\\n t_shang = t_shang + (child.data[0]/t_node.data[0])*np.log(child.data[0]/t_node.data[0])\\n t_node.data.append(-t_shang)\\n for t_n in t_hnodes:\\n t_node = self.tree.get_node(t_n)\\n t_parentnode = self.tree.parent(t_n)\\n if t_h > 1:\\n t_node.data.append((t_node.data[0] / t_sum) * (t_node.data[0]/t_parentnode.data[0]))\\n t_hpro.append((t_node.data[0]/t_sum) * (t_node.data[0]/t_parentnode.data[0]))\\n else:\\n t_node.data.append((t_node.data[0] / t_sum))\\n t_hpro.append((t_node.data[0] / t_sum))\\n\\n t_cpro.append(t_node.data[1])\\n t_ndata = np.array(t_hpro)\\n mean = np.mean(t_ndata)\\n std = np.std(t_ndata,ddof=1)\\n t_sdata = np.array(t_cpro)\\n mean_s = np.mean(t_sdata)\\n std_s = np.std(t_sdata,ddof=1)\\n for t_n in t_hnodes:\\n t_node = self.tree.get_node(t_n)\\n if(std != 0):\\n t_node.data[2] = (t_node.data[2] - mean)/std\\n else:\\n t_node.data[2] = (t_node.data[2] - mean)\\n if(mean_s == 0 and std_s ==0):\\n t_node.data[1] = -100.0\\n continue\\n t_node.data[1] = (t_node.data[1] - mean_s)/std_s\\n t_h = t_h + 1\",\n \"def sample_propensities(mutated_params: torch.Tensor) -> torch.Tensor:\\n return torch.softmax(mutated_params, -1)\",\n \"def transition_model(corpus, page, damping_factor):\\n\\n probability_distribution = dict()\\n\\n #if page has outgoing links\\n if corpus[page]:\\n\\n for link in corpus:\\n probability_distribution[link] = (1 - damping_factor) / len(corpus)\\n\\n if link in corpus[page]:\\n probability_distribution[link] = probability_distribution[link] + (damping_factor / len(corpus[page]))\\n\\n else:\\n\\n for link in corpus:\\n probability_distribution[link] = 1 / len(corpus)\\n\\n return probability_distribution\",\n \"def compute_transition_matrix(M, triang_distr):\\n prob_self = 0.99\\n \\n A = np.zeros((2*M, 2*M))\\n max_step = len(triang_distr) // 2\\n\\n for i in range(M):\\n if i < max_step:\\n A[i, 0:i+max_step] = prob_self * triang_distr[max_step - i:-1] / np.sum(triang_distr[max_step - i:-1])\\n A[i+M, M:i+M+max_step] = prob_self * triang_distr[max_step - i:-1] / np.sum(triang_distr[max_step - i:-1])\\n\\n if i >= max_step and i < M-max_step:\\n A[i, i-max_step:i+max_step+1] = prob_self * triang_distr\\n A[i+M, (i+M)-max_step:(i+M)+max_step+1] = prob_self * triang_distr\\n\\n if i >= M-max_step:\\n A[i, i-max_step:M] = prob_self * triang_distr[0:max_step - (i-M)] / np.sum(triang_distr[0:max_step - (i-M)])\\n A[i+M, i+M-max_step:2*M] = prob_self * triang_distr[0:max_step - (i - M)] / \\\\\\n np.sum(triang_distr[0:max_step - (i - M)])\\n\\n A[i, i+M] = 1 - prob_self\\n A[i+M, i] = 1 - prob_self\\n \\n return A\",\n \"def wsimenv(pts, dist, pcc=True):\\n\\tN = 100\\n\\tx = np.array(pts)\\n\\tn = len(x)\\n\\n\\trgen = get_random()\\n\\t\\n\\tfor e in xrange(N):\\n\\t\\tr = []\\n\\t\\tprev_dtup = None\\n\\t\\tfor dtup in dist:\\n\\t\\t\\tif prev_dtup == None:\\n\\t\\t\\t\\tlb = 0.0\\n\\t\\t\\telse:\\t\\n\\t\\t\\t\\tlb = prev_dtup[0]\\n\\t\\t\\tub = dtup[0] + lb\\n\\t\\t\\tn1 = int(math.ceil(dtup[0]*n))\\n\\t\\n\\t\\t\\tfor i in xrange(n1):\\n\\t\\t\\t\\trvar = rgen.uniform(lb,ub)\\n\\t\\t\\t\\tr.append(dtup[1].cdf_inv(rvar))\\n\\t\\t\\t\\n\\t\\t\\tprev_dtup = dtup\\n\\t\\t\\n\\t\\tif pcc == True:\\n\\t\\t\\tncc = ccdf(r, issorted=False)\\n\\t\\t\\tplt.loglog(ncc[:,0], ncc[:,1], color='#A0A0A0' , linestyle='steps', linewidth=0.5)\\n\\t\\telse:\\n\\t\\t\\tncc = ecdf(r, issorted=False)\\n\\t\\t\\tplt.plot(ncc[:,0], ncc[:,1], color='#A0A0A0', linestyle='steps', linewidth=0.5)\\n\\t\\t\\n\\tif pcc == True:\\n\\t\\tcc = ccdf(x, issorted=True)\\n\\t\\tplt.loglog(cc[:,0],cc[:,1],'k-',linestyle='steps',linewidth=3)\\n\\telse:\\n\\t\\tcc = ecdf(x, issorted=True)\\n\\t\\tplt.plot(cc[:,0],cc[:,1],'k-',linestyle='steps',linewidth=3)\",\n \"def prob4():\\n#raise NotImplementedError(\\\"Problem 4 Incomplete\\\")\\n h = lambda x : x[0] < -1 and x[1] > 1\\n f = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([0,0]),cov=np.eye(2))\\n g = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([-1,1]),cov=np.eye(2))\\n X = np.random.multivariate_normal(mean=np.array([-1,1]),cov=np.eye(2),size=10000)\\n return 1./10000*np.sum(np.apply_along_axis(h,1,X)*np.apply_along_axis(f,1,X)/np.apply_along_axis(g,1,X))\",\n \"def random_distribution(m):\\n urls = []\\n for i in range(m):\\n p = 'https://en.wikipedia.org/wiki/Special:Random'\\n urls.append(p)\\n return Counter(distribution(urls))\",\n \"def cdf(weights):\\r\\n\\treturn np.cumsum(weights) / sum(weights)\",\n \"def sample_mv_stable_discrete(alpha, weights, points, n):\\n assert weights.shape[0] == points.shape[0], str(weights.shape) + str(points.shape)\\n assert len(weights.shape) == 1\\n assert len(points.shape) == 2\\n for point in points:\\n assert np.abs(norm(point) - 1) < 1e-8\\n m = weights.shape[0]\\n k = points.shape[1]\\n if alpha == 1:\\n X = []\\n for _ in range(n):\\n xs = (\\n weights\\n * (\\n sample_stable(alpha=alpha, beta=1, size=[m])\\n + 2 / np.pi * np.log(weights)\\n )\\n )[..., None] * points\\n X.append(np.sum(xs, axis=0))\\n X = np.array(X)\\n else:\\n X = []\\n for _ in range(n):\\n xs = (\\n weights ** (1 / alpha) * sample_stable(alpha=alpha, beta=1, size=[m])\\n )[..., None] * points\\n X.append(np.sum(xs, axis=0))\\n X = np.array(X)\\n assert X.shape == (n, k)\\n return X\",\n \"def sample_chain(T, dc, pi, ttr):\\n c = np.zeros((T, dc), 'd')\\n # t=0\\n c[0,:] = np.argmax(np.random.multinomial(1, pi, size=(dc)))\\n # t>0\\n for t in range(1,T):\\n for i in range(dc):\\n p = ttr[int(c[t-1,i]), :]\\n tmp = np.random.multinomial(1, p, size=(1))\\n c[t, i] = np.argmax(tmp[0])\\n return c\",\n \"def uniform_start_probs(self) -> np.ndarray:\\n return np.ones(self.n_states) / self.n_states\",\n \"def gen_interpolation_coeff(self):\\n\\n L_counts = [0 for x in range(self.N)]\\n ngram_freqs = [self.get_ngram_freq(n) for n in range(0,self.N + 1)]\\n\\n for ngram in self.freq_dist:\\n\\n c_at_n = [0 for x in range(self.N)]\\n p_at_n = [0 for x in range(self.N)]\\n \\n for n_len in range(1, self.N + 1):\\n\\n nom_ngram = ngram[len(ngram) - n_len:]\\n denom_ngram = nom_ngram[:-1]\\n\\n nom_freq = ngram_freqs[n_len]\\n denom_freq = ngram_freqs[n_len - 1 ]\\n\\n nom_count = nom_freq[nom_ngram] - 1\\n denom_count = denom_freq[denom_ngram] - 1\\n\\n if n_len == 1:\\n denom_count = sum(nom_freq.values()) - 1 \\n \\n p_at_n_len = 0\\n if denom_count:\\n p_at_n_len = nom_count / denom_count\\n\\n c_at_n[n_len - 1] = nom_count + 1\\n p_at_n[n_len - 1] = p_at_n_len \\n\\n p_max = max(p_at_n)\\n for i, p_item in enumerate(p_at_n):\\n if p_max == p_item:\\n L_counts[i] += c_at_n[i]\\n\\n total_L = sum(L_counts)\\n return [L_count_item / total_L for L_count_item in L_counts]\",\n \"def random_table(self):\\n rule_set = []\\n for i in range(self.k ** (2 * self.r + 1) - 1):\\n g = np.random.rand()\\n if g > self.lambda_param:\\n g = 0\\n else:\\n g = np.random.randint(1, self.k)\\n rule_set.append(g)\\n rule_set.append(0)\\n return rule_set\",\n \"def Nmatrix(init_par, alpha, delta, obs, sigma_obs, ccoef, N):\\n\\tparallax, v, sigma_v = init_par[:-4], init_par[-4:-1], init_par[-1] \\n\\tplx_obs, mualpha_obs, mudelta_obs = obs[:, 0], obs[:, 1], obs[:, 2]\\n\\n\\tp, q, r = normalTriad(alpha, delta)\\n\\tmualpha_mod = np.dot(np.transpose(p),v)*parallax/_A\\n\\tmudelta_mod = np.dot(np.transpose(q),v)*parallax/_A\\n\\t\\n\\tplx_mod, mualpha_mod, mudelta_mod = parallax, mualpha_mod, mudelta_mod\\n\\tsigma_plx, sigma_mualpha, sigma_mudelta = np.transpose(sigma_obs)\\n\\ta,like, expo, detD = np.ones(N),np.ones(N),np.ones(N), np.ones(N) \\n\\tC = np.zeros((3,3,N),dtype=np.float64)\\n\\tC[0,0,:],C[1,1,:],C[2,2,:] = sigma_plx**2.,sigma_mualpha**2., sigma_mudelta**2.\\n\\tcorr_coefficient_plx_mualpha, corr_coefficient_plx_mudelta, corr_coefficient_mualpha_mudelta = np.zeros(N), np.zeros(N), np.zeros(N)\\n\\tcorr_coefficient_plx_mualpha[:], corr_coefficient_plx_mudelta[:], corr_coefficient_mualpha_mudelta[:] = ccoef[:, 0], ccoef[:, 1], ccoef[:, 2] \\n\\t\\n\\tC[0,1,:], C[0,2,:] = corr_coefficient_plx_mualpha*sigma_plx*sigma_mualpha, corr_coefficient_plx_mudelta*sigma_plx*sigma_mudelta\\n\\tC[1,0,:], C[1,2,:] = corr_coefficient_plx_mualpha*sigma_plx*sigma_mualpha, corr_coefficient_mualpha_mudelta*sigma_mualpha*sigma_mudelta\\n\\tC[2,0,:], C[2,1,:] = corr_coefficient_plx_mudelta*sigma_plx*sigma_mudelta, corr_coefficient_mualpha_mudelta*sigma_mualpha*sigma_mudelta\\n\\tE = np.zeros((3,3,N),dtype=np.float64)\\n\\tE[1,1,:],E[2,2,:] = (sigma_v**2.)*(parallax/_A)**2., (sigma_v**2.)*(parallax/_A)**2.\\n\\tD,invD = np.zeros((3,3,N),dtype=np.float64),np.zeros((3,3,N),dtype=np.float64)\\n\\tD = np.add(E,C)\\n\\tfor i in range(N):\\n\\t\\tdetD[i] = matrix_det(D[:,:,i]) \\n\\t\\tinvD[:,:,i] = matrix_inv(D[:,:,i])\\n\\t\\t\\n\\ta_c = np.ones((3,N))\\n\\ta_c = [plx_obs - plx_mod, mualpha_obs - mualpha_mod, mudelta_obs-mudelta_mod]\\n\\t\\n\\t\\n\\n\\t\\n\\tcprime_pi, cprime_vx, cprime_vy, cprime_vz, = np.ones((3,N)), np.ones((3,N)), \\\\\\n\\t\\t\\t\\t\\t\\t\\tnp.ones((3,N)), np.ones((3,N)), \\n\\tcprime_pi[0,:] = 1.\\n\\tcprime_pi[1,:] = np.dot(np.transpose(p),v)/_A\\n\\tcprime_pi[2,:] = np.dot(np.transpose(q),v)/_A\\n\\t\\n\\tcprime_vx[0,:] = 0.\\n\\tcprime_vx[1,:] = -np.sin(alpha)*plx_mod/_A \\n\\tcprime_vx[2,:] = -np.sin(delta)*np.cos(alpha)*plx_mod/_A\\n\\n\\t\\n\\tcprime_vy[0,:] = 0.\\n\\tcprime_vy[1,:] = np.cos(alpha)*plx_mod/_A \\n\\tcprime_vy[2,:] = -np.sin(delta)*np.sin(alpha)*plx_mod/_A\\n\\n\\tcprime_vz[0,:] = 0.\\n\\tcprime_vz[1,:] = 0. \\n\\tcprime_vz[2,:] = np.cos(delta)*plx_mod/_A\\n\\n\\tdlnd_dpi, dlnd_dsigmav = np.zeros(N), np.zeros(N)\\n\\tde_dpi, de_dsigmav = np.zeros(N), np.zeros(N)\\n\\t\\n\\n\\t### See formula A.5 \\n\\tde_dpi[:] = ((sigma_v/_A)**2.)*2.*plx_mod[:]\\n\\tde_dsigmav[:] = ((plx_mod[:]/_A)**2.)*2.*sigma_v\\n\\t\\n\\tdlnd_dpi[:] = (invD[1,1,:] + invD[2,2,:])*de_dpi[:] \\n\\tdlnd_dsigmav[:] = (invD[1,1,:] + invD[2,2,:])*de_dsigmav[:]\\n\\t\\n\\t\\n\\t\\n\\t### See formula A.7\\n\\thess = np.zeros((N+4, N+4))\\n\\n\\thess_diag_pi, hess_diag_pi_1, hess_diag_pi_2 = np.zeros(N), np.zeros(N), np.zeros(N)\\n\\thess_diag_pi_1[:] = invD[0, 0, :]*cprime_pi[0, :]*cprime_pi[0, :] + invD[0, 1, :]*cprime_pi[0, :]*cprime_pi[1, :] + invD[0, 2, :]*cprime_pi[0, :]*cprime_pi[2, :] + \\\\\\n\\t\\t\\t invD[1, 0, :]*cprime_pi[1, :]*cprime_pi[0, :] + invD[1, 1, :]*cprime_pi[1, :]*cprime_pi[1, :] + invD[1, 2, :]*cprime_pi[1, :]*cprime_pi[2, :] + \\\\\\n\\t\\t \\t invD[2, 0, :]*cprime_pi[2, :]*cprime_pi[0, :] + invD[2, 1, :]*cprime_pi[2, :]*cprime_pi[1, :] + invD[2, 2, :]*cprime_pi[2, :]*cprime_pi[2, :]\\t\\n\\t\\n\\t\\n\\t#hess_diag_pi_2[:] = np.sum(0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dpi[:]*de_dpi[:]) ### Check if it's with or without sum: without!\\n\\t# So correct formula is below.\\n\\thess_diag_pi_2[:] = (0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dpi[:]*de_dpi[:])\\n\\thess_diag_pi[:] = hess_diag_pi_1[:] + hess_diag_pi_2[:]\\t\\n\\n\\t\\n\\thess_diag_vx, hess_diag_vy, hess_diag_vz, hess_diag_sigmav = np.zeros(N), np.zeros(N), np.zeros(N), np.zeros(N)\\n\\thess_pi_vx, hess_pi_vy, hess_pi_vz, hess_pi_sigmav = np.zeros(N), np.zeros(N), np.zeros(N), np.zeros(N)\\n\\thess_diag_vxi, hess_diag_vyi, hess_diag_vzi = np.zeros(N), np.zeros(N), np.zeros(N)\\n\\t\\n\\thess_diag_vxi[:] = invD[0, 0, :]*cprime_vx[0, :]*cprime_vx[0, :] + invD[0, 1, :]*cprime_vx[0, :]*cprime_vx[1, :] + invD[0, 2, :]*cprime_vx[0, :]*cprime_vx[2, :] + \\\\\\n\\t\\t\\t invD[1, 0, :]*cprime_vx[1, :]*cprime_vx[0, :] + invD[1, 1, :]*cprime_vx[1, :]*cprime_vx[1, :] + invD[1, 2, :]*cprime_vx[1, :]*cprime_vx[2, :] + \\\\\\n\\t\\t\\t invD[2, 0, :]*cprime_vx[2, :]*cprime_vx[0, :] + invD[2, 1, :]*cprime_vx[2, :]*cprime_vx[1, :] + invD[2, 2, :]*cprime_vx[2, :]*cprime_vx[2, :] \\t\\t\\n\\t\\n\\thess_diag_vyi[:] = invD[0, 0, :]*cprime_vy[0, :]*cprime_vy[0, :] + invD[0, 1, :]*cprime_vy[0, :]*cprime_vy[1, :] + invD[0, 2, :]*cprime_vy[0, :]*cprime_vy[2, :] +\\\\\\n\\t\\t\\t invD[1, 0, :]*cprime_vy[1, :]*cprime_vy[0, :] + invD[1, 1, :]*cprime_vy[1, :]*cprime_vy[1, :] + invD[1, 2, :]*cprime_vy[1, :]*cprime_vy[2, :] +\\\\\\n\\t\\t\\t invD[2, 0, :]*cprime_vy[2, :]*cprime_vy[0, :] + invD[2, 1, :]*cprime_vy[2, :]*cprime_vy[1, :] + invD[2, 2, :]*cprime_vy[2, :]*cprime_vy[2, :] \\t\\n\\n\\n\\thess_diag_vzi[:] = invD[0, 0, :]*cprime_vz[0, :]*cprime_vz[0, :] + invD[0, 1, :]*cprime_vz[0, :]*cprime_vz[1, :] + invD[0, 2, :]*cprime_vz[0, :]*cprime_vz[2, :] +\\\\\\n\\t\\t\\t invD[1, 0, :]*cprime_vz[1, :]*cprime_vz[0, :] + invD[1, 1, :]*cprime_vz[1, :]*cprime_vz[1, :] + invD[1, 2, :]*cprime_vz[1, :]*cprime_vz[2, :] +\\\\\\n\\t\\t\\t invD[2, 0, :]*cprime_vz[2, :]*cprime_vz[0, :] + invD[2, 1, :]*cprime_vz[2, :]*cprime_vz[1, :] + invD[2, 2, :]*cprime_vz[2, :]*cprime_vz[2, :] \\t\\t\\n\\t\\n\\n\\thess_pi_vx[:] = invD[0, 0, :]*cprime_pi[0,:]*cprime_vx[0, :] + invD[0, 1, :]*cprime_pi[0,:]*cprime_vx[1, :] + invD[0, 2, :]*cprime_pi[0,:]*cprime_vx[2, :] +\\\\\\n\\t\\t\\tinvD[1, 0, :]*cprime_pi[1,:]*cprime_vx[0, :] + invD[1, 1, :]*cprime_pi[1,:]*cprime_vx[1, :] + invD[1, 2, :]*cprime_pi[1,:]*cprime_vx[2, :] +\\\\\\n\\t\\t\\tinvD[2, 0, :]*cprime_pi[2,:]*cprime_vx[0, :] + invD[2, 1, :]*cprime_pi[2,:]*cprime_vx[1, :] + invD[2, 2, :]*cprime_pi[2,:]*cprime_vx[2, :] \\n\\n\\thess_pi_vy[:] = invD[0, 0, :]*cprime_pi[0,:]*cprime_vy[0, :] + invD[0, 1, :]*cprime_pi[0,:]*cprime_vy[1, :] + invD[0, 2, :]*cprime_pi[0,:]*cprime_vy[2, :] +\\\\\\n\\t\\t\\tinvD[1, 0, :]*cprime_pi[1,:]*cprime_vy[0, :] + invD[1, 1, :]*cprime_pi[1,:]*cprime_vy[1, :] + invD[1, 2, :]*cprime_pi[1,:]*cprime_vy[2, :] +\\\\\\n\\t\\t\\tinvD[2, 0, :]*cprime_pi[2,:]*cprime_vy[0, :] + invD[2, 1, :]*cprime_pi[2,:]*cprime_vy[1, :] + invD[2, 2, :]*cprime_pi[2,:]*cprime_vy[2, :] \\n\\n\\thess_pi_vz[:] = invD[0, 0, :]*cprime_pi[0,:]*cprime_vz[0, :] + invD[0, 1, :]*cprime_pi[0,:]*cprime_vz[1, :] + invD[0, 2, :]*cprime_pi[0,:]*cprime_vz[2, :] +\\\\\\n\\t\\t\\tinvD[1, 0, :]*cprime_pi[1,:]*cprime_vz[0, :] + invD[1, 1, :]*cprime_pi[1,:]*cprime_vz[1, :] + invD[1, 2, :]*cprime_pi[1,:]*cprime_vz[2, :] +\\\\\\n\\t\\t\\tinvD[2, 0, :]*cprime_pi[2,:]*cprime_vz[0, :] + invD[2, 1, :]*cprime_pi[2,:]*cprime_vz[1, :] + invD[2, 2, :]*cprime_pi[2,:]*cprime_vz[2, :] \\n\\n\\t\\t\\t\\t\\t\\t\\n\\thess_diag_vx = np.sum(hess_diag_vxi)\\n\\thess_diag_vy = np.sum(hess_diag_vyi)\\n\\thess_diag_vz = np.sum(hess_diag_vzi)\\t\\n\\t\\n\\thess_diag_sigmav = np.sum(0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dsigmav[:]*de_dsigmav[:])\\n\\thess_pi_sigmav[:] = 0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dpi[:]*de_dsigmav[:] \\n\\n\\thess_diag = np.concatenate((hess_diag_pi, np.array([hess_diag_vx, hess_diag_vy, hess_diag_vz, hess_diag_sigmav])))\\n\\t\\n\\tfor i in range(N+4):\\n\\t\\thess[i, i] = hess_diag[i]\\n\\t\\t\\n\\t\\n\\tfor j in range(N):\\n\\t\\t\\thess[j, -4] = hess_pi_vx[j]\\n\\t\\t\\thess[j, -3] = hess_pi_vy[j]\\n\\t\\t\\thess[j, -2] = hess_pi_vz[j]\\n\\t\\t\\thess[j, -1] = hess_pi_sigmav[j]\\n\\t\\t\\thess[-4, j] = hess_pi_vx[j]\\n\\t\\t\\thess[-3, j] = hess_pi_vy[j] \\n\\t\\t\\thess[-2, j] = hess_pi_vz[j]\\n\\t\\t\\thess[-1, j] = hess_pi_sigmav[j]\\n\\t\\t\\t\\n\\n\\t\\n\\t\\n\\tpart_12, part_13, part_23 = np.zeros(N),np.zeros(N),np.zeros(N)\\n\\tfor ia in range(3):\\n\\t\\tfor ib in range(3):\\n\\t\\t\\tpart_12[:] += invD[ia, ib, :]*cprime_vx[ia, :]*cprime_vy[ib, :] \\n\\t\\t\\tpart_13[:] += invD[ia, ib, :]*cprime_vx[ia, :]*cprime_vz[ib, :] \\n\\t\\t\\tpart_23[:] += invD[ia, ib, :]*cprime_vy[ia, :]*cprime_vz[ib, :] \\t\\t\\t\\t\\n\\n\\thess[-4, -3] = np.sum(part_12)\\n\\thess[-3, -4] = hess[-4, -3]\\n\\t\\n\\thess[-4, -2] = np.sum(part_13)\\n\\thess[-2, -4] = hess[-4, -2]\\n\\n\\thess[-3, -2] = np.sum(part_23)\\n\\thess[-2, -3] = hess[-3, -2]\\n\\n\\t#### I am returning here the matrix Njk, which is defined as -E(H),\\n\\t#### where H is the hessian of the likelihood: therefore to obtain the real hessian, one\\n\\t#### should multiply this by '-1' (see function below.)\\n\\treturn hess ### See eq. 18\",\n \"def sample_pdf(bins,\\n weights,\\n N_importance,\\n det=False,\\n eps=1e-5):\\n N_rays, N_samples_ = weights.shape\\n weights = weights + eps # prevent division by zero (don't do inplace op!)\\n pdf = weights / torch.sum(weights, -1, keepdim=True) # (N_rays, N_samples_)\\n cdf = torch.cumsum(pdf, -1) # (N_rays, N_samples), cumulative distribution function\\n cdf = torch.cat([torch.zeros_like(cdf[:, :1]), cdf], -1) # (N_rays, N_samples_+1)\\n # padded to 0~1 inclusive\\n\\n if det:\\n u = torch.linspace(0, 1, N_importance, device=bins.device)\\n u = u.expand(N_rays, N_importance)\\n else:\\n u = torch.rand(N_rays, N_importance, device=bins.device)\\n u = u.contiguous()\\n\\n inds = torch.searchsorted(cdf, u)\\n below = torch.clamp_min(inds - 1, 0)\\n above = torch.clamp_max(inds, N_samples_)\\n\\n inds_sampled = torch.stack([below, above], -1).view(N_rays, 2 * N_importance)\\n cdf_g = torch.gather(cdf, 1, inds_sampled)\\n cdf_g = cdf_g.view(N_rays, N_importance, 2)\\n bins_g = torch.gather(bins, 1, inds_sampled).view(N_rays, N_importance, 2)\\n\\n denom = cdf_g[..., 1] - cdf_g[..., 0]\\n denom[denom < eps] = 1 # denom equals 0 means a bin has weight 0, in which case it will not be sampled\\n # anyway, therefore any value for it is fine (set to 1 here)\\n\\n samples = bins_g[..., 0] + (u - cdf_g[..., 0]) / denom * (bins_g[..., 1] - bins_g[..., 0])\\n return samples\",\n \"def discrete_rv(p):\\n u = np.random.uniform()\\n cdf = np.cumsum(p)\\n j = np.searchsorted(cdf, u)\\n return j\",\n \"def transition_model(corpus, page, damping_factor):\\n prob_distrib = dict()\\n\\n #Initialising probability distribution to 0 for each page\\n for pg in corpus.keys():\\n prob_distrib[pg] = 0\\n\\n if len(corpus[page]) > 0:\\n #With probability damping_factor, assigning links from the page with equal probability\\n for pg in corpus[page]:\\n prob_distrib[pg] += damping_factor/len(corpus[page])\\n\\n #With probability (1-damping_factor), assigning all pages of the corpus with equal probability\\n for pg in corpus.keys():\\n prob_distrib[pg] += (1-damping_factor)/len(corpus.keys())\\n else:\\n #Assigning all pages of the corpus with equal probability\\n for pg in corpus.keys():\\n prob_distrib[pg] += 1/len(corpus.keys())\\n\\n return prob_distrib\",\n \"def dctmtx(n):\\n x,y = np.meshgrid(range(n), range(n))\\n D = np.sqrt(2.0/n) * np.cos(np.pi * (2*x+1) * y / (2*n))\\n D[0] /= np.sqrt(2)\\n return D\",\n \"def generate_transition_trigram_probabilities(transition_bigram_counts, transition_trigram_counts):\\r\\n\\ttransition_trigram_probabilities = dict()\\r\\n\\tfor tag_trigram in transition_trigram_counts:\\r\\n\\t\\ttransition_trigram_probabilities[tag_trigram] = float(transition_trigram_counts[tag_trigram])/transition_bigram_counts[(tag_trigram[0], tag_trigram[1])]\\r\\n\\treturn transition_trigram_probabilities\",\n \"def test_bp_example():\\n signal = np.array([4, 7, 9, 10, 6, 11, 3])\\n\\n pe = permutation_entropy(signal, 2)\\n\\n assert 0.91 < pe < 0.92 # Should be approx 0.918.\\n\\n pe = permutation_entropy(signal, 3)\\n\\n assert 1.52 < pe < 1.53 # Should be approx 1.522.\",\n \"def make_prog(self):\\r\\n\\r\\n self.cnv.clear()\\r\\n cdf = self.df[self.df.L != 0]\\r\\n c0 = cdf['C0'].value_counts().idxmax()\\r\\n c1 = cdf['C1'].value_counts().idxmax()\\r\\n c2 = cdf['C2'].value_counts().idxmax()\\r\\n c3 = cdf['C3'].value_counts().idxmax()\\r\\n self.cnv.extend([c0, c1, c2, c3])\",\n \"def random_times(p):\\n while True:\\n if sum(p.values()) != 1:\\n raise ValueError('Probabilities must sum to unity')\\n r = random.random()\\n remaining = 1\\n for category, probability in p.items():\\n remaining -= probability\\n if remaining <= r:\\n yield category\\n break\",\n \"def generateMatrix_CN(N, sigma):\\n\\n # Setup the diagonal\\n d = 2*numpy.diag(numpy.ones(N-2)*(1+1./sigma))\\n\\n # Consider Neumann BC\\n d[-1,-1] = 1+2./sigma\\n\\n # Setup upper diagonal\\n ud = numpy.diag(numpy.ones(N-3)*-1, 1)\\n\\n # Setup lower diagonal\\n ld = numpy.diag(numpy.ones(N-3)*-1, -1)\\n\\n A = d + ud + ld\\n\\n return A\",\n \"def stateOccupationProbabilityGeneration(self):\\n self.L = zeros((self.noOfEmmittingStates, self.T))\\n\\n for j in range(self.noOfEmmittingStates):\\n for t in range(self.T):\\n self.L[j,t] = (self.alpha[j+1, t+1] * self.beta[j+1, t+1]) / self.observationLikelihood\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60476565","0.59997904","0.58673024","0.58671576","0.5793469","0.5774345","0.5758262","0.5752579","0.57467324","0.5681701","0.5671376","0.5654487","0.56216985","0.5615002","0.56125927","0.56113505","0.56080693","0.55988497","0.55925566","0.5580419","0.557701","0.557319","0.5565872","0.55467975","0.55245095","0.55189985","0.5509733","0.5506849","0.55057424","0.550238","0.5497177","0.5475643","0.5471904","0.5470075","0.54636484","0.545343","0.54501283","0.5440055","0.54387045","0.543644","0.54349965","0.5433801","0.54139256","0.54018366","0.5385126","0.53809327","0.5377705","0.53762543","0.537327","0.5372894","0.53711045","0.53705597","0.5365332","0.5357622","0.53539735","0.5349078","0.5345001","0.53387934","0.53272176","0.53211945","0.5320871","0.5319629","0.53164333","0.53159076","0.5311638","0.53110427","0.53074133","0.5302417","0.5301168","0.52813864","0.5280677","0.5276026","0.5272755","0.5270418","0.5253412","0.5252176","0.52443343","0.52424455","0.52300775","0.5223063","0.52215344","0.5221034","0.5220137","0.5218715","0.52105343","0.5202796","0.52024436","0.5195759","0.5190681","0.5184936","0.51819646","0.51766133","0.51718795","0.51698995","0.51656747","0.5165179","0.5164617","0.5159195","0.51589984","0.5158703","0.51521873"],"string":"[\n \"0.60476565\",\n \"0.59997904\",\n \"0.58673024\",\n \"0.58671576\",\n \"0.5793469\",\n \"0.5774345\",\n \"0.5758262\",\n \"0.5752579\",\n \"0.57467324\",\n \"0.5681701\",\n \"0.5671376\",\n \"0.5654487\",\n \"0.56216985\",\n \"0.5615002\",\n \"0.56125927\",\n \"0.56113505\",\n \"0.56080693\",\n \"0.55988497\",\n \"0.55925566\",\n \"0.5580419\",\n \"0.557701\",\n \"0.557319\",\n \"0.5565872\",\n \"0.55467975\",\n \"0.55245095\",\n \"0.55189985\",\n \"0.5509733\",\n \"0.5506849\",\n \"0.55057424\",\n \"0.550238\",\n \"0.5497177\",\n \"0.5475643\",\n \"0.5471904\",\n \"0.5470075\",\n \"0.54636484\",\n \"0.545343\",\n \"0.54501283\",\n \"0.5440055\",\n \"0.54387045\",\n \"0.543644\",\n \"0.54349965\",\n \"0.5433801\",\n \"0.54139256\",\n \"0.54018366\",\n \"0.5385126\",\n \"0.53809327\",\n \"0.5377705\",\n \"0.53762543\",\n \"0.537327\",\n \"0.5372894\",\n \"0.53711045\",\n \"0.53705597\",\n \"0.5365332\",\n \"0.5357622\",\n \"0.53539735\",\n \"0.5349078\",\n \"0.5345001\",\n \"0.53387934\",\n \"0.53272176\",\n \"0.53211945\",\n \"0.5320871\",\n \"0.5319629\",\n \"0.53164333\",\n \"0.53159076\",\n \"0.5311638\",\n \"0.53110427\",\n \"0.53074133\",\n \"0.5302417\",\n \"0.5301168\",\n \"0.52813864\",\n \"0.5280677\",\n \"0.5276026\",\n \"0.5272755\",\n \"0.5270418\",\n \"0.5253412\",\n \"0.5252176\",\n \"0.52443343\",\n \"0.52424455\",\n \"0.52300775\",\n \"0.5223063\",\n \"0.52215344\",\n \"0.5221034\",\n \"0.5220137\",\n \"0.5218715\",\n \"0.52105343\",\n \"0.5202796\",\n \"0.52024436\",\n \"0.5195759\",\n \"0.5190681\",\n \"0.5184936\",\n \"0.51819646\",\n \"0.51766133\",\n \"0.51718795\",\n \"0.51698995\",\n \"0.51656747\",\n \"0.5165179\",\n \"0.5164617\",\n \"0.5159195\",\n \"0.51589984\",\n \"0.5158703\",\n \"0.51521873\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":76693,"cells":{"query":{"kind":"string","value":"Return true if this term and other are strictly syntactically equivalent. This would tipically be in the magic method `__eq__`, but we use `__eq__` for a different purpose, namely, to be able to use Python expressions such as \"loc(b1)==table\" in order to construct a FOL atom."},"document":{"kind":"string","value":"def is_syntactically_equal(self, other):\n raise NotImplementedError() # To be subclassed"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __eq__(self, other):\n if other is self:\n return True\n if isinstance(other, CoordFunctionSymb):\n if other.parent() != self.parent():\n return False\n else:\n return bool(other._express == self._express)\n else:\n return bool(self._express == other)","def __eq__(self, other):\n from numpy.linalg import norm\n from numpy import array\n\n # Upcast to an Atom\n othercomp = Atom(other)\n\n # Compare Symbols\n sym1 = self.sym\n sym2 = othercomp.sym\n if sym1 != sym2:\n return False\n\n # Compare position\n pos1 = array(self.get_position())\n pos2 = array(othercomp.get_position())\n\n return norm(pos1 - pos2) < 1e-3","def __eq__(self, other):\n return (\n isinstance(other, self.__class__)\n and len(self) == len(other)\n and all(term in self for term in other)\n )","def __eq__(self, other) -> bool:\n if not isinstance(other, NilpotentOrbit):\n return False\n if self.my_type != other.my_type:\n return False\n if self.lie_rank != other.lie_rank:\n return False\n if self.decorator != other.decorator:\n return False\n return self.my_diagram == other.my_diagram","def __eq__(self, other):\n return (other is self) or (isinstance(other, Expr)\n and self.op == other.op and self.args == other.args)","def __eq__(self, other):\n if isinstance(other, Symbol):\n return self.arg == other.arg\n else:\n return False","def __eq__(self, other):\n if type(other) == type(self):\n if other.triple.relation == self.triple.relation:\n if (other.triple.concept1 == self.triple.concept1 \n and other.triple.concept2 == self.triple.concept2):\n return True\n return False","def __eq__(self,other):\n if type(other)==type(\"\"):\n return self.word == other\n return self.word == other.word","def __flt_eq_trs(self, other):\n if self.tears is None:\n return True\n\n return self.tears == other.tears","def __eq__(self, other: 'LTL'):\n if self.formula == other.formula:\n return True\n implied_a = self >= other\n implied_b = self <= other\n return implied_a and implied_b","def __eq__(self: _TT, other: object) -> bool:\n return self.eq(other) # type: ignore","def __eq__(self, other):\n if self is other:\n return True\n\n if not isinstance(other, Basic):\n return self._do_eq_sympify(other)\n\n # check for pure number expr\n if not (self.is_Number and other.is_Number) and (\n type(self) != type(other)):\n return False\n a, b = self._hashable_content(), other._hashable_content()\n if a != b:\n return False\n # check number *in* an expression\n for a, b in zip(a, b):\n if not isinstance(a, Basic):\n continue\n if a.is_Number and type(a) != type(b):\n return False\n return True","def same_as(self, other):\n return super().__eq__(other)","def eq(self: _TT, other: _TT) -> bool: # pylint: disable=invalid-name\n if type(self) != type(other):\n raise TypeError(\"Types do not match\")\n return self.value_in_units(self.default_prefix) == other.value_in_units(self.default_prefix)","def __eq__(self, other):\n return (type(self) == type(other) and\n self.base_location == other.base_location and\n self.fold_path == other.fold_path and\n self.field == other.field)","def __eq__(self, other):\n if (type(self) != type(other) or\n not isinstance(self, other.__class__)):\n return False\n if (isinstance(self, TokKeyword) and\n isinstance(other, TokKeyword)):\n return self._data.lower() == other._data.lower()\n return self._data == other._data","def is_equal(self, other) -> bool:\n if isinstance(other, numbers.Number):\n return not self.num_variables and bool(self.offset == other)\n # todo: performance\n\n try:\n if callable(other.vartype):\n vartype_eq = all(self.vartype(v) == other.vartype(v) for v in self.variables)\n else:\n vartype_eq = all(self.vartype(v) == other.vartype for v in self.variables)\n\n return (vartype_eq\n and self.shape == other.shape # redundant, fast to check\n and self.offset == other.offset\n and self.linear == other.linear\n and self.adj == other.adj)\n except AttributeError:\n return False","def __eq__(self, other):\r\n\r\n return type(self) == type(other) and self.ttype == other.ttype","def __eq__(self, other: t.Any) -> bool:\n return self._op_bool('__eq__', other)","def is_identical(self, other):\n if self.is_input != other.is_input:\n return False\n\n if self.is_raw() and other.is_raw():\n return True\n if self.is_raw() or other.is_raw():\n return False\n return self.structure.is_identical(other.structure)","def __eq__(self, other):\n\n if type(other) != type(self):\n return False\n if other.description != self.description:\n return False\n if other.func != self.func:\n return False\n return True","def __eq__(self, other):\n if self is other:\n return True\n\n if not (type(self) == type(other)):\n return False\n\n assert self.base == other.base\n\n try:\n rands = self.rands\n except NotImplementedError:\n rands = None\n\n if rands:\n return all(s == o for s, o in zip(self.rands, other.rands))\n else:\n return NotImplemented\n\n return False","def __le__(self, other):\n return isinstance(other, RealPred) \\\n and (self.lemma == '?' or self.lemma == other.lemma) \\\n and (self.pos in ('?', 'u') or self.pos == other.pos) \\\n and (self.sense in ('?', 'unknown') or self.sense == other.sense)","def __eq__(self, other: Any) -> bool:\n return (\n super().__eq__(other) and isinstance(other, AtomicField) and self._spark_data_type == other._spark_data_type\n )","def _do_eq_sympify(self, other):\n for superclass in type(other).__mro__:\n conv = _external_converter.get(superclass)\n if conv is not None:\n return self == conv(other)\n if hasattr(other, '_sympy_'):\n return self == other._sympy_()\n return NotImplemented","def __eq__(self, other):\n if isinstance(other, Token):\n return self.text == other.text\n else:\n return self.text == other","def equals(self, other): # -> bool:\n ...","def __eq__(self, other):\n if type(self) is not type(other):\n return False\n if self is other:\n return True\n if len(self.body) != len(other.body):\n return False\n for i in enumerate(len(self.body)):\n if not self.body[i] == other.body[i]:\n return False\n return True","def eq(self, other: Any) -> bool:\n # TODO: Rasswanth: Fix later after the comparison operation\n # relative\n # from .... import Tensor\n\n # if (\n # isinstance(self.child, Tensor)\n # and isinstance(other.child, Tensor)\n # and (self.child != other.child).child.any() # type: ignore\n # ):\n # return False\n\n # if (\n # isinstance(self.child, np.ndarray)\n # and isinstance(other.child, np.ndarray)\n # and (self.child != other.child).any()\n # ):\n # return False\n\n # if self.rank != other.rank:\n # return False\n\n # if self.ring_size != other.ring_size:\n # return False\n\n # if self.nr_parties != other.nr_parties:\n # return False\n\n # return True\n\n # ATTENTION: Why are we getting here now when we never did before?\n if not hasattr(other, \"child\"):\n return self.child == other\n\n return self.child == other.child","def __flt_eq_pos(self, other):\n if self.position is None:\n return True\n\n return (\n self.position == other.position\n and self.is_lean == other.is_lean\n )","def __eq__(self, other):\n try:\n return super().__eq__(other) and self.body == other.body\n except AttributeError:\n return False","def __eq__(self, other: object) -> bool:\n if not isinstance(other, Z2Symmetries):\n return False\n\n return (\n self.symmetries == other.symmetries\n and self.sq_paulis == other.sq_paulis\n and self.sq_list == other.sq_list\n and self.tapering_values == other.tapering_values\n )","def __eq__(self, other):\n if not issubclass(other.__class__, Location):\n return False\n considerations = [\n self.i1 == other.i1,\n self.i2 == other.i2,\n self.axial == other.axial,\n self.label == other.label,\n ]\n return all(considerations)","def is_consistent(self, other):\n return self.name != other.name or self.type is other.type","def is_equal(self, other):\n return (other.__class__ == self.__class__\n and other.subscript == self.subscript\n and other.swept_inames == self.swept_inames)","def __eq__(self, other):\n eq = True\n for attr in ['geocode',\n 'geocodeDict',\n 'geolevel',\n 'parentGeocode',\n 'raw',\n 'raw_housing',\n 'dp',\n 'syn',\n 'syn_unrounded',\n # 'cons',\n # 'invar',\n # 'dp_queries',\n # 'congDistGeocode',\n # 'sldlGeocode',\n # 'slduGeocode',\n ]:\n\n eq = eq and self.__getattribute__(attr) == other.__getattribute__(attr)\n\n #eq = eq and (np.array_equal(self.raw.toDense(), other.raw.toDense()))\n return eq","def __eq__(self, other):\n\n try:\n return self.raw_field == other.raw_field\n except AttributeError:\n return False","def __eq__(self, other) -> bool:\n return (\n self._start is other._start\n and self._end is other._end\n and self._label == other._label\n and self._has_direction is other._has_direction\n )","def __eq__(self: 'RegexTree', other: object) -> bool:\n return (isinstance(other, RegexTree) and\n self.symbol == other.symbol and\n self.children == other.children)","def __eq__(self, other):\n return isinstance(other, self.__class__) and \\\n self.content == other.content and self.justification == other.justification","def __eq__(self, other):\n\n return(self.cell == other.cell and\n self._lastUsedIteration == other._lastUsedIteration and\n (sorted(self.__synapses, key=lambda x: x._ordinal) ==\n sorted(other.__synapses, key=lambda x: x._ordinal)))","def equals(self, other: InputTransform) -> bool:\n return super().equals(other=other) and all(\n t1 == t2 for t1, t2 in zip(self.values(), other.values())\n )","def __eq__(self, other):\n return (type(self) is type(other) and\n self.line == other.line and\n self.line_num == other.line_num and\n six.text_type(self) == six.text_type(other))","def __eq__(self, other):\n try: \n same_num = (self._num == other._num)\n same_den = (self._den == other._den)\n return same_num and same_den\n except AttributeError:\n return (self == Rational.parse_number(other))","def _is_equal_same_type(self, other):\n return True","def __eq__(self, other):\n # Note that tf doesn't override \"==\" and \"!=\", unlike numpy.\n return tf.math.equal(self._ordinals, other.ordinal())","def __eq__(self, other: object) -> bool:\n if isinstance(other, Lintable):\n return bool(self.name == other.name)\n return False","def __flt_eq_nse(self, other):\n if self.nose is None:\n return True\n\n return self.nose == other.nose","def __eq__(self, other):\n assert isinstance(other, Thm), \"cannot compare Thm with %s\" % str(type(other))\n return set(self.hyps) == set(other.hyps) and self.prop == other.prop","def __eq__(self, other):\n\n if isinstance(other, (int, type(Zero()))):\n if (other == 0) & (self.args == []):\n return True\n elif other == 0:\n return False\n self._check_dyadic(other)\n return set(self.args) == set(other.args)","def equal(self, other):\n return (self == other and\n self._rdfs == other._rdfs and\n self._adfs == other._adfs)","def __eq__(self, other):\n if isinstance(other, Field):\n return self.value == other.value and self.location == other.location and self.container_length == other.container_length and self.overflow_to_supplement == other.overflow_to_supplement and self.part == other.part and self.question == other.question\n return False","def equivalent(self, other):\n return id(self) == id(other)","def __eq__(self, other: object) -> bool:\n if not isinstance(other, TextDocument):\n return NotImplemented\n if not self._string and not self._lines:\n return not other._string and not other._lines\n return self.lines == other.lines","def __eq__(self, other):\n return (type(other) == type(self)\n and self.using_default_schema == other.using_default_schema\n and self.settings == other.settings\n and self.schema == other.schema\n and self.comment == other.comment)","def __eq__(self, other) -> bool:\n return str(self) == str(other)","def __eq__(self, other: Any) -> bool:\n # Subclasses should call this as part of their equality checks\n return (\n isinstance(other, BaseField)\n and self._is_nullable == other._is_nullable\n and self._resolve_field_name() == other._resolve_field_name() # may be None == None\n and self._spark_type_class == other._spark_type_class\n and self._metadata == other._metadata # may be None == None\n )","def __eq__(self, other):\n if not isinstance(other, Expression):\n return False\n\n return self.evaluate() == other.evaluate()","def __eq__(self, other):\n for (ngram, value) in self.items():\n if ngram not in other or other[ngram] != value:\n return False\n for (ngram, value) in other.items():\n if ngram not in self or self[ngram] != value:\n return False\n return True","def __eq__(self, rhs):\n return (\n (self.name == rhs.name)\n and (self.args == rhs.args)\n and (self.varargs == rhs.varargs)\n and (self.keywords == rhs.keywords)\n )","def is_exact_correspondence(self, other):\n match = True\n if self.multistate != other.multistate:\n return False\n if self.multistate and other.multistate:\n xf1 = self.fundamental_states\n xf2 = other.fundamental_states\n if len(xf1) != len(xf2):\n match = False\n else:\n f1 = set(xf1)\n f2 = set(xf2)\n for m1 in f1:\n member_match = False\n for m2 in f2:\n if m1.is_exact_correspondence(m2):\n member_match = True\n f2.remove(m2)\n break\n if not member_match:\n match = False\n break\n if match:\n f1 = set(xf1)\n f2 = set(xf2)\n for m2 in f2:\n member_match = False\n for m1 in f1:\n if m1.is_exact_correspondence(m2):\n f1.remove(m1)\n member_match = True\n break\n if not member_match:\n match = False\n break\n return match\n else:\n try:\n return self.symbol.upper() == other.symbol.upper()\n except AttributeError:\n return self.symbol == other.symbol","def __eq__(self, other: 'Origin') -> bool:\n if not isinstance(other, self.__class__):\n return False\n return self.__dict__ == other.__dict__","def __eq__(self, other: Any) -> bool:\n if isinstance(other, InputSpec):\n return type_utils.get_canonical_name_for_outer_generic(\n self.type) == type_utils.get_canonical_name_for_outer_generic(\n other.type) and self.default == other.default\n else:\n return False","def __eq__(self, other):\n # check that is Dta and is same version\n if not self.__class__ == other.__class__: return False\n \n # pertinent header info\n if not self._nvar == other._nvar: return False\n if not self._nobs == other._nobs: return False\n if not self._ds_format == other._ds_format: return False\n #if not self._data_label == other._data_label: return False # keep ?\n \n #descriptors\n if not self._typlist == other._typlist: return False\n if not self._varlist == other._varlist: return False\n # Remove check on srtlist? With this here, data[:, :] != data.\n if not self._srtlist == other._srtlist: return False\n if not self._fmtlist == other._fmtlist: return False\n if not self._lbllist == other._lbllist: return False\n \n # variable labels\n if not self._vlblist == other._vlblist: return False\n \n # expansion fields\n if not self._chrdict == other._chrdict: return False\n \n # data\n if not self._varvals == other._varvals: return False\n \n # value labels\n if not self._vallabs == other._vallabs: return False\n \n return True","def __eq__(self, other):\n return (other is not None and\n ((not self.name and not other.name) or\n self.name == other.name) and\n ((not self.expressions and not other.expressions) or\n self.expressions == other.expressions) and\n self.fields == other.fields and\n dict.__eq__(self.attrs or {}, other.attrs or {}))","def __eq__(self, other) -> bool:\n\n return str(self) == str(other)","def __eq__(self, other):\n if not isinstance(other, Encoder):\n return False\n return (\n self.char2index == other.char2index\n and self.index2char == other.index2char\n and self.tokenizer == other.tokenizer\n )","def dummy_eq(self, other, symbol=None):\n s = self.as_dummy()\n o = _sympify(other)\n o = o.as_dummy()\n\n dummy_symbols = [i for i in s.free_symbols if i.is_Dummy]\n\n if len(dummy_symbols) == 1:\n dummy = dummy_symbols.pop()\n else:\n return s == o\n\n if symbol is None:\n symbols = o.free_symbols\n\n if len(symbols) == 1:\n symbol = symbols.pop()\n else:\n return s == o\n\n tmp = dummy.__class__()\n\n return s.xreplace({dummy: tmp}) == o.xreplace({symbol: tmp})","def __eq__(self, other):\n if other is self:\n return True\n if not isinstance(other, PotentialExpression):\n return False\n if not self.is_parametric:\n return (\n self.expression == other.expression\n and self.independent_variables == other.independent_variables\n )\n else:\n return (\n self.expression == other.expression\n and self.independent_variables == other.independent_variables\n and _are_equal_parameters(self.parameters, other.parameters)\n )","def __eq__(self, other):\n # pylint: disable=too-many-boolean-expressions\n if (type(self) is type(other) and\n self._index == other._index and\n self._drives == other._drives and\n self._controls == other._controls and\n self._measures == other._measures and\n self._acquires == other._acquires):\n return True\n return False","def __eq__(self, other: Any) -> bool:\n if isinstance(other, OutputSpec):\n return type_utils.get_canonical_name_for_outer_generic(\n self.type) == type_utils.get_canonical_name_for_outer_generic(\n other.type)\n else:\n return False","def __eq__(self, other):\n return self is other","def __eq__(self, other):\n return self is other","def __eq__(self, other):\n return ZeroaryOperator.__eq__(self, other) and \\\n self.relation_key == other.relation_key","def match( self, other ):\n\n\t\tif isinstance( other, Operand ):\n\t\t\treturn self._name == other._name and self._range == other._range\n\t\tif isinstance( other, ( Value, Pair) ) and self._name != \"l\":\n\t\t\treturn self._range.check( other ) \n\t\tif isinstance( other, ( Label ) ) and self._name == \"l\":\n\t\t\tif other.value():\n\t\t\t\treturn self._range.check( other )\n\t\t\treturn True\n\t\treturn False","def __eq__(self, other):\n return self._dna == other._dna and \\\n self._exons == other._exons","def __eq__(self, other):\n return (self.name == other.name and\n self.attrs == other.attrs and\n self.range_key == other.range_key and\n self.global_indexes == other.global_indexes and\n self.read_throughput == other.read_throughput and\n self.write_throughput == other.write_throughput)","def __eq__(self, other: SymbolicObject) -> bool:\n\n if isinstance(other, ListObject):\n return self._subobjects == other._subobjects\n else:\n return False","def __eq__(self, other):\n other_ref = other\n if isinstance(other, tuple):\n other_ref = self.__class__(*other)\n elif not isinstance(other, IRIReference):\n try:\n other_ref = self.__class__.from_string(other)\n except TypeError:\n raise TypeError(\n 'Unable to compare {0}() to {1}()'.format(\n type(self).__name__, type(other).__name__))\n\n # See http://tools.ietf.org/html/rfc3986#section-6.2\n return tuple(self) == tuple(other_ref)","def evil_hack(self, other):\n if isinstance(other, FExpr):\n return other == self\n return isinstance(other, self.__class__) and self.id == other.id","def equiv(self, other):\n # FUTURE: once the PiplelineState nodes attribute stores multiple modules,\n # we may need to check that the ordering is consistent. Currently,\n # CAST and the AnnnotatedCast nodes only have a single module, so this is not a concern\n for i, node in enumerate(self.nodes):\n if not node.equiv(other.nodes[i]):\n # printing diff to help locating difference\n # because we do not overwrite the __str__() methods, \n # this has limited usefullness, but its better than nothing\n print(f\"AnnCast equiv failed:\")\n self_lines = str(node).splitlines()\n other_lines = str(other.nodes[i]).splitlines()\n for i, diff in enumerate(difflib.ndiff(self_lines, other_lines)):\n if diff[0]==' ': \n continue\n print(f\"Line {i}: {diff}\")\n\n return False\n \n return True","def is_same_as(self, other) -> bool:\n return self.x == other.x and self.y == other.y","def __eq__(self, other):\n return type(other) is type(self) and other._node is self._node","def __eq__(self, other):\n\n if isinstance(other, (int, type(Zero()))):\n if other == 0:\n if self.args == []:\n return True\n else:\n return False\n\n frame = self.args[0][1]\n for v in frame:\n if expand((self - other) & v) != 0:\n return False\n return True","def equals(self, other: InputTransform) -> bool:\n return super().equals(other=other) and (self.reverse == other.reverse)","def equals(self, other: Schema) -> bool:\n if not isinstance(other, Schema):\n raise TypeError(\n f\"invalid equality comparison between Schema and {type(other)}\"\n )\n return self.__cached_equals__(other)","def equals(self, other: Schema) -> bool:\n if not isinstance(other, Schema):\n raise TypeError(\n f\"invalid equality comparison between Schema and {type(other)}\"\n )\n return self.__cached_equals__(other)","def __eq__(self, other):\n\t\ttry:\n\t\t\treturn (np.array_equal(self.val, other.val) and \n\t\t\t\t\tnp.array_equal(self.der, other.der))\n\t\texcept:\n\t\t\t# Compare scalar Vars with derivative 1 to scalars\n\t\t\tif len(self.val) == 1 and np.array_equal(self.der, [1.]):\n\t\t\t\treturn self.val == other\n\t\t\treturn False","def is_compatible(self, other):\n return self.intervals == other.intervals and\\\n self.nonderived_directions == other.nonderived_directions","def __eq__(self, other) -> bool:\n if other is None or not isinstance(other, Graph):\n name = other.name if other else None\n print(f'{name} is not a Graph object.')\n return False\n\n def match(op1: Operator, op2: Operator) -> bool:\n if not op1.equals(op2):\n print(f'{op1.name} is different.')\n return False\n\n # check input nodes and further\n for i1, i2 in zip(op1.input_ops.values(), op2.input_ops.values()):\n if not match(i1, i2):\n return False\n return True\n\n for o1, o2 in zip(self.get_outputs(), other.get_outputs()):\n if not match(o1, o2):\n return False\n return True","def __eq__(self, other: object) -> bool:\n\n if not isinstance(other, self.__class__):\n return False\n\n if not self.simctl_type == other.simctl_type:\n return False\n\n return self.raw_info == other.raw_info","def __eq__(self, other):\n x_eq = self.x == other.x\n y_eq = self.y == other.y\n return x_eq and y_eq","def __eq__(self, other):\n x_eq = self.x == other.x\n y_eq = self.y == other.y\n return x_eq and y_eq","def __eq__(self, other: Segment) -> bool:\n return any(\n (\n self.start == other.start and self.end == other.end,\n self.start == other.end and self.end == other.start,\n )\n )","def __eq__(self, other):\n if not isinstance(other, DocxStyle):\n return False\n\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n \n if not tools.data_are_equal(self.attrs, other.attrs):\n print('here')\n return False\n \n return tools.data_are_equal(self.components, other.components)","def __eq__(self, other):\n if type(self) is not type(other):\n return NotImplemented\n \n # required\n if self.required != other.required:\n return False\n \n # title\n if self.title != other.title:\n return False\n \n # type\n if self.type is not other.type:\n return False\n \n # values\n if self.values != other.values:\n return False\n \n return True","def __eq__(self, other):\r\n if not hasattr(other, 'name') or not hasattr(other, 'attrs') or not hasattr(other, 'contents') or self.name != other.name or self.attrs != other.attrs or len(self) != len(other):\r\n return False\r\n for i in range(0, len(self.contents)):\r\n if self.contents[i] != other.contents[i]:\r\n return False\r\n return True","def __eq__(self, other):\n if isinstance(other, six.string_types):\n return other == self.name\n\n elif isinstance(other, type(self)):\n return self.xnd_dtype == other.xnd_dtype\n\n else:\n return False","def __eq__(\n self: \"HereditaryStratumOrderedStoreList\",\n other: \"HereditaryStratumOrderedStoreList\",\n ) -> bool:\n # adapted from https://stackoverflow.com/a/4522896\n return (\n isinstance(\n other,\n self.__class__,\n )\n and self.__slots__ == other.__slots__\n and all(\n getter(self) == getter(other)\n for getter in [\n operator.attrgetter(attr) for attr in self.__slots__\n ]\n )\n )"],"string":"[\n \"def __eq__(self, other):\\n if other is self:\\n return True\\n if isinstance(other, CoordFunctionSymb):\\n if other.parent() != self.parent():\\n return False\\n else:\\n return bool(other._express == self._express)\\n else:\\n return bool(self._express == other)\",\n \"def __eq__(self, other):\\n from numpy.linalg import norm\\n from numpy import array\\n\\n # Upcast to an Atom\\n othercomp = Atom(other)\\n\\n # Compare Symbols\\n sym1 = self.sym\\n sym2 = othercomp.sym\\n if sym1 != sym2:\\n return False\\n\\n # Compare position\\n pos1 = array(self.get_position())\\n pos2 = array(othercomp.get_position())\\n\\n return norm(pos1 - pos2) < 1e-3\",\n \"def __eq__(self, other):\\n return (\\n isinstance(other, self.__class__)\\n and len(self) == len(other)\\n and all(term in self for term in other)\\n )\",\n \"def __eq__(self, other) -> bool:\\n if not isinstance(other, NilpotentOrbit):\\n return False\\n if self.my_type != other.my_type:\\n return False\\n if self.lie_rank != other.lie_rank:\\n return False\\n if self.decorator != other.decorator:\\n return False\\n return self.my_diagram == other.my_diagram\",\n \"def __eq__(self, other):\\n return (other is self) or (isinstance(other, Expr)\\n and self.op == other.op and self.args == other.args)\",\n \"def __eq__(self, other):\\n if isinstance(other, Symbol):\\n return self.arg == other.arg\\n else:\\n return False\",\n \"def __eq__(self, other):\\n if type(other) == type(self):\\n if other.triple.relation == self.triple.relation:\\n if (other.triple.concept1 == self.triple.concept1 \\n and other.triple.concept2 == self.triple.concept2):\\n return True\\n return False\",\n \"def __eq__(self,other):\\n if type(other)==type(\\\"\\\"):\\n return self.word == other\\n return self.word == other.word\",\n \"def __flt_eq_trs(self, other):\\n if self.tears is None:\\n return True\\n\\n return self.tears == other.tears\",\n \"def __eq__(self, other: 'LTL'):\\n if self.formula == other.formula:\\n return True\\n implied_a = self >= other\\n implied_b = self <= other\\n return implied_a and implied_b\",\n \"def __eq__(self: _TT, other: object) -> bool:\\n return self.eq(other) # type: ignore\",\n \"def __eq__(self, other):\\n if self is other:\\n return True\\n\\n if not isinstance(other, Basic):\\n return self._do_eq_sympify(other)\\n\\n # check for pure number expr\\n if not (self.is_Number and other.is_Number) and (\\n type(self) != type(other)):\\n return False\\n a, b = self._hashable_content(), other._hashable_content()\\n if a != b:\\n return False\\n # check number *in* an expression\\n for a, b in zip(a, b):\\n if not isinstance(a, Basic):\\n continue\\n if a.is_Number and type(a) != type(b):\\n return False\\n return True\",\n \"def same_as(self, other):\\n return super().__eq__(other)\",\n \"def eq(self: _TT, other: _TT) -> bool: # pylint: disable=invalid-name\\n if type(self) != type(other):\\n raise TypeError(\\\"Types do not match\\\")\\n return self.value_in_units(self.default_prefix) == other.value_in_units(self.default_prefix)\",\n \"def __eq__(self, other):\\n return (type(self) == type(other) and\\n self.base_location == other.base_location and\\n self.fold_path == other.fold_path and\\n self.field == other.field)\",\n \"def __eq__(self, other):\\n if (type(self) != type(other) or\\n not isinstance(self, other.__class__)):\\n return False\\n if (isinstance(self, TokKeyword) and\\n isinstance(other, TokKeyword)):\\n return self._data.lower() == other._data.lower()\\n return self._data == other._data\",\n \"def is_equal(self, other) -> bool:\\n if isinstance(other, numbers.Number):\\n return not self.num_variables and bool(self.offset == other)\\n # todo: performance\\n\\n try:\\n if callable(other.vartype):\\n vartype_eq = all(self.vartype(v) == other.vartype(v) for v in self.variables)\\n else:\\n vartype_eq = all(self.vartype(v) == other.vartype for v in self.variables)\\n\\n return (vartype_eq\\n and self.shape == other.shape # redundant, fast to check\\n and self.offset == other.offset\\n and self.linear == other.linear\\n and self.adj == other.adj)\\n except AttributeError:\\n return False\",\n \"def __eq__(self, other):\\r\\n\\r\\n return type(self) == type(other) and self.ttype == other.ttype\",\n \"def __eq__(self, other: t.Any) -> bool:\\n return self._op_bool('__eq__', other)\",\n \"def is_identical(self, other):\\n if self.is_input != other.is_input:\\n return False\\n\\n if self.is_raw() and other.is_raw():\\n return True\\n if self.is_raw() or other.is_raw():\\n return False\\n return self.structure.is_identical(other.structure)\",\n \"def __eq__(self, other):\\n\\n if type(other) != type(self):\\n return False\\n if other.description != self.description:\\n return False\\n if other.func != self.func:\\n return False\\n return True\",\n \"def __eq__(self, other):\\n if self is other:\\n return True\\n\\n if not (type(self) == type(other)):\\n return False\\n\\n assert self.base == other.base\\n\\n try:\\n rands = self.rands\\n except NotImplementedError:\\n rands = None\\n\\n if rands:\\n return all(s == o for s, o in zip(self.rands, other.rands))\\n else:\\n return NotImplemented\\n\\n return False\",\n \"def __le__(self, other):\\n return isinstance(other, RealPred) \\\\\\n and (self.lemma == '?' or self.lemma == other.lemma) \\\\\\n and (self.pos in ('?', 'u') or self.pos == other.pos) \\\\\\n and (self.sense in ('?', 'unknown') or self.sense == other.sense)\",\n \"def __eq__(self, other: Any) -> bool:\\n return (\\n super().__eq__(other) and isinstance(other, AtomicField) and self._spark_data_type == other._spark_data_type\\n )\",\n \"def _do_eq_sympify(self, other):\\n for superclass in type(other).__mro__:\\n conv = _external_converter.get(superclass)\\n if conv is not None:\\n return self == conv(other)\\n if hasattr(other, '_sympy_'):\\n return self == other._sympy_()\\n return NotImplemented\",\n \"def __eq__(self, other):\\n if isinstance(other, Token):\\n return self.text == other.text\\n else:\\n return self.text == other\",\n \"def equals(self, other): # -> bool:\\n ...\",\n \"def __eq__(self, other):\\n if type(self) is not type(other):\\n return False\\n if self is other:\\n return True\\n if len(self.body) != len(other.body):\\n return False\\n for i in enumerate(len(self.body)):\\n if not self.body[i] == other.body[i]:\\n return False\\n return True\",\n \"def eq(self, other: Any) -> bool:\\n # TODO: Rasswanth: Fix later after the comparison operation\\n # relative\\n # from .... import Tensor\\n\\n # if (\\n # isinstance(self.child, Tensor)\\n # and isinstance(other.child, Tensor)\\n # and (self.child != other.child).child.any() # type: ignore\\n # ):\\n # return False\\n\\n # if (\\n # isinstance(self.child, np.ndarray)\\n # and isinstance(other.child, np.ndarray)\\n # and (self.child != other.child).any()\\n # ):\\n # return False\\n\\n # if self.rank != other.rank:\\n # return False\\n\\n # if self.ring_size != other.ring_size:\\n # return False\\n\\n # if self.nr_parties != other.nr_parties:\\n # return False\\n\\n # return True\\n\\n # ATTENTION: Why are we getting here now when we never did before?\\n if not hasattr(other, \\\"child\\\"):\\n return self.child == other\\n\\n return self.child == other.child\",\n \"def __flt_eq_pos(self, other):\\n if self.position is None:\\n return True\\n\\n return (\\n self.position == other.position\\n and self.is_lean == other.is_lean\\n )\",\n \"def __eq__(self, other):\\n try:\\n return super().__eq__(other) and self.body == other.body\\n except AttributeError:\\n return False\",\n \"def __eq__(self, other: object) -> bool:\\n if not isinstance(other, Z2Symmetries):\\n return False\\n\\n return (\\n self.symmetries == other.symmetries\\n and self.sq_paulis == other.sq_paulis\\n and self.sq_list == other.sq_list\\n and self.tapering_values == other.tapering_values\\n )\",\n \"def __eq__(self, other):\\n if not issubclass(other.__class__, Location):\\n return False\\n considerations = [\\n self.i1 == other.i1,\\n self.i2 == other.i2,\\n self.axial == other.axial,\\n self.label == other.label,\\n ]\\n return all(considerations)\",\n \"def is_consistent(self, other):\\n return self.name != other.name or self.type is other.type\",\n \"def is_equal(self, other):\\n return (other.__class__ == self.__class__\\n and other.subscript == self.subscript\\n and other.swept_inames == self.swept_inames)\",\n \"def __eq__(self, other):\\n eq = True\\n for attr in ['geocode',\\n 'geocodeDict',\\n 'geolevel',\\n 'parentGeocode',\\n 'raw',\\n 'raw_housing',\\n 'dp',\\n 'syn',\\n 'syn_unrounded',\\n # 'cons',\\n # 'invar',\\n # 'dp_queries',\\n # 'congDistGeocode',\\n # 'sldlGeocode',\\n # 'slduGeocode',\\n ]:\\n\\n eq = eq and self.__getattribute__(attr) == other.__getattribute__(attr)\\n\\n #eq = eq and (np.array_equal(self.raw.toDense(), other.raw.toDense()))\\n return eq\",\n \"def __eq__(self, other):\\n\\n try:\\n return self.raw_field == other.raw_field\\n except AttributeError:\\n return False\",\n \"def __eq__(self, other) -> bool:\\n return (\\n self._start is other._start\\n and self._end is other._end\\n and self._label == other._label\\n and self._has_direction is other._has_direction\\n )\",\n \"def __eq__(self: 'RegexTree', other: object) -> bool:\\n return (isinstance(other, RegexTree) and\\n self.symbol == other.symbol and\\n self.children == other.children)\",\n \"def __eq__(self, other):\\n return isinstance(other, self.__class__) and \\\\\\n self.content == other.content and self.justification == other.justification\",\n \"def __eq__(self, other):\\n\\n return(self.cell == other.cell and\\n self._lastUsedIteration == other._lastUsedIteration and\\n (sorted(self.__synapses, key=lambda x: x._ordinal) ==\\n sorted(other.__synapses, key=lambda x: x._ordinal)))\",\n \"def equals(self, other: InputTransform) -> bool:\\n return super().equals(other=other) and all(\\n t1 == t2 for t1, t2 in zip(self.values(), other.values())\\n )\",\n \"def __eq__(self, other):\\n return (type(self) is type(other) and\\n self.line == other.line and\\n self.line_num == other.line_num and\\n six.text_type(self) == six.text_type(other))\",\n \"def __eq__(self, other):\\n try: \\n same_num = (self._num == other._num)\\n same_den = (self._den == other._den)\\n return same_num and same_den\\n except AttributeError:\\n return (self == Rational.parse_number(other))\",\n \"def _is_equal_same_type(self, other):\\n return True\",\n \"def __eq__(self, other):\\n # Note that tf doesn't override \\\"==\\\" and \\\"!=\\\", unlike numpy.\\n return tf.math.equal(self._ordinals, other.ordinal())\",\n \"def __eq__(self, other: object) -> bool:\\n if isinstance(other, Lintable):\\n return bool(self.name == other.name)\\n return False\",\n \"def __flt_eq_nse(self, other):\\n if self.nose is None:\\n return True\\n\\n return self.nose == other.nose\",\n \"def __eq__(self, other):\\n assert isinstance(other, Thm), \\\"cannot compare Thm with %s\\\" % str(type(other))\\n return set(self.hyps) == set(other.hyps) and self.prop == other.prop\",\n \"def __eq__(self, other):\\n\\n if isinstance(other, (int, type(Zero()))):\\n if (other == 0) & (self.args == []):\\n return True\\n elif other == 0:\\n return False\\n self._check_dyadic(other)\\n return set(self.args) == set(other.args)\",\n \"def equal(self, other):\\n return (self == other and\\n self._rdfs == other._rdfs and\\n self._adfs == other._adfs)\",\n \"def __eq__(self, other):\\n if isinstance(other, Field):\\n return self.value == other.value and self.location == other.location and self.container_length == other.container_length and self.overflow_to_supplement == other.overflow_to_supplement and self.part == other.part and self.question == other.question\\n return False\",\n \"def equivalent(self, other):\\n return id(self) == id(other)\",\n \"def __eq__(self, other: object) -> bool:\\n if not isinstance(other, TextDocument):\\n return NotImplemented\\n if not self._string and not self._lines:\\n return not other._string and not other._lines\\n return self.lines == other.lines\",\n \"def __eq__(self, other):\\n return (type(other) == type(self)\\n and self.using_default_schema == other.using_default_schema\\n and self.settings == other.settings\\n and self.schema == other.schema\\n and self.comment == other.comment)\",\n \"def __eq__(self, other) -> bool:\\n return str(self) == str(other)\",\n \"def __eq__(self, other: Any) -> bool:\\n # Subclasses should call this as part of their equality checks\\n return (\\n isinstance(other, BaseField)\\n and self._is_nullable == other._is_nullable\\n and self._resolve_field_name() == other._resolve_field_name() # may be None == None\\n and self._spark_type_class == other._spark_type_class\\n and self._metadata == other._metadata # may be None == None\\n )\",\n \"def __eq__(self, other):\\n if not isinstance(other, Expression):\\n return False\\n\\n return self.evaluate() == other.evaluate()\",\n \"def __eq__(self, other):\\n for (ngram, value) in self.items():\\n if ngram not in other or other[ngram] != value:\\n return False\\n for (ngram, value) in other.items():\\n if ngram not in self or self[ngram] != value:\\n return False\\n return True\",\n \"def __eq__(self, rhs):\\n return (\\n (self.name == rhs.name)\\n and (self.args == rhs.args)\\n and (self.varargs == rhs.varargs)\\n and (self.keywords == rhs.keywords)\\n )\",\n \"def is_exact_correspondence(self, other):\\n match = True\\n if self.multistate != other.multistate:\\n return False\\n if self.multistate and other.multistate:\\n xf1 = self.fundamental_states\\n xf2 = other.fundamental_states\\n if len(xf1) != len(xf2):\\n match = False\\n else:\\n f1 = set(xf1)\\n f2 = set(xf2)\\n for m1 in f1:\\n member_match = False\\n for m2 in f2:\\n if m1.is_exact_correspondence(m2):\\n member_match = True\\n f2.remove(m2)\\n break\\n if not member_match:\\n match = False\\n break\\n if match:\\n f1 = set(xf1)\\n f2 = set(xf2)\\n for m2 in f2:\\n member_match = False\\n for m1 in f1:\\n if m1.is_exact_correspondence(m2):\\n f1.remove(m1)\\n member_match = True\\n break\\n if not member_match:\\n match = False\\n break\\n return match\\n else:\\n try:\\n return self.symbol.upper() == other.symbol.upper()\\n except AttributeError:\\n return self.symbol == other.symbol\",\n \"def __eq__(self, other: 'Origin') -> bool:\\n if not isinstance(other, self.__class__):\\n return False\\n return self.__dict__ == other.__dict__\",\n \"def __eq__(self, other: Any) -> bool:\\n if isinstance(other, InputSpec):\\n return type_utils.get_canonical_name_for_outer_generic(\\n self.type) == type_utils.get_canonical_name_for_outer_generic(\\n other.type) and self.default == other.default\\n else:\\n return False\",\n \"def __eq__(self, other):\\n # check that is Dta and is same version\\n if not self.__class__ == other.__class__: return False\\n \\n # pertinent header info\\n if not self._nvar == other._nvar: return False\\n if not self._nobs == other._nobs: return False\\n if not self._ds_format == other._ds_format: return False\\n #if not self._data_label == other._data_label: return False # keep ?\\n \\n #descriptors\\n if not self._typlist == other._typlist: return False\\n if not self._varlist == other._varlist: return False\\n # Remove check on srtlist? With this here, data[:, :] != data.\\n if not self._srtlist == other._srtlist: return False\\n if not self._fmtlist == other._fmtlist: return False\\n if not self._lbllist == other._lbllist: return False\\n \\n # variable labels\\n if not self._vlblist == other._vlblist: return False\\n \\n # expansion fields\\n if not self._chrdict == other._chrdict: return False\\n \\n # data\\n if not self._varvals == other._varvals: return False\\n \\n # value labels\\n if not self._vallabs == other._vallabs: return False\\n \\n return True\",\n \"def __eq__(self, other):\\n return (other is not None and\\n ((not self.name and not other.name) or\\n self.name == other.name) and\\n ((not self.expressions and not other.expressions) or\\n self.expressions == other.expressions) and\\n self.fields == other.fields and\\n dict.__eq__(self.attrs or {}, other.attrs or {}))\",\n \"def __eq__(self, other) -> bool:\\n\\n return str(self) == str(other)\",\n \"def __eq__(self, other):\\n if not isinstance(other, Encoder):\\n return False\\n return (\\n self.char2index == other.char2index\\n and self.index2char == other.index2char\\n and self.tokenizer == other.tokenizer\\n )\",\n \"def dummy_eq(self, other, symbol=None):\\n s = self.as_dummy()\\n o = _sympify(other)\\n o = o.as_dummy()\\n\\n dummy_symbols = [i for i in s.free_symbols if i.is_Dummy]\\n\\n if len(dummy_symbols) == 1:\\n dummy = dummy_symbols.pop()\\n else:\\n return s == o\\n\\n if symbol is None:\\n symbols = o.free_symbols\\n\\n if len(symbols) == 1:\\n symbol = symbols.pop()\\n else:\\n return s == o\\n\\n tmp = dummy.__class__()\\n\\n return s.xreplace({dummy: tmp}) == o.xreplace({symbol: tmp})\",\n \"def __eq__(self, other):\\n if other is self:\\n return True\\n if not isinstance(other, PotentialExpression):\\n return False\\n if not self.is_parametric:\\n return (\\n self.expression == other.expression\\n and self.independent_variables == other.independent_variables\\n )\\n else:\\n return (\\n self.expression == other.expression\\n and self.independent_variables == other.independent_variables\\n and _are_equal_parameters(self.parameters, other.parameters)\\n )\",\n \"def __eq__(self, other):\\n # pylint: disable=too-many-boolean-expressions\\n if (type(self) is type(other) and\\n self._index == other._index and\\n self._drives == other._drives and\\n self._controls == other._controls and\\n self._measures == other._measures and\\n self._acquires == other._acquires):\\n return True\\n return False\",\n \"def __eq__(self, other: Any) -> bool:\\n if isinstance(other, OutputSpec):\\n return type_utils.get_canonical_name_for_outer_generic(\\n self.type) == type_utils.get_canonical_name_for_outer_generic(\\n other.type)\\n else:\\n return False\",\n \"def __eq__(self, other):\\n return self is other\",\n \"def __eq__(self, other):\\n return self is other\",\n \"def __eq__(self, other):\\n return ZeroaryOperator.__eq__(self, other) and \\\\\\n self.relation_key == other.relation_key\",\n \"def match( self, other ):\\n\\n\\t\\tif isinstance( other, Operand ):\\n\\t\\t\\treturn self._name == other._name and self._range == other._range\\n\\t\\tif isinstance( other, ( Value, Pair) ) and self._name != \\\"l\\\":\\n\\t\\t\\treturn self._range.check( other ) \\n\\t\\tif isinstance( other, ( Label ) ) and self._name == \\\"l\\\":\\n\\t\\t\\tif other.value():\\n\\t\\t\\t\\treturn self._range.check( other )\\n\\t\\t\\treturn True\\n\\t\\treturn False\",\n \"def __eq__(self, other):\\n return self._dna == other._dna and \\\\\\n self._exons == other._exons\",\n \"def __eq__(self, other):\\n return (self.name == other.name and\\n self.attrs == other.attrs and\\n self.range_key == other.range_key and\\n self.global_indexes == other.global_indexes and\\n self.read_throughput == other.read_throughput and\\n self.write_throughput == other.write_throughput)\",\n \"def __eq__(self, other: SymbolicObject) -> bool:\\n\\n if isinstance(other, ListObject):\\n return self._subobjects == other._subobjects\\n else:\\n return False\",\n \"def __eq__(self, other):\\n other_ref = other\\n if isinstance(other, tuple):\\n other_ref = self.__class__(*other)\\n elif not isinstance(other, IRIReference):\\n try:\\n other_ref = self.__class__.from_string(other)\\n except TypeError:\\n raise TypeError(\\n 'Unable to compare {0}() to {1}()'.format(\\n type(self).__name__, type(other).__name__))\\n\\n # See http://tools.ietf.org/html/rfc3986#section-6.2\\n return tuple(self) == tuple(other_ref)\",\n \"def evil_hack(self, other):\\n if isinstance(other, FExpr):\\n return other == self\\n return isinstance(other, self.__class__) and self.id == other.id\",\n \"def equiv(self, other):\\n # FUTURE: once the PiplelineState nodes attribute stores multiple modules,\\n # we may need to check that the ordering is consistent. Currently,\\n # CAST and the AnnnotatedCast nodes only have a single module, so this is not a concern\\n for i, node in enumerate(self.nodes):\\n if not node.equiv(other.nodes[i]):\\n # printing diff to help locating difference\\n # because we do not overwrite the __str__() methods, \\n # this has limited usefullness, but its better than nothing\\n print(f\\\"AnnCast equiv failed:\\\")\\n self_lines = str(node).splitlines()\\n other_lines = str(other.nodes[i]).splitlines()\\n for i, diff in enumerate(difflib.ndiff(self_lines, other_lines)):\\n if diff[0]==' ': \\n continue\\n print(f\\\"Line {i}: {diff}\\\")\\n\\n return False\\n \\n return True\",\n \"def is_same_as(self, other) -> bool:\\n return self.x == other.x and self.y == other.y\",\n \"def __eq__(self, other):\\n return type(other) is type(self) and other._node is self._node\",\n \"def __eq__(self, other):\\n\\n if isinstance(other, (int, type(Zero()))):\\n if other == 0:\\n if self.args == []:\\n return True\\n else:\\n return False\\n\\n frame = self.args[0][1]\\n for v in frame:\\n if expand((self - other) & v) != 0:\\n return False\\n return True\",\n \"def equals(self, other: InputTransform) -> bool:\\n return super().equals(other=other) and (self.reverse == other.reverse)\",\n \"def equals(self, other: Schema) -> bool:\\n if not isinstance(other, Schema):\\n raise TypeError(\\n f\\\"invalid equality comparison between Schema and {type(other)}\\\"\\n )\\n return self.__cached_equals__(other)\",\n \"def equals(self, other: Schema) -> bool:\\n if not isinstance(other, Schema):\\n raise TypeError(\\n f\\\"invalid equality comparison between Schema and {type(other)}\\\"\\n )\\n return self.__cached_equals__(other)\",\n \"def __eq__(self, other):\\n\\t\\ttry:\\n\\t\\t\\treturn (np.array_equal(self.val, other.val) and \\n\\t\\t\\t\\t\\tnp.array_equal(self.der, other.der))\\n\\t\\texcept:\\n\\t\\t\\t# Compare scalar Vars with derivative 1 to scalars\\n\\t\\t\\tif len(self.val) == 1 and np.array_equal(self.der, [1.]):\\n\\t\\t\\t\\treturn self.val == other\\n\\t\\t\\treturn False\",\n \"def is_compatible(self, other):\\n return self.intervals == other.intervals and\\\\\\n self.nonderived_directions == other.nonderived_directions\",\n \"def __eq__(self, other) -> bool:\\n if other is None or not isinstance(other, Graph):\\n name = other.name if other else None\\n print(f'{name} is not a Graph object.')\\n return False\\n\\n def match(op1: Operator, op2: Operator) -> bool:\\n if not op1.equals(op2):\\n print(f'{op1.name} is different.')\\n return False\\n\\n # check input nodes and further\\n for i1, i2 in zip(op1.input_ops.values(), op2.input_ops.values()):\\n if not match(i1, i2):\\n return False\\n return True\\n\\n for o1, o2 in zip(self.get_outputs(), other.get_outputs()):\\n if not match(o1, o2):\\n return False\\n return True\",\n \"def __eq__(self, other: object) -> bool:\\n\\n if not isinstance(other, self.__class__):\\n return False\\n\\n if not self.simctl_type == other.simctl_type:\\n return False\\n\\n return self.raw_info == other.raw_info\",\n \"def __eq__(self, other):\\n x_eq = self.x == other.x\\n y_eq = self.y == other.y\\n return x_eq and y_eq\",\n \"def __eq__(self, other):\\n x_eq = self.x == other.x\\n y_eq = self.y == other.y\\n return x_eq and y_eq\",\n \"def __eq__(self, other: Segment) -> bool:\\n return any(\\n (\\n self.start == other.start and self.end == other.end,\\n self.start == other.end and self.end == other.start,\\n )\\n )\",\n \"def __eq__(self, other):\\n if not isinstance(other, DocxStyle):\\n return False\\n\\n return self.__dict__ == other.__dict__\",\n \"def __eq__(self, other):\\n \\n if not tools.data_are_equal(self.attrs, other.attrs):\\n print('here')\\n return False\\n \\n return tools.data_are_equal(self.components, other.components)\",\n \"def __eq__(self, other):\\n if type(self) is not type(other):\\n return NotImplemented\\n \\n # required\\n if self.required != other.required:\\n return False\\n \\n # title\\n if self.title != other.title:\\n return False\\n \\n # type\\n if self.type is not other.type:\\n return False\\n \\n # values\\n if self.values != other.values:\\n return False\\n \\n return True\",\n \"def __eq__(self, other):\\r\\n if not hasattr(other, 'name') or not hasattr(other, 'attrs') or not hasattr(other, 'contents') or self.name != other.name or self.attrs != other.attrs or len(self) != len(other):\\r\\n return False\\r\\n for i in range(0, len(self.contents)):\\r\\n if self.contents[i] != other.contents[i]:\\r\\n return False\\r\\n return True\",\n \"def __eq__(self, other):\\n if isinstance(other, six.string_types):\\n return other == self.name\\n\\n elif isinstance(other, type(self)):\\n return self.xnd_dtype == other.xnd_dtype\\n\\n else:\\n return False\",\n \"def __eq__(\\n self: \\\"HereditaryStratumOrderedStoreList\\\",\\n other: \\\"HereditaryStratumOrderedStoreList\\\",\\n ) -> bool:\\n # adapted from https://stackoverflow.com/a/4522896\\n return (\\n isinstance(\\n other,\\n self.__class__,\\n )\\n and self.__slots__ == other.__slots__\\n and all(\\n getter(self) == getter(other)\\n for getter in [\\n operator.attrgetter(attr) for attr in self.__slots__\\n ]\\n )\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73922414","0.72952706","0.7224389","0.7203774","0.6997577","0.6990958","0.6936724","0.6910764","0.6866715","0.68585664","0.6852991","0.6825722","0.68223184","0.67959416","0.6793662","0.67728335","0.67344457","0.673216","0.667139","0.66642874","0.66628367","0.66381544","0.66282046","0.66058105","0.6605409","0.6578638","0.6567247","0.65664583","0.65585315","0.65572816","0.6556325","0.65535057","0.6520543","0.6516708","0.6508495","0.6504219","0.64896846","0.648553","0.648223","0.6478428","0.6463603","0.64564645","0.64559305","0.64421713","0.6439751","0.6413685","0.6411537","0.64090383","0.64040524","0.63985294","0.63897145","0.63867044","0.63809437","0.6378849","0.63764745","0.6368522","0.63681555","0.6363957","0.63613534","0.6358853","0.6358098","0.6356341","0.635321","0.63504636","0.6347436","0.6345928","0.63370687","0.63339984","0.63295436","0.63227516","0.63214076","0.6315211","0.6315211","0.630736","0.6304088","0.6294956","0.62945074","0.6293444","0.6292427","0.6289194","0.6282266","0.6277499","0.6274498","0.62740946","0.62729263","0.62711173","0.62711173","0.6269735","0.6261636","0.62587047","0.62539583","0.6253143","0.6253143","0.624769","0.6245797","0.6244695","0.6241009","0.6239868","0.6231738","0.6230136"],"string":"[\n \"0.73922414\",\n \"0.72952706\",\n \"0.7224389\",\n \"0.7203774\",\n \"0.6997577\",\n \"0.6990958\",\n \"0.6936724\",\n \"0.6910764\",\n \"0.6866715\",\n \"0.68585664\",\n \"0.6852991\",\n \"0.6825722\",\n \"0.68223184\",\n \"0.67959416\",\n \"0.6793662\",\n \"0.67728335\",\n \"0.67344457\",\n \"0.673216\",\n \"0.667139\",\n \"0.66642874\",\n \"0.66628367\",\n \"0.66381544\",\n \"0.66282046\",\n \"0.66058105\",\n \"0.6605409\",\n \"0.6578638\",\n \"0.6567247\",\n \"0.65664583\",\n \"0.65585315\",\n \"0.65572816\",\n \"0.6556325\",\n \"0.65535057\",\n \"0.6520543\",\n \"0.6516708\",\n \"0.6508495\",\n \"0.6504219\",\n \"0.64896846\",\n \"0.648553\",\n \"0.648223\",\n \"0.6478428\",\n \"0.6463603\",\n \"0.64564645\",\n \"0.64559305\",\n \"0.64421713\",\n \"0.6439751\",\n \"0.6413685\",\n \"0.6411537\",\n \"0.64090383\",\n \"0.64040524\",\n \"0.63985294\",\n \"0.63897145\",\n \"0.63867044\",\n \"0.63809437\",\n \"0.6378849\",\n \"0.63764745\",\n \"0.6368522\",\n \"0.63681555\",\n \"0.6363957\",\n \"0.63613534\",\n \"0.6358853\",\n \"0.6358098\",\n \"0.6356341\",\n \"0.635321\",\n \"0.63504636\",\n \"0.6347436\",\n \"0.6345928\",\n \"0.63370687\",\n \"0.63339984\",\n \"0.63295436\",\n \"0.63227516\",\n \"0.63214076\",\n \"0.6315211\",\n \"0.6315211\",\n \"0.630736\",\n \"0.6304088\",\n \"0.6294956\",\n \"0.62945074\",\n \"0.6293444\",\n \"0.6292427\",\n \"0.6289194\",\n \"0.6282266\",\n \"0.6277499\",\n \"0.6274498\",\n \"0.62740946\",\n \"0.62729263\",\n \"0.62711173\",\n \"0.62711173\",\n \"0.6269735\",\n \"0.6261636\",\n \"0.62587047\",\n \"0.62539583\",\n \"0.6253143\",\n \"0.6253143\",\n \"0.624769\",\n \"0.6245797\",\n \"0.6244695\",\n \"0.6241009\",\n \"0.6239868\",\n \"0.6231738\",\n \"0.6230136\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7148483"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":76694,"cells":{"query":{"kind":"string","value":"Return a hash of the current object. Meant to be used by TermReference objects to wrap Terms appropriately for use within associative containers."},"document":{"kind":"string","value":"def hash(self):\n raise NotImplementedError() # To be subclassed"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __Hash(self):\n return self._Hash()","def hash(self):\n return self.__hash__()","def hash(self):\n return self.hash_by_id(self.id)","def current_hash(self):","def hash(self):\n return hashlib.sha1(str(self._dict))","def get_hash(self):\n return self.__hash","def __hash__(self):\n return hash(tuple(sorted(self.__dict__.items())))","def __hash__(self):\n return hash(tuple(sorted(self.__dict__.items())))","def __hash__(self):\n return hash(tuple(sorted(self.__dict__.items())))","def __hash__(self):\n return hash(tuple(sorted(self.__dict__.items())))","def hash(self):\n return self._hash","def get_hash(self):\n return freeze_dict(self.get_hash_params())","def hash(self):\r\n if not hasattr(self, '_hash'):\r\n self._hash = conf.lib.clang_hashCursor(self)\r\n\r\n return self._hash","def get_hash(self):\r\n return","def __Hash(self):\n return self._Hash()","def get_hash(self):\n if self.contributes:\n return hash_from_values(self.iter_values())","def __hash__(self):\r\n return hash(tuple(sorted(self.__dict__.items())))","def __hash__(self):\n return self.to_hash()","def __hash__(self):\n return hash((self.SYMBOL, self._.hash_parameters))","def hash(self):\n return Hash.dhash(bytes(self))","def histo_hash ( self ) :\n return self.__histo_hash","def histo_hash ( self ) :\n return self.__histo_hash","def histo_hash ( self ) :\n return self.__histo_hash","def __hash__(self):\n return hash(self.hash)","def internal_hash(self): \n return hash(tuple(sorted(self.hashtriples())))","def __hash__(self):\n return hash(repr(self))","def __hash__(self):\n return hash(repr(self))","def __hash__(self):\n return hash(repr(self))","def _Hash(self):\n out = [self.key.string_id()]\n properties = self._PropList()\n for prop in properties:\n out.append(unicode(getattr(self, prop, '')))\n to_hash = ''.join(out)\n return hashlib.md5(to_hash.encode('utf-8')).hexdigest()","def internal_hash(self):\r\n return _TripleCanonicalizer(self).to_hash()","def HashValue(self) -> _n_0_t_3[_n_0_t_9]:","def hash(self):\n return hashlib.sha256(self.to_json().encode()).hexdigest()","def __hash__(self):\n\t\treturn hash(repr(self))","def __hash__(self):\n return hash(self._tp__get_typed_properties())","def __hash__(self):\n return hash(id(self))","def __hash__(self):\n return hash(id(self))","def innerHash(self) -> str:\r\n\r\n return self.__inner_hash","def __hash__(self):\n hash_value = hash(self.tag)\n for attribute in self.attributes:\n hash_value += hash(attribute)\n return hash_value","def __hash__(self):\n return hash(str(self)) # use the __str__ method to obtain the hashcode","def __hash__(self):\n return hash(str(self)) # use the __str__ method to obtain the hashcode","def __hash__(self):\n return hash(str(self)) # use the __str__ method to obtain the hashcode","def __hash__(self):\n return hash(str(self)) # use the __str__ method to obtain the hashcode","def hash(self):\n\n # deepcopy so that the cached definition property is not modified by the deletes below\n d = deepcopy(self.definition)\n\n # omit version\n if \"podpac_version\" in d:\n del d[\"podpac_version\"]\n\n # omit style in every node\n for k in d:\n if \"style\" in d[k]:\n del d[k][\"style\"]\n\n s = json.dumps(d, separators=(\",\", \":\"), cls=JSONEncoder)\n return hash_alg(s.encode(\"utf-8\")).hexdigest()","def hash(self):\n\n # deepcopy so that the cached definition property is not modified by the deletes below\n d = deepcopy(self.definition)\n\n # omit version\n if \"podpac_version\" in d:\n del d[\"podpac_version\"]\n\n # omit style in every node\n for k in d:\n if \"style\" in d[k]:\n del d[k][\"style\"]\n\n s = json.dumps(d, separators=(\",\", \":\"), cls=JSONEncoder)\n return hash_alg(s.encode(\"utf-8\")).hexdigest()","def __hash__(self):\r\n return hash(self.__key())","def __hash__(self):\n return hash(str(self.__id__))","def __hash__(self):\n return hash(str(self))","def __hash__(self):\n return hash(str(self))","def __hash__(self):\n return hash(str(self))","def __hash__(self):\n hash_content = []\n hash_content.extend(self.analyzer_options)\n hash_content.append(str(self.analyzer_type))\n hash_content.append(self.target[self.lang])\n hash_content.append(self.source)\n return hash(''.join(hash_content))","def __hash__(self):\n return hash(self.get_canonical_identifier())","def __hash__(self, reinit=False):\n if not self.hash_value is None and not reinit:\n return self.hash_value\n elif isinstance(self, Leaf):\n self.hash_value = Hash.leaf_hash(self)\n return self.hash_value\n else:\n self.hash_value = Hash.node_hash(self)\n return self.hash_value","def __hash__(self):\n return hash(self.label())","def __hash__(self):\n return id(self)","def __hash__(self):\n return id(self)","def __hash__(self):\n return id(self)","def __hash__(self):\n return hash(self.joined())","def _data_hash(self):\n\n return _short_hash([getattr(self, _) for _ in self._data_attributes])","def __hash__(self):\n return self['id'].__hash__()","def hash_key(self):","def __hash__(self):\n return hash(self.base_location) ^ hash(self.fold_path) ^ hash(self.field)","def __hash__(self):\n # These entities are not cached, so we wont use their `id` if applicable.\n hash_value = 0\n \n # bot\n hash_value ^= hash(self.bot)\n \n # description\n description = self.description\n if (description is not None):\n hash_value ^= hash(description)\n \n # icon\n hash_value ^= hash(self.icon)\n \n # id\n hash_value ^= self.id\n \n # name\n name = self.name\n if (description is None) or (description != name):\n hash_value ^= hash(name)\n \n return hash_value","def hash(self) -> str:\n return pulumi.get(self, \"hash\")","def outerHash(self) -> str:\r\n\r\n return self.__outer_hash","def get_hash(self) -> str:\n if self.call_hash:\n # Derived state from a call_node.\n return hash_struct([\"Handle\", self.fullname, \"call_hash\", self.key, self.call_hash])\n else:\n # Initial state.\n return hash_struct([\"Handle\", self.fullname, \"init\", self.key, self.args, self.kwargs])","def getHash( self ):\n return self.stats.copy()","def __hash__(self):\n return super().__hash__()","def aggregate_hash(self):\n return self._aggregate_hash","def __hash__(self):\n\n return hash((str(self.type) + str(self.value)))","def __hash__(self):\n return hash(('genes', tuple(self.genes), self.environment))","def __hash__( self ):\n return hash( self.data )","def _trained_hash(self):\n\n if not self._trained: return None\n return _short_hash([getattr(self, _) for _ in self._trained_attributes])","def __hash__(self):\n return self.fam.c_hash(self)","def __hash__(self):\n\n return int(self._hash_value_)","def get_hash(self) -> str:\n return self.__hash.hexdigest()","def extended_hash(self):\n return self._extended_hash","def compute_hash(self):\n block_string = json.dumps(self.__dict__, sort_keys=True)\n return sha256(block_string.encode()).hexdigest()","def compute_hash(self):\n block_string = json.dumps(self.__dict__, sort_keys=True)\n return sha256(block_string.encode()).hexdigest()","def hash(self) -> bytes:","def getHash(self):\n if self.chash:\n return self.chash\n else:\n self.setHash()\n return self.chash","def __hash__(self):\n\n return hash(self._key)","def hashCode(self):\n if self.hashCode == 0:\n self.hashCode = toString().hashCode()\n return self.hashCode","def __hash__(self) -> int:\n return hash(repr(self))","def __hash__(self):\n return hash(str(self.key))","def __hash__(self):\n return hash(str(self.key))","def __hash__(self):\n hashable = tuple(self.pandas_object.values.tobytes())\n if isinstance(self.pandas_object, pd.DataFrame):\n hashable += tuple(self.pandas_object.columns)\n else:\n hashable += tuple(self.pandas_object.name)\n return hash(hashable)","def __hash__(self):\n return self._id","def __hash__(self) -> int:\n return hash(self._hashable_content())","def __hash__(self):\n return hash(self.name)","def __hash__(self):\n return hash(self.name)","def __hash__(self):\n return hash(self.text)","def get_hash(self, composition):\n return","def __hash__(self) -> int:\n return hash(self.__key__())","def __hash__(self):\n return hash(self.get_all_features())","def __hash__(self):\n return self.word.__hash__()","def __hash__(self):\n return hash((self._start, self._end, self._name, self._value))","def __hash__(self):\n return hash(\n (tuple(\n self.vector), self.index, tuple(\n self.labels), self.label))","def hash_string(self):\n return self._hash_string","def __hash__(self):\n return hash(self.description)","def hash_store(self):\n json_store = json.dumps(self.store, sort_keys=True).encode()\n return hashlib.sha256(json_store).hexdigest()"],"string":"[\n \"def __Hash(self):\\n return self._Hash()\",\n \"def hash(self):\\n return self.__hash__()\",\n \"def hash(self):\\n return self.hash_by_id(self.id)\",\n \"def current_hash(self):\",\n \"def hash(self):\\n return hashlib.sha1(str(self._dict))\",\n \"def get_hash(self):\\n return self.__hash\",\n \"def __hash__(self):\\n return hash(tuple(sorted(self.__dict__.items())))\",\n \"def __hash__(self):\\n return hash(tuple(sorted(self.__dict__.items())))\",\n \"def __hash__(self):\\n return hash(tuple(sorted(self.__dict__.items())))\",\n \"def __hash__(self):\\n return hash(tuple(sorted(self.__dict__.items())))\",\n \"def hash(self):\\n return self._hash\",\n \"def get_hash(self):\\n return freeze_dict(self.get_hash_params())\",\n \"def hash(self):\\r\\n if not hasattr(self, '_hash'):\\r\\n self._hash = conf.lib.clang_hashCursor(self)\\r\\n\\r\\n return self._hash\",\n \"def get_hash(self):\\r\\n return\",\n \"def __Hash(self):\\n return self._Hash()\",\n \"def get_hash(self):\\n if self.contributes:\\n return hash_from_values(self.iter_values())\",\n \"def __hash__(self):\\r\\n return hash(tuple(sorted(self.__dict__.items())))\",\n \"def __hash__(self):\\n return self.to_hash()\",\n \"def __hash__(self):\\n return hash((self.SYMBOL, self._.hash_parameters))\",\n \"def hash(self):\\n return Hash.dhash(bytes(self))\",\n \"def histo_hash ( self ) :\\n return self.__histo_hash\",\n \"def histo_hash ( self ) :\\n return self.__histo_hash\",\n \"def histo_hash ( self ) :\\n return self.__histo_hash\",\n \"def __hash__(self):\\n return hash(self.hash)\",\n \"def internal_hash(self): \\n return hash(tuple(sorted(self.hashtriples())))\",\n \"def __hash__(self):\\n return hash(repr(self))\",\n \"def __hash__(self):\\n return hash(repr(self))\",\n \"def __hash__(self):\\n return hash(repr(self))\",\n \"def _Hash(self):\\n out = [self.key.string_id()]\\n properties = self._PropList()\\n for prop in properties:\\n out.append(unicode(getattr(self, prop, '')))\\n to_hash = ''.join(out)\\n return hashlib.md5(to_hash.encode('utf-8')).hexdigest()\",\n \"def internal_hash(self):\\r\\n return _TripleCanonicalizer(self).to_hash()\",\n \"def HashValue(self) -> _n_0_t_3[_n_0_t_9]:\",\n \"def hash(self):\\n return hashlib.sha256(self.to_json().encode()).hexdigest()\",\n \"def __hash__(self):\\n\\t\\treturn hash(repr(self))\",\n \"def __hash__(self):\\n return hash(self._tp__get_typed_properties())\",\n \"def __hash__(self):\\n return hash(id(self))\",\n \"def __hash__(self):\\n return hash(id(self))\",\n \"def innerHash(self) -> str:\\r\\n\\r\\n return self.__inner_hash\",\n \"def __hash__(self):\\n hash_value = hash(self.tag)\\n for attribute in self.attributes:\\n hash_value += hash(attribute)\\n return hash_value\",\n \"def __hash__(self):\\n return hash(str(self)) # use the __str__ method to obtain the hashcode\",\n \"def __hash__(self):\\n return hash(str(self)) # use the __str__ method to obtain the hashcode\",\n \"def __hash__(self):\\n return hash(str(self)) # use the __str__ method to obtain the hashcode\",\n \"def __hash__(self):\\n return hash(str(self)) # use the __str__ method to obtain the hashcode\",\n \"def hash(self):\\n\\n # deepcopy so that the cached definition property is not modified by the deletes below\\n d = deepcopy(self.definition)\\n\\n # omit version\\n if \\\"podpac_version\\\" in d:\\n del d[\\\"podpac_version\\\"]\\n\\n # omit style in every node\\n for k in d:\\n if \\\"style\\\" in d[k]:\\n del d[k][\\\"style\\\"]\\n\\n s = json.dumps(d, separators=(\\\",\\\", \\\":\\\"), cls=JSONEncoder)\\n return hash_alg(s.encode(\\\"utf-8\\\")).hexdigest()\",\n \"def hash(self):\\n\\n # deepcopy so that the cached definition property is not modified by the deletes below\\n d = deepcopy(self.definition)\\n\\n # omit version\\n if \\\"podpac_version\\\" in d:\\n del d[\\\"podpac_version\\\"]\\n\\n # omit style in every node\\n for k in d:\\n if \\\"style\\\" in d[k]:\\n del d[k][\\\"style\\\"]\\n\\n s = json.dumps(d, separators=(\\\",\\\", \\\":\\\"), cls=JSONEncoder)\\n return hash_alg(s.encode(\\\"utf-8\\\")).hexdigest()\",\n \"def __hash__(self):\\r\\n return hash(self.__key())\",\n \"def __hash__(self):\\n return hash(str(self.__id__))\",\n \"def __hash__(self):\\n return hash(str(self))\",\n \"def __hash__(self):\\n return hash(str(self))\",\n \"def __hash__(self):\\n return hash(str(self))\",\n \"def __hash__(self):\\n hash_content = []\\n hash_content.extend(self.analyzer_options)\\n hash_content.append(str(self.analyzer_type))\\n hash_content.append(self.target[self.lang])\\n hash_content.append(self.source)\\n return hash(''.join(hash_content))\",\n \"def __hash__(self):\\n return hash(self.get_canonical_identifier())\",\n \"def __hash__(self, reinit=False):\\n if not self.hash_value is None and not reinit:\\n return self.hash_value\\n elif isinstance(self, Leaf):\\n self.hash_value = Hash.leaf_hash(self)\\n return self.hash_value\\n else:\\n self.hash_value = Hash.node_hash(self)\\n return self.hash_value\",\n \"def __hash__(self):\\n return hash(self.label())\",\n \"def __hash__(self):\\n return id(self)\",\n \"def __hash__(self):\\n return id(self)\",\n \"def __hash__(self):\\n return id(self)\",\n \"def __hash__(self):\\n return hash(self.joined())\",\n \"def _data_hash(self):\\n\\n return _short_hash([getattr(self, _) for _ in self._data_attributes])\",\n \"def __hash__(self):\\n return self['id'].__hash__()\",\n \"def hash_key(self):\",\n \"def __hash__(self):\\n return hash(self.base_location) ^ hash(self.fold_path) ^ hash(self.field)\",\n \"def __hash__(self):\\n # These entities are not cached, so we wont use their `id` if applicable.\\n hash_value = 0\\n \\n # bot\\n hash_value ^= hash(self.bot)\\n \\n # description\\n description = self.description\\n if (description is not None):\\n hash_value ^= hash(description)\\n \\n # icon\\n hash_value ^= hash(self.icon)\\n \\n # id\\n hash_value ^= self.id\\n \\n # name\\n name = self.name\\n if (description is None) or (description != name):\\n hash_value ^= hash(name)\\n \\n return hash_value\",\n \"def hash(self) -> str:\\n return pulumi.get(self, \\\"hash\\\")\",\n \"def outerHash(self) -> str:\\r\\n\\r\\n return self.__outer_hash\",\n \"def get_hash(self) -> str:\\n if self.call_hash:\\n # Derived state from a call_node.\\n return hash_struct([\\\"Handle\\\", self.fullname, \\\"call_hash\\\", self.key, self.call_hash])\\n else:\\n # Initial state.\\n return hash_struct([\\\"Handle\\\", self.fullname, \\\"init\\\", self.key, self.args, self.kwargs])\",\n \"def getHash( self ):\\n return self.stats.copy()\",\n \"def __hash__(self):\\n return super().__hash__()\",\n \"def aggregate_hash(self):\\n return self._aggregate_hash\",\n \"def __hash__(self):\\n\\n return hash((str(self.type) + str(self.value)))\",\n \"def __hash__(self):\\n return hash(('genes', tuple(self.genes), self.environment))\",\n \"def __hash__( self ):\\n return hash( self.data )\",\n \"def _trained_hash(self):\\n\\n if not self._trained: return None\\n return _short_hash([getattr(self, _) for _ in self._trained_attributes])\",\n \"def __hash__(self):\\n return self.fam.c_hash(self)\",\n \"def __hash__(self):\\n\\n return int(self._hash_value_)\",\n \"def get_hash(self) -> str:\\n return self.__hash.hexdigest()\",\n \"def extended_hash(self):\\n return self._extended_hash\",\n \"def compute_hash(self):\\n block_string = json.dumps(self.__dict__, sort_keys=True)\\n return sha256(block_string.encode()).hexdigest()\",\n \"def compute_hash(self):\\n block_string = json.dumps(self.__dict__, sort_keys=True)\\n return sha256(block_string.encode()).hexdigest()\",\n \"def hash(self) -> bytes:\",\n \"def getHash(self):\\n if self.chash:\\n return self.chash\\n else:\\n self.setHash()\\n return self.chash\",\n \"def __hash__(self):\\n\\n return hash(self._key)\",\n \"def hashCode(self):\\n if self.hashCode == 0:\\n self.hashCode = toString().hashCode()\\n return self.hashCode\",\n \"def __hash__(self) -> int:\\n return hash(repr(self))\",\n \"def __hash__(self):\\n return hash(str(self.key))\",\n \"def __hash__(self):\\n return hash(str(self.key))\",\n \"def __hash__(self):\\n hashable = tuple(self.pandas_object.values.tobytes())\\n if isinstance(self.pandas_object, pd.DataFrame):\\n hashable += tuple(self.pandas_object.columns)\\n else:\\n hashable += tuple(self.pandas_object.name)\\n return hash(hashable)\",\n \"def __hash__(self):\\n return self._id\",\n \"def __hash__(self) -> int:\\n return hash(self._hashable_content())\",\n \"def __hash__(self):\\n return hash(self.name)\",\n \"def __hash__(self):\\n return hash(self.name)\",\n \"def __hash__(self):\\n return hash(self.text)\",\n \"def get_hash(self, composition):\\n return\",\n \"def __hash__(self) -> int:\\n return hash(self.__key__())\",\n \"def __hash__(self):\\n return hash(self.get_all_features())\",\n \"def __hash__(self):\\n return self.word.__hash__()\",\n \"def __hash__(self):\\n return hash((self._start, self._end, self._name, self._value))\",\n \"def __hash__(self):\\n return hash(\\n (tuple(\\n self.vector), self.index, tuple(\\n self.labels), self.label))\",\n \"def hash_string(self):\\n return self._hash_string\",\n \"def __hash__(self):\\n return hash(self.description)\",\n \"def hash_store(self):\\n json_store = json.dumps(self.store, sort_keys=True).encode()\\n return hashlib.sha256(json_store).hexdigest()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7544915","0.745015","0.74020696","0.7351971","0.73146653","0.7294813","0.7268655","0.7268655","0.7268655","0.7268655","0.726741","0.7251138","0.7248584","0.72445667","0.7236863","0.7205611","0.7174264","0.7149143","0.7121638","0.7120802","0.71172434","0.71172434","0.71172434","0.7115283","0.7114491","0.7106335","0.7106335","0.7106335","0.7105949","0.71011025","0.7073844","0.7070362","0.7022083","0.7005162","0.69951344","0.69951344","0.698986","0.69817185","0.6975818","0.6975818","0.6975818","0.6975818","0.6947322","0.6947322","0.69465834","0.69434685","0.692129","0.692129","0.692129","0.6888264","0.6877115","0.68755054","0.68637514","0.6853142","0.6853142","0.6853142","0.680985","0.680507","0.68046963","0.67965704","0.6783542","0.6763704","0.6757547","0.67466074","0.6737671","0.6736573","0.67327833","0.67295885","0.6729424","0.67160565","0.67147905","0.6706924","0.66958284","0.66925186","0.6686643","0.6684484","0.6677501","0.6677501","0.6677283","0.66709733","0.66697025","0.66616106","0.66497535","0.66435736","0.66435736","0.6641359","0.6638794","0.66273355","0.66177475","0.66177475","0.6580109","0.65680754","0.65628827","0.6561489","0.6560711","0.6543718","0.65316683","0.6525215","0.6524385","0.6524178"],"string":"[\n \"0.7544915\",\n \"0.745015\",\n \"0.74020696\",\n \"0.7351971\",\n \"0.73146653\",\n \"0.7294813\",\n \"0.7268655\",\n \"0.7268655\",\n \"0.7268655\",\n \"0.7268655\",\n \"0.726741\",\n \"0.7251138\",\n \"0.7248584\",\n \"0.72445667\",\n \"0.7236863\",\n \"0.7205611\",\n \"0.7174264\",\n \"0.7149143\",\n \"0.7121638\",\n \"0.7120802\",\n \"0.71172434\",\n \"0.71172434\",\n \"0.71172434\",\n \"0.7115283\",\n \"0.7114491\",\n \"0.7106335\",\n \"0.7106335\",\n \"0.7106335\",\n \"0.7105949\",\n \"0.71011025\",\n \"0.7073844\",\n \"0.7070362\",\n \"0.7022083\",\n \"0.7005162\",\n \"0.69951344\",\n \"0.69951344\",\n \"0.698986\",\n \"0.69817185\",\n \"0.6975818\",\n \"0.6975818\",\n \"0.6975818\",\n \"0.6975818\",\n \"0.6947322\",\n \"0.6947322\",\n \"0.69465834\",\n \"0.69434685\",\n \"0.692129\",\n \"0.692129\",\n \"0.692129\",\n \"0.6888264\",\n \"0.6877115\",\n \"0.68755054\",\n \"0.68637514\",\n \"0.6853142\",\n \"0.6853142\",\n \"0.6853142\",\n \"0.680985\",\n \"0.680507\",\n \"0.68046963\",\n \"0.67965704\",\n \"0.6783542\",\n \"0.6763704\",\n \"0.6757547\",\n \"0.67466074\",\n \"0.6737671\",\n \"0.6736573\",\n \"0.67327833\",\n \"0.67295885\",\n \"0.6729424\",\n \"0.67160565\",\n \"0.67147905\",\n \"0.6706924\",\n \"0.66958284\",\n \"0.66925186\",\n \"0.6686643\",\n \"0.6684484\",\n \"0.6677501\",\n \"0.6677501\",\n \"0.6677283\",\n \"0.66709733\",\n \"0.66697025\",\n \"0.66616106\",\n \"0.66497535\",\n \"0.66435736\",\n \"0.66435736\",\n \"0.6641359\",\n \"0.6638794\",\n \"0.66273355\",\n \"0.66177475\",\n \"0.66177475\",\n \"0.6580109\",\n \"0.65680754\",\n \"0.65628827\",\n \"0.6561489\",\n \"0.6560711\",\n \"0.6543718\",\n \"0.65316683\",\n \"0.6525215\",\n \"0.6524385\",\n \"0.6524178\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70241517"},"document_rank":{"kind":"string","value":"32"}}},{"rowIdx":76695,"cells":{"query":{"kind":"string","value":"Creates a folder if it does not exists"},"document":{"kind":"string","value":"def create_folder(folder_name):\n if not os.path.exists(folder_name):\n os.makedirs(folder_name)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_folder_if_needed(path):\n if os.path.exists(path):\n print(\"{} dir exists\".format(path))\n else:\n print(\"{} dir does not exist. Creating dir.\".format(path))\n os.mkdir(path)","def create_folder(path):\n if not exists(path):\n os.makedirs(path)","def create_folder(path):\n if not os.path.exists(path):\n os.makedirs(path)","def create_folder(folder):\n if not os.path.exists(folder):\n os.makedirs(folder)","def createFolder(self):\n self.destination = self.getPath() #Find the destination to create the folder\n try:\n os.makedirs(self.destination) #Try and make a folder\n except FileExistsError:\n pass #Otherwise continue if an error is encountered because the file exists already","def create_folder(folder):\n if not os.path.exists(folder):\n os.makedirs(folder)\n logging.debug(\"Folder %s Created!\" % folder)\n else:\n logging.debug(\"Folder %s Exists!\" % folder)","def createFolder(folder):\n folder_ = os.path.join(os.getcwd(),folder)\n if not(os.path.isdir(folder_)):\n os.mkdir(folder_)","def create_folder(path: str):\n if not os.path.exists(path):\n os.makedirs(path)","def create_folder(folder):\n import errno\n try:\n os.makedirs(folder)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise e","def create_folder(name_folder: str):\n try:\n # Create a new direcctory\n os.mkdir(name_folder)\n except FileExistsError:\n # If the direcctory already exits print.\n print(f\"The directory {name_folder} already exists.\")","def _create_folder_if_not_exist(filename):\n os.makedirs(os.path.dirname(filename), exist_ok=True)","def create_folder(folder_name):\n\n try:\n os.makedirs(folder_name)\n except FileExistsError:\n pass","def create_folder(path):\n folder_missing = not os.path.exists(path)\n\n if folder_missing:\n # Using makedirs since the path hierarchy might not fully exist.\n try:\n os.makedirs(path)\n except OSError as e:\n if (e.errno, e.strerror) == (17, 'File exists'):\n print(e)\n else:\n raise\n\n print('Created folder {0}'.format(path))\n\n return folder_missing","def create_folder(path):\n try:\n os.listdir(path)\n except:\n os.makedirs(path)\n else:\n shutil.rmtree(path)\n os.makedirs(path)\n return path","def create_folder(folder):\n flag = True\n if not os.path.exists(folder):\n try:\n os.makedirs(folder)\n initlog('Folder path:%s created by me; ' % folder) \n except Exception, e:\n initlog('failed to create Folder path; %s' % str(e))\n flag = False\n return flag","def create_folder(folder_path: str) -> None:\n if not os.path.exists(folder_path):\n os.makedirs(folder_path)","def mkdir(folder_name: str) -> None:\n if exist(folder_name):\n print(\"The folder is already exist\")\n return \n\n os.mkdir(folder_name)","def create_directory(folder_name):\n if not os.path.exists(folder_name):\n os.makedirs(folder_name)","def create_folder(path: str):\n try:\n Path(path).mkdir(parents=True, exist_ok=True)\n return True\n except:\n print(\"An error occured.\")","def create_test_folder_if_does_not_exist(path):\n print('')\n if os.path.exists(path):\n print(' Skip creation of existing folder: {}'.format(path))\n else:\n print(' Create non-existing test folder: {}'.format(path))\n os.makedirs(path, mode=0o775)","def _create_folder(file_path):\r\n file_base = os.path.dirname(file_path)\r\n if not os.path.exists(file_base):\r\n try:\r\n os.makedirs(file_base)\r\n except OSError as e:\r\n if e.errno != errno.EEXIST:\r\n raise","def CreateFolderIfNotExisting(folder_path, communicator):\n if not os.path.isdir(folder_path) and communicator.MyPID() == 0:\n os.makedirs(folder_path)\n communicator.Barrier()","def create_folders(folder_name):\n\n if os.path.exists(downloads_path + '\\\\' + folder_name):\n pass\n else:\n os.makedirs(folder_name)\n print(f'Folder: {folder_name} has been created in {downloads_path}')","def create_folder(location: str):\n try:\n os.mkdir(location)\n except FileExistsError:\n pass","def create_folder(target_folder):\n try:\n os.makedirs(target_folder)\n except OSError as e:\n pass\n return os.path.exists(target_folder)","def create_folder(target_folder):\n try:\n os.makedirs(target_folder)\n except OSError as e:\n pass\n return os.path.exists(target_folder)","def create_folder(folder):\n\n try:\n os.mkdir(folder, 0740)\n except OSError:\n return False\n else:\n return True","def mkdir_if_not_exists(path):\n if not os.path.exists(path):\n os.makedirs(path)","def create_folder(folders_to_create=[]):\n for f in folders_to_create:\n if not os.path.exists(f):\n os.makedirs(f)","def ensure_folder_exists(folder_path: str) -> None:\n if not os.path.exists(folder_path):\n os.makedirs(folder_path)","def createFolder(self, foldername: str) -> None:\n try:\n if not os.path.exists(foldername):\n os.makedirs(foldername)\n except OSError:\n print('Error: Creating following directory: ' + foldername)","def createFolder(self):\n\n self.directory = \"D:\\\\CompositionHelper\"\n if not os.path.exists(self.directory):\n os.makedirs(self.directory)\n print ('Created new folder')","def create_folder_path(folder_path):\n try:\n if os.path.exists(folder_path):\n shutil.rmtree(folder_path)\n os.makedirs(folder_path)\n except Exception:\n raise Error('Create {folder_path} exception'.format(folder_path))","def create_folder(name):\n try:\n dirname = os.path.dirname(__file__)\n filename = os.path.join(dirname, name)\n os.makedirs(filename)\n return 0\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n return -1","def create_folder(path):\n command = ['mkdir', TEST_DIR]\n file_operation(path, command)","def create_folder():\n directory = \"data/\"\n if not os.path.exists(directory):\n os.makedirs(directory)\n logging.info(\"Data folder created.\")\n else:\n logging.info(\"Data folder already existed.\")","def ensure_folder(*arg):\n if len(arg) == 0:\n raise Exception(\"No input to ensure_folder\")\n path = get_dir(Path(*arg))\n path.mkdir(parents=True, exist_ok=True)","def create_folders(self):\n for f in self.params['folder_names']:\n if not os.path.exists(f):\n print 'Creating folder:\\t%s' % f\n os.system(\"mkdir %s\" % (f))","def create_folders(self):\n for f in self.params['folder_names']:\n if not os.path.exists(f):\n print 'Creating folder:\\t%s' % f\n os.system(\"mkdir %s\" % (f))","def create_folder(self, unformatted_path):\n os.makedirs(self.format_path(unformatted_path), exist_ok=True)","def create_dir(path):\n if not os.path.exists(path):\n os.makedirs(path)","def create_folder(self):\n path = os.path.expanduser('~') + \"/.ip_enrich/\"\n # Does it exist already?\n if os.path.isdir(path):\n return True\n try:\n os.mkdir(path)\n return True\n except Exception as e:\n print (f\"Creation of the directory {path} failed\")\n print (f\"Error {e}\")\n return False","def prepare_folder(path):\n if not os.path.isdir(path):\n os.makedirs(path)","def folder_guard(folder_path):\n if not os.path.isdir(folder_path):\n print('INFO:folder_guard(): Creating folder: ' + folder_path + '...')\n os.mkdir(folder_path)","def _mkdir_if_not_exist(path):\n if not(os.path.isdir(path)):\n os.mkdir(path)\n else:\n _logger.info('Skipping existing directory %s' % path)","def create_dir():\n if check_dir_exist():\n return False\n else:\n os.makedirs(path_structure)\n return True","def check_make(folder_check):\n if not os.path.isdir(folder_check):\n os.mkdir(folder_check)","def mkdir(name_folder,path):\r\n folder_name = os.path.join(path,name_folder)\r\n if not os.path.exists(folder_name):\r\n os.mkdir(folder_name)\r\n else:\r\n raise NameError('path %s already exists'% path)","def mkdir(path):\n if not os.path.exists(path):\n os.mkdir(path)","def create_folders(self):\n\n for f in self.params['folder_names']:\n if not os.path.exists(f):\n print 'Creating folder:\\t%s' % f\n os.system(\"mkdir %s\" % (f))","def mkdir(path):\n\tif not Path(path).exists():\n\t\tPath(path).mkdir(parents=True, exist_ok=True)","def mkdir(folder_path, exist_ok=True):\n\n # Generate dir\n os.makedirs(folder_path, exist_ok=exist_ok)\n\n return True","def mkdir(path):","def createFolder(self):\n raise NotImplementedError","def make_dir_if_needed(path):\n\n if not os.path.exists(path):\n os.makedirs(path)\n return path","def new_dir(folder):\n os.makedirs(folder, exist_ok=True)\n return folder","def create_folder(directory):\n try:\n if not os.path.exists(directory):\n os.makedirs(directory)\n except OSError:\n print('Error: The folder wasn\\'t able to be created: ' + directory)","def create_dir_if_necessary(path):\n try:\n os.makedirs(path)\n except OSError as exception:\n if exception.errno != errno.EEXIST:\n raise","def _create_folders(self):\n if not os.path.exists(os.path.join(BASE_DIR, DIR)):\n os.mkdir(os.path.join(BASE_DIR, DIR))\n directory = os.path.join(BASE_DIR, DIR, self.title)\n if not os.path.exists(directory):\n os.mkdir(directory)\n return directory","def mkdir(path):\n if not os.path.exists(path):\n os.makedirs(path)","def mkdir(path):\n if not os.path.exists(path):\n os.makedirs(path)","def mkdir(path):\n if not os.path.exists(path):\n os.makedirs(path)","def mkdir(path):\n if not os.path.exists(path):\n os.makedirs(path)","def make_dir(self, path):\n import os\n if not os.path.exists(path):\n os.makedirs(path)","def _check_directory(my_folder):\n if not os.path.exists(my_folder):\n os.makedirs(my_folder)","def make_dir(path):\n if os.path.exists(path):\n return False\n else:\n os.makedirs(path, exist_ok=True)\n return True","def create_folder(self):\n self.config.csv_path.mkdir(parents=True, exist_ok=True)\n self.config.images_path.mkdir(parents=True, exist_ok=True)","def _mkdir_p(path):\n if not osp.exists(path):\n os.makedirs(path)","def mkdir_if_notexists(path):\n try:\n os.mkdir(path)\n except FileExistsError:\n pass","def maybe_make_dir(path):\n if not os.path.exists(path):\n os.makedirs(path)","def folder_guard(folder_path):\n \n if not os.path.isdir(folder_path):\n print('INFO:folder_guard(): Creating folder: ' + folder_path + '...')\n os.mkdir(folder_path)","def create_dir(dir_path):\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)","def maybe_makedirs(path_to_create):\n try: \n os.makedirs(path_to_create)\n except OSError:\n if not os.path.isdir(path_to_create):\n raise","def ifnotexistmkdir(directory):\n if not os.path.exists(directory):\n os.mkdir(directory)\n return Path(directory)","def create_dir_if_doesnt_exist(dir_path):\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n return","def create_directory(parent_path, new_folder):\n newdir = os.path.join(parent_path, new_folder)\n if os.path.isdir(newdir):\n return False\n else:\n os.mkdir(newdir)\n return True","def createFolder(directory) -> None:\n try:\n if not os.path.exists(directory):\n os.makedirs(directory)\n except Exception as error:\n print(f\"Error: createFolder({directory}) -> {error}\")","def ensure_dir_exists(path):\n if not os.path.exists(path):\n os.makedirs(path)","def check_folder(filepath):\n if not os.path.exists(filepath):\n os.mkdir(filepath)\n return filepath","def create_folder(path_folder, name_subfolder=None):\n if not name_subfolder:\n if not os.path.exists(path_folder):\n os.makedirs(path_folder)\n else:\n path_result_subolder = os.path.join(path_folder, name_subfolder)\n if not os.path.exists(path_result_subolder):\n os.makedirs(path_result_subolder)","def MakeDir(path):\n if os.path.exists(path):\n return False\n else:\n os.makedirs(path)\n return True","def mkdir(path):\n try:\n os.mkdir(path)\n except FileExistsError:\n pass","def prepare_folders():\n folder_list = [\"./data\", \"./data/stage\", \"./data/spoken\", \"./data/stage_lemmas\", \"./data/spoken_lemmas\"]\n for folder in folder_list:\n if not os.path.exists(folder):\n os.mkdir(folder)\n print(f\"Created folder {folder}\")\n else:\n print(f\"Folder {folder} already existed\")","def mkdir(path):\n if not os.path.isdir(path):\n os.makedirs(path)","def createFolder(folderFullPath):\n os.makedirs(folderFullPath, exist_ok=True)","def mkdir_p(path):\n try:\n os.makedirs(path) # , exist_ok=True\n except OSError:\n pass","def createImageFolder():\n try:\n os.makedirs(imageFolder)\n except FileExistsError:\n # Exists, delete contents instead\n clearImageFolder()","def create_folders():\n os.makedirs(GRID_DIR, exist_ok=True)","def mkdir_p(path):\n if not os.path.exists(path):\n os.makedirs(path)","def mkdir(path):\n try: \n os.mkdir(path)\n except OSError:\n if not os.path.isdir(path):\n raise","def make_dir_if_needed(dir) :\n\tif not exists(dir) :\n\t\tos.makedirs(dir)","def maybe_makedirs(path_to_create):\n try:\n os.makedirs(path_to_create)\n except OSError:\n if not os.path.isdir(path_to_create):\n raise","def new_dir(the_dir):\n try:\n os.makedirs(the_dir)\n except OSError as e:\n if e.errno == errno.EEXIST:\n pass #not a problem if file exists","def create_directory():\n global dirName\n dirName = 'Downloaded Files'\n global folder_path\n if os.path.isdir(dirName) == True:\n print(\"This folder already exists, path:\", os.path.abspath(dirName))\n else:\n os.mkdir(dirName)\n global folder_path\n folder_path = os.path.abspath(dirName)\n print(\"Directory \" , dirName , \" Created \")","def mymkdir(*folders):\n for folder in folders:\n if not os.path.exists(folder):\n os.mkdir(folder)","def mkdir(path: str):\n if not os.path.exists(path):\n os.makedirs(path)","def make_folders(self):\n\t\tfor name in self.folders:\n\t\t\tos.makedirs(self.path+\"/\"+name,exist_ok=True)","def create_directory_if_not_exists(directory_path):\n os.makedirs(directory_path, exist_ok=True)","def create(path, overwrite=False):\n path = os.path.abspath(path)\n if os.path.isfile(path):\n if overwrite:\n pdbox._args.get(\"dryrun\") or os.remove(path)\n else:\n raise ValueError(\"%s is a file\" % path)\n if os.path.isdir(path):\n if overwrite:\n pdbox._args.get(\"dryrun\") or shutil.rmtree(path)\n else:\n raise ValueError(\"%s already exists\" % path)\n\n pdbox._args.get(\"dryrun\") or os.makedirs(path)\n\n pdbox.info(\"Created new folder %s\" % path)\n return None if pdbox._args.get(\"dryrun\") else LocalFolder(path)"],"string":"[\n \"def create_folder_if_needed(path):\\n if os.path.exists(path):\\n print(\\\"{} dir exists\\\".format(path))\\n else:\\n print(\\\"{} dir does not exist. Creating dir.\\\".format(path))\\n os.mkdir(path)\",\n \"def create_folder(path):\\n if not exists(path):\\n os.makedirs(path)\",\n \"def create_folder(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def create_folder(folder):\\n if not os.path.exists(folder):\\n os.makedirs(folder)\",\n \"def createFolder(self):\\n self.destination = self.getPath() #Find the destination to create the folder\\n try:\\n os.makedirs(self.destination) #Try and make a folder\\n except FileExistsError:\\n pass #Otherwise continue if an error is encountered because the file exists already\",\n \"def create_folder(folder):\\n if not os.path.exists(folder):\\n os.makedirs(folder)\\n logging.debug(\\\"Folder %s Created!\\\" % folder)\\n else:\\n logging.debug(\\\"Folder %s Exists!\\\" % folder)\",\n \"def createFolder(folder):\\n folder_ = os.path.join(os.getcwd(),folder)\\n if not(os.path.isdir(folder_)):\\n os.mkdir(folder_)\",\n \"def create_folder(path: str):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def create_folder(folder):\\n import errno\\n try:\\n os.makedirs(folder)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise e\",\n \"def create_folder(name_folder: str):\\n try:\\n # Create a new direcctory\\n os.mkdir(name_folder)\\n except FileExistsError:\\n # If the direcctory already exits print.\\n print(f\\\"The directory {name_folder} already exists.\\\")\",\n \"def _create_folder_if_not_exist(filename):\\n os.makedirs(os.path.dirname(filename), exist_ok=True)\",\n \"def create_folder(folder_name):\\n\\n try:\\n os.makedirs(folder_name)\\n except FileExistsError:\\n pass\",\n \"def create_folder(path):\\n folder_missing = not os.path.exists(path)\\n\\n if folder_missing:\\n # Using makedirs since the path hierarchy might not fully exist.\\n try:\\n os.makedirs(path)\\n except OSError as e:\\n if (e.errno, e.strerror) == (17, 'File exists'):\\n print(e)\\n else:\\n raise\\n\\n print('Created folder {0}'.format(path))\\n\\n return folder_missing\",\n \"def create_folder(path):\\n try:\\n os.listdir(path)\\n except:\\n os.makedirs(path)\\n else:\\n shutil.rmtree(path)\\n os.makedirs(path)\\n return path\",\n \"def create_folder(folder):\\n flag = True\\n if not os.path.exists(folder):\\n try:\\n os.makedirs(folder)\\n initlog('Folder path:%s created by me; ' % folder) \\n except Exception, e:\\n initlog('failed to create Folder path; %s' % str(e))\\n flag = False\\n return flag\",\n \"def create_folder(folder_path: str) -> None:\\n if not os.path.exists(folder_path):\\n os.makedirs(folder_path)\",\n \"def mkdir(folder_name: str) -> None:\\n if exist(folder_name):\\n print(\\\"The folder is already exist\\\")\\n return \\n\\n os.mkdir(folder_name)\",\n \"def create_directory(folder_name):\\n if not os.path.exists(folder_name):\\n os.makedirs(folder_name)\",\n \"def create_folder(path: str):\\n try:\\n Path(path).mkdir(parents=True, exist_ok=True)\\n return True\\n except:\\n print(\\\"An error occured.\\\")\",\n \"def create_test_folder_if_does_not_exist(path):\\n print('')\\n if os.path.exists(path):\\n print(' Skip creation of existing folder: {}'.format(path))\\n else:\\n print(' Create non-existing test folder: {}'.format(path))\\n os.makedirs(path, mode=0o775)\",\n \"def _create_folder(file_path):\\r\\n file_base = os.path.dirname(file_path)\\r\\n if not os.path.exists(file_base):\\r\\n try:\\r\\n os.makedirs(file_base)\\r\\n except OSError as e:\\r\\n if e.errno != errno.EEXIST:\\r\\n raise\",\n \"def CreateFolderIfNotExisting(folder_path, communicator):\\n if not os.path.isdir(folder_path) and communicator.MyPID() == 0:\\n os.makedirs(folder_path)\\n communicator.Barrier()\",\n \"def create_folders(folder_name):\\n\\n if os.path.exists(downloads_path + '\\\\\\\\' + folder_name):\\n pass\\n else:\\n os.makedirs(folder_name)\\n print(f'Folder: {folder_name} has been created in {downloads_path}')\",\n \"def create_folder(location: str):\\n try:\\n os.mkdir(location)\\n except FileExistsError:\\n pass\",\n \"def create_folder(target_folder):\\n try:\\n os.makedirs(target_folder)\\n except OSError as e:\\n pass\\n return os.path.exists(target_folder)\",\n \"def create_folder(target_folder):\\n try:\\n os.makedirs(target_folder)\\n except OSError as e:\\n pass\\n return os.path.exists(target_folder)\",\n \"def create_folder(folder):\\n\\n try:\\n os.mkdir(folder, 0740)\\n except OSError:\\n return False\\n else:\\n return True\",\n \"def mkdir_if_not_exists(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def create_folder(folders_to_create=[]):\\n for f in folders_to_create:\\n if not os.path.exists(f):\\n os.makedirs(f)\",\n \"def ensure_folder_exists(folder_path: str) -> None:\\n if not os.path.exists(folder_path):\\n os.makedirs(folder_path)\",\n \"def createFolder(self, foldername: str) -> None:\\n try:\\n if not os.path.exists(foldername):\\n os.makedirs(foldername)\\n except OSError:\\n print('Error: Creating following directory: ' + foldername)\",\n \"def createFolder(self):\\n\\n self.directory = \\\"D:\\\\\\\\CompositionHelper\\\"\\n if not os.path.exists(self.directory):\\n os.makedirs(self.directory)\\n print ('Created new folder')\",\n \"def create_folder_path(folder_path):\\n try:\\n if os.path.exists(folder_path):\\n shutil.rmtree(folder_path)\\n os.makedirs(folder_path)\\n except Exception:\\n raise Error('Create {folder_path} exception'.format(folder_path))\",\n \"def create_folder(name):\\n try:\\n dirname = os.path.dirname(__file__)\\n filename = os.path.join(dirname, name)\\n os.makedirs(filename)\\n return 0\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise\\n return -1\",\n \"def create_folder(path):\\n command = ['mkdir', TEST_DIR]\\n file_operation(path, command)\",\n \"def create_folder():\\n directory = \\\"data/\\\"\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n logging.info(\\\"Data folder created.\\\")\\n else:\\n logging.info(\\\"Data folder already existed.\\\")\",\n \"def ensure_folder(*arg):\\n if len(arg) == 0:\\n raise Exception(\\\"No input to ensure_folder\\\")\\n path = get_dir(Path(*arg))\\n path.mkdir(parents=True, exist_ok=True)\",\n \"def create_folders(self):\\n for f in self.params['folder_names']:\\n if not os.path.exists(f):\\n print 'Creating folder:\\\\t%s' % f\\n os.system(\\\"mkdir %s\\\" % (f))\",\n \"def create_folders(self):\\n for f in self.params['folder_names']:\\n if not os.path.exists(f):\\n print 'Creating folder:\\\\t%s' % f\\n os.system(\\\"mkdir %s\\\" % (f))\",\n \"def create_folder(self, unformatted_path):\\n os.makedirs(self.format_path(unformatted_path), exist_ok=True)\",\n \"def create_dir(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def create_folder(self):\\n path = os.path.expanduser('~') + \\\"/.ip_enrich/\\\"\\n # Does it exist already?\\n if os.path.isdir(path):\\n return True\\n try:\\n os.mkdir(path)\\n return True\\n except Exception as e:\\n print (f\\\"Creation of the directory {path} failed\\\")\\n print (f\\\"Error {e}\\\")\\n return False\",\n \"def prepare_folder(path):\\n if not os.path.isdir(path):\\n os.makedirs(path)\",\n \"def folder_guard(folder_path):\\n if not os.path.isdir(folder_path):\\n print('INFO:folder_guard(): Creating folder: ' + folder_path + '...')\\n os.mkdir(folder_path)\",\n \"def _mkdir_if_not_exist(path):\\n if not(os.path.isdir(path)):\\n os.mkdir(path)\\n else:\\n _logger.info('Skipping existing directory %s' % path)\",\n \"def create_dir():\\n if check_dir_exist():\\n return False\\n else:\\n os.makedirs(path_structure)\\n return True\",\n \"def check_make(folder_check):\\n if not os.path.isdir(folder_check):\\n os.mkdir(folder_check)\",\n \"def mkdir(name_folder,path):\\r\\n folder_name = os.path.join(path,name_folder)\\r\\n if not os.path.exists(folder_name):\\r\\n os.mkdir(folder_name)\\r\\n else:\\r\\n raise NameError('path %s already exists'% path)\",\n \"def mkdir(path):\\n if not os.path.exists(path):\\n os.mkdir(path)\",\n \"def create_folders(self):\\n\\n for f in self.params['folder_names']:\\n if not os.path.exists(f):\\n print 'Creating folder:\\\\t%s' % f\\n os.system(\\\"mkdir %s\\\" % (f))\",\n \"def mkdir(path):\\n\\tif not Path(path).exists():\\n\\t\\tPath(path).mkdir(parents=True, exist_ok=True)\",\n \"def mkdir(folder_path, exist_ok=True):\\n\\n # Generate dir\\n os.makedirs(folder_path, exist_ok=exist_ok)\\n\\n return True\",\n \"def mkdir(path):\",\n \"def createFolder(self):\\n raise NotImplementedError\",\n \"def make_dir_if_needed(path):\\n\\n if not os.path.exists(path):\\n os.makedirs(path)\\n return path\",\n \"def new_dir(folder):\\n os.makedirs(folder, exist_ok=True)\\n return folder\",\n \"def create_folder(directory):\\n try:\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n except OSError:\\n print('Error: The folder wasn\\\\'t able to be created: ' + directory)\",\n \"def create_dir_if_necessary(path):\\n try:\\n os.makedirs(path)\\n except OSError as exception:\\n if exception.errno != errno.EEXIST:\\n raise\",\n \"def _create_folders(self):\\n if not os.path.exists(os.path.join(BASE_DIR, DIR)):\\n os.mkdir(os.path.join(BASE_DIR, DIR))\\n directory = os.path.join(BASE_DIR, DIR, self.title)\\n if not os.path.exists(directory):\\n os.mkdir(directory)\\n return directory\",\n \"def mkdir(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def mkdir(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def mkdir(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def mkdir(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def make_dir(self, path):\\n import os\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def _check_directory(my_folder):\\n if not os.path.exists(my_folder):\\n os.makedirs(my_folder)\",\n \"def make_dir(path):\\n if os.path.exists(path):\\n return False\\n else:\\n os.makedirs(path, exist_ok=True)\\n return True\",\n \"def create_folder(self):\\n self.config.csv_path.mkdir(parents=True, exist_ok=True)\\n self.config.images_path.mkdir(parents=True, exist_ok=True)\",\n \"def _mkdir_p(path):\\n if not osp.exists(path):\\n os.makedirs(path)\",\n \"def mkdir_if_notexists(path):\\n try:\\n os.mkdir(path)\\n except FileExistsError:\\n pass\",\n \"def maybe_make_dir(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def folder_guard(folder_path):\\n \\n if not os.path.isdir(folder_path):\\n print('INFO:folder_guard(): Creating folder: ' + folder_path + '...')\\n os.mkdir(folder_path)\",\n \"def create_dir(dir_path):\\n if not os.path.exists(dir_path):\\n os.makedirs(dir_path)\",\n \"def maybe_makedirs(path_to_create):\\n try: \\n os.makedirs(path_to_create)\\n except OSError:\\n if not os.path.isdir(path_to_create):\\n raise\",\n \"def ifnotexistmkdir(directory):\\n if not os.path.exists(directory):\\n os.mkdir(directory)\\n return Path(directory)\",\n \"def create_dir_if_doesnt_exist(dir_path):\\n if not os.path.exists(dir_path):\\n os.makedirs(dir_path)\\n return\",\n \"def create_directory(parent_path, new_folder):\\n newdir = os.path.join(parent_path, new_folder)\\n if os.path.isdir(newdir):\\n return False\\n else:\\n os.mkdir(newdir)\\n return True\",\n \"def createFolder(directory) -> None:\\n try:\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n except Exception as error:\\n print(f\\\"Error: createFolder({directory}) -> {error}\\\")\",\n \"def ensure_dir_exists(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def check_folder(filepath):\\n if not os.path.exists(filepath):\\n os.mkdir(filepath)\\n return filepath\",\n \"def create_folder(path_folder, name_subfolder=None):\\n if not name_subfolder:\\n if not os.path.exists(path_folder):\\n os.makedirs(path_folder)\\n else:\\n path_result_subolder = os.path.join(path_folder, name_subfolder)\\n if not os.path.exists(path_result_subolder):\\n os.makedirs(path_result_subolder)\",\n \"def MakeDir(path):\\n if os.path.exists(path):\\n return False\\n else:\\n os.makedirs(path)\\n return True\",\n \"def mkdir(path):\\n try:\\n os.mkdir(path)\\n except FileExistsError:\\n pass\",\n \"def prepare_folders():\\n folder_list = [\\\"./data\\\", \\\"./data/stage\\\", \\\"./data/spoken\\\", \\\"./data/stage_lemmas\\\", \\\"./data/spoken_lemmas\\\"]\\n for folder in folder_list:\\n if not os.path.exists(folder):\\n os.mkdir(folder)\\n print(f\\\"Created folder {folder}\\\")\\n else:\\n print(f\\\"Folder {folder} already existed\\\")\",\n \"def mkdir(path):\\n if not os.path.isdir(path):\\n os.makedirs(path)\",\n \"def createFolder(folderFullPath):\\n os.makedirs(folderFullPath, exist_ok=True)\",\n \"def mkdir_p(path):\\n try:\\n os.makedirs(path) # , exist_ok=True\\n except OSError:\\n pass\",\n \"def createImageFolder():\\n try:\\n os.makedirs(imageFolder)\\n except FileExistsError:\\n # Exists, delete contents instead\\n clearImageFolder()\",\n \"def create_folders():\\n os.makedirs(GRID_DIR, exist_ok=True)\",\n \"def mkdir_p(path):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def mkdir(path):\\n try: \\n os.mkdir(path)\\n except OSError:\\n if not os.path.isdir(path):\\n raise\",\n \"def make_dir_if_needed(dir) :\\n\\tif not exists(dir) :\\n\\t\\tos.makedirs(dir)\",\n \"def maybe_makedirs(path_to_create):\\n try:\\n os.makedirs(path_to_create)\\n except OSError:\\n if not os.path.isdir(path_to_create):\\n raise\",\n \"def new_dir(the_dir):\\n try:\\n os.makedirs(the_dir)\\n except OSError as e:\\n if e.errno == errno.EEXIST:\\n pass #not a problem if file exists\",\n \"def create_directory():\\n global dirName\\n dirName = 'Downloaded Files'\\n global folder_path\\n if os.path.isdir(dirName) == True:\\n print(\\\"This folder already exists, path:\\\", os.path.abspath(dirName))\\n else:\\n os.mkdir(dirName)\\n global folder_path\\n folder_path = os.path.abspath(dirName)\\n print(\\\"Directory \\\" , dirName , \\\" Created \\\")\",\n \"def mymkdir(*folders):\\n for folder in folders:\\n if not os.path.exists(folder):\\n os.mkdir(folder)\",\n \"def mkdir(path: str):\\n if not os.path.exists(path):\\n os.makedirs(path)\",\n \"def make_folders(self):\\n\\t\\tfor name in self.folders:\\n\\t\\t\\tos.makedirs(self.path+\\\"/\\\"+name,exist_ok=True)\",\n \"def create_directory_if_not_exists(directory_path):\\n os.makedirs(directory_path, exist_ok=True)\",\n \"def create(path, overwrite=False):\\n path = os.path.abspath(path)\\n if os.path.isfile(path):\\n if overwrite:\\n pdbox._args.get(\\\"dryrun\\\") or os.remove(path)\\n else:\\n raise ValueError(\\\"%s is a file\\\" % path)\\n if os.path.isdir(path):\\n if overwrite:\\n pdbox._args.get(\\\"dryrun\\\") or shutil.rmtree(path)\\n else:\\n raise ValueError(\\\"%s already exists\\\" % path)\\n\\n pdbox._args.get(\\\"dryrun\\\") or os.makedirs(path)\\n\\n pdbox.info(\\\"Created new folder %s\\\" % path)\\n return None if pdbox._args.get(\\\"dryrun\\\") else LocalFolder(path)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.85329485","0.85087234","0.842846","0.8422511","0.83173305","0.8302552","0.82762134","0.82187194","0.8175441","0.8163295","0.814333","0.814166","0.8140945","0.8131764","0.8117364","0.81125724","0.80800235","0.80615175","0.8053969","0.80054647","0.80003494","0.79758644","0.79720837","0.79417735","0.7903279","0.7903279","0.7893764","0.78881705","0.7881796","0.7864371","0.78263086","0.780592","0.77771544","0.7771857","0.77718544","0.7766389","0.7763888","0.77454305","0.77454305","0.77449775","0.77186733","0.7715243","0.7702188","0.76951927","0.767777","0.76744825","0.76654047","0.7664917","0.7644769","0.7635077","0.76350415","0.76336706","0.7632958","0.762442","0.7617904","0.76170033","0.7612259","0.76006556","0.7596508","0.7593643","0.7593643","0.7593643","0.7593643","0.75747633","0.7568903","0.7562815","0.75538677","0.75445485","0.75385","0.751956","0.7512833","0.7503703","0.7502793","0.74905235","0.74868923","0.748604","0.74856764","0.7484222","0.748342","0.74745464","0.7463552","0.74621284","0.7461302","0.745871","0.7446105","0.74424833","0.7434716","0.7433498","0.7428438","0.74159765","0.74144787","0.7392615","0.73900557","0.73893124","0.73852277","0.7357761","0.73552203","0.7353827","0.73532563"],"string":"[\n \"0.85329485\",\n \"0.85087234\",\n \"0.842846\",\n \"0.8422511\",\n \"0.83173305\",\n \"0.8302552\",\n \"0.82762134\",\n \"0.82187194\",\n \"0.8175441\",\n \"0.8163295\",\n \"0.814333\",\n \"0.814166\",\n \"0.8140945\",\n \"0.8131764\",\n \"0.8117364\",\n \"0.81125724\",\n \"0.80800235\",\n \"0.80615175\",\n \"0.8053969\",\n \"0.80054647\",\n \"0.80003494\",\n \"0.79758644\",\n \"0.79720837\",\n \"0.79417735\",\n \"0.7903279\",\n \"0.7903279\",\n \"0.7893764\",\n \"0.78881705\",\n \"0.7881796\",\n \"0.7864371\",\n \"0.78263086\",\n \"0.780592\",\n \"0.77771544\",\n \"0.7771857\",\n \"0.77718544\",\n \"0.7766389\",\n \"0.7763888\",\n \"0.77454305\",\n \"0.77454305\",\n \"0.77449775\",\n \"0.77186733\",\n \"0.7715243\",\n \"0.7702188\",\n \"0.76951927\",\n \"0.767777\",\n \"0.76744825\",\n \"0.76654047\",\n \"0.7664917\",\n \"0.7644769\",\n \"0.7635077\",\n \"0.76350415\",\n \"0.76336706\",\n \"0.7632958\",\n \"0.762442\",\n \"0.7617904\",\n \"0.76170033\",\n \"0.7612259\",\n \"0.76006556\",\n \"0.7596508\",\n \"0.7593643\",\n \"0.7593643\",\n \"0.7593643\",\n \"0.7593643\",\n \"0.75747633\",\n \"0.7568903\",\n \"0.7562815\",\n \"0.75538677\",\n \"0.75445485\",\n \"0.75385\",\n \"0.751956\",\n \"0.7512833\",\n \"0.7503703\",\n \"0.7502793\",\n \"0.74905235\",\n \"0.74868923\",\n \"0.748604\",\n \"0.74856764\",\n \"0.7484222\",\n \"0.748342\",\n \"0.74745464\",\n \"0.7463552\",\n \"0.74621284\",\n \"0.7461302\",\n \"0.745871\",\n \"0.7446105\",\n \"0.74424833\",\n \"0.7434716\",\n \"0.7433498\",\n \"0.7428438\",\n \"0.74159765\",\n \"0.74144787\",\n \"0.7392615\",\n \"0.73900557\",\n \"0.73893124\",\n \"0.73852277\",\n \"0.7357761\",\n \"0.73552203\",\n \"0.7353827\",\n \"0.73532563\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8088543"},"document_rank":{"kind":"string","value":"17"}}},{"rowIdx":76696,"cells":{"query":{"kind":"string","value":"Given a file returns the number of lines where a certain pattern appears."},"document":{"kind":"string","value":"def count_patterns(pattern, file):\n count = 0\n with open(file, 'r') as f:\n for line in f:\n if re.search(pattern, line):\n count += 1\n print(\"The pattern '{}' appears {} times.\".format(pattern, count))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def line_count(file):\n with open(file, \"r\") as f:\n return sum(1 for line in f)","def count_lines(filename):\n with open(filename, 'rb') as f:\n return sum(1 for line in f)","def count_lines(filename):\r\n with open(filename, 'rb') as f:\r\n return sum(1 for line in f)","def get_file_line_count(a_file):\r\n count = -1\r\n try:\r\n for count, line in enumerate(open(a_file, \"rU\")):\r\n pass\r\n except IOError:\r\n pass\r\n count += 1\r\n return count","def get_file_line_count(a_file):\r\n count = -1\r\n try:\r\n for count, line in enumerate(open(a_file, \"rU\")):\r\n pass\r\n except IOError:\r\n pass\r\n count += 1\r\n return count","def number_of_lines(filename=\"\"):\n with open(filename, encoding='utf-8') as myFile:\n return sum([1 for line in myFile])","def parse_file_count(path, args):\n try:\n fisier = open(path, 'r')\n except IOError:\n print(\"Nu am putut deschide fisierul :\", path)\n return\n n_found = 0\n pattern = args.pattern\n for line in fisier:\n if args.ignore_case:\n line = line.lower()\n pattern = pattern.lower()\n n_found += line.count(pattern)\n\n fisier.close()\n return n_found","def CountLineNumber(filename):\n\n fp = open(os.path.abspath(filename), \"r\");\n lines = 0\n for line in fp.readlines():\n lines = lines + 1\n fp.close()\n return lines","def number_of_lines(filename=\"\"):\n c = 0\n with open(filename) as f:\n for r in f:\n c += 1\n return(c)","def countLines(file_path, keyword):\n count = 0\n\n try:\n with open(file_path, 'r') as f:\n lines = f.readlines()\n if keyword in lines:\n count += 1\n # Catch permission errors.\n except OSError as e:\n print(\"{}. Could not open file {}.\").format(e, file_path)\n return count","def number_of_lines(filename=\"\"):\n count = 0\n with open(filename) as f:\n for lines in f:\n count += 1\n return (count)","def get_n_lines(file):\n return sum(1 for _ in open(file))","def line_count(fname):\n return int(call(['wc', '-l', fname]).strip().split()[0])","def num_lines_in_file(file_name):\n line_idx = -1\n with open(file_name) as file:\n for line_idx, _ in enumerate(file):\n pass\n return line_idx + 1","def number_of_lines(filename=\"\"):\n with open(filename, encoding='UTF8') as a_file:\n\n lineNum = 0\n\n for eachLine in a_file:\n lineNum += 1\n return lineNum","def num_lines(file_name):\n with open(file_name) as file:\n for i, line in enumerate(file):\n pass\n return i + 1","def number_of_lines(filename=\"\"):\n line_number = 0\n with open(filename, encoding='UTF8') as f:\n for line in f:\n line_number += 1\n return line_number","def file_number_of_lines(file_name):\n try:\n item = (0, None)\n with open(file_name) as file_handler:\n for item in enumerate(file_handler):\n pass\n return item[0] + 1\n except IOError:\n return 0","def number_of_lines(filename=\"\"):\n num_lines = 0\n with open(filename, encoding=\"utf-8\") as myFile:\n return myFile.read().count('\\n')","def numLinesInFile(fname):\n with open(fname, 'rb') as f:\n for i, l in enumerate(f):\n pass\n return i + 1","def fileLineCount(fPath):\n\twith open(fPath) as f:\n\t\tfor i, li in enumerate(f):\n\t\t\tpass\n\treturn (i + 1)","def number_of_lines(filename=\"\"):\n\n number_lines = 0\n with open(filename) as file_opened:\n for line in file_opened:\n number_lines += 1\n return number_lines","def number_of_lines(filename=\"\"):\n n_lines = 0\n with open(filename, encoding='utf-8', mode='r') as file:\n for lines in file:\n n_lines += 1\n return n_lines","def number_of_lines(filename=\"\"):\n counter = 0\n with open(filename, \"r\") as my_file:\n for line in my_file:\n counter += 1\n my_file.close()\n return (counter)","def number_of_lines(filename=\"\"):\n n = 0\n if filename == \"\":\n return n\n with open(filename, \"r\") as f:\n for line in f:\n n = n + 1\n return n","def num_lines(fname):\n with open(fname) as f:\n for i, l in enumerate(f):\n pass\n return( i + 1 )","def commentOutLineMatching(pattern,fileName,maxOccurs=None):\n \n file=open(fileName,mode='r')\n pattern=re.compile(pattern)\n fileText=\"\"\n numMatches=0\n if maxOccurs==None:\n maxOccurs=sys.maxsize\n \n for line in file:\n \n if pattern.match(line) and numMatches<maxOccurs:\n fileText+=\"#\"+line\n numMatches+=1\n else:\n fileText+=line\n file.close()\n file=open(fileName,mode='w')\n file.write(fileText)\n file.close()\n return numMatches","def map_count(filename):\n f = open(filename, \"r+\")\n buf = mmap.mmap(f.fileno(), 0)\n lines = 0\n readline = buf.readline\n while readline():\n lines += 1\n return lines","def countLines(file_name, start, end):\r\n\r\n with open(file_name, \"r\") as file:\r\n counter_lines = 0\r\n\r\n for line in islice(file, start, end):\r\n counter_lines += 1\r\n\r\n return counter_lines","def countlines(fn):\n with open(fn, 'rb') as f:\n bufgen = takewhile(\n lambda x: x, (f.read(1024 * 1024) for _ in repeat(None)))\n ln = sum(buf.count(b'\\n') for buf in bufgen)\n return ln","def number_of_lines(filename=\"\"):\n with open(filename, encoding=\"utf-8\") as file:\n text = file.readlines()\n return len(text)","def count_lines(file_uri):\n\n with open(file_uri) as file_obj:\n for i, line in enumerate(file_obj):\n pass\n num_lines = i + 1\n return num_lines","def bufcount(filename):\n\timport gzip\n\tif filename.split('.')[-1] in ['gz','gzip']: f = gzip.open(filename)\n\telse: f = open(filename)\n\tlines = 0\n\tbuf_size = 1024 * 1024\n\tread_f = f.read # loop optimization\n\t\n\tbuf = read_f(buf_size)\n\twhile buf:\n\t\tlines += buf.count('\\n')\n\t\tbuf = read_f(buf_size)\n\t\tf.close\n\treturn lines","def listRows(file):\n\twith open(file) as f:\n\t\tcount = 0\n\t\tfor line in f.readlines():\n\t\t\tcount += 1\n\t\treturn count -1","def find_dimesion(filename):\n file = open(filename,\"r\")\n\n line = file.readline()\n file.close()\n return len(line.split())","def no_of_lines():\n number_of_lines = len(open(FILE_NAME).readlines())\n return number_of_lines","def count_locs(file_type, comment_pattern):\n find = \"find . -name '*.{0}' -print0\".format(file_type)\n sed_pattern = \"'/^\\s*{0}/d;/^\\s*$/d'\".format(comment_pattern)\n\n cmd = \"{0} | xargs -0 sed {1} | wc -l\".format(find, sed_pattern)\n\n return check_output(cmd, shell = True).decode('utf-8').replace('\\n', '')","def count_positions(fname):\r\n with open(fname) as f:\r\n for i, l in enumerate(f):\r\n pass\r\n return i + 1","def file_len(full_path):\n f = open(full_path)\n nr_of_lines = sum(1 for line in f)\n f.close()\n return nr_of_lines","def get_file_lines(filename):\n if not os.path.isfile(filename):\n logging.error(\"[get_file_lines] %s not found\", filename)\n return -1\n\n if not os.access(filename, os.R_OK):\n logging.error(\"[get_file_lines] %s cannot be read\", filename)\n return -1\n\n i = -1\n with open(filename) as f:\n try:\n for i, l in enumerate(f):\n pass\n except UnicodeDecodeError:\n return -1\n return i + 1","def _count_comment_rows(vcf_path):\n vcf_lines_generator = lines_from_vcf(vcf_path)\n\n comment_lines_count = 0\n for line in vcf_lines_generator:\n if line.startswith('#'):\n comment_lines_count += 1\n else:\n vcf_lines_generator.close() # Don't leave the file handle opened\n # Don't continue reading the VCF once the comments section ended\n break\n\n return comment_lines_count","def count_variants(filename, content=None):\n open_fn = gzip.open if is_gz_file(filename) else open\n count = 0\n with open_fn(filename, \"rt\") as ifile:\n for line in ifile:\n if not line.startswith(\"#\"):\n if content:\n if content in line:\n count += 1\n else:\n count += 1\n return count","def get_rows_num(file_name):\n # By default add the first datetime\n row_num = 1\n with open(file_name, \"r\") as file:\n for line in file:\n if line == \"\\n\":\n break\n else:\n row_num += 1\n\n return row_num","def commentaires(file_name_c):\n com = subprocess.run([\"grep\", \"eleves_bis/\" + file_name_c, \"-e\", '/\\*'], stdout=subprocess.PIPE)\n return com.stdout.decode().count(\"\\n\")","def nrows(filename):\n with fopen(filename, 'rb') as fdsc:\n bufgen = takewhile(lambda x: x, (fdsc.read(1024*1024) for _ in repeat(None)))\n return sum([buf.count(b'\\n') for buf in bufgen])","def countSamples(filename):\n with open(filename, \"r\") as f:\n line = f.readline().split(\"\\t\")\n return len(line) - 2","def count_LOC(path):\n re_empty = re.compile(r\"[\\s]*(#|\\n|\\\"\\\"\\\")\")\n re_for = re.compile(r\"for.*in\")\n re_lambda = re.compile(r\"lambda\")\n re_if = re.compile(r\"if.*:\")\n re_def = re.compile(r\"def (?P<fname>\\w+)\\(\")\n\n total_LOC, indent_level = 0, 0\n cur_part = None\n parts = defaultdict(int)\n\n with open(path, 'r') as _file:\n for line in filter(lambda l : not re_empty.match(l), _file):\n\n extra = len( re_for.findall(line) ) - 1 + len( re_lambda.findall(line) ) - 1 + len( re_if.findall(line) ) -1\n\n if extra < 0: extra = 0\n\n total_LOC += 1 + extra\n if cur_part:\n parts[cur_part] += 1 + extra\n\n defs = re_def.search(line)\n if defs:\n cur_part = defs.groupdict()['fname']\n indent_level = first_non_whitespace(line)\n\n cur_indent = first_non_whitespace(line)\n if cur_indent < indent_level:\n cur_part = None\n indent_level = cur_indent\n\n return(total_LOC, parts)","def count_occurrences(text, pattern, d=0):\n return len(find_occurrences(text, pattern, d))","def linecount(x):\n return sum(1 for char in x if char == \"\\n\")","def countsubcatchments(inputfilename=FileSettings.settingsdict['inputfilename']):\r\n global count\r\n with open(inputfilename, 'r') as swmmput:\r\n contents = swmmput.readlines()\r\n count = len(contents)\r\n return(count)","def count_lines(filename):\n with open(filename, 'r', encoding='utf-8') as file:\n lines_count = int()\n for line in file:\n lines_count += 1\n info_tuple = (filename, lines_count)\n return info_tuple","def pattern_count(text, pattern):\n\n count = 0\n len_text = len(text)\n len_pattern = len(pattern)\n for i in range(len_text - len_pattern):\n if pattern in text[i:i + len_pattern]:\n count = count + 1\n else:\n continue\n return count","def main():\n\tcount = 0\n\twith open(FILE, 'r') as f:\n\t\tfor line in f:\n\t\t\tcount += 1\n\tprint('There are ' + str(count) + ' lines in '+str(FILE))","def pattern_count(text, pattern):\n return len([i\n for i in range(0, len(text) - len(pattern) + 1)\n if text[i:i + len(pattern)] == pattern])","def file_len(fname):\n with open(fname) as f:\n for i, l in enumerate(f):\n pass\n Nrows = i + 1\n return Nrows","def grep_me(pattern, fname):\n for line in stream_reader(fname):\n if re.search(pattern, line, re.I):\n print('{}:{}:{}'.format(filename(), filelineno(), line), end='')","def get_lines_in_file(file_path, read=False):\n nLines = 0\n lines = []\n with open(file_path, 'r') as f:\n for line in f:\n nLines += 1\n if read:\n lines.append(line.strip())\n return lines, nLines","def count_pattern(sentence, pattern):\n n = len(pattern)\n counter = 0\n for i in range(len(sentence) - n + 1):\n if sentence[i:i+n] == pattern:\n counter += 1\n\n return counter","def FindHeaderLength():\n\n lookup = 'Lateral um'\n \n with open(filename) as myFile:\n for FoundPosition, line in enumerate(myFile, 1):\n if lookup in line:\n print 'Scan Data found at line:', FoundPosition\n break\n \n return FoundPosition+4","def PatternCount(text, pattern):\n\n count = 0\n for i in range(0, len(text)-len(pattern)+1):\n if text[i:i+len(pattern)] == pattern:\n count += 1\n return count","def find_pattern_in_file(pattern, file_name):\n pattern = re.compile(pattern)\n with open(file_name) as f:\n for line in f:\n for match in re.finditer(pattern,line):\n return match.groups()\n return None","def linesCountingAux(file_name, nProcesses):\r\n\r\n linesPerProcessesList = []\r\n\r\n with open(file_name, \"r\") as file:\r\n lineCounting = 0\r\n\r\n for line in file:\r\n lineCounting += 1 #discover the lines in the text file\r\n\r\n linesPerProcesses = lineCounting // nProcesses\r\n\r\n for number in range(nProcesses):\r\n linesPerProcessesList.append(linesPerProcesses)\r\n if sum(linesPerProcessesList) < lineCounting:\r\n for number in range (lineCounting - sum(linesPerProcessesList)):\r\n linesPerProcessesList[number] += 1\r\n\r\n return linesPerProcessesList","def count_lines(file_obj):\n for idx, line in enumerate(file_obj):\n pass\n file_obj.seek(0)\n return idx + 1","def contig_count(contig):\n return sum([1 for line in open(contig, 'rU').readlines() if line.startswith('>')])","def get_line_length(file_path):\n with open(file_path, 'rb+') as f:\n return len(f.readline())","def _MatchPatternLines(self, in_stream, re_pattern, num_lines=None):\n num_read = 0\n while True:\n line = in_stream.readline()\n if not line:\n return None\n num_read += 1\n m = re_pattern.match(line)\n if m is not None:\n return m\n if num_lines is not None and num_read >= num_lines:\n return None","def test_line_count(self):\n self.assertEqual(analyze_text(self.filename)[0], 11)","def _get_line_number(vcf):\n with open(vcf) as vcf_input_file:\n i = -1\n for line in vcf_input_file:\n i += 1\n return i","def position(file_, pattern):\n pattern = pattern[1:-1]\n pattern = pattern.replace('(', '\\(')\n pattern = pattern.replace(')', '\\)')\n file_obj = open(file_, 'rU')\n for line_number, line in enumerate(file_obj):\n m = re.search(pattern, line)\n if m is not None:\n return line_number, m.pos\n file_obj.close()\n return 0, 0","def calculate_line_number(text):\n return len([line for line in text.split(\"\\n\") if line.strip() != \"\"])","def count_total_line():\n count = 0\n file_count = 0\n for filename in os.listdir('.'):\n if filename.endswith(\".json\"):\n file_count += 1\n with open(filename, 'r', encoding='utf8') as f:\n for line in f:\n count += 1\n print(\"There are {0} lines in {1} json files\".format(count, file_count))","def count_meme_entries(motif_path):\n with open(motif_path, \"r\") as f:\n counter = 0\n for line in f:\n if line[:6] == \"letter\":\n counter += 1\n return counter","def num_lines(self, snapshot: Bug, filepath: str) -> int:\n return len(self._line_offsets(snapshot, filepath))","def trace_file_len(fname):\n try:\n with open(fname) as f:\n for i, l in enumerate(f):\n pass\n return i - 1\n except FileNotFoundError:\n return 0","def file_len(filename):\n with open(filename) as f:\n for i, l in enumerate(f):\n pass\n return i + 1","def test_line_count(self):\n self.assertEqual(analyze_text(self.filename)[0], 4)","def test_line_count(self):\n self.assertEqual(analyze_text(self.filename)[0], 4)","def getKeywordCount(cwd, keyword):\n lines_count = 0\n\n for dirpath, dirnames, filenames in os.walk(cwd):\n for file in filenames:\n file_path = os.path.join(dirpath, file)\n lines_count += countLines(file_path, keyword)\n return lines_count","def count_lines(inFile, outFile):\n \n bodyLines = []\n # Begin iteratin lines, starting at line 'lineNum'\n for line_ in inFile:\n line_ = line_.rstrip('\\n').strip()\n if (not line_) or (line_[0] == '!'):\n continue\n elif line_.upper() in cases:\n print(\"Error: encountered new case before end of last.\"\n \"\\n Have you forgotten an 'END'?\", file=outFile)\n sys.exit()\n elif line_.upper() == \"END\":\n return bodyLines\n else:\n bodyLines.append(line_)","def get_pattern_count(sequence, pattern):\n return len(re.findall(r'(?=' + pattern + ')', sequence))","def get_number_of_paragraph(self):\n file_to_read = f'{self.path}/{self.filename}'\n file = open(file_to_read, 'r', encoding='utf-8')\n string_to_match = '<p>'\n count = 0\n for line in file:\n if string_to_match in line:\n count += 1\n sqlite_for_ht.CreateTable.update_table(f_1, self.filename, 'number_of_paragraph', count)\n print(datetime.now(), '-', 'number_of_paragraph for', self.filename, 'calculated =', count)\n return None","def test_line_count(self):\n\t\tself.assertEqual(analyse_text(self.filename)[0], 4)","def read_file(filename, n, val):\n\tcount, read = 0, 0\n\twith open(filename, 'r') as f:\n\t\tfor value in f.readlines():\n\t\t\tread += 1\n\t\t\tif read > n: break\n\t\t\tif int(value) == val: count += 1\n\treturn count","def grep(pattern, filename):\n rx = re.compile(pattern)\n with open(filename, 'r') as file:\n for line in file:\n if rx.search(line):\n yield line","def file_length(fileName):\n with open(f_pass) as f:\n for i, l in enumerate(f):\n pass\n return i + 1","def file_len(file_name):\n with open(file_name) as f:\n for i, l in enumerate(f):\n pass\n return i + 1","def get_file_counts(filename):\n new_file = open(filename, \"r\")\n d = dict()\n for line in new_file: \n split_line = line.split()\n for word in split_line:\n if word in d:\n d[word] += 1\n else:\n d[word] = 1\n new_file.close()\n return d","def num_data_lines(filepath):\n\tif not file_exists(filepath):\n\t\treturn -1\n\tcount = 0\n\twith open(filepath, 'r') as f:\n\t\twhile read_floats(f):\n\t\t\tcount += 1\n\tf.close()\n\treturn count","def get_filesize(inputfile) -> int:\n with open(inputfile, \"rb\") as f:\n lines = 0\n buf_size = 1024 * 1024\n read_f = f.raw.read\n\n buf = read_f(buf_size)\n while buf:\n lines += buf.count(b\"\\n\")\n buf = read_f(buf_size)\n\n return lines","def file_len(fname):\n \n with open(fname) as f:\n for i, l in enumerate(f):\n pass\n return i + 1","def get_number_lines(running_reward_file, running_loss_file, action_count_file):\n if Path(running_reward_file).exists():\n data = np.loadtxt(running_reward_file).reshape(-1,2)\n return data.shape[0]\n if Path(running_loss_file).exists():\n data = np.loadtxt(running_loss_file).reshape(-1,2)\n return data.shape[0]\n if Path(action_count_file).exists():\n data = np.loadtxt(action_count_file).reshape(-1,2)\n return data.shape[0]\n raise NameError(\"No files to count lines\")","def bpCount(file):\n amount_bp = len(file)\n return amount_bp","def count_entries(path, dedupe=True):\n if dedupe:\n return len(get_identities(path))\n else:\n if not os.path.exists(path):\n return 0\n with open(path, 'rb') as f:\n return sum(1 for line in f)","def countCharacters(file_name, start, end):\r\n\r\n with open(file_name, \"r\") as file:\r\n counter_chars = 0\r\n\r\n for line in islice(file, start, end):\r\n counter_chars += len(line)\r\n\r\n return counter_chars","def line_offsets(fname):\n line_offset = []\n offset = 0\n for _, line in enumerate( open(fname) ):\n line_offset.append(offset)\n offset += len(line)\n return line_offset","def analyzeCppCode(self, sourceFile):\n numLines = 0 # Number of lines of code\n numComments = 0 # Number of comments in the code\n\n f=self.openFile(sourceFile)\n for line in f:\n numLines += 1;\n loc = 0\n while (loc != -1): #count the # of times the '/*' characters appears\n loc = line.find(\"#\", loc)\n if (loc != -1):\n loc += 1\n numComments += 1\n \n loc = 0\n loc = line.find('//', loc) #count the # of times the '//' characters appears\n if (loc != -1):\n loc += 1\n numComments += 1\n \n f.close()\n return numLines, numComments","def count_tests(infile):\n\n ntests = 0\n with open(infile, \"r\") as ff:\n for line in ff:\n if line.strip(): # Jump empty lines\n if summary in line:\n # Count failed tests for each start of file\n ntests += 1\n return ntests","def get_num_examples(path_in):\n i = 0\n with open(path_in, 'r', encoding='utf8') as f:\n for _ in f:\n i += 1\n return i","def skiprows(fn):\n for i, line in enumerate(open(fn)):\n if line.startswith('#'):\n continue\n else:\n break\n return i","def header_len(fname,header_char='#'):\n Nheader = 0\n with open(fname) as f:\n for i, l in enumerate(f):\n if ( (l[0:len(header_char)]==header_char) or (l==\"\\n\") ):\n Nheader += 1\n else:\n break\n\n return Nheader"],"string":"[\n \"def line_count(file):\\n with open(file, \\\"r\\\") as f:\\n return sum(1 for line in f)\",\n \"def count_lines(filename):\\n with open(filename, 'rb') as f:\\n return sum(1 for line in f)\",\n \"def count_lines(filename):\\r\\n with open(filename, 'rb') as f:\\r\\n return sum(1 for line in f)\",\n \"def get_file_line_count(a_file):\\r\\n count = -1\\r\\n try:\\r\\n for count, line in enumerate(open(a_file, \\\"rU\\\")):\\r\\n pass\\r\\n except IOError:\\r\\n pass\\r\\n count += 1\\r\\n return count\",\n \"def get_file_line_count(a_file):\\r\\n count = -1\\r\\n try:\\r\\n for count, line in enumerate(open(a_file, \\\"rU\\\")):\\r\\n pass\\r\\n except IOError:\\r\\n pass\\r\\n count += 1\\r\\n return count\",\n \"def number_of_lines(filename=\\\"\\\"):\\n with open(filename, encoding='utf-8') as myFile:\\n return sum([1 for line in myFile])\",\n \"def parse_file_count(path, args):\\n try:\\n fisier = open(path, 'r')\\n except IOError:\\n print(\\\"Nu am putut deschide fisierul :\\\", path)\\n return\\n n_found = 0\\n pattern = args.pattern\\n for line in fisier:\\n if args.ignore_case:\\n line = line.lower()\\n pattern = pattern.lower()\\n n_found += line.count(pattern)\\n\\n fisier.close()\\n return n_found\",\n \"def CountLineNumber(filename):\\n\\n fp = open(os.path.abspath(filename), \\\"r\\\");\\n lines = 0\\n for line in fp.readlines():\\n lines = lines + 1\\n fp.close()\\n return lines\",\n \"def number_of_lines(filename=\\\"\\\"):\\n c = 0\\n with open(filename) as f:\\n for r in f:\\n c += 1\\n return(c)\",\n \"def countLines(file_path, keyword):\\n count = 0\\n\\n try:\\n with open(file_path, 'r') as f:\\n lines = f.readlines()\\n if keyword in lines:\\n count += 1\\n # Catch permission errors.\\n except OSError as e:\\n print(\\\"{}. Could not open file {}.\\\").format(e, file_path)\\n return count\",\n \"def number_of_lines(filename=\\\"\\\"):\\n count = 0\\n with open(filename) as f:\\n for lines in f:\\n count += 1\\n return (count)\",\n \"def get_n_lines(file):\\n return sum(1 for _ in open(file))\",\n \"def line_count(fname):\\n return int(call(['wc', '-l', fname]).strip().split()[0])\",\n \"def num_lines_in_file(file_name):\\n line_idx = -1\\n with open(file_name) as file:\\n for line_idx, _ in enumerate(file):\\n pass\\n return line_idx + 1\",\n \"def number_of_lines(filename=\\\"\\\"):\\n with open(filename, encoding='UTF8') as a_file:\\n\\n lineNum = 0\\n\\n for eachLine in a_file:\\n lineNum += 1\\n return lineNum\",\n \"def num_lines(file_name):\\n with open(file_name) as file:\\n for i, line in enumerate(file):\\n pass\\n return i + 1\",\n \"def number_of_lines(filename=\\\"\\\"):\\n line_number = 0\\n with open(filename, encoding='UTF8') as f:\\n for line in f:\\n line_number += 1\\n return line_number\",\n \"def file_number_of_lines(file_name):\\n try:\\n item = (0, None)\\n with open(file_name) as file_handler:\\n for item in enumerate(file_handler):\\n pass\\n return item[0] + 1\\n except IOError:\\n return 0\",\n \"def number_of_lines(filename=\\\"\\\"):\\n num_lines = 0\\n with open(filename, encoding=\\\"utf-8\\\") as myFile:\\n return myFile.read().count('\\\\n')\",\n \"def numLinesInFile(fname):\\n with open(fname, 'rb') as f:\\n for i, l in enumerate(f):\\n pass\\n return i + 1\",\n \"def fileLineCount(fPath):\\n\\twith open(fPath) as f:\\n\\t\\tfor i, li in enumerate(f):\\n\\t\\t\\tpass\\n\\treturn (i + 1)\",\n \"def number_of_lines(filename=\\\"\\\"):\\n\\n number_lines = 0\\n with open(filename) as file_opened:\\n for line in file_opened:\\n number_lines += 1\\n return number_lines\",\n \"def number_of_lines(filename=\\\"\\\"):\\n n_lines = 0\\n with open(filename, encoding='utf-8', mode='r') as file:\\n for lines in file:\\n n_lines += 1\\n return n_lines\",\n \"def number_of_lines(filename=\\\"\\\"):\\n counter = 0\\n with open(filename, \\\"r\\\") as my_file:\\n for line in my_file:\\n counter += 1\\n my_file.close()\\n return (counter)\",\n \"def number_of_lines(filename=\\\"\\\"):\\n n = 0\\n if filename == \\\"\\\":\\n return n\\n with open(filename, \\\"r\\\") as f:\\n for line in f:\\n n = n + 1\\n return n\",\n \"def num_lines(fname):\\n with open(fname) as f:\\n for i, l in enumerate(f):\\n pass\\n return( i + 1 )\",\n \"def commentOutLineMatching(pattern,fileName,maxOccurs=None):\\n \\n file=open(fileName,mode='r')\\n pattern=re.compile(pattern)\\n fileText=\\\"\\\"\\n numMatches=0\\n if maxOccurs==None:\\n maxOccurs=sys.maxsize\\n \\n for line in file:\\n \\n if pattern.match(line) and numMatches<maxOccurs:\\n fileText+=\\\"#\\\"+line\\n numMatches+=1\\n else:\\n fileText+=line\\n file.close()\\n file=open(fileName,mode='w')\\n file.write(fileText)\\n file.close()\\n return numMatches\",\n \"def map_count(filename):\\n f = open(filename, \\\"r+\\\")\\n buf = mmap.mmap(f.fileno(), 0)\\n lines = 0\\n readline = buf.readline\\n while readline():\\n lines += 1\\n return lines\",\n \"def countLines(file_name, start, end):\\r\\n\\r\\n with open(file_name, \\\"r\\\") as file:\\r\\n counter_lines = 0\\r\\n\\r\\n for line in islice(file, start, end):\\r\\n counter_lines += 1\\r\\n\\r\\n return counter_lines\",\n \"def countlines(fn):\\n with open(fn, 'rb') as f:\\n bufgen = takewhile(\\n lambda x: x, (f.read(1024 * 1024) for _ in repeat(None)))\\n ln = sum(buf.count(b'\\\\n') for buf in bufgen)\\n return ln\",\n \"def number_of_lines(filename=\\\"\\\"):\\n with open(filename, encoding=\\\"utf-8\\\") as file:\\n text = file.readlines()\\n return len(text)\",\n \"def count_lines(file_uri):\\n\\n with open(file_uri) as file_obj:\\n for i, line in enumerate(file_obj):\\n pass\\n num_lines = i + 1\\n return num_lines\",\n \"def bufcount(filename):\\n\\timport gzip\\n\\tif filename.split('.')[-1] in ['gz','gzip']: f = gzip.open(filename)\\n\\telse: f = open(filename)\\n\\tlines = 0\\n\\tbuf_size = 1024 * 1024\\n\\tread_f = f.read # loop optimization\\n\\t\\n\\tbuf = read_f(buf_size)\\n\\twhile buf:\\n\\t\\tlines += buf.count('\\\\n')\\n\\t\\tbuf = read_f(buf_size)\\n\\t\\tf.close\\n\\treturn lines\",\n \"def listRows(file):\\n\\twith open(file) as f:\\n\\t\\tcount = 0\\n\\t\\tfor line in f.readlines():\\n\\t\\t\\tcount += 1\\n\\t\\treturn count -1\",\n \"def find_dimesion(filename):\\n file = open(filename,\\\"r\\\")\\n\\n line = file.readline()\\n file.close()\\n return len(line.split())\",\n \"def no_of_lines():\\n number_of_lines = len(open(FILE_NAME).readlines())\\n return number_of_lines\",\n \"def count_locs(file_type, comment_pattern):\\n find = \\\"find . -name '*.{0}' -print0\\\".format(file_type)\\n sed_pattern = \\\"'/^\\\\s*{0}/d;/^\\\\s*$/d'\\\".format(comment_pattern)\\n\\n cmd = \\\"{0} | xargs -0 sed {1} | wc -l\\\".format(find, sed_pattern)\\n\\n return check_output(cmd, shell = True).decode('utf-8').replace('\\\\n', '')\",\n \"def count_positions(fname):\\r\\n with open(fname) as f:\\r\\n for i, l in enumerate(f):\\r\\n pass\\r\\n return i + 1\",\n \"def file_len(full_path):\\n f = open(full_path)\\n nr_of_lines = sum(1 for line in f)\\n f.close()\\n return nr_of_lines\",\n \"def get_file_lines(filename):\\n if not os.path.isfile(filename):\\n logging.error(\\\"[get_file_lines] %s not found\\\", filename)\\n return -1\\n\\n if not os.access(filename, os.R_OK):\\n logging.error(\\\"[get_file_lines] %s cannot be read\\\", filename)\\n return -1\\n\\n i = -1\\n with open(filename) as f:\\n try:\\n for i, l in enumerate(f):\\n pass\\n except UnicodeDecodeError:\\n return -1\\n return i + 1\",\n \"def _count_comment_rows(vcf_path):\\n vcf_lines_generator = lines_from_vcf(vcf_path)\\n\\n comment_lines_count = 0\\n for line in vcf_lines_generator:\\n if line.startswith('#'):\\n comment_lines_count += 1\\n else:\\n vcf_lines_generator.close() # Don't leave the file handle opened\\n # Don't continue reading the VCF once the comments section ended\\n break\\n\\n return comment_lines_count\",\n \"def count_variants(filename, content=None):\\n open_fn = gzip.open if is_gz_file(filename) else open\\n count = 0\\n with open_fn(filename, \\\"rt\\\") as ifile:\\n for line in ifile:\\n if not line.startswith(\\\"#\\\"):\\n if content:\\n if content in line:\\n count += 1\\n else:\\n count += 1\\n return count\",\n \"def get_rows_num(file_name):\\n # By default add the first datetime\\n row_num = 1\\n with open(file_name, \\\"r\\\") as file:\\n for line in file:\\n if line == \\\"\\\\n\\\":\\n break\\n else:\\n row_num += 1\\n\\n return row_num\",\n \"def commentaires(file_name_c):\\n com = subprocess.run([\\\"grep\\\", \\\"eleves_bis/\\\" + file_name_c, \\\"-e\\\", '/\\\\*'], stdout=subprocess.PIPE)\\n return com.stdout.decode().count(\\\"\\\\n\\\")\",\n \"def nrows(filename):\\n with fopen(filename, 'rb') as fdsc:\\n bufgen = takewhile(lambda x: x, (fdsc.read(1024*1024) for _ in repeat(None)))\\n return sum([buf.count(b'\\\\n') for buf in bufgen])\",\n \"def countSamples(filename):\\n with open(filename, \\\"r\\\") as f:\\n line = f.readline().split(\\\"\\\\t\\\")\\n return len(line) - 2\",\n \"def count_LOC(path):\\n re_empty = re.compile(r\\\"[\\\\s]*(#|\\\\n|\\\\\\\"\\\\\\\"\\\\\\\")\\\")\\n re_for = re.compile(r\\\"for.*in\\\")\\n re_lambda = re.compile(r\\\"lambda\\\")\\n re_if = re.compile(r\\\"if.*:\\\")\\n re_def = re.compile(r\\\"def (?P<fname>\\\\w+)\\\\(\\\")\\n\\n total_LOC, indent_level = 0, 0\\n cur_part = None\\n parts = defaultdict(int)\\n\\n with open(path, 'r') as _file:\\n for line in filter(lambda l : not re_empty.match(l), _file):\\n\\n extra = len( re_for.findall(line) ) - 1 + len( re_lambda.findall(line) ) - 1 + len( re_if.findall(line) ) -1\\n\\n if extra < 0: extra = 0\\n\\n total_LOC += 1 + extra\\n if cur_part:\\n parts[cur_part] += 1 + extra\\n\\n defs = re_def.search(line)\\n if defs:\\n cur_part = defs.groupdict()['fname']\\n indent_level = first_non_whitespace(line)\\n\\n cur_indent = first_non_whitespace(line)\\n if cur_indent < indent_level:\\n cur_part = None\\n indent_level = cur_indent\\n\\n return(total_LOC, parts)\",\n \"def count_occurrences(text, pattern, d=0):\\n return len(find_occurrences(text, pattern, d))\",\n \"def linecount(x):\\n return sum(1 for char in x if char == \\\"\\\\n\\\")\",\n \"def countsubcatchments(inputfilename=FileSettings.settingsdict['inputfilename']):\\r\\n global count\\r\\n with open(inputfilename, 'r') as swmmput:\\r\\n contents = swmmput.readlines()\\r\\n count = len(contents)\\r\\n return(count)\",\n \"def count_lines(filename):\\n with open(filename, 'r', encoding='utf-8') as file:\\n lines_count = int()\\n for line in file:\\n lines_count += 1\\n info_tuple = (filename, lines_count)\\n return info_tuple\",\n \"def pattern_count(text, pattern):\\n\\n count = 0\\n len_text = len(text)\\n len_pattern = len(pattern)\\n for i in range(len_text - len_pattern):\\n if pattern in text[i:i + len_pattern]:\\n count = count + 1\\n else:\\n continue\\n return count\",\n \"def main():\\n\\tcount = 0\\n\\twith open(FILE, 'r') as f:\\n\\t\\tfor line in f:\\n\\t\\t\\tcount += 1\\n\\tprint('There are ' + str(count) + ' lines in '+str(FILE))\",\n \"def pattern_count(text, pattern):\\n return len([i\\n for i in range(0, len(text) - len(pattern) + 1)\\n if text[i:i + len(pattern)] == pattern])\",\n \"def file_len(fname):\\n with open(fname) as f:\\n for i, l in enumerate(f):\\n pass\\n Nrows = i + 1\\n return Nrows\",\n \"def grep_me(pattern, fname):\\n for line in stream_reader(fname):\\n if re.search(pattern, line, re.I):\\n print('{}:{}:{}'.format(filename(), filelineno(), line), end='')\",\n \"def get_lines_in_file(file_path, read=False):\\n nLines = 0\\n lines = []\\n with open(file_path, 'r') as f:\\n for line in f:\\n nLines += 1\\n if read:\\n lines.append(line.strip())\\n return lines, nLines\",\n \"def count_pattern(sentence, pattern):\\n n = len(pattern)\\n counter = 0\\n for i in range(len(sentence) - n + 1):\\n if sentence[i:i+n] == pattern:\\n counter += 1\\n\\n return counter\",\n \"def FindHeaderLength():\\n\\n lookup = 'Lateral um'\\n \\n with open(filename) as myFile:\\n for FoundPosition, line in enumerate(myFile, 1):\\n if lookup in line:\\n print 'Scan Data found at line:', FoundPosition\\n break\\n \\n return FoundPosition+4\",\n \"def PatternCount(text, pattern):\\n\\n count = 0\\n for i in range(0, len(text)-len(pattern)+1):\\n if text[i:i+len(pattern)] == pattern:\\n count += 1\\n return count\",\n \"def find_pattern_in_file(pattern, file_name):\\n pattern = re.compile(pattern)\\n with open(file_name) as f:\\n for line in f:\\n for match in re.finditer(pattern,line):\\n return match.groups()\\n return None\",\n \"def linesCountingAux(file_name, nProcesses):\\r\\n\\r\\n linesPerProcessesList = []\\r\\n\\r\\n with open(file_name, \\\"r\\\") as file:\\r\\n lineCounting = 0\\r\\n\\r\\n for line in file:\\r\\n lineCounting += 1 #discover the lines in the text file\\r\\n\\r\\n linesPerProcesses = lineCounting // nProcesses\\r\\n\\r\\n for number in range(nProcesses):\\r\\n linesPerProcessesList.append(linesPerProcesses)\\r\\n if sum(linesPerProcessesList) < lineCounting:\\r\\n for number in range (lineCounting - sum(linesPerProcessesList)):\\r\\n linesPerProcessesList[number] += 1\\r\\n\\r\\n return linesPerProcessesList\",\n \"def count_lines(file_obj):\\n for idx, line in enumerate(file_obj):\\n pass\\n file_obj.seek(0)\\n return idx + 1\",\n \"def contig_count(contig):\\n return sum([1 for line in open(contig, 'rU').readlines() if line.startswith('>')])\",\n \"def get_line_length(file_path):\\n with open(file_path, 'rb+') as f:\\n return len(f.readline())\",\n \"def _MatchPatternLines(self, in_stream, re_pattern, num_lines=None):\\n num_read = 0\\n while True:\\n line = in_stream.readline()\\n if not line:\\n return None\\n num_read += 1\\n m = re_pattern.match(line)\\n if m is not None:\\n return m\\n if num_lines is not None and num_read >= num_lines:\\n return None\",\n \"def test_line_count(self):\\n self.assertEqual(analyze_text(self.filename)[0], 11)\",\n \"def _get_line_number(vcf):\\n with open(vcf) as vcf_input_file:\\n i = -1\\n for line in vcf_input_file:\\n i += 1\\n return i\",\n \"def position(file_, pattern):\\n pattern = pattern[1:-1]\\n pattern = pattern.replace('(', '\\\\(')\\n pattern = pattern.replace(')', '\\\\)')\\n file_obj = open(file_, 'rU')\\n for line_number, line in enumerate(file_obj):\\n m = re.search(pattern, line)\\n if m is not None:\\n return line_number, m.pos\\n file_obj.close()\\n return 0, 0\",\n \"def calculate_line_number(text):\\n return len([line for line in text.split(\\\"\\\\n\\\") if line.strip() != \\\"\\\"])\",\n \"def count_total_line():\\n count = 0\\n file_count = 0\\n for filename in os.listdir('.'):\\n if filename.endswith(\\\".json\\\"):\\n file_count += 1\\n with open(filename, 'r', encoding='utf8') as f:\\n for line in f:\\n count += 1\\n print(\\\"There are {0} lines in {1} json files\\\".format(count, file_count))\",\n \"def count_meme_entries(motif_path):\\n with open(motif_path, \\\"r\\\") as f:\\n counter = 0\\n for line in f:\\n if line[:6] == \\\"letter\\\":\\n counter += 1\\n return counter\",\n \"def num_lines(self, snapshot: Bug, filepath: str) -> int:\\n return len(self._line_offsets(snapshot, filepath))\",\n \"def trace_file_len(fname):\\n try:\\n with open(fname) as f:\\n for i, l in enumerate(f):\\n pass\\n return i - 1\\n except FileNotFoundError:\\n return 0\",\n \"def file_len(filename):\\n with open(filename) as f:\\n for i, l in enumerate(f):\\n pass\\n return i + 1\",\n \"def test_line_count(self):\\n self.assertEqual(analyze_text(self.filename)[0], 4)\",\n \"def test_line_count(self):\\n self.assertEqual(analyze_text(self.filename)[0], 4)\",\n \"def getKeywordCount(cwd, keyword):\\n lines_count = 0\\n\\n for dirpath, dirnames, filenames in os.walk(cwd):\\n for file in filenames:\\n file_path = os.path.join(dirpath, file)\\n lines_count += countLines(file_path, keyword)\\n return lines_count\",\n \"def count_lines(inFile, outFile):\\n \\n bodyLines = []\\n # Begin iteratin lines, starting at line 'lineNum'\\n for line_ in inFile:\\n line_ = line_.rstrip('\\\\n').strip()\\n if (not line_) or (line_[0] == '!'):\\n continue\\n elif line_.upper() in cases:\\n print(\\\"Error: encountered new case before end of last.\\\"\\n \\\"\\\\n Have you forgotten an 'END'?\\\", file=outFile)\\n sys.exit()\\n elif line_.upper() == \\\"END\\\":\\n return bodyLines\\n else:\\n bodyLines.append(line_)\",\n \"def get_pattern_count(sequence, pattern):\\n return len(re.findall(r'(?=' + pattern + ')', sequence))\",\n \"def get_number_of_paragraph(self):\\n file_to_read = f'{self.path}/{self.filename}'\\n file = open(file_to_read, 'r', encoding='utf-8')\\n string_to_match = '<p>'\\n count = 0\\n for line in file:\\n if string_to_match in line:\\n count += 1\\n sqlite_for_ht.CreateTable.update_table(f_1, self.filename, 'number_of_paragraph', count)\\n print(datetime.now(), '-', 'number_of_paragraph for', self.filename, 'calculated =', count)\\n return None\",\n \"def test_line_count(self):\\n\\t\\tself.assertEqual(analyse_text(self.filename)[0], 4)\",\n \"def read_file(filename, n, val):\\n\\tcount, read = 0, 0\\n\\twith open(filename, 'r') as f:\\n\\t\\tfor value in f.readlines():\\n\\t\\t\\tread += 1\\n\\t\\t\\tif read > n: break\\n\\t\\t\\tif int(value) == val: count += 1\\n\\treturn count\",\n \"def grep(pattern, filename):\\n rx = re.compile(pattern)\\n with open(filename, 'r') as file:\\n for line in file:\\n if rx.search(line):\\n yield line\",\n \"def file_length(fileName):\\n with open(f_pass) as f:\\n for i, l in enumerate(f):\\n pass\\n return i + 1\",\n \"def file_len(file_name):\\n with open(file_name) as f:\\n for i, l in enumerate(f):\\n pass\\n return i + 1\",\n \"def get_file_counts(filename):\\n new_file = open(filename, \\\"r\\\")\\n d = dict()\\n for line in new_file: \\n split_line = line.split()\\n for word in split_line:\\n if word in d:\\n d[word] += 1\\n else:\\n d[word] = 1\\n new_file.close()\\n return d\",\n \"def num_data_lines(filepath):\\n\\tif not file_exists(filepath):\\n\\t\\treturn -1\\n\\tcount = 0\\n\\twith open(filepath, 'r') as f:\\n\\t\\twhile read_floats(f):\\n\\t\\t\\tcount += 1\\n\\tf.close()\\n\\treturn count\",\n \"def get_filesize(inputfile) -> int:\\n with open(inputfile, \\\"rb\\\") as f:\\n lines = 0\\n buf_size = 1024 * 1024\\n read_f = f.raw.read\\n\\n buf = read_f(buf_size)\\n while buf:\\n lines += buf.count(b\\\"\\\\n\\\")\\n buf = read_f(buf_size)\\n\\n return lines\",\n \"def file_len(fname):\\n \\n with open(fname) as f:\\n for i, l in enumerate(f):\\n pass\\n return i + 1\",\n \"def get_number_lines(running_reward_file, running_loss_file, action_count_file):\\n if Path(running_reward_file).exists():\\n data = np.loadtxt(running_reward_file).reshape(-1,2)\\n return data.shape[0]\\n if Path(running_loss_file).exists():\\n data = np.loadtxt(running_loss_file).reshape(-1,2)\\n return data.shape[0]\\n if Path(action_count_file).exists():\\n data = np.loadtxt(action_count_file).reshape(-1,2)\\n return data.shape[0]\\n raise NameError(\\\"No files to count lines\\\")\",\n \"def bpCount(file):\\n amount_bp = len(file)\\n return amount_bp\",\n \"def count_entries(path, dedupe=True):\\n if dedupe:\\n return len(get_identities(path))\\n else:\\n if not os.path.exists(path):\\n return 0\\n with open(path, 'rb') as f:\\n return sum(1 for line in f)\",\n \"def countCharacters(file_name, start, end):\\r\\n\\r\\n with open(file_name, \\\"r\\\") as file:\\r\\n counter_chars = 0\\r\\n\\r\\n for line in islice(file, start, end):\\r\\n counter_chars += len(line)\\r\\n\\r\\n return counter_chars\",\n \"def line_offsets(fname):\\n line_offset = []\\n offset = 0\\n for _, line in enumerate( open(fname) ):\\n line_offset.append(offset)\\n offset += len(line)\\n return line_offset\",\n \"def analyzeCppCode(self, sourceFile):\\n numLines = 0 # Number of lines of code\\n numComments = 0 # Number of comments in the code\\n\\n f=self.openFile(sourceFile)\\n for line in f:\\n numLines += 1;\\n loc = 0\\n while (loc != -1): #count the # of times the '/*' characters appears\\n loc = line.find(\\\"#\\\", loc)\\n if (loc != -1):\\n loc += 1\\n numComments += 1\\n \\n loc = 0\\n loc = line.find('//', loc) #count the # of times the '//' characters appears\\n if (loc != -1):\\n loc += 1\\n numComments += 1\\n \\n f.close()\\n return numLines, numComments\",\n \"def count_tests(infile):\\n\\n ntests = 0\\n with open(infile, \\\"r\\\") as ff:\\n for line in ff:\\n if line.strip(): # Jump empty lines\\n if summary in line:\\n # Count failed tests for each start of file\\n ntests += 1\\n return ntests\",\n \"def get_num_examples(path_in):\\n i = 0\\n with open(path_in, 'r', encoding='utf8') as f:\\n for _ in f:\\n i += 1\\n return i\",\n \"def skiprows(fn):\\n for i, line in enumerate(open(fn)):\\n if line.startswith('#'):\\n continue\\n else:\\n break\\n return i\",\n \"def header_len(fname,header_char='#'):\\n Nheader = 0\\n with open(fname) as f:\\n for i, l in enumerate(f):\\n if ( (l[0:len(header_char)]==header_char) or (l==\\\"\\\\n\\\") ):\\n Nheader += 1\\n else:\\n break\\n\\n return Nheader\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.79906595","0.7601465","0.7589926","0.7400159","0.7400159","0.7367526","0.7361509","0.7314271","0.7307912","0.7246522","0.72217196","0.71740115","0.7155738","0.7092855","0.7072881","0.7064458","0.70398235","0.7038543","0.7035276","0.7028555","0.7019521","0.6974545","0.696929","0.69293606","0.69266313","0.6907818","0.68913555","0.6849545","0.68060094","0.67846346","0.6757026","0.661889","0.65990454","0.6586989","0.6545936","0.6529705","0.64956236","0.6432328","0.642815","0.6419043","0.6402347","0.6384721","0.6370523","0.6360136","0.6326862","0.63232","0.6299591","0.6284598","0.62707895","0.6253771","0.6213732","0.61710906","0.61661005","0.6140425","0.6127164","0.61227924","0.61224216","0.6121656","0.6113622","0.61012393","0.61006814","0.6078083","0.6051001","0.6046518","0.6040775","0.60348696","0.6029514","0.6019405","0.6019182","0.6019177","0.5997199","0.5991397","0.59728914","0.5971736","0.5963107","0.59565026","0.59565026","0.5954773","0.5945826","0.59380937","0.5935022","0.5924292","0.59146893","0.5913255","0.5912229","0.5902537","0.5899652","0.5891372","0.5886438","0.5883692","0.5881619","0.587707","0.5876283","0.5873141","0.5859815","0.5857686","0.585486","0.58294815","0.58022857","0.5798574"],"string":"[\n \"0.79906595\",\n \"0.7601465\",\n \"0.7589926\",\n \"0.7400159\",\n \"0.7400159\",\n \"0.7367526\",\n \"0.7361509\",\n \"0.7314271\",\n \"0.7307912\",\n \"0.7246522\",\n \"0.72217196\",\n \"0.71740115\",\n \"0.7155738\",\n \"0.7092855\",\n \"0.7072881\",\n \"0.7064458\",\n \"0.70398235\",\n \"0.7038543\",\n \"0.7035276\",\n \"0.7028555\",\n \"0.7019521\",\n \"0.6974545\",\n \"0.696929\",\n \"0.69293606\",\n \"0.69266313\",\n \"0.6907818\",\n \"0.68913555\",\n \"0.6849545\",\n \"0.68060094\",\n \"0.67846346\",\n \"0.6757026\",\n \"0.661889\",\n \"0.65990454\",\n \"0.6586989\",\n \"0.6545936\",\n \"0.6529705\",\n \"0.64956236\",\n \"0.6432328\",\n \"0.642815\",\n \"0.6419043\",\n \"0.6402347\",\n \"0.6384721\",\n \"0.6370523\",\n \"0.6360136\",\n \"0.6326862\",\n \"0.63232\",\n \"0.6299591\",\n \"0.6284598\",\n \"0.62707895\",\n \"0.6253771\",\n \"0.6213732\",\n \"0.61710906\",\n \"0.61661005\",\n \"0.6140425\",\n \"0.6127164\",\n \"0.61227924\",\n \"0.61224216\",\n \"0.6121656\",\n \"0.6113622\",\n \"0.61012393\",\n \"0.61006814\",\n \"0.6078083\",\n \"0.6051001\",\n \"0.6046518\",\n \"0.6040775\",\n \"0.60348696\",\n \"0.6029514\",\n \"0.6019405\",\n \"0.6019182\",\n \"0.6019177\",\n \"0.5997199\",\n \"0.5991397\",\n \"0.59728914\",\n \"0.5971736\",\n \"0.5963107\",\n \"0.59565026\",\n \"0.59565026\",\n \"0.5954773\",\n \"0.5945826\",\n \"0.59380937\",\n \"0.5935022\",\n \"0.5924292\",\n \"0.59146893\",\n \"0.5913255\",\n \"0.5912229\",\n \"0.5902537\",\n \"0.5899652\",\n \"0.5891372\",\n \"0.5886438\",\n \"0.5883692\",\n \"0.5881619\",\n \"0.587707\",\n \"0.5876283\",\n \"0.5873141\",\n \"0.5859815\",\n \"0.5857686\",\n \"0.585486\",\n \"0.58294815\",\n \"0.58022857\",\n \"0.5798574\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79991657"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":76697,"cells":{"query":{"kind":"string","value":"Function to clean the datasets following the given instructions"},"document":{"kind":"string","value":"def clean_dataset(dataset, pollsters, output_name):\n # No banned pollsters\n no_banned = pollsters[pollsters['Banned by 538'] == 'no'].Pollster\n # Interviews with no banned pollster\n dataset = dataset[dataset.pollster.isin(no_banned)]\n # Non tracked interviews\n dataset = dataset[dataset.tracking == False]\n # Reset index\n dataset = dataset.reset_index(drop=True)\n # Save clean data\n dataset.to_csv(\"./data/\" + output_name, index=False)\n # Return clean data\n return dataset"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def clean(args):\n with_dataset(args, Dataset._clean)","def cleaning (data):","def _clean_data(self, dataset):\n dataset.dropna(inplace=True)\n # Problem: handle missing data (in a different way), noisy data, inconsistent data","def clean():\n filter_phase_data()\n combine_phase_data()\n remove_duplicates_phase_data()","def cleaning_Dataset(dataset):\n cols = dataset.select_dtypes([np.number]).columns\n diff = dataset[cols].diff().sum()\n\n dataset = dataset.drop([diff==0].index, axis=1)\n dataset = dataset.drop('adj close', 1)\n dataset = dataset.fillna(method='bfill')\n dataset = dataset[1:-1]\n return dataset","def clean_up_data(self):\n pass","def clean(df):","def clean(self):\n self.df = _data.prune(self.df, [REGEX_PATTERN_GCI, REGEX_PATTERN_DB_ID])\n self.df, _ = _data.remove_totally_failed_tests(self.df)\n self.is_cleaned = True","def clean_data(df):\n \n # Put in code here to execute all main cleaning steps:\n # convert missing value codes into NaNs, ...\n count_miss = df.isnull().sum(axis=0).values #find number of nans for each column\n count_miss = [val for val in count_miss]\n \n drop_cols = []\n\n for ind, val in enumerate(count_miss):\n if val > 200000:\n drop_cols.append(ind)\n \n df_drop_cols = list(azdias.columns[drop_cols])\n df = df.drop(df_drop_cols, axis=1)\n \n for col in range(df.shape[1]): #loop through columns\n column_name = df.columns[col] #get column name\n missing_list = feat_info.iloc[col,3] #get missing_or_unknown column from feature info\n missing_list = missing_list.replace('[','') #remove left bracket from string\n missing_list = missing_list.replace(']','') #remove right bracket from string\n missing_list = missing_list.split(',') #split into individual strings\n \n #find data that is natually missing and continue loop to omit\n if missing_list == ['']:\n continue\n \n else:\n for dat_type in missing_list: \n if df[column_name].dtype == 'object': #find values that contain x\n df.loc[df[column_name] == dat_type, column_name] = np.nan #replace x with nan\n \n else:\n dat_type = int(dat_type) #if no x, convert to integer and replace with nan\n df.loc[df[column_name] == dat_type, column_name] = np.nan\n \n # select, re-encode, and engineer column values.\n \n # encode OST_WEST_KZ\n df.loc[df['OST_WEST_KZ'] == 'W','OST_WEST_KZ'] = 0\n df.loc[df['OST_WEST_KZ'] == 'O','OST_WEST_KZ'] = 1\n \n # Re-encode categorical variable(s) to be kept in the analysis.\n \n \n #get list of attributes with type categorical\n feat_info[feat_info['type'] == 'categorical']\n \n cat_new_cols = [] #initialize\n for i in feat_info[feat_info['type'] == 'categorical']['attribute']:\n cat_new_cols.append(i)\n \n for cols in df.columns:\n if cols in cat_new_cols:\n if df[cols].nunique(dropna=True) > 2: #if the number of unique values is greater than 2 \n df = df.drop(cols, axis=1) #drop from the analysis\n print(\"more than 2 categories: {}\".format(cols))\n \n else:\n if not df[cols].unique()[0] > 0:\n #if not df[cols].unique()[0] > 0:\n dummies = pd.get_dummies(df[cols], prefix=cols)\n df = df.drop(cols, axis=1) #create dummy variable\n df = df.join(dummies)\n print(\"transformed to dummy variable: {}\".format(cols))\n \n # create variable: MOVEMENT\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([1,3,5,8,10,12,14]),'MOVEMENT'] = 1\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([2,4,6,7,9,11,13,15]),'MOVEMENT'] = 2\n \n #Capture Decade\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([1,2]), 'DECADE'] = 40\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([3,4]), 'DECADE'] = 50\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([5,6,7]), 'DECADE'] = 60\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([8,9]), 'DECADE'] = 70\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([10,11,12,13]), 'DECADE'] = 80\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([14,15]), 'DECADE'] = 90\n \n df['CAMEO_INTL_2015'] = df['CAMEO_INTL_2015'].astype(float)\n\n # create new variable: WEALTH\n df.loc[df['CAMEO_INTL_2015'].isin([51,52,53,54,55]), 'WEALTH'] = 1\n df.loc[df['CAMEO_INTL_2015'].isin([41,42,43,44,45]), 'WEALTH'] = 2\n df.loc[df['CAMEO_INTL_2015'].isin([31,32,33,34,35]), 'WEALTH'] = 3\n df.loc[df['CAMEO_INTL_2015'].isin([21,22,23,24,25]), 'WEALTH'] = 4\n df.loc[df['CAMEO_INTL_2015'].isin([11,12,13,14,15]), 'WEALTH'] = 5\n \n # create new variable: LIFE_STAGE\n df.loc[df['CAMEO_INTL_2015'].isin([11,21,31,41,51]),'LIFE_STAGE'] = 1\n df.loc[df['CAMEO_INTL_2015'].isin([12,22,32,42,52]),'LIFE_STAGE'] = 2\n df.loc[df['CAMEO_INTL_2015'].isin([13,23,33,43,53]),'LIFE_STAGE'] = 3\n df.loc[df['CAMEO_INTL_2015'].isin([14,24,34,44,54]),'LIFE_STAGE'] = 4\n df.loc[df['CAMEO_INTL_2015'].isin([15,25,35,45,55]),'LIFE_STAGE'] = 5\n \n # remove selected columns and rows, ...\n df = df.drop('PRAEGENDE_JUGENDJAHRE', axis=1)\n df = df.drop('CAMEO_INTL_2015',axis=1)\n \n # Return the cleaned dataframe.\n return df","def _clean_datafile_set(self):\n file_list = self._meta['sets']['data file']['items']\n for item in file_list[:]:\n collection = item.split('@')[0]\n variable = item.split('@')[1]\n if not variable in self:\n file_list.remove(item)\n elif collection == 'masks':\n for s in self._get_source_ref(variable):\n while s in file_list:\n file_list.remove(s)\n elif self._is_array_item(variable):\n parent = self.parents(variable)[0]\n if not parent in file_list:\n idx = file_list.index(item)\n file_list[idx] = parent\n while item in file_list:\n file_list.remove(item)\n f_list = []\n for item in file_list:\n if not item in f_list: f_list.append(item)\n self._meta['sets']['data file']['items'] = f_list\n return None","def _clean_results(self):\n\t\tif self.file_type == \"Automobile\":\n\t\t\tcols = [\"Year\", \"Mileage\", \"Price\"]\n\t\t\tself.data.Mileage.replace([',', 'mi.', 'nan', ' '], '', regex=True, inplace=True) # Fix mileage column\n\t\t\tself.data.Price.replace([',', '\\$'], '', regex=True, inplace=True) # Always fix price column (, and $ removed)\n\t\t\tself.data[cols] = self.data[cols].apply(pd.to_numeric, errors='coerce') # Coerces errors into NaN values\n\t\t\tself.data.drop(self.data[self.data.Year < 2000].index, inplace=True) # Remove cars made before 2000\n\t\t\tself.data.drop(self.data[self.data.Price > 30000].index, inplace=True) # Remove cars over $30,000\n\t\t\tself.data.drop(self.data[(self.data.Mileage < 1000) | (self.data.Mileage > 300000)].index, inplace=True) # Remove cars with over 300,000 miles\n\t\t\tself.data['Age'] = 2018 - self.data['Year'] # Change years to Age\n\t\telif self.file_type == \"Apartment\":\n\t\t\tself.data.Area.replace(['ft2'], '', regex=True, inplace=True) # Remove ft2 from square footage column\n\t\t\tself.data.Price.replace([',', '\\$'], '', regex=True, inplace=True) # Always fix price column (, and $ removed)\n\t\telse:\n\t\t\tself.data['Street'], self.data['City'], self.data['State'] = self.data['Address'].str.split(',', 2).str\n\t\t\tdel self.data.Address\n\t\t\tself.data.drop(self.data[self.data.Price > 1000000].index, inplace=True) # Remove houses worth more than $1 million\n\n\t\tself.data.replace('^\\s*$', np.nan, regex=True, inplace=True) # Replace all empty values with np.NaN\n\t\tself.data = self.data.dropna(axis=1, how='all') # Remove Null Columns\n\t\tself.data = self.data.apply(pd.to_numeric, errors='ignore') # Coerces errors into NaN values","def cleanData(data):\r\n \r\n indexes = data.index[pd.isnull(data[\"release date\"])]\r\n # data = data.drop(data.index[[2172, 3781, 7245, 12475, 14756, 15292, 49295, 93523, 99723]],axis=0,inplace=False)\r\n data = data.drop(indexes,axis=0,inplace=False)\r\n data = data.drop([\"video release date\", 'IMDb URL'],axis=1)\r\n \r\n \r\n data.gender = data.gender.astype(\"category\")\r\n data.occupation = data.occupation.astype(\"category\")\r\n \r\n categorical_variables = [\"gender\", \"occupation\"]\r\n for variable in categorical_variables:\r\n dummies = pd.get_dummies(data[variable], prefix=variable)\r\n data = pd.concat([data, dummies], axis=1)\r\n data.drop([variable], axis=1, inplace=True)\r\n data['movieID'] = data['movieID'].astype('int')\r\n data['userID'] = data['userID'].astype('int')\r\n \r\n\t#data = dateTime(data)\r\n \r\n return data","def clean(c):","def clean(self):","def _clean(self, dataset):\n # Replace missing values with numpy's NaN. The missing value is\n # usually 1e+20, but values can be like 1.0000002e+20, which is\n # different. Ergo the inequality.\n for var in dataset.data_vars.itervalues():\n if 'missing_value' in var.attrs:\n missing_data_value = var.missing_value\n try:\n var.values[var.values >= missing_data_value] = np.NaN\n except ValueError:\n print \"Encountered ValueError in {0}. Ignoring\".format(var.name)","def cleanData(self):\n clean_data = []\n for file in self.raw_data_file:\n data = RawData(file)\n data.clean()\n data.prepare()\n clean_data.append(data)\n return clean_data","def cleanup(adata, del_prediction=False, del_2nd_moments=False):\n\n if \"pca_fit\" in adata.uns_keys():\n adata.uns[\"pca_fit\"] = None\n if \"velocyto_SVR\" in adata.uns_keys():\n adata.uns[\"velocyto_SVR\"][\"SVR\"] = None\n if \"umap_fit\" in adata.uns_keys():\n adata.uns[\"umap_fit\"][\"fit\"] = None\n if \"velocity_pca_fit\" in adata.uns_keys():\n adata.uns[\"velocity_pca_fit\"] = None\n if \"kmc\" in adata.uns_keys():\n adata.uns[\"kmc\"] = None\n if \"kinetics_heatmap\" in adata.uns_keys():\n adata.uns.pop(\"kinetics_heatmap\")\n if \"hdbscan\" in adata.uns_keys():\n adata.uns.pop(\"hdbscan\")\n\n VF_keys = [i if i.startswith(\"VecFld\") else None for i in adata.uns_keys()]\n for i in VF_keys:\n if i is not None and \"VecFld2D\" in adata.uns[i].keys():\n del adata.uns[i][\"VecFld2D\"]\n\n fate_keys = [i if i.startswith(\"fate\") else None for i in adata.uns_keys()]\n for i in fate_keys:\n if i is not None:\n if adata.uns[i][\"init_cells\"] is not None:\n adata.uns[i][\"init_cells\"] = list(adata.uns[i][\"init_cells\"])\n if \"prediction\" in adata.uns[i].keys():\n if del_prediction:\n del adata.uns[i][\"prediction\"]\n if \"VecFld_true\" in adata.uns[i].keys():\n if adata.uns[i][\"VecFld_true\"] is not None:\n del adata.uns[i][\"VecFld_true\"]\n\n if del_2nd_moments:\n from .tools.utils import remove_2nd_moments\n\n remove_2nd_moments(adata)\n\n return adata","def cleanup(self):\n\n # uninstall sourcedata\n if self.conversion.install_dataset_path.exists():\n # without the ChangeWorkingDir the command does not operate inside\n # of dataset_path\n with utils.ChangeWorkingDir(self.dataset_path):\n datalad.uninstall(\n path=self.conversion.install_dataset_name,\n dataset=self.dataset_path,\n recursive=True\n )\n\n # remove bids conversion\n bids_dir = self._get_bids_dir()\n if bids_dir.exists():\n self.log.info(\"Remove %s\", bids_dir)\n shutil.rmtree(bids_dir)","def data_preparation(self) -> None:\n self.logger.info('data cleaning')\n self.logger.info('num of secs: {}, num of ipo_dates: {}, num of secs with prices: {}'.format(\n len(self.data),\n len(self.ipo_dates),\n len(self.prices)\n ))\n excluded = []\n excluded = [i.lower() for i in excluded]\n self.logger.info(f'number of excluded: {len(excluded)}')\n for i in excluded:\n self.data.pop(i)\n for s in self.data:\n # columns with empty assets sum (empty columns and other situations)\n self.data[s].dropna(axis='columns', how='any', subset=['A_0'], inplace=True)\n # columns with descriptions (polish and english names of values)\n self.data[s].drop(self.data[s].columns[[0, 1]], inplace=True, axis=1)\n\n self.logger.info(f'number of secs after cleaning: {len(self.data)}')\n data_list = [k for k in self.data.values()]\n self.uber_data = pd.concat(data_list, ignore_index=True, axis=1)\n self.uber_data = self.uber_data.transpose()\n self.uber_data = self.uber_data.loc[:, pd.notnull(self.uber_data.columns)]","def transform(self, dataset, labels):\n print(f\"Dropping {len(self.deficient)} deficient features...\")\n dataset.drop(columns=self.deficient, inplace=True)\n print(f\"Scanning {len(dataset)} samples for duplicates...\")\n duplicates = dataset.duplicated()\n print(f\"Dropping {sum(duplicates)} duplicate samples...\")\n dataset.drop(index=dataset.index[duplicates], inplace=True)\n dataset.reset_index(drop=True, inplace=True)\n labels.drop(labels=labels.index[duplicates], inplace=True)\n labels.reset_index(drop=True, inplace=True)\n return dataset, labels","def clean_data():\n pd.set_option('display.max_columns', None)\n try:\n df = pd.read_csv('test1/movie.csv')\n except FileNotFoundError:\n df = pd.read_csv('movie.csv')\n\n df.drop(labels=[\"actor_3_facebook_likes\", \"actor_2_name\",\n \"actor_1_facebook_likes\", \"actor_1_name\",\n \"num_voted_users\",\n \"cast_total_facebook_likes\", \"actor_3_name\",\n \"facenumber_in_poster\", \"movie_imdb_link\",\n \"num_user_for_reviews\", \"actor_2_facebook_likes\",\n \"aspect_ratio\", \"color\", \"num_critic_for_reviews\",\n \"director_facebook_likes\"], axis=1, inplace=True)\n df.dropna(subset=[\"gross\"], axis=0, inplace=True)\n return df","def clean(self):\n pass","def normalize_dataset(self):","def clean(self):\n # Perform the standard ACE cleaning\n max_status = mm_ace.clean(self)\n\n # Replace bad values with NaN and remove times with no valid data\n ecols = ['eflux_38-53', 'eflux_175-315']\n\n # Evaluate the electron flux data\n self[self.data['status_e'] > max_status, ecols] = np.nan\n\n # Evaluate the proton flux data\n pcols = ['pflux_47-68', 'pflux_115-195', 'pflux_310-580',\n 'pflux_795-1193', 'pflux_1060-1900']\n self[self.data['status_p'] > max_status, pcols] = np.nan\n\n # Include both fluxes and the anisotropy index in the removal eval\n eval_cols = ecols + pcols\n eval_cols.append('anis_ind')\n\n # Remove lines without any good data\n good_cols = (np.isfinite(self.data.loc[:, eval_cols])).sum(axis=1)\n bad_index = good_cols[good_cols == 0].index\n self.data = self.data.drop(index=bad_index)\n\n return","def clear_data():\n logger.info(\"Delete Structure instances\")\n Structure.objects.all().delete()\n logger.info(\"Delete StructureType instances\")\n StructureType.objects.all().delete()\n logger.info(\"Delete Industry instances\")\n Industry.objects.all().delete()\n logger.info(\"Delete Price instances\")\n PriceList.objects.all().delete()\n logger.info(\"Delete Stock instances\")\n Stock.objects.all().delete()\n logger.info(\"Delete News instances\")\n News.objects.all().delete()\n logger.info(\"Delete NewsImages instances\")\n NewsImage.objects.all().delete()\n logger.info(\"Delete News Sections instances\")\n NewsCategorySection.objects.all().delete()\n logger.info(\"Delete Analysis instances\")\n AnalysisOpinion.objects.all().delete()\n logger.info(\"Delete Analysis Images instances\")\n AnalysisImage.objects.all().delete()\n logger.info(\"Delete Analysis Sections instances\")\n AnalysisCategorySection.objects.all().delete()","def clean_data(data):\n \n cols = data.columns\n \n #these columns had some extra characters in the strings becuase of encoding issues\n list_to_strip=[\n 'attributes_alcohol',\n 'attributes_restaurantsattire',\n 'attributes_wifi',\n 'attributes_smoking',\n 'attributes_noiselevel',\n ]\n #this removes quotation marks and u's from strings\n \n for col in list_to_strip:\n data[col]=data[col].str.strip(\"u\\'\")\n \n #this replaces the strings None and none with Nan objects\n for col in cols:\n data[col]=data[col].where(data[col]!='None')\n data[col]=data[col].where(data[col]!='none')\n \n #this creates a list of categorical and numerical features\n categorical_features = cols.drop([\n 'review_count',\n 'restaurant',\n 'latitude',\n 'longitude',\n 'business_id',\n 'meanfunny',\n 'meanuseful',\n 'avgwordcount',\n 'maxwordcount',\n 'minwordcount',\n 'avgfunnywordcount',\n 'maxfunnywordcount',\n 'avgusefulwordcount',\n 'maxusefulwordcount',\n 'medianwordcount',\n 'upperquartilewordcount',\n 'lowerquartilewordcount',\n 'target'])\n \n \n numerical_features = [\n 'review_count',\n 'latitude',\n 'longitude',\n 'meanfunny',\n 'meanuseful',\n 'avgwordcount',\n 'maxwordcount',\n 'minwordcount',\n 'avgfunnywordcount',\n 'maxfunnywordcount',\n 'avgusefulwordcount',\n 'maxusefulwordcount',\n 'medianwordcount',\n 'upperquartilewordcount',\n 'lowerquartilewordcount']\n \n #this replaces the categorial nans with 9 as a placeholder and fills numerical nans with 0\n data[categorical_features]=data[categorical_features].fillna(9)\n data[numerical_features]=data[numerical_features].fillna(0)\n \n #this makes all the categorical columns strings\n data[categorical_features]=data[categorical_features].astype(str)\n data = data\n \n return data, numerical_features, categorical_features","def clean_data():\n datapath = Path(os.getcwd()) / \"data\"\n files = [str(file) for file in datapath.glob(\"*.csv\")]\n for file in files:\n if file.endswith(\"confirmed.csv\"):\n Confirmed = pd.read_csv(file)\n elif file.endswith(\"deaths.csv\"):\n Deaths = pd.read_csv(file)\n elif file.endswith(\"recovered.csv\"):\n Recovered = pd.read_csv(file)\n\n dataFrames = [Confirmed, Deaths, Recovered]\n countryList = list(dataFrames[0][\"Country/Region\"]) #list of valid countries\n countryList = list(dict.fromkeys(countryList))\n\n #create country population dictionary and align values with those in countryList\n countriesPop = {}\n countriesPop[\"US\"] = CountryInfo(\"USA\").population()\n countriesPop[\"Czechia\"] = CountryInfo(\"Czech Republic\").population()\n countriesPop[\"Taiwan*\"] = CountryInfo(\"Taiwan\").population()\n countriesPop[\"Korea, South\"] = CountryInfo(\"South Korea\").population()\n countriesPop[\"Eswatini\"] = CountryInfo(\"Swaziland\").population()\n countriesPop[\"Cote d'Ivoire\"] = CountryInfo(\"Ivory Coast\").population()\n\n for country in countryList:\n try:\n countriesPop[country] = CountryInfo(country).population()\n except KeyError:\n pass\n\n #remove unnecessary information from dataframes\n for count in range(len(dataFrames)):\n dataFrames[count] = dataFrames[count].drop(\"Province/State\",axis=1)\n dataFrames[count] = dataFrames[count].drop(\"Lat\",axis=1)\n dataFrames[count] = dataFrames[count].drop(\"Long\",axis=1)\n dataFrames[count] = dataFrames[count].rename(columns={\"Country/Region\": \"Country\"})\n dataFrames[count][\"Country\"] = dataFrames[count][\"Country\"].replace({\"Korea, South\": \"South Korea\"})\n dataFrames[count] = dataFrames[count].groupby(\"Country\").sum()\n\n # create per 100k capita values by dividing country data by population\n ConfirmedPC = dataFrames[0].copy()\n DeathsPC = dataFrames[1].copy()\n RecoveredPC = dataFrames[2].copy()\n countryList.append(\"South Korea\")\n\n for country in countryList:\n try:\n ConfirmedPC.loc[country] = ConfirmedPC.loc[country].divide(countriesPop[country]).multiply(100000) #confirmed cases per 100k inhabitants\n DeathsPC.loc[country] = DeathsPC.loc[country].divide(countriesPop[country]).multiply(100000) #deaths per 100k inhabitants\n RecoveredPC.loc[country] = RecoveredPC.loc[country].divide(countriesPop[country]).multiply(100000) #recovered cases per 100k inhabitants\n except KeyError:\n pass\n\n dataFrames.extend([ConfirmedPC, DeathsPC, RecoveredPC])\n\n return dataFrames, countryList","def dev_clean():\n clean_files(\"csv\", True)\n clean_files(\"jsontxt\", True)","def remove_data():\n # Removing the existing data\n col_answer_given.remove()\n col_answer_not_given.remove()\n col_q_not_given.remove()\n col_to_summarize.remove()","def original_clean():\n # load the data\n dataset = np.genfromtxt(\"wdbc.data\", dtype=np.float, delimiter=',', usecols=(2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n 12, 13, 14, 15, 16, 17, 18, 19,\n 20, 21, 22, 23, 24, 25, 26, 27,\n 28, 29, 30, 31), encoding=None)\n labels = np.genfromtxt(\"wdbc.data\", dtype=None, delimiter=',', usecols=(1), encoding=None)\n temp_labels = np.zeros(len(labels))\n for i in range(len(labels)):\n if labels[i] == 'B':\n temp_labels[i] = 0\n else:\n temp_labels[i] = 1\n # normalize\n temp_data = normalize(dataset)\n return temp_data, temp_labels","def clean_up(self):\n cleaning_sqls = [\"delete from meter where id not in (select meter_id from hymn)\",\n \"delete from author where id not in (select author_id from hymn)\",\n \"delete from category where id not in (select category_id from hymn)\"]\n for sql in cleaning_sqls:\n self.cur.execute(sql)\n self.conn.commit()","def clean(self):\n raise NotImplementedError","def clean(self, quick_clean=False):\r\n date_time('Cleaning')\r\n self.cursor.execute('DELETE FROM citations WHERE citation IS NULL OR citation = \"\" ;')\r\n self.conn.execute(\"VACUUM\")\r\n self.conn.commit()\r\n gc.collect()","def clean(self):\n\n pass","def delete_ds(self, dt):\n\n for k in self.datasets_keys:\n for F in self.datasets[k]:\n if F not in data[k].keys():\n continue \n max_date = data[k][F]['max_date'] \n \"\"\" Deleting unecessary ds \"\"\"\n if dt > max_date : # check max date and check if data is still loaded\n print(blue + 'Memory used before deleting : ' , process.memory_info().rss/1000000000 , cend) \n del data[k][F] \n print(\"*** Erasing dataset: \" , k , ' ' , F ) \n print(blue + 'Memory used after deleting : ' , process.memory_info().rss/1000000000 , cend) \n \n else:\n continue","def cleanup(self):\n if self.cleanup_allowed:\n shutil.rmtree(self.out_dir)\n self.train_df, self.valid_df, self.test_df = None, None, None","def clean():\n clean_files()","def _common_preprocess(self, data):\n\n data = data.drop('id', axis=1) \n data = data.drop(['17', '488', 'B01AF', 'H01AB'], axis=1, errors='ignore')\n\n # drop age outliers\n idx = data[(data['age'] > 99)].index\n data = data.drop(idx)\n\n # drop rows with CKD\n idx = data[((data['585'] != 0) | (data['586'] != 0)) &\n (data['ckd'] == 0)].index\n data = data.drop(idx)\n data = data.drop(['585', '586'], axis=1)\n\n return data","def clean(_context):","def data_cleaning(df):\n df = df.dropna(how='all')\n \n return df","def cleanup():\n cat = CSVCatalog.CSVCatalog()\n cat.drop_table(\"people\", force_drop=True)\n cat.drop_table(\"batting\", force_drop=True)\n cat.drop_table(\"teams\", force_drop=True)","def data_cleaning():\n conn = get_connect()\n conn.execute(\"DELETE FROM championMatchData WHERE kills < 2 AND deaths < 2 AND assists < 2\")\n conn.commit()\n conn.close()\n return","def _clean_data(self, docs: []):\n print('Cleaning data...')\n preprocessed_data = []\n for doc in docs:\n if len(doc) <= constants.MIN_DOC_LENGTH:\n continue\n\n temp_doc = self._remove_urls(doc)\n temp_doc = self._remove_special_chars(temp_doc)\n temp_doc = self._transform_to_lowercase(temp_doc)\n temp_doc = self._remove_stopwords(temp_doc)\n\n preprocessed_data.append(temp_doc)\n\n return preprocessed_data","def clean(self):\n if self.reloading:\n self.cleaned = pd.concat(\n [self.raw[0: self.brkIdx1+1],\n self.raw[self.brkIdx3+1: self.brkIdx4+1]])\n else:\n self.cleaned = self.raw[0: self.brkIdx1+1]\n self.cleaned.reset_index(drop=True, inplace=True) # update idx\n # -- Cubic spline that passes through the data\n sigmaLog = np.log10(self.cleaned['stress'][1:])\n cs = CubicSpline(x=sigmaLog, y=self.cleaned['e'][1:])\n self.eSigmaV = float(cs(np.log10(self.sigmaV))) # void ratio at sigmaV\n return","def cleanup():\n cat = CSVCatalog.CSVCatalog()\n cat.drop_table(\"people\")\n cat.drop_table(\"batting\")\n cat.drop_table(\"teams\")","def clean(self):\n\n for metric in self.metricList:\n listf = glob.glob(\n '{}/*_{}_{}*'.format(self.outDir, metric.name, self.num))\n if len(listf) > 0:\n for val in listf:\n os.system('rm {}'.format(val))","def test_PubChemDataSet_clean_load(self):\n\n df = PubChemDataSet(1).clean_load()\n assert len((df.Activity[df.Activity == 1])) == len((df.Activity[df.Activity == 0]))\n assert None not in [Chem.MolToSmiles(mol) if mol else None for mol in df.rdkit]","def _clean_data(self):\n if not path.exists('auto-mpg.data.txt'):\n logger.info('Could not find auto-mpg.data.txt in the current working directory')\n sys.exit()\n else:\n try:\n with open('auto-mpg.data.txt', 'r') as dirty_data:\n with open('auto-mpg.clean.txt', 'w') as clean_data:\n ## counter for row writes\n counter = 0\n for row in csv.reader(dirty_data):\n clean_data.write(row[0].expandtabs(1) + '\\n')\n counter +=1\n except Exception as e:\n logger.info('File error occurred: {e}. Exiting')\n sys.exit()","def _clean_up(self):","def cleanAll(self):\n for i in range(len(self.asteroid_type) - 1, -1, -1):\n x, y = self.get_coords(self.asteroid_type[i])\n self.del_asteroid(i)\n\n for i in range(len(self.asteroid_id_e) - 1, -1, -1):\n x, y = self.get_coords(self.asteroid_id_e[i])\n self.del_asteroid_e(i)","def load_data_clean(source='local'):\n\n train_bl, test_bl = load_data_raw(source)\n\n train = train_bl.copy()\n train = encode_dtypes(train)\n train = impute_missing(train)\n train = clean_data(train)\n\n test = test_bl.copy()\n test = encode_dtypes(test)\n test = impute_missing(test)\n test = clean_data(test)\n\n return train, test","def _clean_data(self, chart_data):\n return [x for x in chart_data if x]","def clean_data(data):\n data.dropna(inplace=True)\n for feature in data:\n if ((feature != 'lat') and (feature != 'long') and (feature != 'date')):\n data.drop(data[(data[feature] < 0)].index, inplace=True)\n data.drop(data[(data['price'] == 0)].index, inplace=True)\n data.drop(data[(data['bedrooms'] == 0) & (data['bathrooms'] == 0.0)].index, inplace=True)\n return data","def cleanUp():\n pass","def clean_data(data):\n # switch dict to flip the names of the RE data set to match SF\n col_switch = {'fund_id': 'project_id',\n 'gift_count': 'record_count.5',\n 'total': 'sum_of_amount.5'}\n\n # define package items\n dfs = data[0]\n args = data[1]\n\n # clean columns in dataframes\n for df in dfs:\n df.columns = [x.lower() for x in df.columns]\n if 'fund id' in df.columns:\n df.columns = [x.lower().replace(' ', '_') for x in df.columns]\n df['source'] = 'RE'\n df.rename(columns=col_switch, inplace=True)\n else:\n df.columns = [x.lower().replace(' ', '_') for x in df.columns]\n df['source'] = 'SF'\n df.drop(columns=['sum_of_amount', 'record_count', 'sum_of_amount.1', 'record_count.1', 'sum_of_amount.2',\n 'record_count.2', 'sum_of_amount.3',\n 'record_count.3', 'sum_of_amount.4', 'record_count.4'], inplace=True)\n\n # standardize project_IDs\n for df in dfs:\n df['project_id_new'] = df['project_id'].apply(lambda x: x.strip() if isinstance(x, str) else x)\n df['project_id_new'] = df['project_id_new'].apply(\n lambda x: x.replace(' - ', '-').lower() if isinstance(x, str) and '-' in x else x)\n df['project_id_new'] = df['project_id_new'].apply(\n lambda x: x.replace('- ', '-').lower() if isinstance(x, str) and '-' in x else x)\n df['project_id_new'] = df['project_id_new'].apply(\n lambda x: x.replace(' -', '-').lower() if isinstance(x, str) and '-' in x else x)\n df['project_id_new'] = df['project_id_new'].apply(\n lambda x: x.replace('-', '_').lower() if isinstance(x, str) and '-' in x else x)\n df['project_id_new'] = df['project_id_new'].apply(\n lambda x: x.replace(' ', '_').lower() if isinstance(x, str) and ' ' in x else x)\n df['project_id_new'] = df['project_id_new'].apply(lambda x: x.lower() if isinstance(x, str) else x)\n\n # standardize sum_of_amount\n for df in dfs:\n df['sum_of_amount_new'] = df['sum_of_amount.5'].apply(lambda x: x.strip() if isinstance(x, str) else x)\n df['sum_of_amount_new'] = df['sum_of_amount_new'].apply(\n lambda x: x.replace('USD', '').lower() if isinstance(x, str) and 'USD' in x else x)\n df['sum_of_amount_new'] = df['sum_of_amount_new'].apply(\n lambda x: x.replace('$', '').lower() if isinstance(x, str) and '$' in x else x)\n df['sum_of_amount_new'] = df['sum_of_amount_new'].apply(\n lambda x: x.replace(',', '').lower() if isinstance(x, str) and ',' in x else x)\n df['sum_of_amount_new'] = df['sum_of_amount_new'].apply(\n lambda x: float(\"{0:.2f}\".format(float(x))) if isinstance(x, str) else x)\n\n return dfs, args","def _clean(self):\n map(self.__delitem__, self.keys())\n self._original = []\n self._columns = {}\n self._modified, self._deleted = {}, {}","def cleanup_dataset(euctr_cond):\n euctr_cond['date_of_the_global_end_of_the_trial'] = pd.to_datetime(euctr_cond['date_of_the_global_end_of_the_trial'])\n euctr_cond['trial_is_part_of_a_paediatric_investigation_plan'] = (euctr_cond['trial_is_part_of_a_paediatric_investigation_plan'] == True).astype(int)\n euctr_cond['trial_human_pharmacology_phase_i'] = (euctr_cond['trial_human_pharmacology_phase_i']== True).astype(int)\n euctr_cond['trial_therapeutic_exploratory_phase_ii'] = (euctr_cond['trial_therapeutic_exploratory_phase_ii']== True).astype(int)\n euctr_cond['trial_therapeutic_confirmatory_phase_iii'] = (euctr_cond['trial_therapeutic_confirmatory_phase_iii']== True).astype(int)\n euctr_cond['trial_therapeutic_use_phase_iv'] = (euctr_cond['trial_therapeutic_use_phase_iv']== True).astype(int)\n euctr_cond['not_bioequivalence_study'] = (euctr_cond['trial_bioequivalence_study']== False).astype(int)\n euctr_cond['trial_bioequivalence_study'] = (euctr_cond['trial_bioequivalence_study']== True).astype(int)\n euctr_cond['rare_disease_blank'] = (euctr_cond['trial_condition_being_studied_is_a_rare_disease'] == 'Information not present in EudraCT').astype(int)\n euctr_cond['not_rare_disease'] = (euctr_cond['trial_condition_being_studied_is_a_rare_disease'] == 'No').astype(int)\n euctr_cond['trial_condition_being_studied_is_a_rare_disease'] = (euctr_cond['trial_condition_being_studied_is_a_rare_disease'] == 'Yes').astype(int)\n euctr_cond['not_single_blind'] = (euctr_cond['trial_single_blind']== False).astype(int)\n euctr_cond['trial_single_blind'] = (euctr_cond['trial_single_blind']== True).astype(int)\n euctr_cond['not_healthy_volunteers'] = (euctr_cond['subject_healthy_volunteers']== False).astype(int)\n euctr_cond['subject_healthy_volunteers'] = (euctr_cond['subject_healthy_volunteers']== True).astype(int)\n\n # Nick's notebook used pandas.notna, we reimplement a simplified version\n # here for compatibility with pandas 0.19\n def euctr_notna(x):\n return not (x is None or x is np.nan)\n euctr_cond['trial_results'] = (euctr_cond['trial_results'].apply(euctr_notna)).astype(int)\n\n euctr_cond.rename(columns={'full_title_of_the_trial':'full_title', 'name_or_abbreviated_title_of_the_trial_where_available': 'abbreviated_title'}, inplace=True)\n euctr_cond['non_eu'] = euctr_cond.eudract_number_with_country.str.contains('-3rd').astype(int)","def minimal_clean_data_inplace(df):\n # There are some 'unknown' users in train dataset only\n unknown_data_lines = df['sexo'].isnull() & df['age'].isnull() & df['ind_empleado'].isnull() & \\\n df['fecha_alta'].isnull() & df['pais_residencia'].isnull()\n\n logging.info(\"- Number of lines with unknown data : %s\" % unknown_data_lines.sum())\n\n # Remove these users as clients\n _clients = df[unknown_data_lines]['ncodpers'].unique()\n bad_lines = df['ncodpers'].isin(_clients)\n df.drop(df[bad_lines].index, inplace=True)\n\n logging.info(\"- Number of columns with nan : %s\" % df.isnull().any().sum())\n\n # Remove accent\n df.loc[df['nomprov'] == \"CORU\\xc3\\x91A, A\", \"nomprov\"] = \"CORUNA\"\n\n unknown_cols = ['sexo',\n 'ind_empleado',\n 'pais_residencia',\n 'ult_fec_cli_1t',\n 'conyuemp',\n 'canal_entrada',\n 'nomprov',\n 'segmento',\n 'tiprel_1mes',\n 'indrel_1mes']\n # Start with cols -> replace nan with UNKNOWN\n for col in unknown_cols:\n df.loc[df[col].isnull(), col] = \"UNKNOWN\"\n\n # Set unknown renta to -99\n df.loc[df['renta'].isnull(), 'renta'] = -99\n\n # Next `fecha_alta` :\n assert df['fecha_alta'].isnull().sum() == 0, \\\n \"Need to replace nan in 'fecha_alta', count=%s\" % df['fecha_alta'].isnull().sum()\n\n # **Remove 'tipodom' and 'cod_prov' columns**\n df.drop([\"tipodom\", \"cod_prov\"], axis=1, inplace=True)\n \n # Convert 'ind_nuevo' to int\n df['ind_nuevo'] = df['ind_nuevo'].astype(int)\n \n # Remove floating point at string indrel_1mes\n df['indrel_1mes'] = df['indrel_1mes'].apply(lambda x: str(int(float(x))) if len(x) == 3 else x)\n\n if \"ind_nomina_ult1\" in df.columns and \"ind_nom_pens_ult1\" in df.columns:\n # Target labels : `ind_nomina_ult1`, `ind_nom_pens_ult1` : nan -> 0\n # I could try to fill in missing values for products by looking at previous months,\n # but since it's such a small number of values for now I'll take the cheap way out.\n df.loc[df.ind_nomina_ult1.isnull(), \"ind_nomina_ult1\"] = 0\n df.loc[df.ind_nom_pens_ult1.isnull(), \"ind_nom_pens_ult1\"] = 0\n\n # replace 'antiguedad' with the number of months between 'fecha_alta' and 'fecha_dato'\n func1 = lambda x: _to_ym_dec(to_yearmonth(x))\n func2 = lambda x: max(_to_nb_months(x), 0) \n\n v1 = df['fecha_dato'].apply(func1)\n v2 = df['fecha_alta'].apply(func1)\n v3 = (v1 - v2).apply(func2)\n df.loc[:, 'antiguedad'] = v3\n \n # Replace 'ult_fec_cli_1t' by current nb of months from fecha_dato, if negative, set to zero\n mask = df['ult_fec_cli_1t'] == 'UNKNOWN'\n df.loc[mask, 'ult_fec_cli_1t'] = df[mask]['fecha_dato']\n v1 = df['fecha_dato'].apply(func1)\n v2 = df['ult_fec_cli_1t'].apply(func1)\n v3 = (v1 - v2).apply(func2)\n df.loc[:, 'ult_fec_cli_1t'] = v3","def clean_exam():\n data = Exam.objects.all()\n data.delete()","def cleanup():","def get_clean_data(path = 'ucr_offenses_known_monthly_1960_2016_dta/', \n identifier_variables = ['fips_state_county_code', 'state', 'date', 'year', 'zip_code', 'month'], \n crime_category = ['act_aggravated_assault', 'act_simple_assault', 'act_murder', 'act_robbery_total', \n 'act_manslaughter', 'act_theft_total', 'act_mtr_vhc_theft_total', 'act_burglary_total', 'act_rape_total'], \n start_year = 1980, end_year = 2009, selected_area = 'all'):\n all_df = []\n for i in get_filenames(start_year, end_year):\n file = path + i\n print(file)\n each_df = pd.read_stata(file)\n each_df = each_df[identifier_variables + crime_category]\n each_df = each_df[each_df['fips_state_county_code'] == '06001']\n each_df['zipcode'] = each_df['zip_code'].apply(lambda x: str(x)[0:5])\n #split Alameda into West and East Alameda according to zip code\n if selected_area == 'east':\n each_df = each_df[(each_df['zipcode'] == '94550') | (each_df['zipcode'] == '94566') | \n (each_df['zipcode'] == '94586') | (each_df['zipcode'] == '94568') | \n (each_df['zipcode'] == '94588') | (each_df['zipcode'] == '94551')]\n elif selected_area == 'west':\n each_df = each_df[(each_df['zipcode'] != '94550') & (each_df['zipcode'] != '94566') & \n (each_df['zipcode'] != '94586') & (each_df['zipcode'] != '94568') & \n (each_df['zipcode'] != '94588') & (each_df['zipcode'] != '94551') &\n (each_df['zipcode'] != '0') & (each_df['zipcode'] != '0.0') & \n (each_df['zipcode'] != 'not r') & (each_df['zipcode'] != 'missi')]\n each_df.loc[:, 'YearMonth'] = [int(re.sub('-', '', date)[0:6]) for date in each_df.loc[:, 'date']]\n #sum up amount of crimes taken place in each category for each month\n each_df = each_df.groupby(['YearMonth'])[crime_category].sum()\n each_df['crime_sum'] = each_df.sum(axis = 1)\n each_df = each_df['crime_sum'].reset_index()\n all_df.append(each_df)\n df = pd.concat(all_df).fillna(0)\n df = df.sort_values('YearMonth').reset_index()\n #split variable 'YearMonth\" into two variables 'year' and \"month' for Poission regression\n del df['index']\n df['year'] = df['YearMonth'].apply(lambda x: str(x)[:4])\n df['month'] = df['YearMonth'].apply(lambda x: str(x)[4:])\n if selected_area == 'east':\n df.to_csv('east_alameda_crime.csv')\n elif selected_area == 'west':\n df.to_csv('west_alameda_crime.csv')\n else:\n df.to_csv('all_alameda_crime.csv')\n return(df)","def clean_data(self):\n data_clean = []\n for item in self.data:\n if int(item[2]) >= self.seq_length and int(item[2]) <= self.max_frames:# and item[1] in self.classes:\n data_clean.append(item)\n\n return data_clean","def free_finalizer(self, dataset: dict):\n # for gc being late\n if dataset:\n if dataset['vrtx']:\n dataset['vrtx'].release()\n if dataset['indx']:\n dataset['indx'].release()\n dataset.clear()","def clean_data():\n for clean_file in clean_files:\n file_list = [f for f in os.listdir(\".\") if f.endswith(clean_file)]\n for f in file_list:\n os.remove(f)","def clean_up(self):\n pass","def clean_up(self):\n pass","def clean_up(self):\n\t\tpass","def clean_up(self):\n self.fname = None\n self.failed_files = []\n self.custom_failed = []\n self.results = None","def clean_data(data,variables,ignore_na=True,fill_mode=\"mode\"):\n\tnum_rows,num_cols = data.shape\n\tif ignore_na:\n\t\tdata = data.dropna(axis=0,how=\"any\",subset=variables)\n\t\tnum_rows,num_cols = data.shape\n\telse:\n\t\tfor variable in variables:\n\t\t\tif data[variable].isna().sum()>0:\n\t\t\t\tif fill_mode==\"mode\":\n\t\t\t\t\tdata[variable] = data[variable].fillna(data[variable].mode()[0])\n\t\t\t\telse:\n\t\t\t\t\tif variable==\"Age\":\n\t\t\t\t\t\tnan_idx = np.where(data[\"Age\"].isna())[0]\n\t\t\t\t\t\tnorm = [1.,1.]#[8.,3.]\n\t\t\t\t\t\tnew_age = np.zeros((num_rows))\n\t\t\t\t\t\tfor idx in nan_idx:\n\t\t\t\t\t\t\terr = (data[[\"SibSp\",\"Pclass\"]]-data[[\"SibSp\",\"Pclass\"]].iloc[idx])\n\t\t\t\t\t\t\terr /= norm\n\t\t\t\t\t\t\ttotal_err = np.sqrt(np.sum(err**2,axis=1))\n\t\t\t\t\t\t\tif len(data[\"Age\"][total_err==0].mode())>0:\n\t\t\t\t\t\t\t\tnew_age[idx] = data[\"Age\"][total_err==0].mode()[0]\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tnew_age[idx] = data[\"Age\"][total_err<4].mode()[0]\n\t\t\t\t\t\tdata[\"Age\"].fillna(pd.Series(new_age),inplace=True)\n\n\t\t\t\t\tif variable==\"Embarked\":\n\t\t\t\t\t\t# data[variable] = data[variable].fillna(data[variable].mode()[0])\n\t\t\t\t\t\tdata = data.dropna(axis=0,how=\"any\",subset=[\"Embarked\"])\n\t\t\t\t\t\tnum_rows,num_cols = data.shape\n\n\t\t\t\t\tif variable==\"Fare\":\n\t\t\t\t\t\tnan_idx = np.where(data[\"Fare\"].isna())[0]\n\t\t\t\t\t\tnew_fare = np.zeros((num_rows))\n\t\t\t\t\t\tfor idx in nan_idx:\n\t\t\t\t\t\t\ttemp_age = data[\"Age\"].iloc[idx]\n\t\t\t\t\t\t\ttemp_embarked = data[\"Embarked\"].iloc[idx]\n\t\t\t\t\t\t\ttemp_pclass = data[\"Pclass\"].iloc[idx]\n\n\t\t\t\t\t\t\tsimilar_cases = data.loc[(data[\"Embarked\"]==temp_embarked) &\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t (data[\"Pclass\"]==temp_pclass) &\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t (data[\"Age\"]>(temp_age-15)) &\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t (data[\"Age\"]<(temp_age+15)) &\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t (data[\"Fare\"].isna()==False), \"Fare\"]\n\t\t\t\t\t\t\tnew_fare[idx] = similar_cases.median()\n\t\t\t\t\t\tdata[\"Fare\"].fillna(pd.Series(new_fare),inplace=True)\n\n\n\t## format ticket values\n\tduplicates = []\n\tticket_no = np.zeros((num_rows))\n\tfor i,uniq in enumerate(data['Ticket'].unique()):\n\t\tticket_no[data['Ticket']==uniq] = i\n\t## take into account four cases where 'Ticket' is given as LINE\n\tticket_no[data['Ticket']==\"LINE\"] = i + np.arange(np.sum(data['Ticket']==\"LINE\"))\n\tdata[\"ticket_no\"] = pd.Series(ticket_no,dtype=int)\n\n\t## format cabin values, set NaN cabin values to zero\n\tduplicates = []\n\tcabin_no = np.zeros((num_rows))\n\tfor i,uniq in enumerate(data['Cabin'].unique()):\n\t\tif uniq==\"nan\":\n\t\t\tcabin_no[data['Cabin']==uniq] = 0\n\t\tcabin_no[data['Cabin']==uniq] = i + 1\n\tdata[\"cabin_no\"] = pd.Series(cabin_no,dtype=int)","def remove_bad_data(self):\n min_title_len = 9e10\n min_abstract_len = 9e10\n indexes_to_remove = []\n for i in range(len(self.data)):\n title_len = len(self.data[i][\"Title\"])\n if title_len < min_title_len:\n min_title_len = title_len\n\n abstract_len = len(self.data[i][\"Abstract\"])\n if abstract_len < min_abstract_len:\n min_abstract_len = abstract_len\n\n if title_len == 0 or abstract_len == 0:\n if i not in indexes_to_remove:\n indexes_to_remove.append(i)\n\n # start deleting from the end of list to avoid problems with shifting indexes\n indexes_to_remove.reverse()\n for index in indexes_to_remove:\n print(\"Deleting index: \", index)\n del self.data[index]\n\n print(\"Min Title len:\", min_title_len)\n print(\"Min Abstract len:\", min_abstract_len)","def cleanUp(self):\r\n pass","def create_cleaned_dataset(PDBbind_dataset_path, general_set_PDBs_path, refined_set_PDBs_path, output_name, plot = False):\n \n # load dataset\n data = pd.read_csv(PDBbind_dataset_path)\n \n # check for NaNs in affinity data\n if data['-log(Kd/Ki)'].isnull().any() != False:\n print('There are NaNs present in affinity data!')\n \n # create list of PDB id's\n pdbid_list = list(data['PDB ID'])\n \n # remove affinity values that do not have structural data by searching PDBs\n missing = []\n for i in range(len(pdbid_list)):\n pdb = pdbid_list[i]\n if os.path.isdir(str(general_set_PDBs_path) + pdb)==False and os.path.isdir(str(refined_set_PDBs_path) + pdb)==False:\n missing.append(pdb)\n data = data[~np.in1d(data['PDB ID'], list(missing))]\n\n # distinguish PDB id's in general and refined sets\n general_dict = {}\n refined_dict = {}\n for i in range(len(pdbid_list)):\n pdb = pdbid_list[i]\n if os.path.isdir(str(general_set_PDBs_path) + pdb)==True:\n general_dict[pdb] = 'general'\n if os.path.isdir(str(refined_set_PDBs_path) + pdb)==True:\n refined_dict[pdb] = 'refined'\n \n # add 'set' column to data and fill with 'general' or 'refined'\n data['set'] = np.nan\n data.loc[np.in1d(data['PDB ID'], list(general_dict)), 'set'] = 'general'\n data.loc[np.in1d(data['PDB ID'], list(refined_dict)), 'set'] = 'refined'\n \n # write out csv of cleaned dataset\n data[['PDB ID', '-log(Kd/Ki)', 'set']].to_csv(output_name, index=False)\n \n # read in and view the cleaned dataset\n display(pd.read_csv(output_name))\n \n if plot == True:\n # plot affinity distributions for general and refined sets\n grid = sns.FacetGrid(data, row='set', row_order=['general', 'refined'],\n size=3, aspect=2)\n grid.map(sns.distplot, '-log(Kd/Ki)')\n else:\n return","def clean(self):\n # Perform the standard ACE cleaning\n max_status = mm_ace.clean(self)\n\n # Replace bad values with NaN and remove times with no valid data\n self.data = self.data[self.data['status'] <= max_status]\n\n return","def clean(self):\n\t\tfor v in self:\n\t\t\tv.reset_distance()\n\t\t\tv.reset_predecessor()\n\t\t\tv.reset_visited()","def CleanUp(self):\n self.cmd.CleanUp()","def makedata():\n # train\n print('Clean Train Dataset and separate questions')\n df = pd.read_csv(TRAIN_DATASET).replace(np.nan, ' ')\n t = df.shape[0] * 2\n print t\n df['question1'] = cleanText(df['question1'])\n df['question2'] = cleanText(df['question2'])\n\n df.to_csv(os.path.join(rootpathdata_cleaned, 'train.csx'), index=False)\n overallquestions = df['question1'].tolist() + df['question2'].tolist()\n tpm = pd.DataFrame()\n tpm['question'] = overallquestions\n tpm.to_csv(os.path.join(rootpathdata_cleaned, 'train_allquestions.csx'), index=False)\n # test\n\n print('Clean Test Dataset and separate questions')\n df = pd.read_csv(TEST_DATASET).fillna(' ')\n t1 = df.shape[0] * 2\n df['question1'] = cleanText(df['question1'])\n df['question2'] = cleanText(df['question2'])\n df.to_csv(os.path.join(rootpathdata_cleaned, 'test.csx'), index=False)\n\n overallquestions += df['question1'].tolist() + df['question2'].tolist()\n tpm = pd.DataFrame()\n tpm['question'] = overallquestions\n tpm.to_csv(os.path.join(rootpathdata_cleaned, 'test_allquestions.csx'), index=False)\n print len(overallquestions), t1 + t","def clean(data, skip_transformations=False, target=False):\n data = convert_type(data)\n data = category_grouping(data)\n if target:\n target = data[['Target']]\n data = data.drop(columns='Target')\n x_train, x_test, y_train, y_test = f.train_test(data, target)\n x_train, y_train = resample_vals(x_train, y_train)\n x_train = x_train.assign(Train=lambda x: 1)\n x_test = x_test.assign(Train=lambda x: 0)\n data = pd.concat([x_train, x_test])\n data = onehot_features(data)\n data = log_trans(data, test=True)\n data = cap_outliers(data, test=True)\n data = scale(data, test=True)\n x_train = data.loc[data['Train'] == 1]\n x_test = data.loc[data['Train'] == 0]\n return x_train, x_test, y_train, y_test\n data = onehot_features(data)\n if skip_transformations:\n return data\n data = log_trans(data)\n data = cap_outliers(data)\n data = scale(data)\n return data","def _clean_files(self):\n if self.delfiles & 1:\n ProcUtils.remove(self.okm)\n if self.delfiles & 2:\n ProcUtils.remove(self.hkm)\n if self.delfiles & 4:\n ProcUtils.remove(self.qkm)\n if self.delfiles & 8:\n ProcUtils.remove(self.obc)\n\n if self.log is False:\n ProcUtils.remove(self.pcf_file)\n base = os.path.basename(self.okm)\n ProcUtils.remove(os.path.join(self.dirs['run'],\n '.'.join(['LogReport', base])))\n ProcUtils.remove(os.path.join(self.dirs['run'],\n '.'.join(['LogStatus', base])))\n ProcUtils.remove(os.path.join(self.dirs['run'],\n '.'.join(['LogUser', base])))","def clear_brain():\n\n if os.path.exists(os.path.abspath(\"papaya_data\")):\n shutil.rmtree(os.path.abspath(\"papaya_data\"))","def tearDown(cls):\n\n # cls.test_mmp_series_object.clean_out_data_seriesobj()\n # reusable data struct\n cls.test_mmp_series_object.clean_out_data_seriesobj()\n cls.test_dataset_testresults.clear()\n # reusable results file\n # os.remove(cls.temp_file_output_series.name)","def clean_data(input_file, output_file):\n # Create data frame\n data = pd.read_csv(input_file, sep = \";\")\n \n # Remove unnecessary features from data frame\n data = data.drop([\"Name\",\"Ticket\",\"Cabin\"], axis=1)\n \n # Remove NaN values from remaining features\n data = data.dropna()\n \n # Save ready-to-use file\n data.to_csv(output_file, index=False)","def cleanup_and_reset(self):\n self.mem.set(self.mem.META_PLAN, None)\n self.mem.set(self.mem.META_GOALS, None)\n self.mem.set(self.mem.META_CURR_GOAL, None)","def clear_temporary_data(self):\n self.extract_list = []\n self.transform_result = {}","def full_clean():\n response_to_df_csv()\n dirty_data = pd.read_csv(\"./data/dirty_data.csv\")\n cleaned_data = dirty_data\n cleaned_data = drop_cols(cleaned_data)\n cleaned_data = lowercase_columns(cleaned_data)\n cleaned_data = make_numeric(cleaned_data)\n cleaned_data = drop_zero_pay(cleaned_data)\n cleaned_data = add_log_col(cleaned_data)\n cleaned_data.to_csv('./data/cleaned_for_testing.csv')\n\n return cleaned_data","def clean(self):\n self.unique_combinations = {}\n self.reverse_combinations = []\n self.label_count = None","def clean():\n try:\n os.unlink(options.coords + 'mirza_mrna_input' + '.fa')\n os.unlink(options.coords + 'mirza_mirna_input' + '.fa')\n os.unlink(options.coords + 'mirza_mirna_expressions' + '.fa')\n except:\n pass","def _clean_up_temporary_files(dataset_dir):\n return","def clean(self):\n files = ['CHG', 'CHGCAR', 'POSCAR', 'INCAR', 'CONTCAR',\n 'DOSCAR', 'EIGENVAL', 'IBZKPT', 'KPOINTS', 'OSZICAR',\n 'OUTCAR', 'PCDAT', 'POTCAR', 'vasprun.xml',\n 'WAVECAR', 'XDATCAR', 'PROCAR', 'ase-sort.dat',\n 'LOCPOT', 'AECCAR0', 'AECCAR1', 'AECCAR2',\n 'WAVECAR.GTO', 'vasp.out', 'vasp.err']\n for f in files:\n try:\n os.remove(f)\n except OSError:\n pass","def clean(self):\n cursor = self.cnx.cursor()\n cursor.execute(\"DROP TABLE IF EXISTS FileNameFilter_scores\")\n cursor.execute(\"DROP TABLE IF EXISTS FileNameFilter_unique_name\")\n self.cnx.commit()\n cursor.close()","def clean_data(df):\n \n # Put in code here to execute all main cleaning steps:\n # convert missing value codes into NaNs, ...\n################################################################################################################ \n missing_data_stats=pd.DataFrame(df.isnull().sum(),columns=['Naturally_Missing_Values'])\n \n # Loop over all the rows/attributes in feat_info\n for i in range(len(feat_info)):\n att=feat_info['attribute'][i] # Get name of attribute\n codes=feat_info['missing_or_unknown'][i].replace('[',\"\").replace(']',\"\").split(',') # Get values from string\n\n # Loop over all the individual values extratcted from '\tmissing_or_unknown' column\n for j in range(len(codes)):\n if codes[j]!='': # skip empty values as nothing needs to be replaced\n try:\n df.loc[df[att]==int(codes[j]),att] = np.nan # replaces numeric values with NaNs\n except:\n df.loc[df[att]==(codes[j]),att] = np.nan # replaces string values with NaNs\n \n # Merge and plot the naturally missing and overall missing values\n missing_data_stats=pd.merge(missing_data_stats,pd.DataFrame(df.isnull().sum(),\n columns=['Overall_Missing_Values']),left_index=True,right_index=True)\n \n################################################################################################################ \n # Perform an assessment of how much missing data there is in each column of the dataset.\n missing_data_stats['Overall_Missing_Percentage']=(missing_data_stats['Overall_Missing_Values']/len(df))*100\n sorted_missing_data_stats=missing_data_stats.sort_values(by='Overall_Missing_Percentage',ascending=False)\n \n # Investigate patterns in the amount of missing data in each column.\n plt.figure(figsize=(15,30))\n sns.barplot(x='Overall_Missing_Percentage', y=sorted_missing_data_stats.index, \n data=sorted_missing_data_stats);\n plt.title('Percentage of Missing Data by Attribute',size=20)\n \n################################################################################################################\n # remove selected columns and rows, ...\n # Define criteria of outliers removal\n outlier=30 # Outlier if % missing more than\n\n #Get outlier columns and drop them from data\n outlier_columns=sorted_missing_data_stats[sorted_missing_data_stats['Overall_Missing_Percentage']>outlier].index\n for i in outlier_columns:\n df.drop(i,axis=1,inplace=True)\n\n################################################################################################################\n # Sum all the missing values by rows\n nan_count_rows=df.isnull().sum(axis=1).sort_values(ascending=False)\n\n # Plot and visualize the distribution of missing rows\n plt.figure(figsize=(20,8))\n sns.countplot(nan_count_rows)\n\n plt.xlabel('Missing Values in each Row')\n plt.ylabel('Count of Rows')\n plt.title('Count of Rows vs Missing Values in each Row')\n \n \n # Define threshold value\n row_nan_threshold=0\n\n # Rows above threshold\n at=df.loc[nan_count_rows>row_nan_threshold]\n\n # Rows below threshold\n bt=df.loc[nan_count_rows<=row_nan_threshold]\n \n################################################################################################################ \n # select, re-encode, and engineer column values.\n\n # Assess categorical variables: which are binary, which are multi-level, and\n # which one needs to be re-encoded?\n\n # Get all categorical features\n cat_att=feat_info[feat_info['type']=='categorical']['attribute']\n\n # Loop through all the categorical features to identify binary numeric, binary non-numeric and multi-level \n #categories\n bin_num=[] #binary and numeric (type1)\n bin_non_num=[] #binary and non_numeric (type2)\n ml_cat=[] #multi-level categories (type3)\n\n for i in cat_att:\n try:\n # get number of unique values\n uv=bt[i].nunique()\n dt=bt[i].dtype\n\n # For binary variable with numeric value\n if uv==2 and dt!='object':\n bin_num.append(i) #append\n\n # For binary variable with non-numeric value\n elif uv==2 and dt=='object':\n bin_non_num.append(i) #append\n\n # For multi-level categories \n elif uv>2:\n ml_cat.append(i) #append\n\n\n # Exception for columns which are already dropped from data\n except Exception as e:\n print('Note=> Column does not exist: ', e)\n\n print('\\nBinary_Numeric:\\n', bin_num , '\\n\\nBinary_Non_Numeric:\\n' , bin_non_num , '\\n\\nMulti-Level:\\n' , ml_cat)\n \n################################################################################################################\n # Import Libraries\n from sklearn.preprocessing import LabelEncoder\n\n # Initialize encoder\n le=LabelEncoder()\n\n # columns list to encode\n col_list=bin_non_num+ml_cat\n\n # Create a dataframe of encoded features\n bt_encoded=bt.copy(deep=True)\n\n # Encode all columns in column list\n for cols in col_list:\n bt_encoded[cols]=le.fit_transform(bt[cols])\n\n################################################################################################################\n # Create a list of all mixed features\n mixed_att=feat_info[feat_info['type']=='mixed']['attribute']\n\n for i in mixed_att:\n # Investigate \"PRAEGENDE_JUGENDJAHRE\" and engineer two new variables.\n if i=='PRAEGENDE_JUGENDJAHRE':\n bt_encoded.loc[(bt_encoded[i]<=2) & (bt_encoded[i]>=1) ,i+'_Decade']=1\n bt_encoded.loc[(bt_encoded[i]<=4) & (bt_encoded[i]>=3),i+'_Decade']=2\n bt_encoded.loc[(bt_encoded[i]<=7) & (bt_encoded[i]>=5),i+'_Decade']=3\n bt_encoded.loc[(bt_encoded[i]<=9) & (bt_encoded[i]>=8),i+'_Decade']=4\n bt_encoded.loc[(bt_encoded[i]<=13) & (bt_encoded[i]>=10),i+'_Decade']=5 \n bt_encoded.loc[(bt_encoded[i]<=15) & (bt_encoded[i]>=14),i+'_Decade']=6\n\n bt_encoded[i+'_Movement']=1\n bt_encoded.loc[(bt_encoded[i]==1) | (bt_encoded[i]==3) |\n (bt_encoded[i]==5) | (bt_encoded[i]==8) |\n (bt_encoded[i]==10) | (bt_encoded[i]==12) |\n (bt_encoded[i]==14) ,i+'_Movement']=2\n\n # Investigate \"CAMEO_INTL_2015\" and engineer two new variables.\n elif i=='CAMEO_INTL_2015':\n bt_encoded[i+'_Wealth']=bt_encoded[i].apply(lambda x:int(x[0]))\n bt_encoded[i+'_Life_Stage']=bt_encoded[i].apply(lambda x:int(x[1]))\n\n # Investigate \"LP_LEBENSPHASE_GROB\" and engineer a new variables (for low,avg,high income groups) \n elif i=='LP_LEBENSPHASE_GROB':\n #low income\n bt_encoded[i+'_income']=1 \n\n #avg income\n bt_encoded.loc[(bt_encoded[i]==3) | (bt_encoded[i]==5) |\n (bt_encoded[i]==8) | (bt_encoded[i]==11) |\n (bt_encoded[i]==12) ,i+'_income']=2 \n #high income\n bt_encoded.loc[(bt_encoded[i]==9) ,i+'_income']=3 \n\n\n # Investigate \"WOHNLAGE\" and engineer a new variables (for Rural and Non Rural Flag) \n elif i=='WOHNLAGE':\n bt_encoded[i+'_Rural']=1\n bt_encoded.loc[(bt_encoded[i]==7) | (bt_encoded[i]==8),i+'_Rural']=2\n\n\n # Investigate \"PLZ8_BAUMAX\" and engineer a new variables (for family and business buildings) \n elif i=='PLZ8_BAUMAX':\n bt_encoded[i+'_Bldg_Type']=1\n bt_encoded.loc[bt_encoded[i]==5,i+'_Bldg_Type']=2\n \n################################################################################################################\n # Return the cleaned dataframe.\n #Remove original mixed columns from final dataset if new columns have been engineered\n for i in mixed_att:\n try:\n bt_encoded.drop(i,axis=1,inplace=True) \n except Exception as e:\n print('Note=> Column does not exist: ', e)\n return bt_encoded","def clean_dataset(args, min_class_count=5):\n classes_count = get_classes_count(args)\n print(f\"cleaning dataset of {len(args)} arguments\")\n classes_to_remove = []\n for c in classes_count.keys():\n if classes_count[c] < min_class_count:\n classes_to_remove.append(c)\n\n clean_args = [a for a in args if a.frame_id not in classes_to_remove]\n removed_args = [a for a in args if a.frame_id in classes_to_remove]\n print(f\"removed {len(removed_args)} arguments from dataset. {len(clean_args)} remaining.\")\n return clean_args, removed_args","def clean(self):\n\n if (self.clean_level == 'dusty') | (self.clean_level == 'clean'):\n idx, = np.where(self['B_flag'] == 0)\n self.data = self[idx, :]\n\n return","def cleaning(df, file=\"proteinGroups\"):\r\n columns = df.columns\r\n if file == \"proteinGroups\":\r\n if (\"Potential contaminant\" not in columns) or\\\r\n (\"Reverse\" not in columns) or\\\r\n (\"Only identified by site\" not in columns):\r\n print(\"Is this data already cleaned?\\nMandatory columns for cleaning not present in data!\")\r\n print(\"Returning provided dataframe!\")\r\n return df\r\n df = df[(df['Potential contaminant'].isnull()) &\r\n (df['Reverse'].isnull()) &\r\n (df['Only identified by site'].isnull())]\r\n df.drop(['Potential contaminant',\"Reverse\", 'Only identified by site'], axis=1, inplace=True)\r\n elif (file == \"Phospho (STY)\") or (file == \"evidence\") or (file == \"modificationSpecificPeptides\"):\r\n if (\"Potential contaminant\" not in columns) or\\\r\n (\"Reverse\" not in columns):\r\n print(\"Is this data already cleaned?\\nMandatory columns for cleaning not present in data!\")\r\n print(\"Returning provided dataframe!\")\r\n return df\r\n df = df[(df['Potential contaminant'].isnull()) &\r\n (df['Reverse'].isnull())]\r\n df.drop(['Potential contaminant',\"Reverse\"], axis=1, inplace=True)\r\n return df","def clean():\n clean_flatbuffer_binaries()\n clean_webp_textures()","def preprocess_dataset(dataset=None, remove_missing=60, remove_empty_rows=True):\n print('feature size before dropping:{}'.format(dataset.shape[1]))\n dataset_after_drop = dataset.dropna(thresh=dataset.shape[0]*remove_missing/100, how='all',axis=1)\n print('feature size after dropping:{}'.format(dataset_after_drop.shape[1]))\n print('row size before dropping:{}'.format(dataset_after_drop.shape[0]))\n if remove_empty_rows is True:\n df_final = dataset_after_drop.dropna(inplace=False).reset_index (drop=True)\n print('row size after dropping:{}'.format(df_final.shape[0]))\n print('---------------')\n print('final shape:{}'.format(df_final.shape))\n return df_final\n else:\n return dataset_after_drop","def clean_dataset(behaviors_source, behaviors_target, news_source,\n news_target):\n print(f\"Clean up {behaviors_source}\")\n behaviors = pd.read_table(behaviors_source,\n header=None,\n usecols=[2, 3],\n names=['clicked_news', 'impressions'])\n behaviors.impressions = behaviors.impressions.str.split()\n behaviors = behaviors.explode('impressions').reset_index(drop=True)\n behaviors['candidate_news'], behaviors[\n 'clicked'] = behaviors.impressions.str.split('-').str\n behaviors.clicked_news.fillna('', inplace=True)\n behaviors.to_csv(behaviors_target,\n sep='\\t',\n index=False,\n columns=['clicked_news', 'candidate_news', 'clicked'])\n\n print(f\"Clean up {news_source}\")\n news = pd.read_table(news_source,\n header=None,\n usecols=[0, 3, 6],\n names=['id', 'title', 'entities'])\n news.to_csv(news_target, sep='\\t', index=False)","def clean_data_fragments(self) -> None:\n read_path: Path = Path(os.environ[\"DATA_PATH\"]) / \"fragments\"\n try:\n shutil.rmtree(read_path / \"__MACOSX\")\n except FileNotFoundError:\n print('Folder \"__MACOSX\" already removed.')\n\n # delete non-binarized images\n frag_paths: list = list((read_path / \"image-data\").iterdir())\n frags_binarized: list = [fp for fp in frag_paths if \"binarized\" in fp.name]\n frags_delete: set = set(frag_paths).difference(set(frags_binarized))\n for frag in frags_delete:\n frag.unlink()\n frag_paths = frags_binarized\n for frag_path in frag_paths:\n # Python 3.8 hack, seems to be supported without str() on 3.9\n shutil.move(str(frag_path.resolve()), str(read_path.resolve()))\n\n (read_path / \"image-data\").rmdir() # delete empty folder","def clean_object(metadata, analysistype):\n for sample in metadata:\n try:\n delattr(sample[analysistype], \"targetnames\")\n except AttributeError:\n pass\n try:\n delattr(sample[analysistype], \"targets\")\n except AttributeError:\n pass\n try:\n delattr(sample[analysistype], \"dnaseq\")\n except AttributeError:\n pass\n try:\n delattr(sample[analysistype], \"protseq\")\n except AttributeError:\n pass","def clean(self):\n self.clean_rally_conf()\n rally.RallyBase.clean_rally_logs()\n if self.image_alt:\n self.cloud.delete_image(self.image_alt)\n if self.flavor_alt:\n self.orig_cloud.delete_flavor(self.flavor_alt.id)\n super().clean()","def collect_garbage(self, required_inputs):"],"string":"[\n \"def clean(args):\\n with_dataset(args, Dataset._clean)\",\n \"def cleaning (data):\",\n \"def _clean_data(self, dataset):\\n dataset.dropna(inplace=True)\\n # Problem: handle missing data (in a different way), noisy data, inconsistent data\",\n \"def clean():\\n filter_phase_data()\\n combine_phase_data()\\n remove_duplicates_phase_data()\",\n \"def cleaning_Dataset(dataset):\\n cols = dataset.select_dtypes([np.number]).columns\\n diff = dataset[cols].diff().sum()\\n\\n dataset = dataset.drop([diff==0].index, axis=1)\\n dataset = dataset.drop('adj close', 1)\\n dataset = dataset.fillna(method='bfill')\\n dataset = dataset[1:-1]\\n return dataset\",\n \"def clean_up_data(self):\\n pass\",\n \"def clean(df):\",\n \"def clean(self):\\n self.df = _data.prune(self.df, [REGEX_PATTERN_GCI, REGEX_PATTERN_DB_ID])\\n self.df, _ = _data.remove_totally_failed_tests(self.df)\\n self.is_cleaned = True\",\n \"def clean_data(df):\\n \\n # Put in code here to execute all main cleaning steps:\\n # convert missing value codes into NaNs, ...\\n count_miss = df.isnull().sum(axis=0).values #find number of nans for each column\\n count_miss = [val for val in count_miss]\\n \\n drop_cols = []\\n\\n for ind, val in enumerate(count_miss):\\n if val > 200000:\\n drop_cols.append(ind)\\n \\n df_drop_cols = list(azdias.columns[drop_cols])\\n df = df.drop(df_drop_cols, axis=1)\\n \\n for col in range(df.shape[1]): #loop through columns\\n column_name = df.columns[col] #get column name\\n missing_list = feat_info.iloc[col,3] #get missing_or_unknown column from feature info\\n missing_list = missing_list.replace('[','') #remove left bracket from string\\n missing_list = missing_list.replace(']','') #remove right bracket from string\\n missing_list = missing_list.split(',') #split into individual strings\\n \\n #find data that is natually missing and continue loop to omit\\n if missing_list == ['']:\\n continue\\n \\n else:\\n for dat_type in missing_list: \\n if df[column_name].dtype == 'object': #find values that contain x\\n df.loc[df[column_name] == dat_type, column_name] = np.nan #replace x with nan\\n \\n else:\\n dat_type = int(dat_type) #if no x, convert to integer and replace with nan\\n df.loc[df[column_name] == dat_type, column_name] = np.nan\\n \\n # select, re-encode, and engineer column values.\\n \\n # encode OST_WEST_KZ\\n df.loc[df['OST_WEST_KZ'] == 'W','OST_WEST_KZ'] = 0\\n df.loc[df['OST_WEST_KZ'] == 'O','OST_WEST_KZ'] = 1\\n \\n # Re-encode categorical variable(s) to be kept in the analysis.\\n \\n \\n #get list of attributes with type categorical\\n feat_info[feat_info['type'] == 'categorical']\\n \\n cat_new_cols = [] #initialize\\n for i in feat_info[feat_info['type'] == 'categorical']['attribute']:\\n cat_new_cols.append(i)\\n \\n for cols in df.columns:\\n if cols in cat_new_cols:\\n if df[cols].nunique(dropna=True) > 2: #if the number of unique values is greater than 2 \\n df = df.drop(cols, axis=1) #drop from the analysis\\n print(\\\"more than 2 categories: {}\\\".format(cols))\\n \\n else:\\n if not df[cols].unique()[0] > 0:\\n #if not df[cols].unique()[0] > 0:\\n dummies = pd.get_dummies(df[cols], prefix=cols)\\n df = df.drop(cols, axis=1) #create dummy variable\\n df = df.join(dummies)\\n print(\\\"transformed to dummy variable: {}\\\".format(cols))\\n \\n # create variable: MOVEMENT\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([1,3,5,8,10,12,14]),'MOVEMENT'] = 1\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([2,4,6,7,9,11,13,15]),'MOVEMENT'] = 2\\n \\n #Capture Decade\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([1,2]), 'DECADE'] = 40\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([3,4]), 'DECADE'] = 50\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([5,6,7]), 'DECADE'] = 60\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([8,9]), 'DECADE'] = 70\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([10,11,12,13]), 'DECADE'] = 80\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([14,15]), 'DECADE'] = 90\\n \\n df['CAMEO_INTL_2015'] = df['CAMEO_INTL_2015'].astype(float)\\n\\n # create new variable: WEALTH\\n df.loc[df['CAMEO_INTL_2015'].isin([51,52,53,54,55]), 'WEALTH'] = 1\\n df.loc[df['CAMEO_INTL_2015'].isin([41,42,43,44,45]), 'WEALTH'] = 2\\n df.loc[df['CAMEO_INTL_2015'].isin([31,32,33,34,35]), 'WEALTH'] = 3\\n df.loc[df['CAMEO_INTL_2015'].isin([21,22,23,24,25]), 'WEALTH'] = 4\\n df.loc[df['CAMEO_INTL_2015'].isin([11,12,13,14,15]), 'WEALTH'] = 5\\n \\n # create new variable: LIFE_STAGE\\n df.loc[df['CAMEO_INTL_2015'].isin([11,21,31,41,51]),'LIFE_STAGE'] = 1\\n df.loc[df['CAMEO_INTL_2015'].isin([12,22,32,42,52]),'LIFE_STAGE'] = 2\\n df.loc[df['CAMEO_INTL_2015'].isin([13,23,33,43,53]),'LIFE_STAGE'] = 3\\n df.loc[df['CAMEO_INTL_2015'].isin([14,24,34,44,54]),'LIFE_STAGE'] = 4\\n df.loc[df['CAMEO_INTL_2015'].isin([15,25,35,45,55]),'LIFE_STAGE'] = 5\\n \\n # remove selected columns and rows, ...\\n df = df.drop('PRAEGENDE_JUGENDJAHRE', axis=1)\\n df = df.drop('CAMEO_INTL_2015',axis=1)\\n \\n # Return the cleaned dataframe.\\n return df\",\n \"def _clean_datafile_set(self):\\n file_list = self._meta['sets']['data file']['items']\\n for item in file_list[:]:\\n collection = item.split('@')[0]\\n variable = item.split('@')[1]\\n if not variable in self:\\n file_list.remove(item)\\n elif collection == 'masks':\\n for s in self._get_source_ref(variable):\\n while s in file_list:\\n file_list.remove(s)\\n elif self._is_array_item(variable):\\n parent = self.parents(variable)[0]\\n if not parent in file_list:\\n idx = file_list.index(item)\\n file_list[idx] = parent\\n while item in file_list:\\n file_list.remove(item)\\n f_list = []\\n for item in file_list:\\n if not item in f_list: f_list.append(item)\\n self._meta['sets']['data file']['items'] = f_list\\n return None\",\n \"def _clean_results(self):\\n\\t\\tif self.file_type == \\\"Automobile\\\":\\n\\t\\t\\tcols = [\\\"Year\\\", \\\"Mileage\\\", \\\"Price\\\"]\\n\\t\\t\\tself.data.Mileage.replace([',', 'mi.', 'nan', ' '], '', regex=True, inplace=True) # Fix mileage column\\n\\t\\t\\tself.data.Price.replace([',', '\\\\$'], '', regex=True, inplace=True) # Always fix price column (, and $ removed)\\n\\t\\t\\tself.data[cols] = self.data[cols].apply(pd.to_numeric, errors='coerce') # Coerces errors into NaN values\\n\\t\\t\\tself.data.drop(self.data[self.data.Year < 2000].index, inplace=True) # Remove cars made before 2000\\n\\t\\t\\tself.data.drop(self.data[self.data.Price > 30000].index, inplace=True) # Remove cars over $30,000\\n\\t\\t\\tself.data.drop(self.data[(self.data.Mileage < 1000) | (self.data.Mileage > 300000)].index, inplace=True) # Remove cars with over 300,000 miles\\n\\t\\t\\tself.data['Age'] = 2018 - self.data['Year'] # Change years to Age\\n\\t\\telif self.file_type == \\\"Apartment\\\":\\n\\t\\t\\tself.data.Area.replace(['ft2'], '', regex=True, inplace=True) # Remove ft2 from square footage column\\n\\t\\t\\tself.data.Price.replace([',', '\\\\$'], '', regex=True, inplace=True) # Always fix price column (, and $ removed)\\n\\t\\telse:\\n\\t\\t\\tself.data['Street'], self.data['City'], self.data['State'] = self.data['Address'].str.split(',', 2).str\\n\\t\\t\\tdel self.data.Address\\n\\t\\t\\tself.data.drop(self.data[self.data.Price > 1000000].index, inplace=True) # Remove houses worth more than $1 million\\n\\n\\t\\tself.data.replace('^\\\\s*$', np.nan, regex=True, inplace=True) # Replace all empty values with np.NaN\\n\\t\\tself.data = self.data.dropna(axis=1, how='all') # Remove Null Columns\\n\\t\\tself.data = self.data.apply(pd.to_numeric, errors='ignore') # Coerces errors into NaN values\",\n \"def cleanData(data):\\r\\n \\r\\n indexes = data.index[pd.isnull(data[\\\"release date\\\"])]\\r\\n # data = data.drop(data.index[[2172, 3781, 7245, 12475, 14756, 15292, 49295, 93523, 99723]],axis=0,inplace=False)\\r\\n data = data.drop(indexes,axis=0,inplace=False)\\r\\n data = data.drop([\\\"video release date\\\", 'IMDb URL'],axis=1)\\r\\n \\r\\n \\r\\n data.gender = data.gender.astype(\\\"category\\\")\\r\\n data.occupation = data.occupation.astype(\\\"category\\\")\\r\\n \\r\\n categorical_variables = [\\\"gender\\\", \\\"occupation\\\"]\\r\\n for variable in categorical_variables:\\r\\n dummies = pd.get_dummies(data[variable], prefix=variable)\\r\\n data = pd.concat([data, dummies], axis=1)\\r\\n data.drop([variable], axis=1, inplace=True)\\r\\n data['movieID'] = data['movieID'].astype('int')\\r\\n data['userID'] = data['userID'].astype('int')\\r\\n \\r\\n\\t#data = dateTime(data)\\r\\n \\r\\n return data\",\n \"def clean(c):\",\n \"def clean(self):\",\n \"def _clean(self, dataset):\\n # Replace missing values with numpy's NaN. The missing value is\\n # usually 1e+20, but values can be like 1.0000002e+20, which is\\n # different. Ergo the inequality.\\n for var in dataset.data_vars.itervalues():\\n if 'missing_value' in var.attrs:\\n missing_data_value = var.missing_value\\n try:\\n var.values[var.values >= missing_data_value] = np.NaN\\n except ValueError:\\n print \\\"Encountered ValueError in {0}. Ignoring\\\".format(var.name)\",\n \"def cleanData(self):\\n clean_data = []\\n for file in self.raw_data_file:\\n data = RawData(file)\\n data.clean()\\n data.prepare()\\n clean_data.append(data)\\n return clean_data\",\n \"def cleanup(adata, del_prediction=False, del_2nd_moments=False):\\n\\n if \\\"pca_fit\\\" in adata.uns_keys():\\n adata.uns[\\\"pca_fit\\\"] = None\\n if \\\"velocyto_SVR\\\" in adata.uns_keys():\\n adata.uns[\\\"velocyto_SVR\\\"][\\\"SVR\\\"] = None\\n if \\\"umap_fit\\\" in adata.uns_keys():\\n adata.uns[\\\"umap_fit\\\"][\\\"fit\\\"] = None\\n if \\\"velocity_pca_fit\\\" in adata.uns_keys():\\n adata.uns[\\\"velocity_pca_fit\\\"] = None\\n if \\\"kmc\\\" in adata.uns_keys():\\n adata.uns[\\\"kmc\\\"] = None\\n if \\\"kinetics_heatmap\\\" in adata.uns_keys():\\n adata.uns.pop(\\\"kinetics_heatmap\\\")\\n if \\\"hdbscan\\\" in adata.uns_keys():\\n adata.uns.pop(\\\"hdbscan\\\")\\n\\n VF_keys = [i if i.startswith(\\\"VecFld\\\") else None for i in adata.uns_keys()]\\n for i in VF_keys:\\n if i is not None and \\\"VecFld2D\\\" in adata.uns[i].keys():\\n del adata.uns[i][\\\"VecFld2D\\\"]\\n\\n fate_keys = [i if i.startswith(\\\"fate\\\") else None for i in adata.uns_keys()]\\n for i in fate_keys:\\n if i is not None:\\n if adata.uns[i][\\\"init_cells\\\"] is not None:\\n adata.uns[i][\\\"init_cells\\\"] = list(adata.uns[i][\\\"init_cells\\\"])\\n if \\\"prediction\\\" in adata.uns[i].keys():\\n if del_prediction:\\n del adata.uns[i][\\\"prediction\\\"]\\n if \\\"VecFld_true\\\" in adata.uns[i].keys():\\n if adata.uns[i][\\\"VecFld_true\\\"] is not None:\\n del adata.uns[i][\\\"VecFld_true\\\"]\\n\\n if del_2nd_moments:\\n from .tools.utils import remove_2nd_moments\\n\\n remove_2nd_moments(adata)\\n\\n return adata\",\n \"def cleanup(self):\\n\\n # uninstall sourcedata\\n if self.conversion.install_dataset_path.exists():\\n # without the ChangeWorkingDir the command does not operate inside\\n # of dataset_path\\n with utils.ChangeWorkingDir(self.dataset_path):\\n datalad.uninstall(\\n path=self.conversion.install_dataset_name,\\n dataset=self.dataset_path,\\n recursive=True\\n )\\n\\n # remove bids conversion\\n bids_dir = self._get_bids_dir()\\n if bids_dir.exists():\\n self.log.info(\\\"Remove %s\\\", bids_dir)\\n shutil.rmtree(bids_dir)\",\n \"def data_preparation(self) -> None:\\n self.logger.info('data cleaning')\\n self.logger.info('num of secs: {}, num of ipo_dates: {}, num of secs with prices: {}'.format(\\n len(self.data),\\n len(self.ipo_dates),\\n len(self.prices)\\n ))\\n excluded = []\\n excluded = [i.lower() for i in excluded]\\n self.logger.info(f'number of excluded: {len(excluded)}')\\n for i in excluded:\\n self.data.pop(i)\\n for s in self.data:\\n # columns with empty assets sum (empty columns and other situations)\\n self.data[s].dropna(axis='columns', how='any', subset=['A_0'], inplace=True)\\n # columns with descriptions (polish and english names of values)\\n self.data[s].drop(self.data[s].columns[[0, 1]], inplace=True, axis=1)\\n\\n self.logger.info(f'number of secs after cleaning: {len(self.data)}')\\n data_list = [k for k in self.data.values()]\\n self.uber_data = pd.concat(data_list, ignore_index=True, axis=1)\\n self.uber_data = self.uber_data.transpose()\\n self.uber_data = self.uber_data.loc[:, pd.notnull(self.uber_data.columns)]\",\n \"def transform(self, dataset, labels):\\n print(f\\\"Dropping {len(self.deficient)} deficient features...\\\")\\n dataset.drop(columns=self.deficient, inplace=True)\\n print(f\\\"Scanning {len(dataset)} samples for duplicates...\\\")\\n duplicates = dataset.duplicated()\\n print(f\\\"Dropping {sum(duplicates)} duplicate samples...\\\")\\n dataset.drop(index=dataset.index[duplicates], inplace=True)\\n dataset.reset_index(drop=True, inplace=True)\\n labels.drop(labels=labels.index[duplicates], inplace=True)\\n labels.reset_index(drop=True, inplace=True)\\n return dataset, labels\",\n \"def clean_data():\\n pd.set_option('display.max_columns', None)\\n try:\\n df = pd.read_csv('test1/movie.csv')\\n except FileNotFoundError:\\n df = pd.read_csv('movie.csv')\\n\\n df.drop(labels=[\\\"actor_3_facebook_likes\\\", \\\"actor_2_name\\\",\\n \\\"actor_1_facebook_likes\\\", \\\"actor_1_name\\\",\\n \\\"num_voted_users\\\",\\n \\\"cast_total_facebook_likes\\\", \\\"actor_3_name\\\",\\n \\\"facenumber_in_poster\\\", \\\"movie_imdb_link\\\",\\n \\\"num_user_for_reviews\\\", \\\"actor_2_facebook_likes\\\",\\n \\\"aspect_ratio\\\", \\\"color\\\", \\\"num_critic_for_reviews\\\",\\n \\\"director_facebook_likes\\\"], axis=1, inplace=True)\\n df.dropna(subset=[\\\"gross\\\"], axis=0, inplace=True)\\n return df\",\n \"def clean(self):\\n pass\",\n \"def normalize_dataset(self):\",\n \"def clean(self):\\n # Perform the standard ACE cleaning\\n max_status = mm_ace.clean(self)\\n\\n # Replace bad values with NaN and remove times with no valid data\\n ecols = ['eflux_38-53', 'eflux_175-315']\\n\\n # Evaluate the electron flux data\\n self[self.data['status_e'] > max_status, ecols] = np.nan\\n\\n # Evaluate the proton flux data\\n pcols = ['pflux_47-68', 'pflux_115-195', 'pflux_310-580',\\n 'pflux_795-1193', 'pflux_1060-1900']\\n self[self.data['status_p'] > max_status, pcols] = np.nan\\n\\n # Include both fluxes and the anisotropy index in the removal eval\\n eval_cols = ecols + pcols\\n eval_cols.append('anis_ind')\\n\\n # Remove lines without any good data\\n good_cols = (np.isfinite(self.data.loc[:, eval_cols])).sum(axis=1)\\n bad_index = good_cols[good_cols == 0].index\\n self.data = self.data.drop(index=bad_index)\\n\\n return\",\n \"def clear_data():\\n logger.info(\\\"Delete Structure instances\\\")\\n Structure.objects.all().delete()\\n logger.info(\\\"Delete StructureType instances\\\")\\n StructureType.objects.all().delete()\\n logger.info(\\\"Delete Industry instances\\\")\\n Industry.objects.all().delete()\\n logger.info(\\\"Delete Price instances\\\")\\n PriceList.objects.all().delete()\\n logger.info(\\\"Delete Stock instances\\\")\\n Stock.objects.all().delete()\\n logger.info(\\\"Delete News instances\\\")\\n News.objects.all().delete()\\n logger.info(\\\"Delete NewsImages instances\\\")\\n NewsImage.objects.all().delete()\\n logger.info(\\\"Delete News Sections instances\\\")\\n NewsCategorySection.objects.all().delete()\\n logger.info(\\\"Delete Analysis instances\\\")\\n AnalysisOpinion.objects.all().delete()\\n logger.info(\\\"Delete Analysis Images instances\\\")\\n AnalysisImage.objects.all().delete()\\n logger.info(\\\"Delete Analysis Sections instances\\\")\\n AnalysisCategorySection.objects.all().delete()\",\n \"def clean_data(data):\\n \\n cols = data.columns\\n \\n #these columns had some extra characters in the strings becuase of encoding issues\\n list_to_strip=[\\n 'attributes_alcohol',\\n 'attributes_restaurantsattire',\\n 'attributes_wifi',\\n 'attributes_smoking',\\n 'attributes_noiselevel',\\n ]\\n #this removes quotation marks and u's from strings\\n \\n for col in list_to_strip:\\n data[col]=data[col].str.strip(\\\"u\\\\'\\\")\\n \\n #this replaces the strings None and none with Nan objects\\n for col in cols:\\n data[col]=data[col].where(data[col]!='None')\\n data[col]=data[col].where(data[col]!='none')\\n \\n #this creates a list of categorical and numerical features\\n categorical_features = cols.drop([\\n 'review_count',\\n 'restaurant',\\n 'latitude',\\n 'longitude',\\n 'business_id',\\n 'meanfunny',\\n 'meanuseful',\\n 'avgwordcount',\\n 'maxwordcount',\\n 'minwordcount',\\n 'avgfunnywordcount',\\n 'maxfunnywordcount',\\n 'avgusefulwordcount',\\n 'maxusefulwordcount',\\n 'medianwordcount',\\n 'upperquartilewordcount',\\n 'lowerquartilewordcount',\\n 'target'])\\n \\n \\n numerical_features = [\\n 'review_count',\\n 'latitude',\\n 'longitude',\\n 'meanfunny',\\n 'meanuseful',\\n 'avgwordcount',\\n 'maxwordcount',\\n 'minwordcount',\\n 'avgfunnywordcount',\\n 'maxfunnywordcount',\\n 'avgusefulwordcount',\\n 'maxusefulwordcount',\\n 'medianwordcount',\\n 'upperquartilewordcount',\\n 'lowerquartilewordcount']\\n \\n #this replaces the categorial nans with 9 as a placeholder and fills numerical nans with 0\\n data[categorical_features]=data[categorical_features].fillna(9)\\n data[numerical_features]=data[numerical_features].fillna(0)\\n \\n #this makes all the categorical columns strings\\n data[categorical_features]=data[categorical_features].astype(str)\\n data = data\\n \\n return data, numerical_features, categorical_features\",\n \"def clean_data():\\n datapath = Path(os.getcwd()) / \\\"data\\\"\\n files = [str(file) for file in datapath.glob(\\\"*.csv\\\")]\\n for file in files:\\n if file.endswith(\\\"confirmed.csv\\\"):\\n Confirmed = pd.read_csv(file)\\n elif file.endswith(\\\"deaths.csv\\\"):\\n Deaths = pd.read_csv(file)\\n elif file.endswith(\\\"recovered.csv\\\"):\\n Recovered = pd.read_csv(file)\\n\\n dataFrames = [Confirmed, Deaths, Recovered]\\n countryList = list(dataFrames[0][\\\"Country/Region\\\"]) #list of valid countries\\n countryList = list(dict.fromkeys(countryList))\\n\\n #create country population dictionary and align values with those in countryList\\n countriesPop = {}\\n countriesPop[\\\"US\\\"] = CountryInfo(\\\"USA\\\").population()\\n countriesPop[\\\"Czechia\\\"] = CountryInfo(\\\"Czech Republic\\\").population()\\n countriesPop[\\\"Taiwan*\\\"] = CountryInfo(\\\"Taiwan\\\").population()\\n countriesPop[\\\"Korea, South\\\"] = CountryInfo(\\\"South Korea\\\").population()\\n countriesPop[\\\"Eswatini\\\"] = CountryInfo(\\\"Swaziland\\\").population()\\n countriesPop[\\\"Cote d'Ivoire\\\"] = CountryInfo(\\\"Ivory Coast\\\").population()\\n\\n for country in countryList:\\n try:\\n countriesPop[country] = CountryInfo(country).population()\\n except KeyError:\\n pass\\n\\n #remove unnecessary information from dataframes\\n for count in range(len(dataFrames)):\\n dataFrames[count] = dataFrames[count].drop(\\\"Province/State\\\",axis=1)\\n dataFrames[count] = dataFrames[count].drop(\\\"Lat\\\",axis=1)\\n dataFrames[count] = dataFrames[count].drop(\\\"Long\\\",axis=1)\\n dataFrames[count] = dataFrames[count].rename(columns={\\\"Country/Region\\\": \\\"Country\\\"})\\n dataFrames[count][\\\"Country\\\"] = dataFrames[count][\\\"Country\\\"].replace({\\\"Korea, South\\\": \\\"South Korea\\\"})\\n dataFrames[count] = dataFrames[count].groupby(\\\"Country\\\").sum()\\n\\n # create per 100k capita values by dividing country data by population\\n ConfirmedPC = dataFrames[0].copy()\\n DeathsPC = dataFrames[1].copy()\\n RecoveredPC = dataFrames[2].copy()\\n countryList.append(\\\"South Korea\\\")\\n\\n for country in countryList:\\n try:\\n ConfirmedPC.loc[country] = ConfirmedPC.loc[country].divide(countriesPop[country]).multiply(100000) #confirmed cases per 100k inhabitants\\n DeathsPC.loc[country] = DeathsPC.loc[country].divide(countriesPop[country]).multiply(100000) #deaths per 100k inhabitants\\n RecoveredPC.loc[country] = RecoveredPC.loc[country].divide(countriesPop[country]).multiply(100000) #recovered cases per 100k inhabitants\\n except KeyError:\\n pass\\n\\n dataFrames.extend([ConfirmedPC, DeathsPC, RecoveredPC])\\n\\n return dataFrames, countryList\",\n \"def dev_clean():\\n clean_files(\\\"csv\\\", True)\\n clean_files(\\\"jsontxt\\\", True)\",\n \"def remove_data():\\n # Removing the existing data\\n col_answer_given.remove()\\n col_answer_not_given.remove()\\n col_q_not_given.remove()\\n col_to_summarize.remove()\",\n \"def original_clean():\\n # load the data\\n dataset = np.genfromtxt(\\\"wdbc.data\\\", dtype=np.float, delimiter=',', usecols=(2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\\n 12, 13, 14, 15, 16, 17, 18, 19,\\n 20, 21, 22, 23, 24, 25, 26, 27,\\n 28, 29, 30, 31), encoding=None)\\n labels = np.genfromtxt(\\\"wdbc.data\\\", dtype=None, delimiter=',', usecols=(1), encoding=None)\\n temp_labels = np.zeros(len(labels))\\n for i in range(len(labels)):\\n if labels[i] == 'B':\\n temp_labels[i] = 0\\n else:\\n temp_labels[i] = 1\\n # normalize\\n temp_data = normalize(dataset)\\n return temp_data, temp_labels\",\n \"def clean_up(self):\\n cleaning_sqls = [\\\"delete from meter where id not in (select meter_id from hymn)\\\",\\n \\\"delete from author where id not in (select author_id from hymn)\\\",\\n \\\"delete from category where id not in (select category_id from hymn)\\\"]\\n for sql in cleaning_sqls:\\n self.cur.execute(sql)\\n self.conn.commit()\",\n \"def clean(self):\\n raise NotImplementedError\",\n \"def clean(self, quick_clean=False):\\r\\n date_time('Cleaning')\\r\\n self.cursor.execute('DELETE FROM citations WHERE citation IS NULL OR citation = \\\"\\\" ;')\\r\\n self.conn.execute(\\\"VACUUM\\\")\\r\\n self.conn.commit()\\r\\n gc.collect()\",\n \"def clean(self):\\n\\n pass\",\n \"def delete_ds(self, dt):\\n\\n for k in self.datasets_keys:\\n for F in self.datasets[k]:\\n if F not in data[k].keys():\\n continue \\n max_date = data[k][F]['max_date'] \\n \\\"\\\"\\\" Deleting unecessary ds \\\"\\\"\\\"\\n if dt > max_date : # check max date and check if data is still loaded\\n print(blue + 'Memory used before deleting : ' , process.memory_info().rss/1000000000 , cend) \\n del data[k][F] \\n print(\\\"*** Erasing dataset: \\\" , k , ' ' , F ) \\n print(blue + 'Memory used after deleting : ' , process.memory_info().rss/1000000000 , cend) \\n \\n else:\\n continue\",\n \"def cleanup(self):\\n if self.cleanup_allowed:\\n shutil.rmtree(self.out_dir)\\n self.train_df, self.valid_df, self.test_df = None, None, None\",\n \"def clean():\\n clean_files()\",\n \"def _common_preprocess(self, data):\\n\\n data = data.drop('id', axis=1) \\n data = data.drop(['17', '488', 'B01AF', 'H01AB'], axis=1, errors='ignore')\\n\\n # drop age outliers\\n idx = data[(data['age'] > 99)].index\\n data = data.drop(idx)\\n\\n # drop rows with CKD\\n idx = data[((data['585'] != 0) | (data['586'] != 0)) &\\n (data['ckd'] == 0)].index\\n data = data.drop(idx)\\n data = data.drop(['585', '586'], axis=1)\\n\\n return data\",\n \"def clean(_context):\",\n \"def data_cleaning(df):\\n df = df.dropna(how='all')\\n \\n return df\",\n \"def cleanup():\\n cat = CSVCatalog.CSVCatalog()\\n cat.drop_table(\\\"people\\\", force_drop=True)\\n cat.drop_table(\\\"batting\\\", force_drop=True)\\n cat.drop_table(\\\"teams\\\", force_drop=True)\",\n \"def data_cleaning():\\n conn = get_connect()\\n conn.execute(\\\"DELETE FROM championMatchData WHERE kills < 2 AND deaths < 2 AND assists < 2\\\")\\n conn.commit()\\n conn.close()\\n return\",\n \"def _clean_data(self, docs: []):\\n print('Cleaning data...')\\n preprocessed_data = []\\n for doc in docs:\\n if len(doc) <= constants.MIN_DOC_LENGTH:\\n continue\\n\\n temp_doc = self._remove_urls(doc)\\n temp_doc = self._remove_special_chars(temp_doc)\\n temp_doc = self._transform_to_lowercase(temp_doc)\\n temp_doc = self._remove_stopwords(temp_doc)\\n\\n preprocessed_data.append(temp_doc)\\n\\n return preprocessed_data\",\n \"def clean(self):\\n if self.reloading:\\n self.cleaned = pd.concat(\\n [self.raw[0: self.brkIdx1+1],\\n self.raw[self.brkIdx3+1: self.brkIdx4+1]])\\n else:\\n self.cleaned = self.raw[0: self.brkIdx1+1]\\n self.cleaned.reset_index(drop=True, inplace=True) # update idx\\n # -- Cubic spline that passes through the data\\n sigmaLog = np.log10(self.cleaned['stress'][1:])\\n cs = CubicSpline(x=sigmaLog, y=self.cleaned['e'][1:])\\n self.eSigmaV = float(cs(np.log10(self.sigmaV))) # void ratio at sigmaV\\n return\",\n \"def cleanup():\\n cat = CSVCatalog.CSVCatalog()\\n cat.drop_table(\\\"people\\\")\\n cat.drop_table(\\\"batting\\\")\\n cat.drop_table(\\\"teams\\\")\",\n \"def clean(self):\\n\\n for metric in self.metricList:\\n listf = glob.glob(\\n '{}/*_{}_{}*'.format(self.outDir, metric.name, self.num))\\n if len(listf) > 0:\\n for val in listf:\\n os.system('rm {}'.format(val))\",\n \"def test_PubChemDataSet_clean_load(self):\\n\\n df = PubChemDataSet(1).clean_load()\\n assert len((df.Activity[df.Activity == 1])) == len((df.Activity[df.Activity == 0]))\\n assert None not in [Chem.MolToSmiles(mol) if mol else None for mol in df.rdkit]\",\n \"def _clean_data(self):\\n if not path.exists('auto-mpg.data.txt'):\\n logger.info('Could not find auto-mpg.data.txt in the current working directory')\\n sys.exit()\\n else:\\n try:\\n with open('auto-mpg.data.txt', 'r') as dirty_data:\\n with open('auto-mpg.clean.txt', 'w') as clean_data:\\n ## counter for row writes\\n counter = 0\\n for row in csv.reader(dirty_data):\\n clean_data.write(row[0].expandtabs(1) + '\\\\n')\\n counter +=1\\n except Exception as e:\\n logger.info('File error occurred: {e}. Exiting')\\n sys.exit()\",\n \"def _clean_up(self):\",\n \"def cleanAll(self):\\n for i in range(len(self.asteroid_type) - 1, -1, -1):\\n x, y = self.get_coords(self.asteroid_type[i])\\n self.del_asteroid(i)\\n\\n for i in range(len(self.asteroid_id_e) - 1, -1, -1):\\n x, y = self.get_coords(self.asteroid_id_e[i])\\n self.del_asteroid_e(i)\",\n \"def load_data_clean(source='local'):\\n\\n train_bl, test_bl = load_data_raw(source)\\n\\n train = train_bl.copy()\\n train = encode_dtypes(train)\\n train = impute_missing(train)\\n train = clean_data(train)\\n\\n test = test_bl.copy()\\n test = encode_dtypes(test)\\n test = impute_missing(test)\\n test = clean_data(test)\\n\\n return train, test\",\n \"def _clean_data(self, chart_data):\\n return [x for x in chart_data if x]\",\n \"def clean_data(data):\\n data.dropna(inplace=True)\\n for feature in data:\\n if ((feature != 'lat') and (feature != 'long') and (feature != 'date')):\\n data.drop(data[(data[feature] < 0)].index, inplace=True)\\n data.drop(data[(data['price'] == 0)].index, inplace=True)\\n data.drop(data[(data['bedrooms'] == 0) & (data['bathrooms'] == 0.0)].index, inplace=True)\\n return data\",\n \"def cleanUp():\\n pass\",\n \"def clean_data(data):\\n # switch dict to flip the names of the RE data set to match SF\\n col_switch = {'fund_id': 'project_id',\\n 'gift_count': 'record_count.5',\\n 'total': 'sum_of_amount.5'}\\n\\n # define package items\\n dfs = data[0]\\n args = data[1]\\n\\n # clean columns in dataframes\\n for df in dfs:\\n df.columns = [x.lower() for x in df.columns]\\n if 'fund id' in df.columns:\\n df.columns = [x.lower().replace(' ', '_') for x in df.columns]\\n df['source'] = 'RE'\\n df.rename(columns=col_switch, inplace=True)\\n else:\\n df.columns = [x.lower().replace(' ', '_') for x in df.columns]\\n df['source'] = 'SF'\\n df.drop(columns=['sum_of_amount', 'record_count', 'sum_of_amount.1', 'record_count.1', 'sum_of_amount.2',\\n 'record_count.2', 'sum_of_amount.3',\\n 'record_count.3', 'sum_of_amount.4', 'record_count.4'], inplace=True)\\n\\n # standardize project_IDs\\n for df in dfs:\\n df['project_id_new'] = df['project_id'].apply(lambda x: x.strip() if isinstance(x, str) else x)\\n df['project_id_new'] = df['project_id_new'].apply(\\n lambda x: x.replace(' - ', '-').lower() if isinstance(x, str) and '-' in x else x)\\n df['project_id_new'] = df['project_id_new'].apply(\\n lambda x: x.replace('- ', '-').lower() if isinstance(x, str) and '-' in x else x)\\n df['project_id_new'] = df['project_id_new'].apply(\\n lambda x: x.replace(' -', '-').lower() if isinstance(x, str) and '-' in x else x)\\n df['project_id_new'] = df['project_id_new'].apply(\\n lambda x: x.replace('-', '_').lower() if isinstance(x, str) and '-' in x else x)\\n df['project_id_new'] = df['project_id_new'].apply(\\n lambda x: x.replace(' ', '_').lower() if isinstance(x, str) and ' ' in x else x)\\n df['project_id_new'] = df['project_id_new'].apply(lambda x: x.lower() if isinstance(x, str) else x)\\n\\n # standardize sum_of_amount\\n for df in dfs:\\n df['sum_of_amount_new'] = df['sum_of_amount.5'].apply(lambda x: x.strip() if isinstance(x, str) else x)\\n df['sum_of_amount_new'] = df['sum_of_amount_new'].apply(\\n lambda x: x.replace('USD', '').lower() if isinstance(x, str) and 'USD' in x else x)\\n df['sum_of_amount_new'] = df['sum_of_amount_new'].apply(\\n lambda x: x.replace('$', '').lower() if isinstance(x, str) and '$' in x else x)\\n df['sum_of_amount_new'] = df['sum_of_amount_new'].apply(\\n lambda x: x.replace(',', '').lower() if isinstance(x, str) and ',' in x else x)\\n df['sum_of_amount_new'] = df['sum_of_amount_new'].apply(\\n lambda x: float(\\\"{0:.2f}\\\".format(float(x))) if isinstance(x, str) else x)\\n\\n return dfs, args\",\n \"def _clean(self):\\n map(self.__delitem__, self.keys())\\n self._original = []\\n self._columns = {}\\n self._modified, self._deleted = {}, {}\",\n \"def cleanup_dataset(euctr_cond):\\n euctr_cond['date_of_the_global_end_of_the_trial'] = pd.to_datetime(euctr_cond['date_of_the_global_end_of_the_trial'])\\n euctr_cond['trial_is_part_of_a_paediatric_investigation_plan'] = (euctr_cond['trial_is_part_of_a_paediatric_investigation_plan'] == True).astype(int)\\n euctr_cond['trial_human_pharmacology_phase_i'] = (euctr_cond['trial_human_pharmacology_phase_i']== True).astype(int)\\n euctr_cond['trial_therapeutic_exploratory_phase_ii'] = (euctr_cond['trial_therapeutic_exploratory_phase_ii']== True).astype(int)\\n euctr_cond['trial_therapeutic_confirmatory_phase_iii'] = (euctr_cond['trial_therapeutic_confirmatory_phase_iii']== True).astype(int)\\n euctr_cond['trial_therapeutic_use_phase_iv'] = (euctr_cond['trial_therapeutic_use_phase_iv']== True).astype(int)\\n euctr_cond['not_bioequivalence_study'] = (euctr_cond['trial_bioequivalence_study']== False).astype(int)\\n euctr_cond['trial_bioequivalence_study'] = (euctr_cond['trial_bioequivalence_study']== True).astype(int)\\n euctr_cond['rare_disease_blank'] = (euctr_cond['trial_condition_being_studied_is_a_rare_disease'] == 'Information not present in EudraCT').astype(int)\\n euctr_cond['not_rare_disease'] = (euctr_cond['trial_condition_being_studied_is_a_rare_disease'] == 'No').astype(int)\\n euctr_cond['trial_condition_being_studied_is_a_rare_disease'] = (euctr_cond['trial_condition_being_studied_is_a_rare_disease'] == 'Yes').astype(int)\\n euctr_cond['not_single_blind'] = (euctr_cond['trial_single_blind']== False).astype(int)\\n euctr_cond['trial_single_blind'] = (euctr_cond['trial_single_blind']== True).astype(int)\\n euctr_cond['not_healthy_volunteers'] = (euctr_cond['subject_healthy_volunteers']== False).astype(int)\\n euctr_cond['subject_healthy_volunteers'] = (euctr_cond['subject_healthy_volunteers']== True).astype(int)\\n\\n # Nick's notebook used pandas.notna, we reimplement a simplified version\\n # here for compatibility with pandas 0.19\\n def euctr_notna(x):\\n return not (x is None or x is np.nan)\\n euctr_cond['trial_results'] = (euctr_cond['trial_results'].apply(euctr_notna)).astype(int)\\n\\n euctr_cond.rename(columns={'full_title_of_the_trial':'full_title', 'name_or_abbreviated_title_of_the_trial_where_available': 'abbreviated_title'}, inplace=True)\\n euctr_cond['non_eu'] = euctr_cond.eudract_number_with_country.str.contains('-3rd').astype(int)\",\n \"def minimal_clean_data_inplace(df):\\n # There are some 'unknown' users in train dataset only\\n unknown_data_lines = df['sexo'].isnull() & df['age'].isnull() & df['ind_empleado'].isnull() & \\\\\\n df['fecha_alta'].isnull() & df['pais_residencia'].isnull()\\n\\n logging.info(\\\"- Number of lines with unknown data : %s\\\" % unknown_data_lines.sum())\\n\\n # Remove these users as clients\\n _clients = df[unknown_data_lines]['ncodpers'].unique()\\n bad_lines = df['ncodpers'].isin(_clients)\\n df.drop(df[bad_lines].index, inplace=True)\\n\\n logging.info(\\\"- Number of columns with nan : %s\\\" % df.isnull().any().sum())\\n\\n # Remove accent\\n df.loc[df['nomprov'] == \\\"CORU\\\\xc3\\\\x91A, A\\\", \\\"nomprov\\\"] = \\\"CORUNA\\\"\\n\\n unknown_cols = ['sexo',\\n 'ind_empleado',\\n 'pais_residencia',\\n 'ult_fec_cli_1t',\\n 'conyuemp',\\n 'canal_entrada',\\n 'nomprov',\\n 'segmento',\\n 'tiprel_1mes',\\n 'indrel_1mes']\\n # Start with cols -> replace nan with UNKNOWN\\n for col in unknown_cols:\\n df.loc[df[col].isnull(), col] = \\\"UNKNOWN\\\"\\n\\n # Set unknown renta to -99\\n df.loc[df['renta'].isnull(), 'renta'] = -99\\n\\n # Next `fecha_alta` :\\n assert df['fecha_alta'].isnull().sum() == 0, \\\\\\n \\\"Need to replace nan in 'fecha_alta', count=%s\\\" % df['fecha_alta'].isnull().sum()\\n\\n # **Remove 'tipodom' and 'cod_prov' columns**\\n df.drop([\\\"tipodom\\\", \\\"cod_prov\\\"], axis=1, inplace=True)\\n \\n # Convert 'ind_nuevo' to int\\n df['ind_nuevo'] = df['ind_nuevo'].astype(int)\\n \\n # Remove floating point at string indrel_1mes\\n df['indrel_1mes'] = df['indrel_1mes'].apply(lambda x: str(int(float(x))) if len(x) == 3 else x)\\n\\n if \\\"ind_nomina_ult1\\\" in df.columns and \\\"ind_nom_pens_ult1\\\" in df.columns:\\n # Target labels : `ind_nomina_ult1`, `ind_nom_pens_ult1` : nan -> 0\\n # I could try to fill in missing values for products by looking at previous months,\\n # but since it's such a small number of values for now I'll take the cheap way out.\\n df.loc[df.ind_nomina_ult1.isnull(), \\\"ind_nomina_ult1\\\"] = 0\\n df.loc[df.ind_nom_pens_ult1.isnull(), \\\"ind_nom_pens_ult1\\\"] = 0\\n\\n # replace 'antiguedad' with the number of months between 'fecha_alta' and 'fecha_dato'\\n func1 = lambda x: _to_ym_dec(to_yearmonth(x))\\n func2 = lambda x: max(_to_nb_months(x), 0) \\n\\n v1 = df['fecha_dato'].apply(func1)\\n v2 = df['fecha_alta'].apply(func1)\\n v3 = (v1 - v2).apply(func2)\\n df.loc[:, 'antiguedad'] = v3\\n \\n # Replace 'ult_fec_cli_1t' by current nb of months from fecha_dato, if negative, set to zero\\n mask = df['ult_fec_cli_1t'] == 'UNKNOWN'\\n df.loc[mask, 'ult_fec_cli_1t'] = df[mask]['fecha_dato']\\n v1 = df['fecha_dato'].apply(func1)\\n v2 = df['ult_fec_cli_1t'].apply(func1)\\n v3 = (v1 - v2).apply(func2)\\n df.loc[:, 'ult_fec_cli_1t'] = v3\",\n \"def clean_exam():\\n data = Exam.objects.all()\\n data.delete()\",\n \"def cleanup():\",\n \"def get_clean_data(path = 'ucr_offenses_known_monthly_1960_2016_dta/', \\n identifier_variables = ['fips_state_county_code', 'state', 'date', 'year', 'zip_code', 'month'], \\n crime_category = ['act_aggravated_assault', 'act_simple_assault', 'act_murder', 'act_robbery_total', \\n 'act_manslaughter', 'act_theft_total', 'act_mtr_vhc_theft_total', 'act_burglary_total', 'act_rape_total'], \\n start_year = 1980, end_year = 2009, selected_area = 'all'):\\n all_df = []\\n for i in get_filenames(start_year, end_year):\\n file = path + i\\n print(file)\\n each_df = pd.read_stata(file)\\n each_df = each_df[identifier_variables + crime_category]\\n each_df = each_df[each_df['fips_state_county_code'] == '06001']\\n each_df['zipcode'] = each_df['zip_code'].apply(lambda x: str(x)[0:5])\\n #split Alameda into West and East Alameda according to zip code\\n if selected_area == 'east':\\n each_df = each_df[(each_df['zipcode'] == '94550') | (each_df['zipcode'] == '94566') | \\n (each_df['zipcode'] == '94586') | (each_df['zipcode'] == '94568') | \\n (each_df['zipcode'] == '94588') | (each_df['zipcode'] == '94551')]\\n elif selected_area == 'west':\\n each_df = each_df[(each_df['zipcode'] != '94550') & (each_df['zipcode'] != '94566') & \\n (each_df['zipcode'] != '94586') & (each_df['zipcode'] != '94568') & \\n (each_df['zipcode'] != '94588') & (each_df['zipcode'] != '94551') &\\n (each_df['zipcode'] != '0') & (each_df['zipcode'] != '0.0') & \\n (each_df['zipcode'] != 'not r') & (each_df['zipcode'] != 'missi')]\\n each_df.loc[:, 'YearMonth'] = [int(re.sub('-', '', date)[0:6]) for date in each_df.loc[:, 'date']]\\n #sum up amount of crimes taken place in each category for each month\\n each_df = each_df.groupby(['YearMonth'])[crime_category].sum()\\n each_df['crime_sum'] = each_df.sum(axis = 1)\\n each_df = each_df['crime_sum'].reset_index()\\n all_df.append(each_df)\\n df = pd.concat(all_df).fillna(0)\\n df = df.sort_values('YearMonth').reset_index()\\n #split variable 'YearMonth\\\" into two variables 'year' and \\\"month' for Poission regression\\n del df['index']\\n df['year'] = df['YearMonth'].apply(lambda x: str(x)[:4])\\n df['month'] = df['YearMonth'].apply(lambda x: str(x)[4:])\\n if selected_area == 'east':\\n df.to_csv('east_alameda_crime.csv')\\n elif selected_area == 'west':\\n df.to_csv('west_alameda_crime.csv')\\n else:\\n df.to_csv('all_alameda_crime.csv')\\n return(df)\",\n \"def clean_data(self):\\n data_clean = []\\n for item in self.data:\\n if int(item[2]) >= self.seq_length and int(item[2]) <= self.max_frames:# and item[1] in self.classes:\\n data_clean.append(item)\\n\\n return data_clean\",\n \"def free_finalizer(self, dataset: dict):\\n # for gc being late\\n if dataset:\\n if dataset['vrtx']:\\n dataset['vrtx'].release()\\n if dataset['indx']:\\n dataset['indx'].release()\\n dataset.clear()\",\n \"def clean_data():\\n for clean_file in clean_files:\\n file_list = [f for f in os.listdir(\\\".\\\") if f.endswith(clean_file)]\\n for f in file_list:\\n os.remove(f)\",\n \"def clean_up(self):\\n pass\",\n \"def clean_up(self):\\n pass\",\n \"def clean_up(self):\\n\\t\\tpass\",\n \"def clean_up(self):\\n self.fname = None\\n self.failed_files = []\\n self.custom_failed = []\\n self.results = None\",\n \"def clean_data(data,variables,ignore_na=True,fill_mode=\\\"mode\\\"):\\n\\tnum_rows,num_cols = data.shape\\n\\tif ignore_na:\\n\\t\\tdata = data.dropna(axis=0,how=\\\"any\\\",subset=variables)\\n\\t\\tnum_rows,num_cols = data.shape\\n\\telse:\\n\\t\\tfor variable in variables:\\n\\t\\t\\tif data[variable].isna().sum()>0:\\n\\t\\t\\t\\tif fill_mode==\\\"mode\\\":\\n\\t\\t\\t\\t\\tdata[variable] = data[variable].fillna(data[variable].mode()[0])\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif variable==\\\"Age\\\":\\n\\t\\t\\t\\t\\t\\tnan_idx = np.where(data[\\\"Age\\\"].isna())[0]\\n\\t\\t\\t\\t\\t\\tnorm = [1.,1.]#[8.,3.]\\n\\t\\t\\t\\t\\t\\tnew_age = np.zeros((num_rows))\\n\\t\\t\\t\\t\\t\\tfor idx in nan_idx:\\n\\t\\t\\t\\t\\t\\t\\terr = (data[[\\\"SibSp\\\",\\\"Pclass\\\"]]-data[[\\\"SibSp\\\",\\\"Pclass\\\"]].iloc[idx])\\n\\t\\t\\t\\t\\t\\t\\terr /= norm\\n\\t\\t\\t\\t\\t\\t\\ttotal_err = np.sqrt(np.sum(err**2,axis=1))\\n\\t\\t\\t\\t\\t\\t\\tif len(data[\\\"Age\\\"][total_err==0].mode())>0:\\n\\t\\t\\t\\t\\t\\t\\t\\tnew_age[idx] = data[\\\"Age\\\"][total_err==0].mode()[0]\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tnew_age[idx] = data[\\\"Age\\\"][total_err<4].mode()[0]\\n\\t\\t\\t\\t\\t\\tdata[\\\"Age\\\"].fillna(pd.Series(new_age),inplace=True)\\n\\n\\t\\t\\t\\t\\tif variable==\\\"Embarked\\\":\\n\\t\\t\\t\\t\\t\\t# data[variable] = data[variable].fillna(data[variable].mode()[0])\\n\\t\\t\\t\\t\\t\\tdata = data.dropna(axis=0,how=\\\"any\\\",subset=[\\\"Embarked\\\"])\\n\\t\\t\\t\\t\\t\\tnum_rows,num_cols = data.shape\\n\\n\\t\\t\\t\\t\\tif variable==\\\"Fare\\\":\\n\\t\\t\\t\\t\\t\\tnan_idx = np.where(data[\\\"Fare\\\"].isna())[0]\\n\\t\\t\\t\\t\\t\\tnew_fare = np.zeros((num_rows))\\n\\t\\t\\t\\t\\t\\tfor idx in nan_idx:\\n\\t\\t\\t\\t\\t\\t\\ttemp_age = data[\\\"Age\\\"].iloc[idx]\\n\\t\\t\\t\\t\\t\\t\\ttemp_embarked = data[\\\"Embarked\\\"].iloc[idx]\\n\\t\\t\\t\\t\\t\\t\\ttemp_pclass = data[\\\"Pclass\\\"].iloc[idx]\\n\\n\\t\\t\\t\\t\\t\\t\\tsimilar_cases = data.loc[(data[\\\"Embarked\\\"]==temp_embarked) &\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t (data[\\\"Pclass\\\"]==temp_pclass) &\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t (data[\\\"Age\\\"]>(temp_age-15)) &\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t (data[\\\"Age\\\"]<(temp_age+15)) &\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t (data[\\\"Fare\\\"].isna()==False), \\\"Fare\\\"]\\n\\t\\t\\t\\t\\t\\t\\tnew_fare[idx] = similar_cases.median()\\n\\t\\t\\t\\t\\t\\tdata[\\\"Fare\\\"].fillna(pd.Series(new_fare),inplace=True)\\n\\n\\n\\t## format ticket values\\n\\tduplicates = []\\n\\tticket_no = np.zeros((num_rows))\\n\\tfor i,uniq in enumerate(data['Ticket'].unique()):\\n\\t\\tticket_no[data['Ticket']==uniq] = i\\n\\t## take into account four cases where 'Ticket' is given as LINE\\n\\tticket_no[data['Ticket']==\\\"LINE\\\"] = i + np.arange(np.sum(data['Ticket']==\\\"LINE\\\"))\\n\\tdata[\\\"ticket_no\\\"] = pd.Series(ticket_no,dtype=int)\\n\\n\\t## format cabin values, set NaN cabin values to zero\\n\\tduplicates = []\\n\\tcabin_no = np.zeros((num_rows))\\n\\tfor i,uniq in enumerate(data['Cabin'].unique()):\\n\\t\\tif uniq==\\\"nan\\\":\\n\\t\\t\\tcabin_no[data['Cabin']==uniq] = 0\\n\\t\\tcabin_no[data['Cabin']==uniq] = i + 1\\n\\tdata[\\\"cabin_no\\\"] = pd.Series(cabin_no,dtype=int)\",\n \"def remove_bad_data(self):\\n min_title_len = 9e10\\n min_abstract_len = 9e10\\n indexes_to_remove = []\\n for i in range(len(self.data)):\\n title_len = len(self.data[i][\\\"Title\\\"])\\n if title_len < min_title_len:\\n min_title_len = title_len\\n\\n abstract_len = len(self.data[i][\\\"Abstract\\\"])\\n if abstract_len < min_abstract_len:\\n min_abstract_len = abstract_len\\n\\n if title_len == 0 or abstract_len == 0:\\n if i not in indexes_to_remove:\\n indexes_to_remove.append(i)\\n\\n # start deleting from the end of list to avoid problems with shifting indexes\\n indexes_to_remove.reverse()\\n for index in indexes_to_remove:\\n print(\\\"Deleting index: \\\", index)\\n del self.data[index]\\n\\n print(\\\"Min Title len:\\\", min_title_len)\\n print(\\\"Min Abstract len:\\\", min_abstract_len)\",\n \"def cleanUp(self):\\r\\n pass\",\n \"def create_cleaned_dataset(PDBbind_dataset_path, general_set_PDBs_path, refined_set_PDBs_path, output_name, plot = False):\\n \\n # load dataset\\n data = pd.read_csv(PDBbind_dataset_path)\\n \\n # check for NaNs in affinity data\\n if data['-log(Kd/Ki)'].isnull().any() != False:\\n print('There are NaNs present in affinity data!')\\n \\n # create list of PDB id's\\n pdbid_list = list(data['PDB ID'])\\n \\n # remove affinity values that do not have structural data by searching PDBs\\n missing = []\\n for i in range(len(pdbid_list)):\\n pdb = pdbid_list[i]\\n if os.path.isdir(str(general_set_PDBs_path) + pdb)==False and os.path.isdir(str(refined_set_PDBs_path) + pdb)==False:\\n missing.append(pdb)\\n data = data[~np.in1d(data['PDB ID'], list(missing))]\\n\\n # distinguish PDB id's in general and refined sets\\n general_dict = {}\\n refined_dict = {}\\n for i in range(len(pdbid_list)):\\n pdb = pdbid_list[i]\\n if os.path.isdir(str(general_set_PDBs_path) + pdb)==True:\\n general_dict[pdb] = 'general'\\n if os.path.isdir(str(refined_set_PDBs_path) + pdb)==True:\\n refined_dict[pdb] = 'refined'\\n \\n # add 'set' column to data and fill with 'general' or 'refined'\\n data['set'] = np.nan\\n data.loc[np.in1d(data['PDB ID'], list(general_dict)), 'set'] = 'general'\\n data.loc[np.in1d(data['PDB ID'], list(refined_dict)), 'set'] = 'refined'\\n \\n # write out csv of cleaned dataset\\n data[['PDB ID', '-log(Kd/Ki)', 'set']].to_csv(output_name, index=False)\\n \\n # read in and view the cleaned dataset\\n display(pd.read_csv(output_name))\\n \\n if plot == True:\\n # plot affinity distributions for general and refined sets\\n grid = sns.FacetGrid(data, row='set', row_order=['general', 'refined'],\\n size=3, aspect=2)\\n grid.map(sns.distplot, '-log(Kd/Ki)')\\n else:\\n return\",\n \"def clean(self):\\n # Perform the standard ACE cleaning\\n max_status = mm_ace.clean(self)\\n\\n # Replace bad values with NaN and remove times with no valid data\\n self.data = self.data[self.data['status'] <= max_status]\\n\\n return\",\n \"def clean(self):\\n\\t\\tfor v in self:\\n\\t\\t\\tv.reset_distance()\\n\\t\\t\\tv.reset_predecessor()\\n\\t\\t\\tv.reset_visited()\",\n \"def CleanUp(self):\\n self.cmd.CleanUp()\",\n \"def makedata():\\n # train\\n print('Clean Train Dataset and separate questions')\\n df = pd.read_csv(TRAIN_DATASET).replace(np.nan, ' ')\\n t = df.shape[0] * 2\\n print t\\n df['question1'] = cleanText(df['question1'])\\n df['question2'] = cleanText(df['question2'])\\n\\n df.to_csv(os.path.join(rootpathdata_cleaned, 'train.csx'), index=False)\\n overallquestions = df['question1'].tolist() + df['question2'].tolist()\\n tpm = pd.DataFrame()\\n tpm['question'] = overallquestions\\n tpm.to_csv(os.path.join(rootpathdata_cleaned, 'train_allquestions.csx'), index=False)\\n # test\\n\\n print('Clean Test Dataset and separate questions')\\n df = pd.read_csv(TEST_DATASET).fillna(' ')\\n t1 = df.shape[0] * 2\\n df['question1'] = cleanText(df['question1'])\\n df['question2'] = cleanText(df['question2'])\\n df.to_csv(os.path.join(rootpathdata_cleaned, 'test.csx'), index=False)\\n\\n overallquestions += df['question1'].tolist() + df['question2'].tolist()\\n tpm = pd.DataFrame()\\n tpm['question'] = overallquestions\\n tpm.to_csv(os.path.join(rootpathdata_cleaned, 'test_allquestions.csx'), index=False)\\n print len(overallquestions), t1 + t\",\n \"def clean(data, skip_transformations=False, target=False):\\n data = convert_type(data)\\n data = category_grouping(data)\\n if target:\\n target = data[['Target']]\\n data = data.drop(columns='Target')\\n x_train, x_test, y_train, y_test = f.train_test(data, target)\\n x_train, y_train = resample_vals(x_train, y_train)\\n x_train = x_train.assign(Train=lambda x: 1)\\n x_test = x_test.assign(Train=lambda x: 0)\\n data = pd.concat([x_train, x_test])\\n data = onehot_features(data)\\n data = log_trans(data, test=True)\\n data = cap_outliers(data, test=True)\\n data = scale(data, test=True)\\n x_train = data.loc[data['Train'] == 1]\\n x_test = data.loc[data['Train'] == 0]\\n return x_train, x_test, y_train, y_test\\n data = onehot_features(data)\\n if skip_transformations:\\n return data\\n data = log_trans(data)\\n data = cap_outliers(data)\\n data = scale(data)\\n return data\",\n \"def _clean_files(self):\\n if self.delfiles & 1:\\n ProcUtils.remove(self.okm)\\n if self.delfiles & 2:\\n ProcUtils.remove(self.hkm)\\n if self.delfiles & 4:\\n ProcUtils.remove(self.qkm)\\n if self.delfiles & 8:\\n ProcUtils.remove(self.obc)\\n\\n if self.log is False:\\n ProcUtils.remove(self.pcf_file)\\n base = os.path.basename(self.okm)\\n ProcUtils.remove(os.path.join(self.dirs['run'],\\n '.'.join(['LogReport', base])))\\n ProcUtils.remove(os.path.join(self.dirs['run'],\\n '.'.join(['LogStatus', base])))\\n ProcUtils.remove(os.path.join(self.dirs['run'],\\n '.'.join(['LogUser', base])))\",\n \"def clear_brain():\\n\\n if os.path.exists(os.path.abspath(\\\"papaya_data\\\")):\\n shutil.rmtree(os.path.abspath(\\\"papaya_data\\\"))\",\n \"def tearDown(cls):\\n\\n # cls.test_mmp_series_object.clean_out_data_seriesobj()\\n # reusable data struct\\n cls.test_mmp_series_object.clean_out_data_seriesobj()\\n cls.test_dataset_testresults.clear()\\n # reusable results file\\n # os.remove(cls.temp_file_output_series.name)\",\n \"def clean_data(input_file, output_file):\\n # Create data frame\\n data = pd.read_csv(input_file, sep = \\\";\\\")\\n \\n # Remove unnecessary features from data frame\\n data = data.drop([\\\"Name\\\",\\\"Ticket\\\",\\\"Cabin\\\"], axis=1)\\n \\n # Remove NaN values from remaining features\\n data = data.dropna()\\n \\n # Save ready-to-use file\\n data.to_csv(output_file, index=False)\",\n \"def cleanup_and_reset(self):\\n self.mem.set(self.mem.META_PLAN, None)\\n self.mem.set(self.mem.META_GOALS, None)\\n self.mem.set(self.mem.META_CURR_GOAL, None)\",\n \"def clear_temporary_data(self):\\n self.extract_list = []\\n self.transform_result = {}\",\n \"def full_clean():\\n response_to_df_csv()\\n dirty_data = pd.read_csv(\\\"./data/dirty_data.csv\\\")\\n cleaned_data = dirty_data\\n cleaned_data = drop_cols(cleaned_data)\\n cleaned_data = lowercase_columns(cleaned_data)\\n cleaned_data = make_numeric(cleaned_data)\\n cleaned_data = drop_zero_pay(cleaned_data)\\n cleaned_data = add_log_col(cleaned_data)\\n cleaned_data.to_csv('./data/cleaned_for_testing.csv')\\n\\n return cleaned_data\",\n \"def clean(self):\\n self.unique_combinations = {}\\n self.reverse_combinations = []\\n self.label_count = None\",\n \"def clean():\\n try:\\n os.unlink(options.coords + 'mirza_mrna_input' + '.fa')\\n os.unlink(options.coords + 'mirza_mirna_input' + '.fa')\\n os.unlink(options.coords + 'mirza_mirna_expressions' + '.fa')\\n except:\\n pass\",\n \"def _clean_up_temporary_files(dataset_dir):\\n return\",\n \"def clean(self):\\n files = ['CHG', 'CHGCAR', 'POSCAR', 'INCAR', 'CONTCAR',\\n 'DOSCAR', 'EIGENVAL', 'IBZKPT', 'KPOINTS', 'OSZICAR',\\n 'OUTCAR', 'PCDAT', 'POTCAR', 'vasprun.xml',\\n 'WAVECAR', 'XDATCAR', 'PROCAR', 'ase-sort.dat',\\n 'LOCPOT', 'AECCAR0', 'AECCAR1', 'AECCAR2',\\n 'WAVECAR.GTO', 'vasp.out', 'vasp.err']\\n for f in files:\\n try:\\n os.remove(f)\\n except OSError:\\n pass\",\n \"def clean(self):\\n cursor = self.cnx.cursor()\\n cursor.execute(\\\"DROP TABLE IF EXISTS FileNameFilter_scores\\\")\\n cursor.execute(\\\"DROP TABLE IF EXISTS FileNameFilter_unique_name\\\")\\n self.cnx.commit()\\n cursor.close()\",\n \"def clean_data(df):\\n \\n # Put in code here to execute all main cleaning steps:\\n # convert missing value codes into NaNs, ...\\n################################################################################################################ \\n missing_data_stats=pd.DataFrame(df.isnull().sum(),columns=['Naturally_Missing_Values'])\\n \\n # Loop over all the rows/attributes in feat_info\\n for i in range(len(feat_info)):\\n att=feat_info['attribute'][i] # Get name of attribute\\n codes=feat_info['missing_or_unknown'][i].replace('[',\\\"\\\").replace(']',\\\"\\\").split(',') # Get values from string\\n\\n # Loop over all the individual values extratcted from '\\tmissing_or_unknown' column\\n for j in range(len(codes)):\\n if codes[j]!='': # skip empty values as nothing needs to be replaced\\n try:\\n df.loc[df[att]==int(codes[j]),att] = np.nan # replaces numeric values with NaNs\\n except:\\n df.loc[df[att]==(codes[j]),att] = np.nan # replaces string values with NaNs\\n \\n # Merge and plot the naturally missing and overall missing values\\n missing_data_stats=pd.merge(missing_data_stats,pd.DataFrame(df.isnull().sum(),\\n columns=['Overall_Missing_Values']),left_index=True,right_index=True)\\n \\n################################################################################################################ \\n # Perform an assessment of how much missing data there is in each column of the dataset.\\n missing_data_stats['Overall_Missing_Percentage']=(missing_data_stats['Overall_Missing_Values']/len(df))*100\\n sorted_missing_data_stats=missing_data_stats.sort_values(by='Overall_Missing_Percentage',ascending=False)\\n \\n # Investigate patterns in the amount of missing data in each column.\\n plt.figure(figsize=(15,30))\\n sns.barplot(x='Overall_Missing_Percentage', y=sorted_missing_data_stats.index, \\n data=sorted_missing_data_stats);\\n plt.title('Percentage of Missing Data by Attribute',size=20)\\n \\n################################################################################################################\\n # remove selected columns and rows, ...\\n # Define criteria of outliers removal\\n outlier=30 # Outlier if % missing more than\\n\\n #Get outlier columns and drop them from data\\n outlier_columns=sorted_missing_data_stats[sorted_missing_data_stats['Overall_Missing_Percentage']>outlier].index\\n for i in outlier_columns:\\n df.drop(i,axis=1,inplace=True)\\n\\n################################################################################################################\\n # Sum all the missing values by rows\\n nan_count_rows=df.isnull().sum(axis=1).sort_values(ascending=False)\\n\\n # Plot and visualize the distribution of missing rows\\n plt.figure(figsize=(20,8))\\n sns.countplot(nan_count_rows)\\n\\n plt.xlabel('Missing Values in each Row')\\n plt.ylabel('Count of Rows')\\n plt.title('Count of Rows vs Missing Values in each Row')\\n \\n \\n # Define threshold value\\n row_nan_threshold=0\\n\\n # Rows above threshold\\n at=df.loc[nan_count_rows>row_nan_threshold]\\n\\n # Rows below threshold\\n bt=df.loc[nan_count_rows<=row_nan_threshold]\\n \\n################################################################################################################ \\n # select, re-encode, and engineer column values.\\n\\n # Assess categorical variables: which are binary, which are multi-level, and\\n # which one needs to be re-encoded?\\n\\n # Get all categorical features\\n cat_att=feat_info[feat_info['type']=='categorical']['attribute']\\n\\n # Loop through all the categorical features to identify binary numeric, binary non-numeric and multi-level \\n #categories\\n bin_num=[] #binary and numeric (type1)\\n bin_non_num=[] #binary and non_numeric (type2)\\n ml_cat=[] #multi-level categories (type3)\\n\\n for i in cat_att:\\n try:\\n # get number of unique values\\n uv=bt[i].nunique()\\n dt=bt[i].dtype\\n\\n # For binary variable with numeric value\\n if uv==2 and dt!='object':\\n bin_num.append(i) #append\\n\\n # For binary variable with non-numeric value\\n elif uv==2 and dt=='object':\\n bin_non_num.append(i) #append\\n\\n # For multi-level categories \\n elif uv>2:\\n ml_cat.append(i) #append\\n\\n\\n # Exception for columns which are already dropped from data\\n except Exception as e:\\n print('Note=> Column does not exist: ', e)\\n\\n print('\\\\nBinary_Numeric:\\\\n', bin_num , '\\\\n\\\\nBinary_Non_Numeric:\\\\n' , bin_non_num , '\\\\n\\\\nMulti-Level:\\\\n' , ml_cat)\\n \\n################################################################################################################\\n # Import Libraries\\n from sklearn.preprocessing import LabelEncoder\\n\\n # Initialize encoder\\n le=LabelEncoder()\\n\\n # columns list to encode\\n col_list=bin_non_num+ml_cat\\n\\n # Create a dataframe of encoded features\\n bt_encoded=bt.copy(deep=True)\\n\\n # Encode all columns in column list\\n for cols in col_list:\\n bt_encoded[cols]=le.fit_transform(bt[cols])\\n\\n################################################################################################################\\n # Create a list of all mixed features\\n mixed_att=feat_info[feat_info['type']=='mixed']['attribute']\\n\\n for i in mixed_att:\\n # Investigate \\\"PRAEGENDE_JUGENDJAHRE\\\" and engineer two new variables.\\n if i=='PRAEGENDE_JUGENDJAHRE':\\n bt_encoded.loc[(bt_encoded[i]<=2) & (bt_encoded[i]>=1) ,i+'_Decade']=1\\n bt_encoded.loc[(bt_encoded[i]<=4) & (bt_encoded[i]>=3),i+'_Decade']=2\\n bt_encoded.loc[(bt_encoded[i]<=7) & (bt_encoded[i]>=5),i+'_Decade']=3\\n bt_encoded.loc[(bt_encoded[i]<=9) & (bt_encoded[i]>=8),i+'_Decade']=4\\n bt_encoded.loc[(bt_encoded[i]<=13) & (bt_encoded[i]>=10),i+'_Decade']=5 \\n bt_encoded.loc[(bt_encoded[i]<=15) & (bt_encoded[i]>=14),i+'_Decade']=6\\n\\n bt_encoded[i+'_Movement']=1\\n bt_encoded.loc[(bt_encoded[i]==1) | (bt_encoded[i]==3) |\\n (bt_encoded[i]==5) | (bt_encoded[i]==8) |\\n (bt_encoded[i]==10) | (bt_encoded[i]==12) |\\n (bt_encoded[i]==14) ,i+'_Movement']=2\\n\\n # Investigate \\\"CAMEO_INTL_2015\\\" and engineer two new variables.\\n elif i=='CAMEO_INTL_2015':\\n bt_encoded[i+'_Wealth']=bt_encoded[i].apply(lambda x:int(x[0]))\\n bt_encoded[i+'_Life_Stage']=bt_encoded[i].apply(lambda x:int(x[1]))\\n\\n # Investigate \\\"LP_LEBENSPHASE_GROB\\\" and engineer a new variables (for low,avg,high income groups) \\n elif i=='LP_LEBENSPHASE_GROB':\\n #low income\\n bt_encoded[i+'_income']=1 \\n\\n #avg income\\n bt_encoded.loc[(bt_encoded[i]==3) | (bt_encoded[i]==5) |\\n (bt_encoded[i]==8) | (bt_encoded[i]==11) |\\n (bt_encoded[i]==12) ,i+'_income']=2 \\n #high income\\n bt_encoded.loc[(bt_encoded[i]==9) ,i+'_income']=3 \\n\\n\\n # Investigate \\\"WOHNLAGE\\\" and engineer a new variables (for Rural and Non Rural Flag) \\n elif i=='WOHNLAGE':\\n bt_encoded[i+'_Rural']=1\\n bt_encoded.loc[(bt_encoded[i]==7) | (bt_encoded[i]==8),i+'_Rural']=2\\n\\n\\n # Investigate \\\"PLZ8_BAUMAX\\\" and engineer a new variables (for family and business buildings) \\n elif i=='PLZ8_BAUMAX':\\n bt_encoded[i+'_Bldg_Type']=1\\n bt_encoded.loc[bt_encoded[i]==5,i+'_Bldg_Type']=2\\n \\n################################################################################################################\\n # Return the cleaned dataframe.\\n #Remove original mixed columns from final dataset if new columns have been engineered\\n for i in mixed_att:\\n try:\\n bt_encoded.drop(i,axis=1,inplace=True) \\n except Exception as e:\\n print('Note=> Column does not exist: ', e)\\n return bt_encoded\",\n \"def clean_dataset(args, min_class_count=5):\\n classes_count = get_classes_count(args)\\n print(f\\\"cleaning dataset of {len(args)} arguments\\\")\\n classes_to_remove = []\\n for c in classes_count.keys():\\n if classes_count[c] < min_class_count:\\n classes_to_remove.append(c)\\n\\n clean_args = [a for a in args if a.frame_id not in classes_to_remove]\\n removed_args = [a for a in args if a.frame_id in classes_to_remove]\\n print(f\\\"removed {len(removed_args)} arguments from dataset. {len(clean_args)} remaining.\\\")\\n return clean_args, removed_args\",\n \"def clean(self):\\n\\n if (self.clean_level == 'dusty') | (self.clean_level == 'clean'):\\n idx, = np.where(self['B_flag'] == 0)\\n self.data = self[idx, :]\\n\\n return\",\n \"def cleaning(df, file=\\\"proteinGroups\\\"):\\r\\n columns = df.columns\\r\\n if file == \\\"proteinGroups\\\":\\r\\n if (\\\"Potential contaminant\\\" not in columns) or\\\\\\r\\n (\\\"Reverse\\\" not in columns) or\\\\\\r\\n (\\\"Only identified by site\\\" not in columns):\\r\\n print(\\\"Is this data already cleaned?\\\\nMandatory columns for cleaning not present in data!\\\")\\r\\n print(\\\"Returning provided dataframe!\\\")\\r\\n return df\\r\\n df = df[(df['Potential contaminant'].isnull()) &\\r\\n (df['Reverse'].isnull()) &\\r\\n (df['Only identified by site'].isnull())]\\r\\n df.drop(['Potential contaminant',\\\"Reverse\\\", 'Only identified by site'], axis=1, inplace=True)\\r\\n elif (file == \\\"Phospho (STY)\\\") or (file == \\\"evidence\\\") or (file == \\\"modificationSpecificPeptides\\\"):\\r\\n if (\\\"Potential contaminant\\\" not in columns) or\\\\\\r\\n (\\\"Reverse\\\" not in columns):\\r\\n print(\\\"Is this data already cleaned?\\\\nMandatory columns for cleaning not present in data!\\\")\\r\\n print(\\\"Returning provided dataframe!\\\")\\r\\n return df\\r\\n df = df[(df['Potential contaminant'].isnull()) &\\r\\n (df['Reverse'].isnull())]\\r\\n df.drop(['Potential contaminant',\\\"Reverse\\\"], axis=1, inplace=True)\\r\\n return df\",\n \"def clean():\\n clean_flatbuffer_binaries()\\n clean_webp_textures()\",\n \"def preprocess_dataset(dataset=None, remove_missing=60, remove_empty_rows=True):\\n print('feature size before dropping:{}'.format(dataset.shape[1]))\\n dataset_after_drop = dataset.dropna(thresh=dataset.shape[0]*remove_missing/100, how='all',axis=1)\\n print('feature size after dropping:{}'.format(dataset_after_drop.shape[1]))\\n print('row size before dropping:{}'.format(dataset_after_drop.shape[0]))\\n if remove_empty_rows is True:\\n df_final = dataset_after_drop.dropna(inplace=False).reset_index (drop=True)\\n print('row size after dropping:{}'.format(df_final.shape[0]))\\n print('---------------')\\n print('final shape:{}'.format(df_final.shape))\\n return df_final\\n else:\\n return dataset_after_drop\",\n \"def clean_dataset(behaviors_source, behaviors_target, news_source,\\n news_target):\\n print(f\\\"Clean up {behaviors_source}\\\")\\n behaviors = pd.read_table(behaviors_source,\\n header=None,\\n usecols=[2, 3],\\n names=['clicked_news', 'impressions'])\\n behaviors.impressions = behaviors.impressions.str.split()\\n behaviors = behaviors.explode('impressions').reset_index(drop=True)\\n behaviors['candidate_news'], behaviors[\\n 'clicked'] = behaviors.impressions.str.split('-').str\\n behaviors.clicked_news.fillna('', inplace=True)\\n behaviors.to_csv(behaviors_target,\\n sep='\\\\t',\\n index=False,\\n columns=['clicked_news', 'candidate_news', 'clicked'])\\n\\n print(f\\\"Clean up {news_source}\\\")\\n news = pd.read_table(news_source,\\n header=None,\\n usecols=[0, 3, 6],\\n names=['id', 'title', 'entities'])\\n news.to_csv(news_target, sep='\\\\t', index=False)\",\n \"def clean_data_fragments(self) -> None:\\n read_path: Path = Path(os.environ[\\\"DATA_PATH\\\"]) / \\\"fragments\\\"\\n try:\\n shutil.rmtree(read_path / \\\"__MACOSX\\\")\\n except FileNotFoundError:\\n print('Folder \\\"__MACOSX\\\" already removed.')\\n\\n # delete non-binarized images\\n frag_paths: list = list((read_path / \\\"image-data\\\").iterdir())\\n frags_binarized: list = [fp for fp in frag_paths if \\\"binarized\\\" in fp.name]\\n frags_delete: set = set(frag_paths).difference(set(frags_binarized))\\n for frag in frags_delete:\\n frag.unlink()\\n frag_paths = frags_binarized\\n for frag_path in frag_paths:\\n # Python 3.8 hack, seems to be supported without str() on 3.9\\n shutil.move(str(frag_path.resolve()), str(read_path.resolve()))\\n\\n (read_path / \\\"image-data\\\").rmdir() # delete empty folder\",\n \"def clean_object(metadata, analysistype):\\n for sample in metadata:\\n try:\\n delattr(sample[analysistype], \\\"targetnames\\\")\\n except AttributeError:\\n pass\\n try:\\n delattr(sample[analysistype], \\\"targets\\\")\\n except AttributeError:\\n pass\\n try:\\n delattr(sample[analysistype], \\\"dnaseq\\\")\\n except AttributeError:\\n pass\\n try:\\n delattr(sample[analysistype], \\\"protseq\\\")\\n except AttributeError:\\n pass\",\n \"def clean(self):\\n self.clean_rally_conf()\\n rally.RallyBase.clean_rally_logs()\\n if self.image_alt:\\n self.cloud.delete_image(self.image_alt)\\n if self.flavor_alt:\\n self.orig_cloud.delete_flavor(self.flavor_alt.id)\\n super().clean()\",\n \"def collect_garbage(self, required_inputs):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7972246","0.78305405","0.770086","0.7358835","0.7004315","0.6960603","0.6881473","0.68532133","0.67468274","0.6705054","0.6648155","0.66471887","0.66403866","0.6639197","0.6567839","0.6521795","0.64767987","0.6475485","0.64592075","0.63912","0.63877296","0.63706595","0.6357867","0.63522524","0.634157","0.6331682","0.6324391","0.63144606","0.6312341","0.63078004","0.6298474","0.62843573","0.62785125","0.6274412","0.6267191","0.62610924","0.62350243","0.62233144","0.6218916","0.6204278","0.620087","0.6184506","0.61840254","0.61767995","0.61508983","0.61436504","0.6141183","0.61355656","0.6132905","0.6129441","0.61189896","0.61101514","0.6106826","0.610548","0.6091539","0.6085897","0.60849166","0.6075513","0.60741395","0.60724217","0.6067116","0.6060838","0.60441506","0.602703","0.6024641","0.6024641","0.6023189","0.6022174","0.60181916","0.60162294","0.60094166","0.60073036","0.60007155","0.5995908","0.5981636","0.59796226","0.59684116","0.59657323","0.5963346","0.5961446","0.59575766","0.5942519","0.59406173","0.59340626","0.593322","0.593317","0.59273297","0.59169185","0.59146214","0.5913298","0.59046954","0.5903483","0.59008414","0.58888936","0.5877329","0.58719355","0.5871472","0.5871163","0.5858921","0.58550715"],"string":"[\n \"0.7972246\",\n \"0.78305405\",\n \"0.770086\",\n \"0.7358835\",\n \"0.7004315\",\n \"0.6960603\",\n \"0.6881473\",\n \"0.68532133\",\n \"0.67468274\",\n \"0.6705054\",\n \"0.6648155\",\n \"0.66471887\",\n \"0.66403866\",\n \"0.6639197\",\n \"0.6567839\",\n \"0.6521795\",\n \"0.64767987\",\n \"0.6475485\",\n \"0.64592075\",\n \"0.63912\",\n \"0.63877296\",\n \"0.63706595\",\n \"0.6357867\",\n \"0.63522524\",\n \"0.634157\",\n \"0.6331682\",\n \"0.6324391\",\n \"0.63144606\",\n \"0.6312341\",\n \"0.63078004\",\n \"0.6298474\",\n \"0.62843573\",\n \"0.62785125\",\n \"0.6274412\",\n \"0.6267191\",\n \"0.62610924\",\n \"0.62350243\",\n \"0.62233144\",\n \"0.6218916\",\n \"0.6204278\",\n \"0.620087\",\n \"0.6184506\",\n \"0.61840254\",\n \"0.61767995\",\n \"0.61508983\",\n \"0.61436504\",\n \"0.6141183\",\n \"0.61355656\",\n \"0.6132905\",\n \"0.6129441\",\n \"0.61189896\",\n \"0.61101514\",\n \"0.6106826\",\n \"0.610548\",\n \"0.6091539\",\n \"0.6085897\",\n \"0.60849166\",\n \"0.6075513\",\n \"0.60741395\",\n \"0.60724217\",\n \"0.6067116\",\n \"0.6060838\",\n \"0.60441506\",\n \"0.602703\",\n \"0.6024641\",\n \"0.6024641\",\n \"0.6023189\",\n \"0.6022174\",\n \"0.60181916\",\n \"0.60162294\",\n \"0.60094166\",\n \"0.60073036\",\n \"0.60007155\",\n \"0.5995908\",\n \"0.5981636\",\n \"0.59796226\",\n \"0.59684116\",\n \"0.59657323\",\n \"0.5963346\",\n \"0.5961446\",\n \"0.59575766\",\n \"0.5942519\",\n \"0.59406173\",\n \"0.59340626\",\n \"0.593322\",\n \"0.593317\",\n \"0.59273297\",\n \"0.59169185\",\n \"0.59146214\",\n \"0.5913298\",\n \"0.59046954\",\n \"0.5903483\",\n \"0.59008414\",\n \"0.58888936\",\n \"0.5877329\",\n \"0.58719355\",\n \"0.5871472\",\n \"0.5871163\",\n \"0.5858921\",\n \"0.58550715\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63390654"},"document_rank":{"kind":"string","value":"25"}}},{"rowIdx":76698,"cells":{"query":{"kind":"string","value":"Given a dataset with a string column , filter rows containing a certain pattern."},"document":{"kind":"string","value":"def pattern_search(pattern, dataset, column):\n # Filter\n dataset = dataset[dataset[column].str.contains(pattern, regex=True)]\n # Reset index\n dataset = dataset.reset_index(drop=True)\n # Return\n return dataset"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def filter(self, filter_strings):\n if filter_strings is None:\n filter_strings = []\n\n result = RowSet()\n for row in self.rows:\n match = True\n for s in filter_strings:\n if not row.filter(s):\n match = False\n break\n if match:\n result.append(row)\n return result","def filter_blacklist(df, path):\n f = open(path)\n patterns = [e.strip() for e in f.readlines()]\n f.close()\n\n def run_filter(x):\n for pat in patterns:\n if fnmatch.fnmatch(x, pat):\n return True\n return False\n\n index = df[df[PROD_NM].apply(run_filter)].index\n df.drop(columns=[PROD_NM], index=index, inplace=True)","def search_by_substring(db, table, column, substring):\n\n condition = column + \" LIKE \\'%\" + substring + \"%\\'\"\n result = select_columns(db, table, \"*\", condition=condition)\n\n return result","def remove(df, pattern):\n return df[~df.index.isin(df.query(pattern).index)]","def filter(df, predicate):\n if not df:\n return []\n\n return [row for row in df if predicate(row)]","def filter_by_regular(filename):\n turnstile_data = pandas.read_csv(filename)\n turnstile_data = turnstile_data[turnstile_data.DESCn == 'REGULAR']\n return turnstile_data","def prune(df, regex_list):\n for regex_pattern in regex_list:\n df = df[~df.case_action.str.contains(regex_pattern)]\n return df","def filterRows(function, rows):\n return [y for y in rows if function(y)]","def search_premade(phrase = '*',\n language = 'en',\n url = 'http://data.ssb.no/api/v0/dataset'):\n url = '{url}?lang={language}'.format(\n url = url,\n language = language)\n \n df = pd.read_html(url)\n df = df[0]\n df.index = df['ID']\n df = df.iloc[:,[0,1]]\n df = df.sort_index()\n phrase = phrase.lower()\n \n if phrase != '*':\n df = df[(df.iloc[:,0].str.lower().str.contains(phrase)) | \n (df.iloc[:,1].str.lower().str.contains(phrase)) ]\n return df","def filter_table_with_keyword(self, table: Table, name: str, *args):\n self._requires_table(table)\n\n def condition(row: Row) -> bool:\n return BuiltIn().run_keyword(name, row, *args)\n\n before = len(table)\n table.filter_all(condition)\n after = len(table)\n\n self.logger.info(\"Removed %d row(s)\", before - after)","def filterIEDBFile(filename, field, search):\n X = pd.read_csv(filename)\n cols = ['PubMed ID','Author','Journal','Year','T Cell ID','MHC Allele Name',\n 'Epitope Linear Sequence','Epitope Source Organism Name']\n y = X[X[field].str.contains(search)]\n print y[cols]\n y.to_csv('filtered.csv',cols=cols)\n return y","def filter_column(col, row):\n return col == column","def filter_on_regexp(strings, pattern):\n filtered_strings = filter(lambda x: re.search(pattern, x), strings)\n return filtered_strings","def where_string(self, column_name: str, comparison: str, value: str) -> \"SpaceDelimitedTextPattern\":\n return jsii.invoke(self, \"whereString\", [column_name, comparison, value])","def _filter_data(self, pattern):\n removed = []\n filtered = []\n for param in self.data:\n if not param[0].startswith(pattern):\n filtered.append(param)\n else:\n removed.append(param)\n self.data = filtered\n return removed","def filter_same_helix(df):\n helixpattern = re.compile(r\"\"\"^(..)\\w+\\s+\\1\"\"\")#For detecting same-helix contacts, the ones like 1.22x22 1.54x54\n helixfilter = df['Position'].str.contains(helixpattern)\n df = df[~helixfilter]\n return(df)","def _filter(self, col: str, val: Any) -> pd.DataFrame:\n return self._df[self._df[col] == val]","def filter_by_isin(df: pd.DataFrame, column: str, values: Iterable) -> pd.DataFrame:\n # First, create a \"map\" series from all possible values in the column => whether they should pass the filter\n all_ids = df[column].unique()\n is_id_relevant = pd.Series(np.zeros(len(all_ids)), index=all_ids).astype('bool') # Default false\n is_id_relevant.loc[values] = True\n\n # Create a boolean mask for column, based on the mapping above. Grab the raw array.\n mask = is_id_relevant[df[column]].values\n # Apply mask\n return df[mask]","def filter_rows(self, **kwds):\n if kwds:\n normalize = lambda v: (v,) if isinstance(v, str) else v\n kwds = dict((k, normalize(v)) for k, v in kwds.items())\n matches_kwds = lambda row: all(row[k] in v for k, v in kwds.items())\n return filter(matches_kwds, self.__iter__())\n return self.__iter__()","def _filter(self, row):\n if not self._head:\n self._head = self._create_head(row)\n if self._args.head:\n return row\n\n if 'cond' not in self._state:\n self._state['cond'] = self._replace_fields(self._args.cond)\n\n r = list(map(self._convert, row))\n if eval(self._state['cond']):\n return row","def filter_by_primary_compartment(df, compartments):\n df = df[df['Compartment'].str.startswith(tuple(compartments))]\n return df","def _filter_valid_id(df, col):\n df = df[(df[col].str.isnumeric().replace({np.nan: False})) &\n (df[col] != '0') &\n (df[col] != 0)]\n return df","def filterLines(weatherRDDRecord):\n fieldsList = weatherRDDRecord.split(\",\")\n #return len(fieldsList)\n if any(i.isdigit() for i in fieldsList[0]):\n return True\n else:\n return False","def filter_column(self, column=None, values=[]):\n if column is None:\n return self.df\n\n columns = self.df.columns\n if column not in columns:\n raise ColumnNameError(\"Column does not exist in the dataframe!\")\n\n return self.df.filter(col(column).isin(values))","def _description_filter(self, df):\n df = df[df['Description'].str.contains(\"and\")]\n df['Description'] = df['Description'].map(lambda x: x[:MAX_DESC_LENGTH])\n return df","def filter_rows(self, **kwargs):\n filtered = self._data.copy()\n for colname, values in kwargs.items():\n values = [values] if type(values) == str else values\n filtered = filtered[filtered[colname].isin(values)]\n return self._copy(filtered)","def remove(data, pattern):\n return [''.join(filter(pattern, str)) for str in data]","def filter(self, pattern):\n if isinstance(pattern, REGEX_TYPE):\n func = tools.filter_regex\n elif pattern.startswith('/'):\n pattern = re.compile(pattern.strip('/'))\n func = tools.filter_regex\n else:\n func = tools.filter_wildcard\n\n return SeeResult(func(self, pattern))","def filterDataset(dat, dataset):\n #\n dat = dat[dat['organism'].isin(dataset)]\n no_mmei_index = dat['mmei']=='no'\n nonstop_index = dat['mutstop']=='no'\n zerofit_index = dat['fitness'].abs()>1e-4\n mutwt_index = dat['mutwt']=='no'\n dat = dat[no_mmei_index & nonstop_index & zerofit_index & mutwt_index]\n #print \"Filtered data\"\n return dat","def grep(requestContext, seriesList, pattern):\n regex = re.compile(pattern)\n return [s for s in seriesList if regex.search(s.name)]","def exact_search(string, row):\n clear_screen()\n found = False\n for item in row:\n if string.lower() in item[\"Task\"].lower() \\\n or string.lower() in item[\"Notes\"].lower():\n print_entry(item)\n found = True\n if found is False:\n print(\"No Entries Found..\")","def match_name(pattern, rows):\n matching = []\n for row in rows:\n # Use regex matching to check whether first name or last name contains the pattern\n if re.search(r'%s' % pattern.lower(), row[0].lower()) != None or re.search(r'%s' % pattern.lower(), row[1].lower()) != None:\n matching.append(row)\n\n # print the matched records\n print_records(matching)","def filter_by_date(df, date_string, before_or_after):\n filter_date = pd.to_datetime(date_string)\n if before_or_after == \"before\":\n return df[df['timestamp'] < filter_date].copy()\n elif before_or_after == \"after\":\n return df[df['timestamp'] >= filter_date + pd.DateOffset(days=1)].copy()\n else:\n return print(\"Invalid argument.\")","def match_email(pattern, rows):\n matching = []\n for row in rows:\n # Use regex matching to check whether email contains the pattern\n if re.search(r'%s' % pattern.lower(), row[2].lower()) != None:\n matching.append(row)\n\n # print the matched records\n print_records(matching)","def filter_data(self):\n self.data = filter_pandas(self.data, self.filters)","def _data_filter(data: str) -> list:\n\n return PROXIES_DATA_REGEX.findall(str(data))","def delete_by_substring(db, table, column, substring):\n mycursor = db.cursor()\n sql = \"DELETE FROM \" + table + \" WHERE \" + column + \" LIKE \\'%\" + substring + \"%\\'\"\n mycursor.execute(sql)\n\n db.commit()","def minus_df(df):\n return df[df[\"content\"].str.contains(\"(^|[^0-9])\\-[0-9]+\")]","def filter(self, filterstring):\n if filterstring not in self.FILTERS:\n raise ValueError(\"{dataset} must be in {d for d in self.DATASETS}\")\n self.query[\"filter\"] = filterstring\n return self","def plus_df(df):\n return df[df[\"content\"].str.contains(\"\\+[0-9]+\")]","def match_list(column, patterns):\n for pattern in patterns:\n if pattern.match(column):\n return True\n return False","def filter_df(df, month, day):\n\n #filter by month\n df = df.loc[df['month'] == month]\n\n #filter by day\n df = df.loc[df['day_of_week'] == day]\n\n return df","def filter(self, row):\r\n return list(itertools.compress(row, self.selectors))","def filter_ignoring_case(self, pattern):\n return self.filter(re.compile(pattern, re.I))","def category_match(self,single_word, input_dataframe):\n temp_set = self.index_dict[single_word]\n category_list = list(temp_set)\n filtered_df = input_dataframe.loc[category_list]\n return filtered_df","def filter_row(col, rw):\n return rw == row","def filter_by_string(cls, query_string, db_cursor, args):\n db_cursor.execute(query_string, args)\n if db_cursor.fetchone():\n return False\n return True","def filter_table_by_column(\n self, table: Table, column: Column, operator: str, value: Any\n ):\n self._requires_table(table)\n\n condition = to_condition(operator, value)\n\n before = len(table)\n table.filter_by_column(column, condition)\n after = len(table)\n\n self.logger.info(\"Filtered %d rows\", after - before)","async def filter(message, *args, **kwargs):\r\n table = args[0]\r\n condition = \" \".join(args[1:])\r\n if not condition: output = DataTables[table]\r\n else: output = [e for e in DataTables[table] if safe_eval(condition, e)]\r\n fields = [\"ID\"]\r\n if \"name\" in DataTables[table][0]: fields.append(\"name\")\r\n if kwargs.get(\"fields\"):\r\n fields.extend((f.strip(\" \") for f in kwargs[\"fields\"].strip('\"').split(\",\")))\r\n if output: await reply(message, f\"```{tablestr(output, fields=fields)}```\")\r\n else: await reply(message, \"no match found\")","def filter_row(row: OrderedDict, sampling_features: set) -> bool:\n\n return row['feature_of_interest'] in sampling_features","def search_actions(strings):\n\tpattern = '|'.join(strings)\n\n\tb = actions[actions.action.str.contains(pattern)] \\\n\t\t.loc[:, ['session', 'bill_id']] \\\n\t\t.drop_duplicates()\n\n\treturn b","def filterfeatures(df):\n\tfilter_arr = []\n\tfor f in df.columns:\n\t\tif not '.l' in f and not '.r' in f and not '.std' in f and f != 'weight' and f != 'class':\n\t\t\t# filter_arr.append(f.rstrip('.mean'))\n\t\t\tfilter_arr.append(f)\n\treturn filter_arr","def filt(seq, lst):\n regex = \"(\" + \")|(\".join(seq) + \")\"\n regex = re.compile(regex)\n slst = list(filter(regex.search, lst))\n return slst\n\n\n # still need a checkUsername function ","def remove_other_elements(data):\n charset = ['F','l','B','r','I','i','M','g','L','b','a','e','K','V','d','R','Z','G','A','Y','u']\n x = []\n for i in range(data.shape[0]):\n for j in range(len(data.iloc[i,1])):\n if data.iloc[i,1][j] in charset:\n x.append(i)\n break\n df = data[(True^data['Index'].isin(x))]\n df.reset_index(drop=True, inplace=True)\n return df","def filter_by_names(df, names_list):\n return df.ix[names_list]","def filterOptions(self, phrases,columnlist):\n filtered=[]\n for col in columnlist:\n outerFlag = False\n for phrase in phrases:\n innerFlag = True\n for word in phrase:\n if word.lower() not in col.lower(): innerFlag = False #Requires all words of phrase to be in col\n if innerFlag: outerFlag = True #Requires at least one phrase to be in col\n if outerFlag: filtered.append(col)\n if not filtered: filtered = ['']\n return filtered","def reg_csv_search(arg):\n results_list = []\n with open('work_log.csv', 'r') as file:\n for row in file:\n if re.search(arg, str(row)):\n results_list.append(row)\n results_sort(results_list)","def filter_lobster_by_elements(e1, e2, ILOBSTER):\n e1_regex = '^' + e1 + '\\d+$'\n e2_regex = '^' + e2 + '\\d+$'\n mask = ((ILOBSTER['atomMU'].str.contains(e1_regex) & ILOBSTER['atomNU'].str.contains(e2_regex)) | \\\n (ILOBSTER['atomNU'].str.contains(e1_regex) & ILOBSTER['atomMU'].str.contains(e2_regex)))\n return ILOBSTER[mask]","def filter_by_column(self, column: Column, condition: CellCondition):\n\n def _check_cell(index: int) -> bool:\n cell = self.get_cell(index, column)\n return condition(cell)\n\n self._filter(_check_cell)","def search_string(self, string, ignore_case=False):\n for ypos in range(self.model_dimensions[\"rows\"]):\n line = self.string_get(ypos + 1, 1, self.model_dimensions[\"columns\"])\n if ignore_case:\n line = line.lower()\n if string in line:\n return True\n return False","def filterData(records):\n def isInteresting(record):\n if record[VO_ISSUER] in ('/DC=ch/DC=cern/OU=computers/CN=voms.cern.ch', '/DC=ch/DC=cern/OU=computers/CN=lcg-voms.cern.ch'):\n return True\n if record[VO_NAME] in ('atlas', 'cms', 'alice'):\n return True\n if record[USERSN] == '/C=SI/O=SiGNET/O=IJS/OU=F9/CN=Andrej Filipcic':\n return True\n if record[USERSN] in ('aliprod', '/aliprod'):\n return True\n if record[USERSN].startswith(ALIEN_USER_PREFIX):\n return True\n\n return False\n\n return [ r for r in records if isInteresting(r) ]","def column_wildcard(self) -> Optional[pulumi.Input['DataCellsFilterColumnWildcardArgs']]:\n return pulumi.get(self, \"column_wildcard\")","def search_element_of_table(path, table, nameColumn, data):\n conn = sqlite3.connect(path)\n c = conn.cursor()\n to_return = \"<tr>\"\n for row in c.execute('SELECT * FROM '+table +' where '+nameColumn+\" like '%\"+data+\"%'\").fetchall():\n for x in row:\n to_return = to_return + \"<td>\"+ str(x) + \"</td>\"\n to_return = to_return + \"</tr>\"\n\n conn.close()\n return to_return","def filterAcceptsRow(self, row, parent):\n if not self.__customFilterEnabled:\n return super(\n _WorkerFilterProxyModel, self).filterAcceptsRow(row, parent)\n\n if not self.__regex:\n return True\n\n model = self.sourceModel()\n idx = model.index(row, 0, parent)\n if not idx.isValid():\n return False\n\n obj = model.data(idx, _WorkerModel.ObjectRole)\n if not obj:\n return False\n\n result = self.__regex.match(obj.name)\n if not result:\n return False\n return True","def WhereMatches(self, pattern):\n regex = re.compile(match_util.ExpandRegexIdentifierPlaceholder(pattern))\n return self.Filter(lambda s: (\n regex.search(s.source_path) or\n regex.search(s.object_path) or\n regex.search(s.full_name) or\n s.full_name is not s.template_name and regex.search(s.template_name) or\n s.full_name is not s.name and regex.search(s.name)))","def split_on_column_value(\n df, column_name: str, batch_identifiers: dict\n ) -> pyspark.DataFrame:\n return df.filter(F.col(column_name) == batch_identifiers[column_name])","def search_by_contains(self, tl):\n print(\"Search by string\")\n string = input(\"Please enter search string: \")\n return tl.findall_contains(string)","def filter_existing_samples(df, sample_id_colname, remote_samples):\n remote_sample_ids = remote_samples['entity:sample_id'].tolist()\n df_clean = df[~df[sample_id_colname].isin(remote_sample_ids)]\n return df_clean","def filter_data(data,filters):\n final_filter = pd.Series(np.array([True] * data.shape[0]))\n for attribute, value in filters:\n final_filter &= data[attribute] == value\n return data[final_filter]","def filter_inbound_statements_grid(self, column_name, filter_item_text):\n self.grid_filter_with_textbox(self.inbound_statement_grid_div_id, column_name, filter_item_text, self.inbound_statements_grid_filter_position)","def sql_filter(my_table='', colName='', var='', **kw):\n\tif (my_table=='') or (colName=='') or (var==''):\n\t\treturn dict(sql='',clauseTables=[])\n\telse:\n\t\tsql = my_table+\".\"+colName+\" LIKE '%\"+var+\"%'\"\n\t\treturn dict(sql=sql,clauseTables=[])","def grep(string,list):\n import re\n expr = re.compile(string)\n return filter(expr.search,list)","def column_wildcard(self) -> pulumi.Output[Optional['outputs.DataCellsFilterColumnWildcard']]:\n return pulumi.get(self, \"column_wildcard\")","def regex_catch(self, column):\n ## considering currency_list and metric_list are globally defined.\n pattern_dict = {\"Column_name\": column, \"email-id\": False, \"website\": False, \"Percentage\": False,\n \"CurrencyCol\": {\"value\": False, \"currency\": None}, \"MetricCol\": {\"value\": False, \"metric\": None},\n \"SepSymbols\": {\"value\": False, \"Symbol\": None}}\n column_val = self.data_frame[column].astype(str).str.strip()\n df1 = self.data_frame[column_val.apply(validate_email)]\n if df1.shape[0] >= (0.8 * self.data_frame.shape[0]):\n pattern_dict[\"email-id\"] = True\n elif column_val.str.contains(\"%$\", na=True).all():\n pattern_dict[\"Percentage\"] = True\n elif column_val.str.contains(\"^https:|^http:|^www.\", na=True).all():\n pattern_dict[\"website\"] = True\n elif column_val.str.contains(\"[0-9]+[.]{0,1}[0-9]*\\s*[Aa-zZ]{1,2}$\").all():\n metric = list(map(lambda x: re.sub(\"[0-9]+[.]{0,1}[0-9]*\\s*\", \"\", x), column_val))\n if len(set(metric)) == 1:\n if metric[0] in Metric_list:\n pattern_dict[\"MetricCol\"][\"value\"] = True\n pattern_dict[\"MetricCol\"][\"metric\"] = metric[0]\n elif column_val.str.contains(\"([0-9]+[.]{0,1}[0-9]*\\s*\\W$)|(^\\W[0-9]+[.]{0,1}[0-9]*)\").all():\n currency = list(map(lambda x: re.sub(\"[0-9.\\s]+\", \"\", x), column_val))\n if len(set(currency)) == 1:\n if currency[0] in currency_list:\n pattern_dict[\"CurrencyCol\"][\"value\"] = True\n pattern_dict[\"CurrencyCol\"][\"currency\"] = currency[0]\n elif column_val.str.contains(\"\\S+\\s*[\\W_]+\\s*\\S+\").all():\n seperators = list(map(lambda x: re.sub(\"\\s*[a-zA-Z0-9]+$\", \"\", x),\n list(map(lambda x: re.sub('^[a-zA-Z0-9]+\\s*', '', x), column_val))))\n if len(set(seperators)) == 1:\n if seperators[0] == \"\":\n seperators[0] = ' '\n pattern_dict[\"SepSymbols\"][\"value\"] = True\n pattern_dict[\"SepSymbols\"][\"Symbol\"] = seperators[0]\n return pattern_dict","def byStringContains(self, paramName, value, invert=False):\n\t\timport revitron\n\t\tself.applyFilter(\n\t\t revitron.DB.FilterStringRule,\n\t\t paramName,\n\t\t value,\n\t\t revitron.DB.FilterStringContains(),\n\t\t invert\n\t\t)\n\t\treturn self","def remove_rows_with_non_english_movies(df):\n df = df[df['original_language'] == 'en']\n return df","def filter_macro(out, scope, args, children):\n len(args) == 2 or syntax_error(\"'filter' macro takes exactly 2 arguments.\")\n regex, l = args\n if not isinstance(l, list):\n syntax_error(\"Invalid list in 'filter' macro: '%s'\" % str(list))\n if not isinstance(regex, str):\n syntax_error(\"Invalid regex in 'filter' macro: '%s'\" % str(regex))\n def match(s):\n return re.search(regex, s)\n return list(filter(match, l))","def filter_data(data, filter_dict):\n for key, match_string in filter_dict.items():\n if key not in data:\n logger.warning(\"{0} doesn't match a top level key\".format(key))\n continue\n values = data[key]\n matcher = re.compile(match_string)\n if isinstance(values, list):\n values = [v for v in values if matcher.search(v)]\n elif isinstance(values, dict):\n values = dict((k, v) for k, v in values.items() if matcher.search(k))\n else:\n raise MiuraException(\"cannot filter a {0}\".format(type(values)))\n data[key] = values","def filter_nmt_file(filename,filter_fn=None):\r\n \r\n if filter_fn is None:\r\n filter_fn = lambda en : en.lower().startswith('i am') or \\\r\n en.lower().startswith('he is') or \\\r\n en.lower().startswith('she is') or \\\r\n en.lower().startswith('they are') or \\\r\n en.lower().startswith('you are') or \\\r\n en.lower().startswith('we are')\r\n \r\n filtered_lines = []\r\n with open(filename) as file:\r\n lines = file.readlines()\r\n for line in lines:\r\n text = line.split('\\t')\r\n en = text[0]\r\n fra = text[1]\r\n if filter_fn(en):\r\n filtered_lines.append(en.lower() + '\\t' + fra.lower())\r\n\r\n return filtered_lines","def filter_chants_with_F_clef(chants, logger=None):\n contains_F_clef = chants.volpiano.str.contains('2') == True\n return chants[contains_F_clef == False]","def test_filters(self):\n data = pd.DataFrame({'a': [1, 2, 3], 'ab': [6, 5, 4], 'c2': [1, 0, 1]})\n\n # test regex selector\n selector = ItemSelector(regex='[b2]$')\n selected_data = selector.transform(data)\n expected_data = pd.DataFrame({'ab': [6, 5, 4], 'c2': [1, 0, 1]})\n pd.testing.assert_frame_equal(selected_data, expected_data)\n\n # test items selector\n selector = ItemSelector(items=['a', 'c2'])\n selected_data = selector.transform(data)\n expected_data = pd.DataFrame({'a': [1, 2, 3], 'c2': [1, 0, 1]})\n pd.testing.assert_frame_equal(selected_data, expected_data)\n\n # test like selector\n selector = ItemSelector(like='a')\n selected_data = selector.transform(data)\n expected_data = pd.DataFrame({'a': [1, 2, 3], 'ab': [6, 5, 4]})\n pd.testing.assert_frame_equal(selected_data, expected_data)","def filter_by_country(df, cont_list):\n return df.filter((df.country.isin(cont_list)))","def filterRawNodes(self, data):\n def filterFun(str):\n if ('/' in str):\n return True\n else:\n return False\n return filter(filterFun, data)","def clean_rows_cat_values(df: pd.DataFrame, col: str, values: list) -> pd.DataFrame:\n\n # create mask to filter df with rows that have\n # the indicated values in the indicated column\n index = df.columns.get_loc(col)\n mask = [df.iloc[row, index] not in values for row in range(len(df))]\n\n # print original dataframe shape\n print(f\"Shape of the original dataframe: \\n{df.shape}\\n\")\n\n # filter df\n df = df.iloc[mask]\n df.reset_index(drop=True, inplace=True)\n print(\n f\"Shape after removing rows with values equal to\\n{values}\\nin column '{col}'':\"\n )\n print(df.shape, \"\\n\")\n\n return df","def pattern_filter(patterns, name):\n return [pat for pat in patterns if fnmatch.fnmatchcase(name, pat)]","def byStringContainsOneInCsv(self, paramName, csv, invert=False):\n\t\treturn self.byOneInCsv('byStringContains', paramName, csv, invert)","def apply_word_filter(self, fn):\n self._apply_filter(lambda ng, f: any(fn(w) for w in ng))","def any_term(self, row):\n return any(term in row for term in self.search_terms)","def execute(self):\n return super(BeefRecipes, self).execute().where(lower(col('ingredients')).like(\"%beef%\"))","def lst_and_pattern (filer_lst, pattern):\r\n new_filter_lst=[]\r\n for word in filer_lst:\r\n if word_and_pattern(word,pattern):\r\n new_filter_lst.append(word)\r\n return new_filter_lst","def filter_significant_email(data):\n frame_to_choose = []\n for ind, row in data.iterrows():\n if len(row[\"RawText\"]) > 500:\n frame_to_choose.append(True)\n else:\n frame_to_choose.append(False)\n\n return data[frame_to_choose]","def crossfilter_by_tag(self,df, tag):\n col,spec= list(tag.items())[0]\n return df[df[col]==spec]","def where(self, cond):\n return self.filter(lambda x: _(x).contains(cond))","def test_extract_column_zero_and_one_correct_content():\n data = \"\"\"kylebarnes@hotmail.com,Records manager\njoe70@yahoo.com,Network engineer\ntorresjames@white.info,Electrical engineer\nshawkins@watson.com,Science writer\"\"\"\n result = extract.extract_data_given_column(data, 0)\n assert \"shawkins@watson.com\" in result\n result = extract.extract_data_given_column(data, 1)\n assert \"Records manager\" in result","def simple_search(self, pattern):\n query = Q()\n for ptn in pattern.split():\n for field in SEARCH_FIELDS:\n query |= Q(**{'%s__icontains' % field: ptn})\n return self.get_queryset().filter(query)","def filterBySequence(wordlist, sequence):\n filtered_list = [entry[\"word\"] for entry in wordlist if entry[\"sequence\"].startswith(sequence)]\n return filtered_list","def subset(self, *args, **kwargs):\n\t\tgs = copy.copy(self)\n\t\tqueryStrings = kwargs['queryStrings'] if 'queryStrings' in kwargs else args[0] if args else []\n\t\tif isinstance(queryStrings, str) or isinstance(queryStrings, bytes):\t# assume queryString was specified as a string\n\t\t\tqueryStrings = [queryStrings]\n\t\n\t\tif len(queryStrings)==0:\n\t\t\treturn gs\n\t\t\t\n\t\tdf = gs._dataframe\n\t\t\n\t\tif 'species' in kwargs and kwargs['species'] in ['MusMusculus','HomoSapiens']:\n\t\t\tdf = df[df['Species']==kwargs['species']]\n\t\t\t\n\t\tcaseSensitive = kwargs.get('caseSensitive', False)\n\t\tif not caseSensitive: queryStrings = [item.lower() for item in queryStrings]\n\t\t\n\t\tmatchSubstring = kwargs.get('matchSubstring', True)\n\t\t\n\t\t# determine which columns to search\n\t\tsearchColumns = kwargs.get('searchColumns')\n\t\tallColumns = ['EnsemblId','EntrezId','GeneSymbol','Synonyms','Description']\n\t\tif searchColumns:\n\t\t\tsearchColumns = ['EnsemblId' if item=='GeneId' or item=='geneId' else item for item in searchColumns]\n\t\tif searchColumns and len(set(allColumns).intersection(set(searchColumns)))>0:\n\t\t\tsearchColumns = list(set(allColumns).intersection(set(searchColumns)))\n\t\telse:\n\t\t\tsearchColumns = allColumns\n\t\t\n\t\trowsToKeep = set()\n\t\tdf = df.reset_index()\n\t\tfor column in searchColumns:\n\t\t\tfor rowIndex,value in df[column].iteritems():\n\t\t\t\tif rowIndex in rowsToKeep or not value: continue\n\t\t\t\tif not caseSensitive: value = value.lower()\n\t\t\t\tfor queryString in queryStrings:\n\t\t\t\t\tif matchSubstring and queryString in value or (not matchSubstring and queryString==value):\n\t\t\t\t\t\trowsToKeep.add(rowIndex)\n\t\t\t\t\t\tbreak\n\n\t\tgs._dataframe = df.loc[list(rowsToKeep),:].set_index('EnsemblId')\n\t\treturn gs","def date_filter(frame, date_column, year):\n frame[date_column] = pd.to_datetime(frame[date_column])\n frame = frame[frame[date_column] > pd.Timestamp(year, 1, 1)]\n return frame","def test_string_in_filter_with_declared_filter(query):\n Person.objects.create(name=\"John\")\n Person.objects.create(name=\"Michael\")\n Person.objects.create(name=\"Angela\")\n\n schema = Schema(query=query)\n\n query = \"\"\"\n query {\n people (names: \"John,Michael\") {\n edges {\n node {\n name\n }\n }\n }\n }\n \"\"\"\n result = schema.execute(query)\n assert not result.errors\n assert result.data[\"people\"][\"edges\"] == [\n {\"node\": {\"name\": \"John\"}},\n {\"node\": {\"name\": \"Michael\"}},\n ]","def _s_filter(cls, arg):\n return cls.query.filter_by(name=arg)"],"string":"[\n \"def filter(self, filter_strings):\\n if filter_strings is None:\\n filter_strings = []\\n\\n result = RowSet()\\n for row in self.rows:\\n match = True\\n for s in filter_strings:\\n if not row.filter(s):\\n match = False\\n break\\n if match:\\n result.append(row)\\n return result\",\n \"def filter_blacklist(df, path):\\n f = open(path)\\n patterns = [e.strip() for e in f.readlines()]\\n f.close()\\n\\n def run_filter(x):\\n for pat in patterns:\\n if fnmatch.fnmatch(x, pat):\\n return True\\n return False\\n\\n index = df[df[PROD_NM].apply(run_filter)].index\\n df.drop(columns=[PROD_NM], index=index, inplace=True)\",\n \"def search_by_substring(db, table, column, substring):\\n\\n condition = column + \\\" LIKE \\\\'%\\\" + substring + \\\"%\\\\'\\\"\\n result = select_columns(db, table, \\\"*\\\", condition=condition)\\n\\n return result\",\n \"def remove(df, pattern):\\n return df[~df.index.isin(df.query(pattern).index)]\",\n \"def filter(df, predicate):\\n if not df:\\n return []\\n\\n return [row for row in df if predicate(row)]\",\n \"def filter_by_regular(filename):\\n turnstile_data = pandas.read_csv(filename)\\n turnstile_data = turnstile_data[turnstile_data.DESCn == 'REGULAR']\\n return turnstile_data\",\n \"def prune(df, regex_list):\\n for regex_pattern in regex_list:\\n df = df[~df.case_action.str.contains(regex_pattern)]\\n return df\",\n \"def filterRows(function, rows):\\n return [y for y in rows if function(y)]\",\n \"def search_premade(phrase = '*',\\n language = 'en',\\n url = 'http://data.ssb.no/api/v0/dataset'):\\n url = '{url}?lang={language}'.format(\\n url = url,\\n language = language)\\n \\n df = pd.read_html(url)\\n df = df[0]\\n df.index = df['ID']\\n df = df.iloc[:,[0,1]]\\n df = df.sort_index()\\n phrase = phrase.lower()\\n \\n if phrase != '*':\\n df = df[(df.iloc[:,0].str.lower().str.contains(phrase)) | \\n (df.iloc[:,1].str.lower().str.contains(phrase)) ]\\n return df\",\n \"def filter_table_with_keyword(self, table: Table, name: str, *args):\\n self._requires_table(table)\\n\\n def condition(row: Row) -> bool:\\n return BuiltIn().run_keyword(name, row, *args)\\n\\n before = len(table)\\n table.filter_all(condition)\\n after = len(table)\\n\\n self.logger.info(\\\"Removed %d row(s)\\\", before - after)\",\n \"def filterIEDBFile(filename, field, search):\\n X = pd.read_csv(filename)\\n cols = ['PubMed ID','Author','Journal','Year','T Cell ID','MHC Allele Name',\\n 'Epitope Linear Sequence','Epitope Source Organism Name']\\n y = X[X[field].str.contains(search)]\\n print y[cols]\\n y.to_csv('filtered.csv',cols=cols)\\n return y\",\n \"def filter_column(col, row):\\n return col == column\",\n \"def filter_on_regexp(strings, pattern):\\n filtered_strings = filter(lambda x: re.search(pattern, x), strings)\\n return filtered_strings\",\n \"def where_string(self, column_name: str, comparison: str, value: str) -> \\\"SpaceDelimitedTextPattern\\\":\\n return jsii.invoke(self, \\\"whereString\\\", [column_name, comparison, value])\",\n \"def _filter_data(self, pattern):\\n removed = []\\n filtered = []\\n for param in self.data:\\n if not param[0].startswith(pattern):\\n filtered.append(param)\\n else:\\n removed.append(param)\\n self.data = filtered\\n return removed\",\n \"def filter_same_helix(df):\\n helixpattern = re.compile(r\\\"\\\"\\\"^(..)\\\\w+\\\\s+\\\\1\\\"\\\"\\\")#For detecting same-helix contacts, the ones like 1.22x22 1.54x54\\n helixfilter = df['Position'].str.contains(helixpattern)\\n df = df[~helixfilter]\\n return(df)\",\n \"def _filter(self, col: str, val: Any) -> pd.DataFrame:\\n return self._df[self._df[col] == val]\",\n \"def filter_by_isin(df: pd.DataFrame, column: str, values: Iterable) -> pd.DataFrame:\\n # First, create a \\\"map\\\" series from all possible values in the column => whether they should pass the filter\\n all_ids = df[column].unique()\\n is_id_relevant = pd.Series(np.zeros(len(all_ids)), index=all_ids).astype('bool') # Default false\\n is_id_relevant.loc[values] = True\\n\\n # Create a boolean mask for column, based on the mapping above. Grab the raw array.\\n mask = is_id_relevant[df[column]].values\\n # Apply mask\\n return df[mask]\",\n \"def filter_rows(self, **kwds):\\n if kwds:\\n normalize = lambda v: (v,) if isinstance(v, str) else v\\n kwds = dict((k, normalize(v)) for k, v in kwds.items())\\n matches_kwds = lambda row: all(row[k] in v for k, v in kwds.items())\\n return filter(matches_kwds, self.__iter__())\\n return self.__iter__()\",\n \"def _filter(self, row):\\n if not self._head:\\n self._head = self._create_head(row)\\n if self._args.head:\\n return row\\n\\n if 'cond' not in self._state:\\n self._state['cond'] = self._replace_fields(self._args.cond)\\n\\n r = list(map(self._convert, row))\\n if eval(self._state['cond']):\\n return row\",\n \"def filter_by_primary_compartment(df, compartments):\\n df = df[df['Compartment'].str.startswith(tuple(compartments))]\\n return df\",\n \"def _filter_valid_id(df, col):\\n df = df[(df[col].str.isnumeric().replace({np.nan: False})) &\\n (df[col] != '0') &\\n (df[col] != 0)]\\n return df\",\n \"def filterLines(weatherRDDRecord):\\n fieldsList = weatherRDDRecord.split(\\\",\\\")\\n #return len(fieldsList)\\n if any(i.isdigit() for i in fieldsList[0]):\\n return True\\n else:\\n return False\",\n \"def filter_column(self, column=None, values=[]):\\n if column is None:\\n return self.df\\n\\n columns = self.df.columns\\n if column not in columns:\\n raise ColumnNameError(\\\"Column does not exist in the dataframe!\\\")\\n\\n return self.df.filter(col(column).isin(values))\",\n \"def _description_filter(self, df):\\n df = df[df['Description'].str.contains(\\\"and\\\")]\\n df['Description'] = df['Description'].map(lambda x: x[:MAX_DESC_LENGTH])\\n return df\",\n \"def filter_rows(self, **kwargs):\\n filtered = self._data.copy()\\n for colname, values in kwargs.items():\\n values = [values] if type(values) == str else values\\n filtered = filtered[filtered[colname].isin(values)]\\n return self._copy(filtered)\",\n \"def remove(data, pattern):\\n return [''.join(filter(pattern, str)) for str in data]\",\n \"def filter(self, pattern):\\n if isinstance(pattern, REGEX_TYPE):\\n func = tools.filter_regex\\n elif pattern.startswith('/'):\\n pattern = re.compile(pattern.strip('/'))\\n func = tools.filter_regex\\n else:\\n func = tools.filter_wildcard\\n\\n return SeeResult(func(self, pattern))\",\n \"def filterDataset(dat, dataset):\\n #\\n dat = dat[dat['organism'].isin(dataset)]\\n no_mmei_index = dat['mmei']=='no'\\n nonstop_index = dat['mutstop']=='no'\\n zerofit_index = dat['fitness'].abs()>1e-4\\n mutwt_index = dat['mutwt']=='no'\\n dat = dat[no_mmei_index & nonstop_index & zerofit_index & mutwt_index]\\n #print \\\"Filtered data\\\"\\n return dat\",\n \"def grep(requestContext, seriesList, pattern):\\n regex = re.compile(pattern)\\n return [s for s in seriesList if regex.search(s.name)]\",\n \"def exact_search(string, row):\\n clear_screen()\\n found = False\\n for item in row:\\n if string.lower() in item[\\\"Task\\\"].lower() \\\\\\n or string.lower() in item[\\\"Notes\\\"].lower():\\n print_entry(item)\\n found = True\\n if found is False:\\n print(\\\"No Entries Found..\\\")\",\n \"def match_name(pattern, rows):\\n matching = []\\n for row in rows:\\n # Use regex matching to check whether first name or last name contains the pattern\\n if re.search(r'%s' % pattern.lower(), row[0].lower()) != None or re.search(r'%s' % pattern.lower(), row[1].lower()) != None:\\n matching.append(row)\\n\\n # print the matched records\\n print_records(matching)\",\n \"def filter_by_date(df, date_string, before_or_after):\\n filter_date = pd.to_datetime(date_string)\\n if before_or_after == \\\"before\\\":\\n return df[df['timestamp'] < filter_date].copy()\\n elif before_or_after == \\\"after\\\":\\n return df[df['timestamp'] >= filter_date + pd.DateOffset(days=1)].copy()\\n else:\\n return print(\\\"Invalid argument.\\\")\",\n \"def match_email(pattern, rows):\\n matching = []\\n for row in rows:\\n # Use regex matching to check whether email contains the pattern\\n if re.search(r'%s' % pattern.lower(), row[2].lower()) != None:\\n matching.append(row)\\n\\n # print the matched records\\n print_records(matching)\",\n \"def filter_data(self):\\n self.data = filter_pandas(self.data, self.filters)\",\n \"def _data_filter(data: str) -> list:\\n\\n return PROXIES_DATA_REGEX.findall(str(data))\",\n \"def delete_by_substring(db, table, column, substring):\\n mycursor = db.cursor()\\n sql = \\\"DELETE FROM \\\" + table + \\\" WHERE \\\" + column + \\\" LIKE \\\\'%\\\" + substring + \\\"%\\\\'\\\"\\n mycursor.execute(sql)\\n\\n db.commit()\",\n \"def minus_df(df):\\n return df[df[\\\"content\\\"].str.contains(\\\"(^|[^0-9])\\\\-[0-9]+\\\")]\",\n \"def filter(self, filterstring):\\n if filterstring not in self.FILTERS:\\n raise ValueError(\\\"{dataset} must be in {d for d in self.DATASETS}\\\")\\n self.query[\\\"filter\\\"] = filterstring\\n return self\",\n \"def plus_df(df):\\n return df[df[\\\"content\\\"].str.contains(\\\"\\\\+[0-9]+\\\")]\",\n \"def match_list(column, patterns):\\n for pattern in patterns:\\n if pattern.match(column):\\n return True\\n return False\",\n \"def filter_df(df, month, day):\\n\\n #filter by month\\n df = df.loc[df['month'] == month]\\n\\n #filter by day\\n df = df.loc[df['day_of_week'] == day]\\n\\n return df\",\n \"def filter(self, row):\\r\\n return list(itertools.compress(row, self.selectors))\",\n \"def filter_ignoring_case(self, pattern):\\n return self.filter(re.compile(pattern, re.I))\",\n \"def category_match(self,single_word, input_dataframe):\\n temp_set = self.index_dict[single_word]\\n category_list = list(temp_set)\\n filtered_df = input_dataframe.loc[category_list]\\n return filtered_df\",\n \"def filter_row(col, rw):\\n return rw == row\",\n \"def filter_by_string(cls, query_string, db_cursor, args):\\n db_cursor.execute(query_string, args)\\n if db_cursor.fetchone():\\n return False\\n return True\",\n \"def filter_table_by_column(\\n self, table: Table, column: Column, operator: str, value: Any\\n ):\\n self._requires_table(table)\\n\\n condition = to_condition(operator, value)\\n\\n before = len(table)\\n table.filter_by_column(column, condition)\\n after = len(table)\\n\\n self.logger.info(\\\"Filtered %d rows\\\", after - before)\",\n \"async def filter(message, *args, **kwargs):\\r\\n table = args[0]\\r\\n condition = \\\" \\\".join(args[1:])\\r\\n if not condition: output = DataTables[table]\\r\\n else: output = [e for e in DataTables[table] if safe_eval(condition, e)]\\r\\n fields = [\\\"ID\\\"]\\r\\n if \\\"name\\\" in DataTables[table][0]: fields.append(\\\"name\\\")\\r\\n if kwargs.get(\\\"fields\\\"):\\r\\n fields.extend((f.strip(\\\" \\\") for f in kwargs[\\\"fields\\\"].strip('\\\"').split(\\\",\\\")))\\r\\n if output: await reply(message, f\\\"```{tablestr(output, fields=fields)}```\\\")\\r\\n else: await reply(message, \\\"no match found\\\")\",\n \"def filter_row(row: OrderedDict, sampling_features: set) -> bool:\\n\\n return row['feature_of_interest'] in sampling_features\",\n \"def search_actions(strings):\\n\\tpattern = '|'.join(strings)\\n\\n\\tb = actions[actions.action.str.contains(pattern)] \\\\\\n\\t\\t.loc[:, ['session', 'bill_id']] \\\\\\n\\t\\t.drop_duplicates()\\n\\n\\treturn b\",\n \"def filterfeatures(df):\\n\\tfilter_arr = []\\n\\tfor f in df.columns:\\n\\t\\tif not '.l' in f and not '.r' in f and not '.std' in f and f != 'weight' and f != 'class':\\n\\t\\t\\t# filter_arr.append(f.rstrip('.mean'))\\n\\t\\t\\tfilter_arr.append(f)\\n\\treturn filter_arr\",\n \"def filt(seq, lst):\\n regex = \\\"(\\\" + \\\")|(\\\".join(seq) + \\\")\\\"\\n regex = re.compile(regex)\\n slst = list(filter(regex.search, lst))\\n return slst\\n\\n\\n # still need a checkUsername function \",\n \"def remove_other_elements(data):\\n charset = ['F','l','B','r','I','i','M','g','L','b','a','e','K','V','d','R','Z','G','A','Y','u']\\n x = []\\n for i in range(data.shape[0]):\\n for j in range(len(data.iloc[i,1])):\\n if data.iloc[i,1][j] in charset:\\n x.append(i)\\n break\\n df = data[(True^data['Index'].isin(x))]\\n df.reset_index(drop=True, inplace=True)\\n return df\",\n \"def filter_by_names(df, names_list):\\n return df.ix[names_list]\",\n \"def filterOptions(self, phrases,columnlist):\\n filtered=[]\\n for col in columnlist:\\n outerFlag = False\\n for phrase in phrases:\\n innerFlag = True\\n for word in phrase:\\n if word.lower() not in col.lower(): innerFlag = False #Requires all words of phrase to be in col\\n if innerFlag: outerFlag = True #Requires at least one phrase to be in col\\n if outerFlag: filtered.append(col)\\n if not filtered: filtered = ['']\\n return filtered\",\n \"def reg_csv_search(arg):\\n results_list = []\\n with open('work_log.csv', 'r') as file:\\n for row in file:\\n if re.search(arg, str(row)):\\n results_list.append(row)\\n results_sort(results_list)\",\n \"def filter_lobster_by_elements(e1, e2, ILOBSTER):\\n e1_regex = '^' + e1 + '\\\\d+$'\\n e2_regex = '^' + e2 + '\\\\d+$'\\n mask = ((ILOBSTER['atomMU'].str.contains(e1_regex) & ILOBSTER['atomNU'].str.contains(e2_regex)) | \\\\\\n (ILOBSTER['atomNU'].str.contains(e1_regex) & ILOBSTER['atomMU'].str.contains(e2_regex)))\\n return ILOBSTER[mask]\",\n \"def filter_by_column(self, column: Column, condition: CellCondition):\\n\\n def _check_cell(index: int) -> bool:\\n cell = self.get_cell(index, column)\\n return condition(cell)\\n\\n self._filter(_check_cell)\",\n \"def search_string(self, string, ignore_case=False):\\n for ypos in range(self.model_dimensions[\\\"rows\\\"]):\\n line = self.string_get(ypos + 1, 1, self.model_dimensions[\\\"columns\\\"])\\n if ignore_case:\\n line = line.lower()\\n if string in line:\\n return True\\n return False\",\n \"def filterData(records):\\n def isInteresting(record):\\n if record[VO_ISSUER] in ('/DC=ch/DC=cern/OU=computers/CN=voms.cern.ch', '/DC=ch/DC=cern/OU=computers/CN=lcg-voms.cern.ch'):\\n return True\\n if record[VO_NAME] in ('atlas', 'cms', 'alice'):\\n return True\\n if record[USERSN] == '/C=SI/O=SiGNET/O=IJS/OU=F9/CN=Andrej Filipcic':\\n return True\\n if record[USERSN] in ('aliprod', '/aliprod'):\\n return True\\n if record[USERSN].startswith(ALIEN_USER_PREFIX):\\n return True\\n\\n return False\\n\\n return [ r for r in records if isInteresting(r) ]\",\n \"def column_wildcard(self) -> Optional[pulumi.Input['DataCellsFilterColumnWildcardArgs']]:\\n return pulumi.get(self, \\\"column_wildcard\\\")\",\n \"def search_element_of_table(path, table, nameColumn, data):\\n conn = sqlite3.connect(path)\\n c = conn.cursor()\\n to_return = \\\"<tr>\\\"\\n for row in c.execute('SELECT * FROM '+table +' where '+nameColumn+\\\" like '%\\\"+data+\\\"%'\\\").fetchall():\\n for x in row:\\n to_return = to_return + \\\"<td>\\\"+ str(x) + \\\"</td>\\\"\\n to_return = to_return + \\\"</tr>\\\"\\n\\n conn.close()\\n return to_return\",\n \"def filterAcceptsRow(self, row, parent):\\n if not self.__customFilterEnabled:\\n return super(\\n _WorkerFilterProxyModel, self).filterAcceptsRow(row, parent)\\n\\n if not self.__regex:\\n return True\\n\\n model = self.sourceModel()\\n idx = model.index(row, 0, parent)\\n if not idx.isValid():\\n return False\\n\\n obj = model.data(idx, _WorkerModel.ObjectRole)\\n if not obj:\\n return False\\n\\n result = self.__regex.match(obj.name)\\n if not result:\\n return False\\n return True\",\n \"def WhereMatches(self, pattern):\\n regex = re.compile(match_util.ExpandRegexIdentifierPlaceholder(pattern))\\n return self.Filter(lambda s: (\\n regex.search(s.source_path) or\\n regex.search(s.object_path) or\\n regex.search(s.full_name) or\\n s.full_name is not s.template_name and regex.search(s.template_name) or\\n s.full_name is not s.name and regex.search(s.name)))\",\n \"def split_on_column_value(\\n df, column_name: str, batch_identifiers: dict\\n ) -> pyspark.DataFrame:\\n return df.filter(F.col(column_name) == batch_identifiers[column_name])\",\n \"def search_by_contains(self, tl):\\n print(\\\"Search by string\\\")\\n string = input(\\\"Please enter search string: \\\")\\n return tl.findall_contains(string)\",\n \"def filter_existing_samples(df, sample_id_colname, remote_samples):\\n remote_sample_ids = remote_samples['entity:sample_id'].tolist()\\n df_clean = df[~df[sample_id_colname].isin(remote_sample_ids)]\\n return df_clean\",\n \"def filter_data(data,filters):\\n final_filter = pd.Series(np.array([True] * data.shape[0]))\\n for attribute, value in filters:\\n final_filter &= data[attribute] == value\\n return data[final_filter]\",\n \"def filter_inbound_statements_grid(self, column_name, filter_item_text):\\n self.grid_filter_with_textbox(self.inbound_statement_grid_div_id, column_name, filter_item_text, self.inbound_statements_grid_filter_position)\",\n \"def sql_filter(my_table='', colName='', var='', **kw):\\n\\tif (my_table=='') or (colName=='') or (var==''):\\n\\t\\treturn dict(sql='',clauseTables=[])\\n\\telse:\\n\\t\\tsql = my_table+\\\".\\\"+colName+\\\" LIKE '%\\\"+var+\\\"%'\\\"\\n\\t\\treturn dict(sql=sql,clauseTables=[])\",\n \"def grep(string,list):\\n import re\\n expr = re.compile(string)\\n return filter(expr.search,list)\",\n \"def column_wildcard(self) -> pulumi.Output[Optional['outputs.DataCellsFilterColumnWildcard']]:\\n return pulumi.get(self, \\\"column_wildcard\\\")\",\n \"def regex_catch(self, column):\\n ## considering currency_list and metric_list are globally defined.\\n pattern_dict = {\\\"Column_name\\\": column, \\\"email-id\\\": False, \\\"website\\\": False, \\\"Percentage\\\": False,\\n \\\"CurrencyCol\\\": {\\\"value\\\": False, \\\"currency\\\": None}, \\\"MetricCol\\\": {\\\"value\\\": False, \\\"metric\\\": None},\\n \\\"SepSymbols\\\": {\\\"value\\\": False, \\\"Symbol\\\": None}}\\n column_val = self.data_frame[column].astype(str).str.strip()\\n df1 = self.data_frame[column_val.apply(validate_email)]\\n if df1.shape[0] >= (0.8 * self.data_frame.shape[0]):\\n pattern_dict[\\\"email-id\\\"] = True\\n elif column_val.str.contains(\\\"%$\\\", na=True).all():\\n pattern_dict[\\\"Percentage\\\"] = True\\n elif column_val.str.contains(\\\"^https:|^http:|^www.\\\", na=True).all():\\n pattern_dict[\\\"website\\\"] = True\\n elif column_val.str.contains(\\\"[0-9]+[.]{0,1}[0-9]*\\\\s*[Aa-zZ]{1,2}$\\\").all():\\n metric = list(map(lambda x: re.sub(\\\"[0-9]+[.]{0,1}[0-9]*\\\\s*\\\", \\\"\\\", x), column_val))\\n if len(set(metric)) == 1:\\n if metric[0] in Metric_list:\\n pattern_dict[\\\"MetricCol\\\"][\\\"value\\\"] = True\\n pattern_dict[\\\"MetricCol\\\"][\\\"metric\\\"] = metric[0]\\n elif column_val.str.contains(\\\"([0-9]+[.]{0,1}[0-9]*\\\\s*\\\\W$)|(^\\\\W[0-9]+[.]{0,1}[0-9]*)\\\").all():\\n currency = list(map(lambda x: re.sub(\\\"[0-9.\\\\s]+\\\", \\\"\\\", x), column_val))\\n if len(set(currency)) == 1:\\n if currency[0] in currency_list:\\n pattern_dict[\\\"CurrencyCol\\\"][\\\"value\\\"] = True\\n pattern_dict[\\\"CurrencyCol\\\"][\\\"currency\\\"] = currency[0]\\n elif column_val.str.contains(\\\"\\\\S+\\\\s*[\\\\W_]+\\\\s*\\\\S+\\\").all():\\n seperators = list(map(lambda x: re.sub(\\\"\\\\s*[a-zA-Z0-9]+$\\\", \\\"\\\", x),\\n list(map(lambda x: re.sub('^[a-zA-Z0-9]+\\\\s*', '', x), column_val))))\\n if len(set(seperators)) == 1:\\n if seperators[0] == \\\"\\\":\\n seperators[0] = ' '\\n pattern_dict[\\\"SepSymbols\\\"][\\\"value\\\"] = True\\n pattern_dict[\\\"SepSymbols\\\"][\\\"Symbol\\\"] = seperators[0]\\n return pattern_dict\",\n \"def byStringContains(self, paramName, value, invert=False):\\n\\t\\timport revitron\\n\\t\\tself.applyFilter(\\n\\t\\t revitron.DB.FilterStringRule,\\n\\t\\t paramName,\\n\\t\\t value,\\n\\t\\t revitron.DB.FilterStringContains(),\\n\\t\\t invert\\n\\t\\t)\\n\\t\\treturn self\",\n \"def remove_rows_with_non_english_movies(df):\\n df = df[df['original_language'] == 'en']\\n return df\",\n \"def filter_macro(out, scope, args, children):\\n len(args) == 2 or syntax_error(\\\"'filter' macro takes exactly 2 arguments.\\\")\\n regex, l = args\\n if not isinstance(l, list):\\n syntax_error(\\\"Invalid list in 'filter' macro: '%s'\\\" % str(list))\\n if not isinstance(regex, str):\\n syntax_error(\\\"Invalid regex in 'filter' macro: '%s'\\\" % str(regex))\\n def match(s):\\n return re.search(regex, s)\\n return list(filter(match, l))\",\n \"def filter_data(data, filter_dict):\\n for key, match_string in filter_dict.items():\\n if key not in data:\\n logger.warning(\\\"{0} doesn't match a top level key\\\".format(key))\\n continue\\n values = data[key]\\n matcher = re.compile(match_string)\\n if isinstance(values, list):\\n values = [v for v in values if matcher.search(v)]\\n elif isinstance(values, dict):\\n values = dict((k, v) for k, v in values.items() if matcher.search(k))\\n else:\\n raise MiuraException(\\\"cannot filter a {0}\\\".format(type(values)))\\n data[key] = values\",\n \"def filter_nmt_file(filename,filter_fn=None):\\r\\n \\r\\n if filter_fn is None:\\r\\n filter_fn = lambda en : en.lower().startswith('i am') or \\\\\\r\\n en.lower().startswith('he is') or \\\\\\r\\n en.lower().startswith('she is') or \\\\\\r\\n en.lower().startswith('they are') or \\\\\\r\\n en.lower().startswith('you are') or \\\\\\r\\n en.lower().startswith('we are')\\r\\n \\r\\n filtered_lines = []\\r\\n with open(filename) as file:\\r\\n lines = file.readlines()\\r\\n for line in lines:\\r\\n text = line.split('\\\\t')\\r\\n en = text[0]\\r\\n fra = text[1]\\r\\n if filter_fn(en):\\r\\n filtered_lines.append(en.lower() + '\\\\t' + fra.lower())\\r\\n\\r\\n return filtered_lines\",\n \"def filter_chants_with_F_clef(chants, logger=None):\\n contains_F_clef = chants.volpiano.str.contains('2') == True\\n return chants[contains_F_clef == False]\",\n \"def test_filters(self):\\n data = pd.DataFrame({'a': [1, 2, 3], 'ab': [6, 5, 4], 'c2': [1, 0, 1]})\\n\\n # test regex selector\\n selector = ItemSelector(regex='[b2]$')\\n selected_data = selector.transform(data)\\n expected_data = pd.DataFrame({'ab': [6, 5, 4], 'c2': [1, 0, 1]})\\n pd.testing.assert_frame_equal(selected_data, expected_data)\\n\\n # test items selector\\n selector = ItemSelector(items=['a', 'c2'])\\n selected_data = selector.transform(data)\\n expected_data = pd.DataFrame({'a': [1, 2, 3], 'c2': [1, 0, 1]})\\n pd.testing.assert_frame_equal(selected_data, expected_data)\\n\\n # test like selector\\n selector = ItemSelector(like='a')\\n selected_data = selector.transform(data)\\n expected_data = pd.DataFrame({'a': [1, 2, 3], 'ab': [6, 5, 4]})\\n pd.testing.assert_frame_equal(selected_data, expected_data)\",\n \"def filter_by_country(df, cont_list):\\n return df.filter((df.country.isin(cont_list)))\",\n \"def filterRawNodes(self, data):\\n def filterFun(str):\\n if ('/' in str):\\n return True\\n else:\\n return False\\n return filter(filterFun, data)\",\n \"def clean_rows_cat_values(df: pd.DataFrame, col: str, values: list) -> pd.DataFrame:\\n\\n # create mask to filter df with rows that have\\n # the indicated values in the indicated column\\n index = df.columns.get_loc(col)\\n mask = [df.iloc[row, index] not in values for row in range(len(df))]\\n\\n # print original dataframe shape\\n print(f\\\"Shape of the original dataframe: \\\\n{df.shape}\\\\n\\\")\\n\\n # filter df\\n df = df.iloc[mask]\\n df.reset_index(drop=True, inplace=True)\\n print(\\n f\\\"Shape after removing rows with values equal to\\\\n{values}\\\\nin column '{col}'':\\\"\\n )\\n print(df.shape, \\\"\\\\n\\\")\\n\\n return df\",\n \"def pattern_filter(patterns, name):\\n return [pat for pat in patterns if fnmatch.fnmatchcase(name, pat)]\",\n \"def byStringContainsOneInCsv(self, paramName, csv, invert=False):\\n\\t\\treturn self.byOneInCsv('byStringContains', paramName, csv, invert)\",\n \"def apply_word_filter(self, fn):\\n self._apply_filter(lambda ng, f: any(fn(w) for w in ng))\",\n \"def any_term(self, row):\\n return any(term in row for term in self.search_terms)\",\n \"def execute(self):\\n return super(BeefRecipes, self).execute().where(lower(col('ingredients')).like(\\\"%beef%\\\"))\",\n \"def lst_and_pattern (filer_lst, pattern):\\r\\n new_filter_lst=[]\\r\\n for word in filer_lst:\\r\\n if word_and_pattern(word,pattern):\\r\\n new_filter_lst.append(word)\\r\\n return new_filter_lst\",\n \"def filter_significant_email(data):\\n frame_to_choose = []\\n for ind, row in data.iterrows():\\n if len(row[\\\"RawText\\\"]) > 500:\\n frame_to_choose.append(True)\\n else:\\n frame_to_choose.append(False)\\n\\n return data[frame_to_choose]\",\n \"def crossfilter_by_tag(self,df, tag):\\n col,spec= list(tag.items())[0]\\n return df[df[col]==spec]\",\n \"def where(self, cond):\\n return self.filter(lambda x: _(x).contains(cond))\",\n \"def test_extract_column_zero_and_one_correct_content():\\n data = \\\"\\\"\\\"kylebarnes@hotmail.com,Records manager\\njoe70@yahoo.com,Network engineer\\ntorresjames@white.info,Electrical engineer\\nshawkins@watson.com,Science writer\\\"\\\"\\\"\\n result = extract.extract_data_given_column(data, 0)\\n assert \\\"shawkins@watson.com\\\" in result\\n result = extract.extract_data_given_column(data, 1)\\n assert \\\"Records manager\\\" in result\",\n \"def simple_search(self, pattern):\\n query = Q()\\n for ptn in pattern.split():\\n for field in SEARCH_FIELDS:\\n query |= Q(**{'%s__icontains' % field: ptn})\\n return self.get_queryset().filter(query)\",\n \"def filterBySequence(wordlist, sequence):\\n filtered_list = [entry[\\\"word\\\"] for entry in wordlist if entry[\\\"sequence\\\"].startswith(sequence)]\\n return filtered_list\",\n \"def subset(self, *args, **kwargs):\\n\\t\\tgs = copy.copy(self)\\n\\t\\tqueryStrings = kwargs['queryStrings'] if 'queryStrings' in kwargs else args[0] if args else []\\n\\t\\tif isinstance(queryStrings, str) or isinstance(queryStrings, bytes):\\t# assume queryString was specified as a string\\n\\t\\t\\tqueryStrings = [queryStrings]\\n\\t\\n\\t\\tif len(queryStrings)==0:\\n\\t\\t\\treturn gs\\n\\t\\t\\t\\n\\t\\tdf = gs._dataframe\\n\\t\\t\\n\\t\\tif 'species' in kwargs and kwargs['species'] in ['MusMusculus','HomoSapiens']:\\n\\t\\t\\tdf = df[df['Species']==kwargs['species']]\\n\\t\\t\\t\\n\\t\\tcaseSensitive = kwargs.get('caseSensitive', False)\\n\\t\\tif not caseSensitive: queryStrings = [item.lower() for item in queryStrings]\\n\\t\\t\\n\\t\\tmatchSubstring = kwargs.get('matchSubstring', True)\\n\\t\\t\\n\\t\\t# determine which columns to search\\n\\t\\tsearchColumns = kwargs.get('searchColumns')\\n\\t\\tallColumns = ['EnsemblId','EntrezId','GeneSymbol','Synonyms','Description']\\n\\t\\tif searchColumns:\\n\\t\\t\\tsearchColumns = ['EnsemblId' if item=='GeneId' or item=='geneId' else item for item in searchColumns]\\n\\t\\tif searchColumns and len(set(allColumns).intersection(set(searchColumns)))>0:\\n\\t\\t\\tsearchColumns = list(set(allColumns).intersection(set(searchColumns)))\\n\\t\\telse:\\n\\t\\t\\tsearchColumns = allColumns\\n\\t\\t\\n\\t\\trowsToKeep = set()\\n\\t\\tdf = df.reset_index()\\n\\t\\tfor column in searchColumns:\\n\\t\\t\\tfor rowIndex,value in df[column].iteritems():\\n\\t\\t\\t\\tif rowIndex in rowsToKeep or not value: continue\\n\\t\\t\\t\\tif not caseSensitive: value = value.lower()\\n\\t\\t\\t\\tfor queryString in queryStrings:\\n\\t\\t\\t\\t\\tif matchSubstring and queryString in value or (not matchSubstring and queryString==value):\\n\\t\\t\\t\\t\\t\\trowsToKeep.add(rowIndex)\\n\\t\\t\\t\\t\\t\\tbreak\\n\\n\\t\\tgs._dataframe = df.loc[list(rowsToKeep),:].set_index('EnsemblId')\\n\\t\\treturn gs\",\n \"def date_filter(frame, date_column, year):\\n frame[date_column] = pd.to_datetime(frame[date_column])\\n frame = frame[frame[date_column] > pd.Timestamp(year, 1, 1)]\\n return frame\",\n \"def test_string_in_filter_with_declared_filter(query):\\n Person.objects.create(name=\\\"John\\\")\\n Person.objects.create(name=\\\"Michael\\\")\\n Person.objects.create(name=\\\"Angela\\\")\\n\\n schema = Schema(query=query)\\n\\n query = \\\"\\\"\\\"\\n query {\\n people (names: \\\"John,Michael\\\") {\\n edges {\\n node {\\n name\\n }\\n }\\n }\\n }\\n \\\"\\\"\\\"\\n result = schema.execute(query)\\n assert not result.errors\\n assert result.data[\\\"people\\\"][\\\"edges\\\"] == [\\n {\\\"node\\\": {\\\"name\\\": \\\"John\\\"}},\\n {\\\"node\\\": {\\\"name\\\": \\\"Michael\\\"}},\\n ]\",\n \"def _s_filter(cls, arg):\\n return cls.query.filter_by(name=arg)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6799201","0.59878045","0.5795762","0.5779332","0.5757394","0.574811","0.5722822","0.57140815","0.56623626","0.56492823","0.5607389","0.5594936","0.5588069","0.5576866","0.5554488","0.5542753","0.55349904","0.5508957","0.54967576","0.546392","0.5438134","0.5436342","0.5420617","0.53819543","0.5339109","0.5327195","0.53084165","0.5279155","0.52772003","0.5276534","0.52764976","0.52183247","0.5166152","0.5164565","0.51614434","0.5160724","0.51261187","0.51067966","0.51052064","0.5095426","0.5091405","0.50857306","0.5085492","0.505596","0.5050752","0.5041762","0.50268745","0.5024737","0.501454","0.5011043","0.500782","0.50049204","0.4996563","0.49833843","0.49786302","0.4955001","0.4954341","0.49422458","0.4926527","0.49159607","0.48988688","0.48986614","0.48886362","0.488793","0.48865014","0.48855636","0.48847556","0.4866523","0.48517013","0.4849282","0.48443058","0.48321107","0.4820419","0.48137996","0.4799312","0.47991723","0.47971877","0.47934398","0.47898793","0.47823158","0.47816873","0.47715285","0.4766327","0.4755991","0.47512525","0.47496077","0.4747086","0.47433487","0.47393885","0.47389722","0.47387147","0.4738542","0.47315925","0.4724259","0.47236964","0.47202757","0.47185007","0.47156546","0.47078365","0.47071594"],"string":"[\n \"0.6799201\",\n \"0.59878045\",\n \"0.5795762\",\n \"0.5779332\",\n \"0.5757394\",\n \"0.574811\",\n \"0.5722822\",\n \"0.57140815\",\n \"0.56623626\",\n \"0.56492823\",\n \"0.5607389\",\n \"0.5594936\",\n \"0.5588069\",\n \"0.5576866\",\n \"0.5554488\",\n \"0.5542753\",\n \"0.55349904\",\n \"0.5508957\",\n \"0.54967576\",\n \"0.546392\",\n \"0.5438134\",\n \"0.5436342\",\n \"0.5420617\",\n \"0.53819543\",\n \"0.5339109\",\n \"0.5327195\",\n \"0.53084165\",\n \"0.5279155\",\n \"0.52772003\",\n \"0.5276534\",\n \"0.52764976\",\n \"0.52183247\",\n \"0.5166152\",\n \"0.5164565\",\n \"0.51614434\",\n \"0.5160724\",\n \"0.51261187\",\n \"0.51067966\",\n \"0.51052064\",\n \"0.5095426\",\n \"0.5091405\",\n \"0.50857306\",\n \"0.5085492\",\n \"0.505596\",\n \"0.5050752\",\n \"0.5041762\",\n \"0.50268745\",\n \"0.5024737\",\n \"0.501454\",\n \"0.5011043\",\n \"0.500782\",\n \"0.50049204\",\n \"0.4996563\",\n \"0.49833843\",\n \"0.49786302\",\n \"0.4955001\",\n \"0.4954341\",\n \"0.49422458\",\n \"0.4926527\",\n \"0.49159607\",\n \"0.48988688\",\n \"0.48986614\",\n \"0.48886362\",\n \"0.488793\",\n \"0.48865014\",\n \"0.48855636\",\n \"0.48847556\",\n \"0.4866523\",\n \"0.48517013\",\n \"0.4849282\",\n \"0.48443058\",\n \"0.48321107\",\n \"0.4820419\",\n \"0.48137996\",\n \"0.4799312\",\n \"0.47991723\",\n \"0.47971877\",\n \"0.47934398\",\n \"0.47898793\",\n \"0.47823158\",\n \"0.47816873\",\n \"0.47715285\",\n \"0.4766327\",\n \"0.4755991\",\n \"0.47512525\",\n \"0.47496077\",\n \"0.4747086\",\n \"0.47433487\",\n \"0.47393885\",\n \"0.47389722\",\n \"0.47387147\",\n \"0.4738542\",\n \"0.47315925\",\n \"0.4724259\",\n \"0.47236964\",\n \"0.47202757\",\n \"0.47185007\",\n \"0.47156546\",\n \"0.47078365\",\n \"0.47071594\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7813946"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":76699,"cells":{"query":{"kind":"string","value":"Given a dataset with pollsters column and the pollsters grade in traditional format, it merges the columns and floors the grade."},"document":{"kind":"string","value":"def grade_cleaning(dataset, pollsters):\n # Merge grades\n dataset_grades = pd.merge(dataset, pollsters[[\"Pollster\", \"538 Grade\", \"Predictive Plus-Minus\"]],\n how='inner', left_on='pollster', right_on='Pollster')\n dataset_grades = dataset_grades.drop(['Pollster'], axis=1)\n # Transform grades\n dataset_grades[\"538 Grade\"] = dataset_grades[\"538 Grade\"].apply(lambda x: sorted(x)[-1])\n # Save dataset\n dataset_grades.to_csv(\"./data/concern_polls_grades.csv\", index=False)\n # Return\n return dataset_grades"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def organize_data(scores, stds):\n\n scores = pd.Series(list(scores.values()),\n index=map(str, scores.keys()),\n name=\"SCORE MEAN\")\n scores.index.name = \"AUXILIARY LOSS WEIGHTS\"\n stds = pd.Series(list(stds.values()),\n index=map(str, stds.keys()),\n name=\"SCORE STD\")\n stds.index.name = \"AUXILIARY LOSS WEIGHTS\"\n return scores, stds","def merge_data(agg_cases, lk_info, geolocation_data):\n merged_df = pd.merge(agg_cases, lk_info, left_on='IdLandkreis', right_on = 'Key')\n merged_df[\"RelativFall\"] = merged_df[\"AnzahlFall\"] / merged_df[\"Bev Insgesamt\"]\n merged_df[\"RelativTodesfall\"] = merged_df[\"AnzahlTodesfall\"] / merged_df[\"Bev Insgesamt\"]\n merged_df = pd.merge(merged_df, geolocation_data, left_on=\"Key\", right_on=\"cca_2\")\n return merged_df","def combine_arb_rows(df_gimmes):\n\n df_gimmes = df_gimmes.merge(df_gimmes, how='inner',\n left_on='Underdog_A',\n right_on='Favorite_B')\n\n df_gimmes.drop(['Favorite_B_x', 'Favorite_B_y', 'ML_B_x',\n 'ML_B_y', 'Date_y', 'ML_A_x', 'ML_A_y'],\n axis=1,\n inplace=True)\n\n return df_gimmes.rename({'Source_x': 'Source_A',\n 'Source_y': 'Source_B',\n 'best_ML_A_x': 'best_ML_A',\n 'best_ML_A_y': 'best_ML_B',\n 'Underdog_A_x': 'Underdog_A',\n 'Underdog_A_y': 'Underdog_B'},\n axis=1)","def convert_attribute(df):\n\n df['Popularity'] = ['Oversold' if x == '(1.0, 2.0]' else 'Unpopular' if x == '(-1.0, 0.0]' or x == '(0.0, 0.5]' else 'Popular' for x in df['percent_of_cap_binRange1']]\n\n return df","def merge_rowdicts(\n list_of_rowdicts, psm_colnames_to_merge_multiple_values, joinchar=\"<|>\"\n):\n merged_d = {}\n fieldnames = []\n for rowdict in list_of_rowdicts:\n for k in rowdict.keys():\n if k not in fieldnames:\n fieldnames.append(k)\n for fieldname in fieldnames:\n values = []\n for d in list_of_rowdicts:\n if fieldname in d.keys():\n values.append(d[fieldname])\n if fieldname in psm_colnames_to_merge_multiple_values.keys():\n no_empty_values = [v for v in values if v != \"\"]\n values_as_floats = [float(value) for value in no_empty_values]\n\n if psm_colnames_to_merge_multiple_values[fieldname] == \"max_value\":\n merged_d[fieldname] = max(values_as_floats)\n\n elif psm_colnames_to_merge_multiple_values[fieldname] == \"min_value\":\n merged_d[fieldname] = min(values_as_floats)\n\n elif psm_colnames_to_merge_multiple_values[fieldname] == \"avg_value\":\n merged_d[fieldname] = sum(values_as_floats) / len(values_as_floats)\n\n elif psm_colnames_to_merge_multiple_values[fieldname] == \"most_frequent\":\n value_occurences = Counter(no_empty_values)\n most_common_value, most_occurences = value_occurences.most_common(1)[0]\n value_occurences_dict = dict(value_occurences)\n final_values = []\n for value in no_empty_values:\n if value in final_values:\n continue\n if value_occurences_dict[value] == most_occurences:\n final_values.append(value)\n merged_d[fieldname] = joinchar.join(final_values)\n\n else:\n if len(set(values)) == 1:\n merged_d[fieldname] = values[0]\n else:\n no_empty_values = [v for v in values if v != \"\"]\n if len(set(no_empty_values)) == 1:\n merged_d[fieldname] = no_empty_values[0]\n else:\n merged_d[fieldname] = joinchar.join(values)\n return merged_d","def merge_race_loops(self,race_data,loop_data,race_merge_cols = ['Driver','race_name'],\n loop_merge_cols = ['Driver','race_name'],loop_cols = ['Fastest Lap']):\n return race_data.merge(loop_data[loop_merge_cols+loop_cols],left_on = race_merge_cols,\n right_on = loop_merge_cols,suffixes = ('','_l'))","def standardize(train_data_jets, test_data_jets):\n \n nbr_jets = train_data_jets.shape[0]\n \n for jet in range(nbr_jets):\n # extract features for standardization\n train_data_features = train_data_jets[jet][:,2:] \n test_data_features = test_data_jets[jet][:,2:] \n # store train mean and std without considering nan values\n train_mean = np.nanmean(train_data_features, axis=0)\n train_std = np.nanstd(train_data_features, axis=0)\n # standardize train and test data with train mean and std\n train_data_features = (train_data_features - train_mean) / train_std\n test_data_features = (test_data_features - train_mean) / train_std\n # insert standardized features into original dataset with predictions\n train_data_jets[jet][:,2:] = train_data_features\n test_data_jets[jet][:,2:] = test_data_features\n \n return train_data_jets, test_data_jets","def rescaleData(data,column_names):\n for column_name in column_names:\n min_value = np.min(data[column_name])\n max_value = np.max(data[column_name])\n data[column_name] = (data[column_name] - min_value) / (max_value - min_value)","def get_grades(self, force=False):\n df = self._fetch_status_data(force)\n df = df[['Name', 'Grade', 'Final grade']].copy()\n\n gradere = re.compile(r'Grade(\\d+(.\\d+)?).*')\n\n # raw grade column is \"GradeXX / YY\"\n df['Grade'] = pandas.to_numeric(\n df.Grade.str.replace(gradere, lambda m: m.group(1)),\n errors='coerce',\n )\n\n return df","def normalizeData(meanAndStd, dataset):\n\n for i in range(len(dataset)):\n for j in range(len(dataset[i])-1):\n mean = meanAndStd[j][\"mean\"]\n std = meanAndStd[j][\"std\"]\n dataset[i][j] = (dataset[i][j] - mean)/std","def _split_flattened(data, split_ratio, seed=default.DEFAULT_CV_RANDOM_SEED):\n\n check.argument_numeric(split_ratio, 0, 1)\n\n pc = np.sum(data.values != 0)\n gs_count = int(split_ratio * pc)\n idx = _make_shuffled_index(pc, seed=seed)\n\n pr_idx = data.values[data.values != 0].copy()\n gs_idx = data.values[data.values != 0].copy()\n\n pr_idx[idx[0:gs_count]] = 0\n gs_idx[idx[gs_count:]] = 0\n\n gs = data.values.copy()\n pr = data.values.copy()\n\n gs[gs != 0] = gs_idx\n pr[pr != 0] = pr_idx\n\n priors_data = pd.DataFrame(pr, index=data.index, columns=data.columns)\n gold_standard = pd.DataFrame(gs, index=data.index, columns=data.columns)\n\n return priors_data, gold_standard","def normalize_dataset(self):","def processData(twcrDat, era20cDat, eraintDat, merraDat, erafiveDat):\n #merge everything\n twcr_era20c = pd.merge(twcrDat, era20cDat, on='tg', how='left')\n twcr_era20c_eraint = pd.merge(twcr_era20c, eraintDat, on='tg', how='left')\n twcr_era20c_eraint_merra = pd.merge(twcr_era20c_eraint, merraDat, on='tg', how='left')\n twcr_era20c_eraint_merra_erafive = pd.merge(twcr_era20c_eraint_merra, erafiveDat, on='tg', how='left')\n\n \n\n allCorr = twcr_era20c_eraint_merra_erafive[['tg', 'lon', 'lat', 'corrTwcr', 'corrEra20c', \\\n 'corrEraint', 'corrMerra', 'corrErafive']]\n allCorr.columns = ['tg', 'lon', 'lat', '20CR', 'ERA-20C', 'ERA-Interim', 'MERRA', 'ERA-FIVE']\n allRMSE = twcr_era20c_eraint_merra_erafive[['tg', 'lon', 'lat', 'rmseTwcr', 'rmseEra20c',\\\n 'rmseEraint', 'rmseMerra', 'rmseErafive']]\n allRMSE.columns = ['tg', 'lon', 'lat', '20CR', 'ERA-20C', 'ERA-Interim', 'MERRA', 'ERA-FIVE']\n allNSE = twcr_era20c_eraint_merra_erafive[['tg', 'lon', 'lat', 'nseTwcr', 'nseEra20c',\\\n 'nseEraint', 'nseMerra', 'nseErafive']]\n allNSE.columns = ['tg', 'lon', 'lat', '20CR', 'ERA-20C', 'ERA-Interim', 'MERRA', 'ERA-FIVE']\n \n \n #get max corr values \n allCorr['Correlation'] = allCorr.iloc[:,3:8].max(axis = 1)\n allCorr['Reanalysis'] = allCorr.iloc[:, 3:8].idxmax(axis = 1)\n\n #get min rmse values - change to cms for visibility\n allRMSE['RMSE(cm)'] = allRMSE.iloc[:,3:8].min(axis = 1)*100\n allRMSE['Reanalysis'] = allRMSE.iloc[:, 3:8].idxmin(axis = 1)\n\n #get max nse values \n# <<<<<<< HEAD\n# allNSE['NSE(%)'] = allNSE.iloc[:,3:8].max(axis = 1)*100\n# =======\n ##normalize NSE values\n allNSE['NNSE'] = 1/(2 - allNSE.iloc[:,3:8].max(axis = 1))\n# >>>>>>> kiko-stuff\n allNSE['Reanalysis'] = allNSE.iloc[:, 3:8].idxmax(axis = 1)\n \n # #save metrics results \n allCorr.to_csv(\"allCorr.csv\")\n allRMSE.to_csv(\"allRMSE.csv\")\n allNSE.to_csv(\"allNSE.csv\")\n \n return allCorr, allRMSE, allNSE","def normalize (a_data,a_column,b_method='MinMax') :\n if b_method == 'MinMax' :\n loc_scaler = __minmax()\n a_data[a_column] = loc_scaler.fit_transform(a_data[a_column])\n elif b_method == 'Standard' :\n loc_scaler = __standard()\n a_data[a_column] = loc_scaler.fit_transform(a_data[a_column])","def bilateralize(ds):\n ds_ROIs = ds.copy('deep')\n ds_ROIs.sa['bilat_ROIs'] = [label.split(' ')[-1] for label in ds_ROIs.sa.all_ROIs]\n mv.h5save(results_dir + 'ds_ROIs.hdf5', ds_ROIs)\n print('Combined lateralized ROIs for the provided dataset and saved the dataset.')\n return ds_ROIs","def process_data_p2(data, documentation, geodata):\r\n documentation_state = documentation.get_column(\"ST_FIPS\")\r\n mean_per_state = data.groupby(\"ST_FIPS\", as_index=False).mean()\r\n mean_per_state[\"ST_FIPS\"] = mean_per_state[\"ST_FIPS\"] \\\r\n .map(documentation_state.values_mapping)\r\n return geodata.merge(mean_per_state, how=\"left\",\r\n left_on=\"name\", right_on=\"ST_FIPS\")","def icdconvert(df,col_icd='icd',icd_version=9):\n if icd_version==9:\n source='icd9'\n target='icd10'\n df_gem=load_icd9to10()\n elif icd_version==10:\n source='icd10'\n target='icd9'\n df_gem=load_icd10to9()\n else:\n print('invalid icd version {}, please set curicd to 9 or 10'.format(icd_version))\n return None\n \n print('Comment: because of the discrepancy between icd9 and icd10, diagnosis codes may be mapped to many codes or no codes')\n \n output=df.merge(df_gem,how='left',left_on=col_icd,right_on=source)\n print('{:.2f}% mapped'.format(output[target].notnull().mean()*100))\n if 'flag' in output.columns:\n output.drop('flag',axis=1,inplace=True)\n if col_icd!=source in output.columns:\n output.drop(source,axis=1,inplace=True)\n \n return output","def divide_dataset_by_jet(data):\n \n # Mask for 23rd column PRI_jet_num (categorical feature) \n cols = list(range(0,24)) + list(range(25,32))\n \n data_0jet = np.copy(data[data[:,24] == '0'])\n data_0jet = np.asarray(data_0jet[:, cols], dtype=float)\n \n data_1jet = np.copy(data[data[:,24] == '1'])\n data_1jet = np.asarray(data_1jet[:, cols], dtype=float)\n \n data_2jet = np.copy(data[data[:,24] == '2'])\n data_2jet = np.asarray(data_2jet[:, cols], dtype=float)\n \n data_3jet = np.copy(data[data[:,24] == '3'])\n data_3jet = np.asarray(data_3jet[:, cols], dtype=float)\n \n return np.array([data_0jet, data_1jet, data_2jet, data_3jet], dtype=object)","def merge_dataframe(df, column_name='population'):\n df['snp'] = df.chrom.astype(str) + '.' + df.position.astype(str)\n print('Getting open and closed combined data, this might take a while.')\n # Takes about 20 minutes\n open_closed = get_open_closed_counts(df)\n keep_cols = [\n 'snp', 'chrom', 'position', 'rsid', 'pre_freq', 'post_freq',\n 'pre_variance', 'post_variance', 'beta', 'p_value', 'z_value', 'ref',\n 'alt', 'depth', 'ref_depth', 'alt_depth', 'snp_postfreq', 'snp_prefreq',\n 'population'\n ]\n comb_functions = {\n 'chrom': first, 'position': first, 'rsid': first,\n 'ref': first, 'alt': first,\n 'pre_freq': np.median, 'post_freq': np.median,\n 'pre_variance': np.median, 'post_variance': np.median,\n 'beta': np.median, 'z_value': np.median, 'depth': np.median,\n 'ref_depth': np.median, 'alt_depth': np.median,\n 'snp_postfreq': np.median, 'snp_prefreq': np.median,\n column_name: ','.join, 'p_value': fishers\n }\n print('Collapsing dataframe, this also takes a while.')\n # Takes about 10 minutes\n df = df[keep_cols].groupby('snp').agg(comb_functions)\n # Split the fishers statistics\n df['fishers_value'], df['fishers_p'] = df.p_value.str\n print('Merging in open/closed data')\n df = pd.merge(df, open_closed, left_index=True, right_index=True)\n print('Organizing')\n final_cols = [\n 'chrom', 'position', 'rsid', 'fishers_p', 'ref', 'alt', 'open_best',\n 'closed_best', 'pop_count', column_name, 'pops_agree',\n 'most_pops_agree', 'all_open_closed', 'open_closed_freqs',\n 'fishers_value', 'pre_freq', 'post_freq', 'pre_variance',\n 'post_variance', 'beta', 'z_value', 'depth', 'ref_depth', 'alt_depth',\n 'snp_postfreq', 'snp_prefreq'\n ]\n final_names = [\n 'chrom', 'position', 'rsid', 'fishers_p', 'ref', 'alt', 'open_best',\n 'closed_best', column_name + '_count', column_name + 's', column_name +\n 's_agree', 'most_{}s_agree'.format(column_name), 'all_open_closed',\n 'open_closed_freqs', 'fishers_value', 'pre_freq_median',\n 'post_freq_median', 'pre_variance_median', 'post_variance_median',\n 'beta_median', 'z_value_median', 'depth_median', 'ref_depth_median',\n 'alt_depth_median', 'snp_postfreq_median', 'snp_prefreq_median'\n ]\n df = df[final_cols]\n df.columns = final_names\n print('Done')\n return df","def calculate_questions_grade():\n import numpy as np\n with open('qqq.csv') as fo:\n headers_list = fo.readline().strip().split(',')\n headers_list.append('questions_grade')\n data_lines = []\n scores = []\n \n for line in fo:\n splits = [f.strip() for f in line.split(',')]\n # splits = [int(s) if get_type(s) is int else s for s in splits]\n scores.append(int(splits[-1]))\n data_lines.append(splits)\n\n std_dev = np.std(scores)\n grades = [score*1.0/std_dev for score in scores]\n max_grade = max(grades)\n grades = [g*10/max_grade for g in grades]\n\n graded_lines = [headers_list]\n for key, split_line in enumerate(data_lines):\n split_line.append(round(grades[key], 2))\n graded_lines.append(split_line)\n\n with open('qqq2.csv', 'w') as fp:\n a = csv.writer(fp, delimiter=',')\n a.writerows(graded_lines)","def _map_raw_data_to_standard(self):\n data_df = self._raw_data.copy()\n\n # if only 1 RT col, split into 2\n if self._map_cols['goRT'] == self._map_cols['stopRT']:\n data_df[self._standards['columns']['goRT']] = np.where(\n data_df[self._map_cols['condition']] == self._map_codes['go'],\n data_df[self._map_cols['goRT']],\n None)\n data_df[self._standards['columns']['stopRT']] = np.where(\n data_df[self._map_cols['condition']] ==\n self._map_codes['stop'],\n data_df[self._map_cols['stopRT']],\n None)\n del data_df[self._map_cols['goRT']]\n else:\n data_df.loc[\n data_df[self._map_cols['condition']] !=\n self._map_codes['go'],\n self._map_cols['goRT']] = None\n data_df.loc[\n data_df[self._map_cols['condition']] !=\n self._map_codes['stop'],\n self._map_cols['stopRT']] = None\n\n # drop SSDs of non-stop Trials\n data_df.loc[\n data_df[self._map_cols['condition']] != self._map_codes['stop'],\n self._map_cols['SSD']] = None\n\n # add block column if not present\n if self._map_cols['block'] not in data_df.columns:\n data_df[self._map_cols['block']] = 1\n\n # recompute choice accuracy if missing / flagged\n if (self._map_cols['choice_accuracy'] not in self._raw_data.columns) |\\\n self._compute_acc_col:\n corr_code = self._map_codes['correct']\n incorr_code = self._map_codes['incorrect']\n data_df[self._map_cols['choice_accuracy']] = np.where(\n data_df[self._map_cols['response']] == data_df[\n self._map_cols['correct_response']],\n corr_code,\n incorr_code)\n\n # map columns, key codes to standard\n rename_column_dict = {self._map_cols[col]: self._standards['columns']\n [col] for col in self._map_cols.keys()}\n data_df = data_df.rename(columns=rename_column_dict)\n\n # map key codes to various columns\n condition_map = {\n self._map_codes['go']: self._standards['key_codes']['go'],\n self._map_codes['stop']: self._standards['key_codes']['stop'],\n }\n acc_map = {\n self._map_codes['correct']: self._standards['key_codes']['correct'],\n self._map_codes['incorrect']: self._standards['key_codes']['incorrect'],\n }\n no_response_map = {\n self._map_codes['noResponse']: self._standards['key_codes']['noResponse']\n }\n cols_n_maps = [(self._standards['columns']['condition'], condition_map),\n (self._standards['columns']['choice_accuracy'], acc_map),\n (self._standards['columns']['goRT'], no_response_map),\n (self._standards['columns']['stopRT'], no_response_map)]\n for col, map_dict in cols_n_maps:\n data_df[col] = data_df[col].map(lambda x: map_dict.get(x,x))\n\n assert self._is_preprocessed(data_df)\n self._transformed_data = data_df","def merge(self):\n for i in range(len(self.main_grid_values)):\n for j in range(len(self.main_grid_values) - 1):\n if self.main_grid_values[i][j] == self.main_grid_values[i][j+1]:\n self.score_value.set(str(int(self.score_value.get()) + self.main_grid_values[i][j]*2))\n self.main_grid_values[i][j] *= 2\n self.main_grid_values[i][j+1] = 0","def preprocess(table):\n # drop the column Zeitraum\n table = table.drop('Zeitraum', axis=1)\n # drop the rows containing the true results of the elections\n Idx = np.where(table.Befragte=='Bundestagswahl')[0]\n Idx = np.append(Idx, np.where(table['CDU/CSU'].str.contains('Umfrage'))[0])\n table = table.drop(Idx)\n table.index = np.arange(table.shape[0])\n # replace the strings %,-\n table = table.replace('%', '', regex=True)\n table = table.replace(',', '.', regex=True)\n table = table.replace('[–?]', '', regex=True)\n # fix the column Befragte !!!!!!!!!!!!!!\n table.Befragte = table.Befragte.replace('[T • ?≈O • .]', '', regex=True)\n # replace all empty entries with NaN\n table = table.replace('', 'NaN', regex=True)\n\n # if the colomn Sonstige contains entries with more than one number\n try: \n table.Sonstige = table.Sonstige.astype(float)\n except ValueError:\n for i, n in enumerate(table.Sonstige):\n if len(n) > 2:\n digits = np.array([digit for digit in np.arange(10).astype(str) if digit in n])\n table.Sonstige[i] = digits.astype(int).sum()\n table.Sonstige = table.Sonstige.astype(float)\n\n # convert all numbers to float\n table[table.keys()[1:]] = table[table.keys()[1:]].astype(float)\n # convert the date to type date\n table.Datum = pd.to_datetime(table.Datum, format='%d.%m.%Y').dt.date\n return table","def get_student_grade_summary_data(request, course, get_grades=True, get_raw_scores=False, use_offline=False):\r\n course_key = course.id\r\n enrolled_students = User.objects.filter(\r\n courseenrollment__course_id=course_key,\r\n courseenrollment__is_active=1,\r\n ).prefetch_related(\"groups\").order_by('username')\r\n\r\n header = [_('ID'), _('Username'), _('Full Name'), _('edX email'), _('External email')]\r\n\r\n datatable = {'header': header, 'students': enrolled_students}\r\n data = []\r\n\r\n gtab = GradeTable()\r\n\r\n for student in enrolled_students:\r\n datarow = [student.id, student.username, student.profile.name, student.email]\r\n try:\r\n datarow.append(student.externalauthmap.external_email)\r\n except: # ExternalAuthMap.DoesNotExist\r\n datarow.append('')\r\n\r\n if get_grades:\r\n gradeset = student_grades(student, request, course, keep_raw_scores=get_raw_scores, use_offline=use_offline)\r\n log.debug('student={0}, gradeset={1}'.format(student, gradeset))\r\n with gtab.add_row(student.id) as add_grade:\r\n if get_raw_scores:\r\n # TODO (ichuang) encode Score as dict instead of as list, so score[0] -> score['earned']\r\n for score in gradeset['raw_scores']:\r\n add_grade(score.section, getattr(score, 'earned', score[0]))\r\n else:\r\n for grade_item in gradeset['section_breakdown']:\r\n add_grade(grade_item['label'], grade_item['percent'])\r\n student.grades = gtab.get_grade(student.id)\r\n\r\n data.append(datarow)\r\n\r\n # if getting grades, need to do a second pass, and add grades to each datarow;\r\n # on the first pass we don't know all the graded components\r\n if get_grades:\r\n for datarow in data:\r\n # get grades for student\r\n sgrades = gtab.get_grade(datarow[0])\r\n datarow += sgrades\r\n\r\n # get graded components and add to table header\r\n assignments = gtab.get_graded_components()\r\n header += assignments\r\n datatable['assignments'] = assignments\r\n\r\n datatable['data'] = data\r\n return datatable","def normalize_columns_together(headers, data):\n\tcolumn_matrix=data.get_data(headers)\n\tmax=column_matrix.max()\n\tprint \"The maximum:\t \", max\n\tmin=column_matrix.min()\n\tprint \"The minimum:\t \", min\n\trange=max-min\n\tprint \"range: \", range\n\tcolumn_matrix=column_matrix-min\n\tnormalized=column_matrix/range\n\treturn normalized","def preprocess_lab_data(self):\n print(\"Processing dataset from the labs ...\")\n self.full_dataset[\"score\"] = self.full_dataset[\"score\"].apply(lambda x: -1 if x==0 else 1)","def right_to_left(data, key, col_tuple, col_tuples, re_tuples,\n re_triple, cnt):\n first_col = col_tuples[0]\n second_col = col_tuples[1]\n if col_tuple == first_col or col_tuple == second_col:\n if col_tuple == first_col:\n data[\"tmp_0\"] = data[col_tuple[1]].astype(str).str.extract(re_triple[cnt][0].format(key),\n expand=True)\n else:\n data[\"tmp_0\"] = data[col_tuple[1]].astype(str).str.extract(re_triple[cnt][0],\n expand=True)\n data[\"tmp_1\"] = data[\"tmp_0\"].str.extract(re_triple[cnt][1],\n expand=True)\n if col_tuple == first_col:\n regex = re_triple[cnt][2].format(key)\n data[\"tmp_0\"] = data[\"tmp_0\"].str.extract(regex, expand=True)\n else:\n regex = re_triple[cnt][2]\n data[\"tmp_0\"] = data[\"tmp_0\"].str.extract(regex, expand=True)\n tmp_0_clean = data[\"tmp_0\"].str.strip()\n data[col_tuple[0]] = data[col_tuple[0]].fillna(tmp_0_clean)\n if col_tuple == first_col:\n data[col_tuple[1]] = data[col_tuple[1]].replace(re_triple[cnt][0].format(key),\n np.NaN, regex=True)\n else:\n data[col_tuple[1]] = data[col_tuple[1]].replace(re_triple[cnt][0][:-15]+\")\",\n np.NaN, regex=True)\n data[col_tuple[1]] = data[col_tuple[1]].replace(re_triple[cnt][0],\n np.NaN, regex=True)\n tmp_1_clean = data[\"tmp_1\"].str.strip()\n data[col_tuple[1]] = data[col_tuple[1]].fillna(tmp_1_clean)\n data = data.drop(columns=\"tmp_0\")\n data = data.drop(columns=\"tmp_1\")\n return data","def groupby_grade(master_dataframe):\n\tno_name_df = master_dataframe.drop(['last_name', 'first_name', 'email'], axis=1)\n\tgrouped = list(no_name_df.groupby(['grade']))\n\tnine, ten, eleven, twelve = grouped[0][1], grouped[1][1], grouped[2][1], grouped[3][1]\n\n\treturn nine, ten, eleven, twelve","def normalise(raw_data, normalise_by_column=False):\n data = raw_data\n if normalise_by_column:\n #normaliza valores usando o maximo de cada coluna\n col_maxes = raw_data.max(axis=0)\n #divide cada valor pelo maximo correspondente de cada coluna\n data = raw_data / col_maxes[np.newaxis,:] \n else:\n #divide todos os valores pelo maximo do dataset (tudo na mesma escala)\n data = raw_data / raw_data.max()\n\n return data","def format_dataset(dataset):\n X = []\n y = []\n\n for user in dataset:\n X.append(''.join(dataset[user][0]))\n y.append(dataset[user][1])\n\n return X, y","def merge_adjustment_factor(*dataframes, with_self=True):\n\n # TODO : Maybe it would be better to select the better of the two\n # adjustement factors. For students rated low by their team, their rating\n # would improve the score and for students that rated themselves lower than\n # their team would get a boost too.\n\n if with_self:\n col = 'Adj Factor (w/ Self)'\n else:\n col = 'Adj Factor (w/o Self)'\n\n data = {}\n\n for i, df in enumerate(dataframes):\n data['P{}'.format(i + 1)] = df[col]\n\n data['Student Name'] = df['Student Name']\n\n data = pd.DataFrame(data)\n\n data = data.dropna() # if student drops and is deleted after peer eval\n\n # calculate a slope value, improvement metric, that characterizes whether\n # the students' ranking improved over time or didn't, positive values are\n # improvements and negative means the got rated worse over time\n\n x_vals = list(range(len(dataframes)))\n slopes = []\n means = []\n stds = []\n adjusted_scores = []\n\n # I weight the later evals more than the prior ones because the later ones\n # tend to be more serious because the stakes become more real as the class\n # progresses.\n eval_names = ['P1', 'P2', 'P3', 'P4']\n weights = [0.85, 0.90, 0.95, 1.0]\n\n for idx, row in data.iterrows():\n y_vals = row[eval_names[:len(dataframes)]].values.astype(float)\n\n # Weight the latter reviews more than the earlier reviews.\n mean = np.average(y_vals, weights=weights[:len(dataframes)])\n\n # Calculate a \"slope\" val that indicates how little or how much\n # improvement there was.\n opt, _ = curve_fit(lambda x, slope, intercept: slope * x + intercept,\n x_vals, y_vals)\n improvement = opt[0]\n\n # If the student was rated low but improved over time, bump their\n # factor up based on the improvement. Also, don't allow any factor's\n # lower than 0.75. A factor of 0.75 can drop the grade two letter\n # grades (based on 85).\n if mean < 0.95 and improvement > 0.0:\n adjusted_score = mean + 1.5 * improvement\n else:\n adjusted_score = max([0.75, mean])\n\n adjusted_score = mean\n\n means.append(mean)\n stds.append(y_vals.std())\n slopes.append(improvement)\n adjusted_scores.append(adjusted_score)\n\n data['Improvement'] = slopes\n data['Mean Adj Factor'] = means\n data['STD Adj Factor'] = stds\n data['Final Adj Factor'] = adjusted_scores\n\n return data","def test_Smoother_multicol(self):\n enames = [self.ename, \"SID79\"]\n bnames = [self.bname, \"BIR79\"]\n out_df = sm.Excess_Risk.by_col(self.df, enames, bnames)\n outcols = [\"{}-{}_excess_risk\".format(e, b) for e, b in zip(enames, bnames)]\n er79 = np.array([0.000000, 2.796607, 0.8383863, 1.217479, 0.943811])\n answers = [self.er, er79]\n for col, answer in zip(outcols, answer):\n self.assertIn(out_df.columns, col)\n np.testing.assert_allclose(\n out_df[col].values[:5], answer, rtol=RTOL, atol=ATOL\n )","def _run_alg(data, agg_col, cat_cols, model, null_responses):\n agg_units = sorted(set(data[agg_col]), key=lambda x: (str(type(x)), x))\n outlier_scores = collections.defaultdict(dict)\n agg_to_data = {}\n agg_col_to_data = {}\n for agg_unit in agg_units:\n # TODO: could this be smarter and remove data each time? maybe no savings.\n # TODO: support numpy only again\n agg_to_data[agg_unit] = data[data[agg_col] == agg_unit]\n agg_col_to_data[agg_unit] = {}\n \n for col in cat_cols:\n col_vals = sorted(set(data[col]), key=lambda x: (str(type(x)), x))\n col_vals = [c for c in col_vals if c not in null_responses]\n frequencies = {}\n for agg_unit in agg_units:\n frequencies[agg_unit],grouped = _get_frequencies(data, col, col_vals, agg_col, agg_unit, agg_to_data)\n agg_col_to_data[agg_unit][col] = grouped\n outlier_scores_for_col, expected_frequencies_for_col, p_values_for_col = model.compute_outlier_scores(frequencies)\n for agg_unit in agg_units:\n outlier_scores[agg_unit][col] = {'score': outlier_scores_for_col[agg_unit],\n 'observed_freq': frequencies[agg_unit],\n 'expected_freq': expected_frequencies_for_col[agg_unit],\n 'p_value': p_values_for_col[agg_unit]}\n return outlier_scores, agg_col_to_data","def fillgaps(column1,column2,train,test):\n ddict={}\n d1=test[[column1,column2]].dropna().values\n d2=train[[column1,column2]].dropna().values\n c1=np.array(d1[:,0].tolist()+d2[:,0].tolist())\n c2=np.array(d1[:,1].tolist()+d2[:,1].tolist())\n for ic1 in np.unique(c1):\n ddict[ic1]=(c2[c1==ic1].mean(),c2[c1==ic1].std())\n full_data = [train, test]\n for dataset in full_data:\n for missing in np.where(np.isnan(dataset[column2]))[0]:\n m,s=ddict[dataset[column1][missing]]\n if s<=0:\n dataset[column2][missing]=m\n else:\n dataset[column2][missing]=np.random.normal(loc=m,scale=s,size=1)\n return (train,test)","def collate_molgraphs(data):\n assert len(data[0]) in [3, 4], \\\n 'Expect the tuple to be of length 3 or 4, got {:d}'.format(len(data[0]))\n if len(data[0]) == 3:\n smiles, graphs, labels = map(list, zip(*data))\n masks = None\n else:\n smiles, graphs, labels, masks = map(list, zip(*data))\n \n bg = dgl.batch(graphs)\n bg.set_n_initializer(dgl.init.zero_initializer)\n bg.set_e_initializer(dgl.init.zero_initializer)\n labels = torch.stack(labels, dim=0)\n \n if masks is None:\n masks = torch.ones(labels.shape)\n else:\n masks = torch.stack(masks, dim=0)\n return smiles, bg, labels, masks","def import_current_grades(filename):\n\t# import relevant csv file columns into pandas dataframe\n\tcg_df = pd.read_csv(filename, usecols=['Student ID', 'Grade Level', 'Student Name',\n\t\t'Period', 'Course Name', 'CAvg'])\n\t\n\t# create dictionary that maps Student ID to Grade Level\n\td = {}\n\tfor row in cg_df.iterrows():\n\t\t# get the student ID number\n\t\tid_num = row[1][0]\n\t\t# get the student's grade level\n\t\tgrade = row[1][2]\n\t\tif id_num not in d:\n\t\t\td[id_num] = grade\n\n\t# collapse dataframe based on student ID, identify cols by Period, \n\t# and fill in grade averages\n\tcg_df=cg_df.pivot(index='Student ID', columns='Period', values='CAvg').reset_index().set_index('Student ID')\n\t\n\t# rename the columns\n\tcg_df.rename(index=int, columns={'Student ID':'ID', '01 Per':'p1', \n\t\t'02 Per':'p2', '03 Per':'p3', '04 Per':'p4', '05 Per':'p5',\n\t\t'06 Per':'p6', '07 Per':'p7', '08 Per':'p8', 'Grade Level': 'grade'}, inplace=True)\n\n\t# add the grade level column back in\n\tcg_df['grade_level'] = cg_df.index.to_series().map(d)\n\n\t# obtain sub-dataframes of each grade\n\tnine = cg_df['grade_level'] == 9\n\tten = cg_df['grade_level'] == 10\n\televen = cg_df['grade_level'] == 11\n\ttwelve = cg_df['grade_level'] == 12\n\n\t# fill NAs with appropirate Lunch Periods\n\tcg_df[nine].p3.fillna(value='Lunch', inplace=True)\n\tcg_df[ten].p4.fillna(value='Lunch', inplace=True)\n\tcg_df[eleven].p5.fillna(value='Lunch', inplace=True)\n\n\t# concatenate back into a single DataFrame\n\t#cg_df = pd.concat([df9, df10, df11, df12])\n\n\t# fill in Periods 3, 4, 7, 8 based on 3/4 and 7/8 grades\n\tfor row in cg_df.iterrows():\n\t\tif row[1][3]:\n\t\t\tper_3_4_grade = row[1][3]\n\t\t\trow[1][2] = per_3_4_grade\n\t\t\trow[1][4] = per_3_4_grade\n\t\telif row[1][8]:\n\t\t\tper_7_8_grade = row[1][8]\n\t\t\trow[1][7] = per_7_8_grade\n\t\n\t# drop cols for periods 3/4 and 7/8\n\tcg_df.drop(['03/04 Per', '07/08 Per', '09 Per'], axis=1, inplace=True)\t\n\t\n\t# fill the NaNs with -1.0\n\tcg_df.fillna(-1.0, inplace=True)\n\t\n\t# # round the grades down to nearest ten\n\tcg_df['p1_r'] = cg_df.p1.apply(round_grade)\n\tcg_df['p2_r'] = cg_df.p2.apply(round_grade)\n\tcg_df['p3_r'] = cg_df.p3.apply(round_grade)\n\tcg_df['p4_r'] = cg_df.p4.apply(round_grade)\n\tcg_df['p5_r'] = cg_df.p5.apply(round_grade)\n\tcg_df['p6_r'] = cg_df.p6.apply(round_grade)\n\tcg_df['p7_r'] = cg_df.p7.apply(round_grade)\n\tcg_df['p8_r'] = cg_df.p8.apply(round_grade)\n\t\n\t# add the letter grade columns\n\tcg_df['p1_letter'] = cg_df.p1_r.apply(letter_grade)\n\tcg_df['p2_letter'] = cg_df.p2_r.apply(letter_grade)\n\tcg_df['p3_letter'] = cg_df.p3_r.apply(letter_grade)\n\tcg_df['p4_letter'] = cg_df.p4_r.apply(letter_grade)\n\tcg_df['p5_letter'] = cg_df.p5_r.apply(letter_grade)\n\tcg_df['p6_letter'] = cg_df.p6_r.apply(letter_grade)\n\tcg_df['p7_letter'] = cg_df.p7_r.apply(letter_grade)\n\tcg_df['p8_letter'] = cg_df.p8_r.apply(letter_grade)\n\t\n\t# add percentage signs\n\tcg_df['p1'] = cg_df.p1.apply(add_percentage)\n\tcg_df['p2'] = cg_df.p2.apply(add_percentage)\n\tcg_df['p3'] = cg_df.p3.apply(add_percentage)\n\tcg_df['p4'] = cg_df.p4.apply(add_percentage)\n\tcg_df['p5'] = cg_df.p5.apply(add_percentage)\n\tcg_df['p6'] = cg_df.p6.apply(add_percentage)\n\tcg_df['p7'] = cg_df.p7.apply(add_percentage)\n\tcg_df['p8'] = cg_df.p8.apply(add_percentage)\n\t\n\t# drop cols for periods 3/4 and 7/8\n\tcg_df.drop(['p1_r', 'p2_r', 'p3_r', 'p4_r', 'p5_r', 'p6_r', 'p7_r', 'p8_r'], axis=1, inplace=True)\t\n\t\n\t# replace values\n\tcg_df.replace('-1.0%', '-', inplace=True)\n\t\n\treturn cg_df","def _process_data(self):\r\n # Rename columns to match final feature class\r\n self._rename_columns()\r\n # Add point ID column\r\n self._add_pointid()\r\n # Sort rows by transect id and timestamp\r\n self._sort_rows()\r\n # Fill Null records with a value\r\n self._fill_nulls()\r\n # Set site_code to lower case\r\n self._lower_site_code()\r\n # Create survey_id\r\n self._calc_survey_id()\r\n # Calculate nativesg column if at least one of the veg columns is a Native seagrass type\r\n if set(self.veg_columns).intersection(set(NATIVESG_CODES)) > 0:\r\n self.nativesg_columns = list(set(self.veg_columns).intersection(set(NATIVESG_CODES)))\r\n self._calc_nativesg()\r\n #\r","def comparison_stats(df, els=None):\n if els is None:\n els = ['Li', 'Mg', 'Al', 'P', 'Ti', 'Y', 'La', 'Ce', 'Pr', 'Nd', 'Sm',\n 'Eu', 'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Hf', 'Pb', 'Th',\n 'U']\n \n yl_stats = []\n \n for i, e in enumerate(els):\n x = df.loc[:, e + '_rd'].values\n yl = df.loc[:, e + '_la'].values\n \n yl_stats.append(summary_stats(x, yl, e))\n \n yl_stats = pd.concat(yl_stats).T\n \n return yl_stats.T","def disagg(vec:gpd.GeoDataFrame):\n\t\t# Split GeometryCollections\n\t\tno_coll = []\n\t\tfor i, row in vec.iterrows():\n\t\t\tgeom = row.geometry\n\t\t\tif geom.type == 'GeometryCollection':\n\t\t\t\tfor part in geom:\n\t\t\t\t\trow2 = row.copy()\n\t\t\t\t\trow2.geometry = part\n\t\t\t\t\tno_coll.append(row2)\n\n\t\t\telse:\n\t\t\t\t\tno_coll.append(row) \n\n\t\t# Split Multi geomries\n\t\tres = []\n\t\tfor row in no_coll:\n\t\t\tgeom = row.geometry\n\t\t\tif geom.type.startswith('Multi'):\n\t\t\t\tfor part in geom:\n\t\t\t\t\trow2 = row.copy()\n\t\t\t\t\trow2.geometry = part\n\t\t\t\t\tres.append(row2)\n\t\t\telse:\n\t\t\t\t\tres.append(row)\n\n\t\treturn gpd.GeoDataFrame(res, crs=vec.crs).reset_index(drop=True)","def dataset_1():\n csv = pd.read_csv('resources/dataset_1.csv') # Pandas loads the CSV file as a DataFrame object\n csv.fillna('', inplace=True) # Pandas fills empty celles with NaN. We replace every Nan value with an emtpy string.\n csv.num_rue = csv.num_rue.apply(str) # Cast street numbers to strings\n # Create a new column named 'address' which concatenates the columns ['num_rue', 'cpltnum_ru', 'type_rue', 'article_ru', 'nom_rue']\n # csv[['num_rue', 'cpltnum_ru', 'type_rue', 'article_ru', 'nom_rue']] select a subset of the table 'csv'.\n # .agg(' '.join, axis=1) is equivalent to merge the selected cells of every lines as 'num_rue' + ' ' + 'cpltnum_ru' + ' ' + 'type_rue' + ' ' + 'article_ru' + ' ' + 'nom_rue'\n csv['address'] = csv[['num_rue', 'cpltnum_ru', 'type_rue', 'article_ru', 'nom_rue']].agg(' '.join, axis=1)\n return csv","def standardize_relations(dset_df, db='DTC'):\n relation_cols = dict(ChEMBL='Standard Relation', DTC='standard_relation')\n rel_col = relation_cols[db]\n\n dset_df[rel_col].fillna('=', inplace=True)\n ops = dset_df[rel_col].values\n if db == 'ChEMBL':\n # Remove annoying quotes around operators\n ops = [op.lstrip(\"'\").rstrip(\"'\") for op in ops]\n op_dict = {\n \">\": \">\",\n \">=\": \">\",\n \"<\": \"<\",\n \"<=\": \"<\",\n \"=\": \"=\"\n }\n ops = np.array([op_dict.get(op, \"@\") for op in ops])\n dset_df[rel_col] = ops\n dset_df = dset_df[dset_df[rel_col] != \"@\"]\n return dset_df","def preprocess_data(data):\n def deal_line(line):\n pos, rating = line.split(',')\n row, col = pos.split(\"_\")\n row = row.replace(\"r\", \"\")\n col = col.replace(\"c\", \"\")\n return int(row), int(col), float(rating)\n\n def statistics(data):\n row = set([line[0] for line in data])\n col = set([line[1] for line in data])\n return min(row), max(row), min(col), max(col)\n\n # parse each line\n data = [deal_line(line) for line in data]\n\n # do statistics on the dataset.\n min_row, max_row, min_col, max_col = statistics(data)\n print(\"number of items: {}, number of users: {}\".format(max_row, max_col))\n\n # build rating matrix.\n ratings = sp.lil_matrix((max_row, max_col))\n for row, col, rating in data:\n ratings[row - 1, col - 1] = rating\n return ratings","def _normalize(self, data):\n norm_data = []\n\n for row in data:\n norm_row = []\n\n for column in row:\n # custom format strings for specific objects\n if isinstance(column, float):\n format_str = '{{:.{}f}}'.format(2)\n item = format_str.format(column)\n\n elif isinstance(column, datetime):\n item = column.strftime('%Y-%m-%d %H:%M')\n\n else:\n item = str(column)\n\n norm_row.append(item)\n\n norm_data.append(norm_row)\n\n return norm_data","def RGBratios(dataset,fil):\n dom_fil = get_dominant_color(fil)\n dataset['R_B_ratio'] = dataset.R/dataset.B\n dataset['R_G_ratio'] = dataset.R/dataset.G\n dataset['G_B_ratio'] = dataset.G/dataset.B\n filter_RB = dom_fil[0]/dom_fil[2]\n filter_RG = dom_fil[0]/dom_fil[1]\n filter_GB = dom_fil[1]/dom_fil[2]\n norm = []\n for array in list(dataset['DominatColour']):\n norm.append( np.linalg.norm(array))\n dataset['Dominant_norm'] = norm\n\n norm = []\n for array in list(dataset['AverageColour']):\n norm.append( np.linalg.norm(array))\n\n\n dataset['DiffRatio_R_B'] = dataset.R_B_ratio-filter_RB\n dataset['DiffRatio_R_G'] = dataset.R_G_ratio-filter_RG\n dataset['DiffRatio_RG_B'] = dataset.G_B_ratio-filter_GB\n dataset['Average_norm'] = norm\n return dataset","def get_score_summary(fname):\n gradedata = {}\n fhandler = open(fname, 'r')\n rest_data = csv.reader(fhandler)\n for row in rest_data:\n if row[10] not in ['P', '', 'GRADE']:\n gradedata[row[0]] = [row[1], row[10]]\n gradedata.update(gradedata)\n fhandler.close()\n\n gradereview = {}\n for value in gradedata.itervalues():\n if value[0] not in gradereview.iterkeys():\n count1 = 1\n count2 = GRADESCL[value[1]]\n else:\n count1 = gradereview[value[0]][0] + 1\n count2 = gradereview[value[0]][1] + GRADESCL[value[1]]\n gradereview[value[0]] = (count1, count2)\n gradereview.update(gradereview)\n grade = {}\n for key in gradereview.iterkeys():\n count1 = gradereview[key][0]\n count2 = gradereview[key][1]/gradereview[key][0]\n grade[key] = (count1, count2)\n return grade","def updateTags(data):\n df = data\n df['Tag'] = np.where(df['godScore'] == 4, 'Keep', df['Tag'])\n df['Tag'] = np.where(df['godScore'] == 0, 'Junk', df['Tag'])\n\n return df","def transform_data(data, cols, standardize = True, log_trans = True):\r\n data = data[cols]\r\n if log_trans:\r\n y = np.log(data)\r\n y[y == -np.inf] = 0\r\n y[y == np.inf] = 0\r\n data = (y- data.mean(axis = 0)) / data.std(axis = 0)\r\n\r\n if standardize:\r\n data = (data- data.mean(axis = 0)) / data.std(axis = 0)\r\n\r\n return data","def merge_on_fip(df, directory):\n pol_fip_data = pd.read_csv(directory, index_col=0)\n df = df.merge(pol_fip_data, left_on='GESTFIPS', right_on='fips' ,how='left')\n df.drop(columns=['fips'], inplace=True)\n \n return df","def join_survey_data(survey, deezer):\n\n\n df = survey.rename(columns={'Age': 'user_age', 'Gender': 'user_gender',\n 'deezer_id': 'media_id'})\n\n for index, row in df.iterrows():\n if pd.isnull(row['time']):\n continue\n time = row['time'].split(',')\n if row['user_gender'] == 'Male':\n user_gender = 1\n else:\n user_gender = 0\n if time == None:\n if row['rating'] == 0:\n for i in [1480513129, 1479067262, 1478675619]:\n new = pd.DataFrame(np.array([[999999, i, row['media_id'],\n 999999, 0, 20001010, 1, 0,\n 999, 1, user_gender,\n row['user_id'], None,\n row['user_age'], 0]]),\n columns=['genre_id', 'ts_listen',\n 'media_id', 'album_id',\n 'context_type',\n 'release_date',\n 'platform_name',\n 'platform_family',\n 'media_duration',\n 'listen_type', 'user_gender',\n 'user_id', 'artist_id',\n 'user_age',\n 'is_listened'])\n deezer = deezer.append(new)\n elif 'Anytime' in time:\n for i in [1480513129, 1479067262, 1478675619]:\n new = pd.DataFrame(np.array([[999999, i, row['media_id'],\n 999999, 0, 20001010, 1, 0, 999,\n 1, user_gender,\n row['user_id'], None,\n row['user_age'], 0]]),\n columns=['genre_id', 'ts_listen',\n 'media_id', 'album_id',\n 'context_type',\n 'release_date', 'platform_name',\n 'platform_family',\n 'media_duration',\n 'listen_type', 'user_gender',\n 'user_id', 'artist_id', 'user_age',\n 'is_listened'])\n deezer = deezer.append(new)\n else:\n t_dict = {'Morning': 0, 'Afternoon': 0, 'Evening': 0}\n for t in time:\n t_dict[t] = 1\n for i in [('Morning', 1480513129), ('Afternoon', 1479067262),\n ('Evening', 1478675619)]:\n new = pd.DataFrame(np.array([[999999, i[1], row['media_id'],\n 999999, 0, 20001010, 1, 0, 999,\n 1, user_gender,\n row['user_id'], None,\n row['user_age'], t_dict[i[0]]]]),\n columns=['genre_id',\n 'ts_listen',\n 'media_id',\n 'album_id',\n 'context_type',\n 'release_date', 'platform_name',\n 'platform_family',\n 'media_duration',\n 'listen_type', 'user_gender',\n 'user_id', 'artist_id', 'user_age',\n 'is_listened'])\n deezer = deezer.append(new)\n\n return deezer","def fix_bad_column_blue():\r\n\r\n science = iraf.hselect('blue????.fits', '$I', 'TURRET == \"APERTURE\" & LAMPS == \"0000000\"', Stdout=1)\r\n if len(science) > 0:\r\n f = open('science_dump', 'w')\r\n for fn in science:\r\n f.write('%s\\n' % fn)\r\n f.close()\r\n science = iraf.hselect('@science_dump', '$I', 'TURRET == \"APERTURE\" & LAMPS == \"0000000\" & AIRMASS > 1.01', Stdout=1)\r\n if len(science)>0:\r\n os.unlink('science_dump')\r\n f = pyfits.open(science[0])\r\n bad_column = f[0].data[1608,:].argmin() + 1\r\n f.close()\r\n f = open('bluebpm', 'w')\r\n f.write('%d %d 1 2835\\n' % (bad_column, bad_column))\r\n f.close()\r\n iraf.fixpix('blue????.fits', \"bluebpm\")","def regroup(data):\n rating = df.groupby(\"Rating\").max()\n rating.dtypes\n rating.shape\n rating.columns\n\n return rating","def test_restaurant_grades(dataset,camis_id):\n #get grades and date for a specified camis_id as g\n g=dataset.GRADE.iloc[np.where(dataset.CAMIS==camis_id)] \n date=dataset['GRADE DATE'].iloc[np.where(dataset.CAMIS==camis_id)]\n date=pd.Series([datetime.datetime.strptime(d,\"%m/%d/%Y\") for d in date],index=g.index,name='GRADE DATE')\n g_date=pd.concat([g,date],axis=1)\n #sort grade according to date\n g_date=g_date.sort(columns='GRADE DATE',axis=0) \n \n return test_grade(list(g_date.GRADE))","def _collect_foll(all_detailed_cells, targeted_gids: pd.Series):\n all_foll_gids = {}\n\n for sim_gid, cells in pbar(all_detailed_cells.items(), total=len(all_detailed_cells), desc='sim'):\n targeted_gid = targeted_gids.loc[sim_gid]\n\n sb.CAT.add_cats_cells(cells)\n cells = cells.drop(targeted_gid)\n\n foll_ei_types = cells.loc[cells['frm_cat'] == 'foll', 'ei_type']\n\n all_foll_gids[sim_gid] = {\n f'{ei_type}_foll_gids': tuple(gids)\n for ei_type, gids in foll_ei_types.groupby(foll_ei_types).groups.items()}\n\n all_foll_gids = pd.DataFrame.from_dict(all_foll_gids, orient='index')\n\n # fillna doesn't like taking empty tuples\n for col, values in all_foll_gids.items():\n all_foll_gids.loc[all_foll_gids[col].isna(), col] = tuple()\n\n all_foll_gids = all_foll_gids.rename_axis(index='sim_gid')\n\n foll_counts = all_foll_gids.applymap(len)\n foll_counts.columns = [f'{col[0]}_foll_count'for col in foll_counts]\n\n all_foll_gids = pd.concat([all_foll_gids, foll_counts], axis=1)\n\n return all_foll_gids","def normalize_ratings_per_archetype(ratings):\n archetype_cols = [c for c in ratings.columns if c != \"name\"]\n n_cards = len(ratings[\"monored\"])\n for arch_col in archetype_cols:\n ratings[arch_col] = ratings[arch_col] / (ratings[arch_col].sum() / n_cards)\n return ratings","def g_date(data): \n #pull out data from the most original dataset and modified the date by year\n g=data.GRADE\n date=data['GRADE DATE'] \n date=[datetime.datetime.strptime(d, \"%m/%d/%Y\") for d in date]\n\n d_by_year=[] \n for i in range(len(date)): \n d_by_year.append(datetime.datetime(date[i].year,1,1))\n\n d_by_year=pd.Series(d_by_year,index=g.index,name=\"GRADE DATE\")\n return pd.concat([g,d_by_year],axis=1)","def grouped_colormaps():\n mlab = ['autumn','winter','spring','summer',\n 'gray','bone','copper','pink',\n 'cool','hot',\n 'hsv','jet','spectral',\n #'binary', # Seems to be a reverse of gray\n 'prism','flag']\n mlab_r = [m+'_r' for m in mlab]\n brewer = ['Accent','Dark2',\n 'Spectral',\n 'Paired',\n 'Blues','Greens','Greys','Oranges','Purples','Reds',\n 'Pastel1','Pastel2',\n 'Set1','Set2','Set3',\n 'BrBG','BuGn','BuPu','GnBu',\n 'OrRd',\n 'PiYG','PRGn','PuBu','PuBuGn',\n 'PuOr','PuRd',\n 'RdBu','RdGy','RdPu',\n 'RdYlBu','RdYlGn',\n 'YlGn','YlGnBu','YlOrBr','YlOrRd',\n ]\n brewer_r = [m+'_r' for m in brewer]\n gist = ['gist_ncar','gist_rainbow',\n 'gist_stern','gist_earth',\n 'gist_gray','gist_heat',\n #'gist_yarg', # Seems to be a reverse of gray\n ]\n gist_r = [m+'_r' for m in gist]\n\n return gist + [None] + mlab + [None] + brewer","def get_col2id( self, ratios_standardized, db ):\n\t\tcol_info_collection = db.col_info\n\t\tcol_name = []\n\t\tcol_id = []\n\t\tfor i in col_info_collection.find():\n\t\t\tcol_name.append(i[\"egrin2_col_name\"])\n\t\t\tcol_id.append(i[\"col_id\"])\n\t\tfor i in ratios_standardized.columns.values:\n\t \t\tif i not in col_name:\n\t \t\t\tcol_name.append( i )\n\t \t\t\tif len(col_id) > 0:\n\t\t\t\t\tcol_id.append(max(col_id)+1)\n\t\t\t\telse:\n\t\t\t\t\tcol_id.append(0)\n\t \tcol_info = pd.DataFrame( zip( col_id, col_name ), index = col_name, columns = [ \"col_id\", \"egrin2_col_name\"] )\n\t \treturn col_info","def merge_ranges():","def _convert_catstrength(self, df, col_name, med_val, low_val):\n\n val_cts = df[col_name].value_counts()\n val_dict = {}\n\n for name in val_cts[val_cts > med_val].index:\n val_dict[name] = 'High' + col_name\n\n for name in val_cts[(val_cts <= med_val) & (val_cts > low_val)].index:\n val_dict[name] = 'Med' + col_name\n\n for name in val_cts[val_cts <= low_val].index:\n val_dict[name] = 'Low' + col_name\n\n new_col_name = 'Mapped{}'.format(col_name)\n\n df[new_col_name] = df[col_name].map(val_dict)","def normalize(data, name):\n cols = list(data.columns)\n vals = data.values\n for i in range(len(vals)):\n v = vals[i]\n l = np.sum(v[0:len(v)-1])\n if l != 0:\n t = v[0:len(v)-1]/l\n v = np.append(t, v[-1])\n vals[i] = v\n write_data(vals, cols, name)","def _prepare_data_to_aug(\n self, col: pd.Series, freq=0.2\n ) -> Tuple[pd.Series, pd.Series]:","def fix_ranks(df, ranks='Current Ranks', programs='Programs'):\n\n # Create columns based on unique program values\n if programs in df:\n unique_programs = set(df[programs].unique())\n if np.nan in unique_programs:\n unique_programs.remove(np.nan)\n for value in unique_programs.copy():\n val_list = value.split(', ')\n if len(val_list) > 1:\n for comma_split_value in value.split(', '):\n if comma_split_value not in unique_programs:\n unique_programs.add(comma_split_value)\n unique_programs.remove(value)\n\n for x in unique_programs:\n if x not in df.columns.values:\n df[x] = \"\"\n\n\n # Assign Ranks to respective columns\n for index, x in df[ranks].iteritems():\n\n new_col = str(df.iloc[index][programs])\n x = str(x)\n\n # If there are commas\n\n if ',' in new_col or x:\n new_col = new_col.split(', ')\n x = x.split(', ')\n\n for rank, col in zip(x, new_col):\n df.at[index, col] = rank\n\n # No commas\n\n else:\n if df.at[index, col].notnull():\n print('value already exists: ' + df.loc[index, col])\n else:\n df.set_value(index, new_col, x)\n\n\n # Get rid of 'nan' in cells\n df[df == 'nan'] = np.nan\n return df","def aggregate_results(scores, stds):\n\n scores = pd.DataFrame(scores.values(),\n index=scores.keys(),\n columns=[\"SCORE MEAN\", ])\n stds = pd.DataFrame(stds.values(),\n index=stds.keys(),\n columns=[\"SCORE STD\", ])\n scores.index.name = param_names\n stds.index.name = param_names\n data = []\n for param_gropby_levels in ((0,), (1,), (2,), (3, 4), (5,)):\n aggregate_scores = scores.groupby(level=param_gropby_levels).mean()\n aggregate_stds = scores.groupby(level=param_gropby_levels).std()\n data.append((aggregate_scores, aggregate_stds))\n return data","def _process_aux_data(self, right_dataframe) -> List[Tuple[int, WeldType]]:\n import willump.evaluation.willump_executor as wexec\n\n join_col_field_types = []\n join_col_type_map = {}\n join_col_lookup_string = \"\"\n for column, col_type in zip(self.left_df_type.column_names, self.left_df_type.field_types):\n if column in self.join_col_names:\n assert(isinstance(col_type, WeldVec))\n col_type = col_type.elemType\n join_col_field_types.append(col_type)\n join_col_type_map[column] = col_type\n col_right_df_index = list(right_dataframe.columns).index(column)\n join_col_lookup_string += \"%s(lookup(_inp%d, i)),\" % (str(join_col_type_map[column]), col_right_df_index)\n if len(self.join_col_names) > 1:\n join_col_weld_type = WeldStruct(join_col_field_types)\n join_col_lookup_string = \"{\" + join_col_lookup_string + \"}\"\n else:\n join_col_weld_type = join_col_field_types[0]\n join_col_lookup_string = join_col_lookup_string[:-1]\n\n types_string = \"{\"\n row_types_list = []\n types_list = []\n right_df_column_names = []\n for i, column in enumerate(right_dataframe):\n col_weld_type: WeldType = numpy_type_to_weld_type(right_dataframe[column].values.dtype)\n if column not in self.join_col_names:\n types_string += str(col_weld_type) + \",\"\n row_types_list.append(col_weld_type)\n types_list.append(WeldVec(col_weld_type))\n right_df_column_names.append(column)\n\n types_string = types_string[:-1] + \"}\"\n\n values_string = \"{\"\n for i, column in enumerate(right_dataframe):\n if column not in self.join_col_names:\n values_string += \"lookup(_inp%d, i),\" % i\n values_string = values_string[:-1] + \"}\"\n\n weld_program = \\\n \"\"\"\n result(for(_inp%d,\n dictmerger[JOIN_COL_TYPE, %s, +],\n | bs: dictmerger[JOIN_COL_TYPE, %s, +], i: i64, x |\n merge(bs, {JOIN_COL_LOOKUP, %s})\n ))\n \"\"\" % (0, types_string, types_string, values_string)\n weld_program = weld_program.replace(\"JOIN_COL_TYPE\", str(join_col_weld_type))\n weld_program = weld_program.replace(\"JOIN_COL_LOOKUP\", join_col_lookup_string)\n\n input_arg_types = {}\n input_arg_list = []\n for i, column in enumerate(right_dataframe):\n input_name = \"input%d\" % i\n input_arg_types[input_name] = \\\n WeldVec(numpy_type_to_weld_type(right_dataframe[column].values.dtype))\n input_arg_list.append(input_name)\n\n module_name = wexec.compile_weld_program(weld_program,\n input_arg_types,\n input_names=input_arg_list,\n output_names=[],\n base_filename=\"hash_join_dataframe_indexer\")\n\n hash_join_dataframe_indexer = importlib.import_module(module_name)\n input_args = []\n for column in right_dataframe:\n input_args.append(right_dataframe[column].values)\n input_args = tuple(input_args)\n\n indexed_right_dataframe = hash_join_dataframe_indexer.caller_func(*input_args)\n self.right_df_row_type = WeldPandas(row_types_list, right_df_column_names)\n self.right_df_type = WeldPandas(types_list, right_df_column_names)\n return [(indexed_right_dataframe, WeldDict(join_col_weld_type, self.right_df_row_type))]","def enrich_dataframe(df, name):\n if(name == 'taux_incidence'):\n df['taux_incidence'] = df['P']*100000/df['pop']\n if(name == 'taux_positivite'):\n df['taux_positivite'] = df['P']/df['T'] * 100\n if(name == 'taux_occupation'):\n df['TO'] = df['TO']*100\n if(name == 'vaccins_vaccines_couv_majeurs'):\n df['couv_complet'] = 100 * df['n_cum_complet'] / df['pop']\n if(name == 'vaccins_vaccines_couv_ado_majeurs'):\n df['couv_complet'] = 100 * df['n_cum_complet'] / df['pop']\n if(name == 'taux_classes_fermees'):\n df['taux_classes'] = 100* df['nombre_classes_fermees'] / df['nombre_total_classes']\n if(name == 'taux_structures_fermees'):\n df['taux_structures'] = 100* df['nombre_structures_fermees'] / df['nombre_total_structures']\n\n \n \n return df","def clean_unit_errors(merged_df):\n merged_df[\"postprocess_height_cat\"] = merged_df[\"height_cat\"]\n merged_df[\"postprocess_height_cat\"] = merged_df[\n \"postprocess_height_cat\"\n ].cat.add_categories([\"Include-UH\", \"Include-UL\"])\n merged_df[\"postprocess_weight_cat\"] = merged_df[\"weight_cat\"]\n merged_df[\"postprocess_weight_cat\"] = merged_df[\n \"postprocess_weight_cat\"\n ].cat.add_categories([\"Include-UH\", \"Include-UL\"])\n merged_df.loc[merged_df[\"height_cat\"] == \"Unit-Error-Low\", \"height\"] = (\n merged_df.loc[merged_df[\"height_cat\"] == \"Unit-Error-Low\", \"height\"] * 2.54\n )\n merged_df.loc[merged_df[\"height_cat\"] == \"Unit-Error-High\", \"height\"] = (\n merged_df.loc[merged_df[\"height_cat\"] == \"Unit-Error-High\", \"height\"] / 2.54\n )\n merged_df.loc[merged_df[\"weight_cat\"] == \"Unit-Error-Low\", \"weight\"] = (\n merged_df.loc[merged_df[\"weight_cat\"] == \"Unit-Error-Low\", \"weight\"] * 2.2046\n )\n merged_df.loc[merged_df[\"weight_cat\"] == \"Unit-Error-High\", \"weight\"] = (\n merged_df.loc[merged_df[\"weight_cat\"] == \"Unit-Error-High\", \"weight\"] / 2.2046\n )\n merged_df.loc[\n merged_df[\"height_cat\"] == \"Unit-Error-Low\", \"postprocess_height_cat\"\n ] = \"Include-UL\"\n merged_df.loc[\n merged_df[\"height_cat\"] == \"Unit-Error-High\", \"postprocess_height_cat\"\n ] = \"Include-UH\"\n merged_df.loc[\n merged_df[\"weight_cat\"] == \"Unit-Error-Low\", \"postprocess_weight_cat\"\n ] = \"Include-UL\"\n merged_df.loc[\n merged_df[\"weight_cat\"] == \"Unit-Error-High\", \"postprocess_weight_cat\"\n ] = \"Include-UH\"\n merged_df[\"bmi\"] = merged_df[\"weight\"] / ((merged_df[\"height\"] / 100) ** 2)\n return merged_df","def merge_breached():\n # read breach and CMS data\n breachdf = pd.read_csv(updated_breach_file_name, encoding='latin1')\n breachdf.rename(columns=lambda x: x.strip(), inplace=True)\n print(breachdf.isnull().sum().sum(), \"null columns\")\n\n df = pd.read_csv(CMS_file_name, encoding='latin1')\n df.rename(columns=lambda x: x.strip(), inplace=True)\n\n print(\"dataframes read\")\n\n # merge data\n new_df = df.merge(breachdf, left_on='FAC_NAME', right_on='FAC_NAME', how='outer')\n print(\"merged\", new_df)\n\n new_df.to_csv(merged_file_name, index=False)\n print(\"Written to\", merged_file_name)","def combine_measurements(row, columns_to_combine):\n return sum(row[column_index] * percentage for column_index,\n percentage in columns_to_combine)","def aggregate_statistics(self, stat_col, stat_agg):\n precision_sums = stat_col[self.key_precision]\n recall_sums = stat_col[self.key_recall]\n f1score_sums = stat_col[self.key_f1score]\n supports = stat_col[self.key_f1score+'_support']\n\n # Special case - no samples!\n if sum(supports) == 0:\n stat_agg[self.key_precision] = 0\n stat_agg[self.key_precision+'_std'] = 0\n stat_agg[self.key_recall] = 0\n stat_agg[self.key_recall+'_std'] = 0\n stat_agg[self.key_f1score] = 0\n stat_agg[self.key_f1score+'_std'] = 0\n\n else: \n # Else: calculate weighted precision.\n precisions_avg = np.average(precision_sums, weights=supports)\n precisions_var = np.average((precision_sums-precisions_avg)**2, weights=supports)\n \n stat_agg[self.key_precision] = precisions_avg\n stat_agg[self.key_precision+'_std'] = math.sqrt(precisions_var)\n\n # Calculate weighted recall.\n recalls_avg = np.average(recall_sums, weights=supports)\n recalls_var = np.average((recall_sums-recalls_avg)**2, weights=supports)\n\n stat_agg[self.key_recall] = recalls_avg\n stat_agg[self.key_recall+'_std'] = math.sqrt(recalls_var)\n\n # Calculate weighted f1 score.\n f1scores_avg = np.average(f1score_sums, weights=supports)\n f1scores_var = np.average((f1score_sums-f1scores_avg)**2, weights=supports)\n\n stat_agg[self.key_f1score] = f1scores_avg\n stat_agg[self.key_f1score+'_std'] = math.sqrt(f1scores_var)","def merge_specimen_data(\n self, mouse_df: DataFrame, embryo_df: DataFrame\n ) -> DataFrame:\n specimen_cols = [\n \"_centreID\",\n \"_specimenID\",\n \"_colonyID\",\n \"_isBaseline\",\n \"_productionCentre\",\n \"_phenotypingCentre\",\n \"phenotyping_consortium\",\n ]\n\n # Select the relevant columns for specimen data\n mouse_specimen_df = mouse_df.select(*specimen_cols)\n if embryo_df is not None:\n embryo_specimen_df = embryo_df.select(*specimen_cols)\n\n # Merge both the mouse specimen data and the embryo specimen data in one DataFrame\n specimen_df = mouse_specimen_df.union(embryo_specimen_df)\n else:\n specimen_df = mouse_specimen_df\n return specimen_df","def FeatureValueFormat(singlegene):\n comp_exon = np.zeros((len(singlegene['exons']),), dtype=np.object)\n for i in range(len(singlegene['exons'])):\n comp_exon[i]= np.array(singlegene['exons'][i])\n singlegene['exons'] = comp_exon\n comp_cds = np.zeros((len(singlegene['cds_exons']),), dtype=np.object)\n for i in range(len(singlegene['cds_exons'])):\n comp_cds[i]= np.array(singlegene['cds_exons'][i])\n singlegene['cds_exons'] = comp_cds\n comp_utr3 = np.zeros((len(singlegene['utr3_exons']),), dtype=np.object)\n for i in range(len(singlegene['utr3_exons'])):\n comp_utr3[i]= np.array(singlegene['utr3_exons'][i])\n singlegene['utr3_exons'] = comp_utr3\n comp_utr5 = np.zeros((len(singlegene['utr5_exons']),), dtype=np.object)\n for i in range(len(singlegene['utr5_exons'])):\n comp_utr5[i]= np.array(singlegene['utr5_exons'][i])\n singlegene['utr5_exons'] = comp_utr5\n comp_transcripts = np.zeros((len(singlegene['transcripts']),), dtype=np.object)\n for i in range(len(singlegene['transcripts'])):\n comp_transcripts[i]= np.array(singlegene['transcripts'][i])\n singlegene['transcripts'] = comp_transcripts\n comp_tss = np.zeros((len(singlegene['tss']),), dtype=np.object)\n for i in range(len(singlegene['tss'])):\n comp_tss[i]= np.array(singlegene['tss'][i])\n singlegene['tss'] = comp_tss\n comp_tis = np.zeros((len(singlegene['tis']),), dtype=np.object)\n for i in range(len(singlegene['tis'])):\n comp_tis[i]= np.array(singlegene['tis'][i])\n singlegene['tis'] = comp_tis\n comp_cleave = np.zeros((len(singlegene['cleave']),), dtype=np.object)\n for i in range(len(singlegene['cleave'])):\n comp_cleave[i]= np.array(singlegene['cleave'][i])\n singlegene['cleave'] = comp_cleave\n comp_cdsStop = np.zeros((len(singlegene['cdsStop']),), dtype=np.object)\n for i in range(len(singlegene['cdsStop'])):\n comp_cdsStop[i]= np.array(singlegene['cdsStop'][i])\n singlegene['cdsStop'] = comp_cdsStop\n\n return singlegene","def mergeAggregatedCsvData(self, contexts, obj, aggData1, aggData2):\n return aggData1 + aggData2","def combine_data(self):\n\t\tself.Full_E = None\n\t\tself.Imaginary_Spectrum = None\n\t\tif self.raw_file is not None:\n\t\t\tlogger.info(\"Convert to scattering factors\")\n\t\t\tself.NearEdgeData = data.convert_data(self.raw_file,self.DataTypeCombo.GetValue(),'ASF')\n#\t\t\tif self.InvertDataCheckBox.GetValue():\n#\t\t\t\tself.NearEdgeData[:,1] = numpy.abs(self.NearEdgeData[:,1] - 2*numpy.mean(self.NearEdgeData[:,1]))\n\t\tlogger.info(\"Combine Data\")\n\t\t# Get splice points\n\t\tsplice_eV = numpy.array([10.0, 30000.0]) # Henke limits\n\t\tif self.SpliceText1.GetValue() == \"Start\":\n\t\t\tif self.raw_file is not None:\n\t\t\t\tsplice_eV[0] = self.NearEdgeData[0, 0]\n\t\telse:\n\t\t\tsplice_eV[0] = float(self.SpliceText1.GetValue())\n\t\tif self.SpliceText2.GetValue() == \"End\":\n\t\t\tif self.raw_file is not None:\n\t\t\t\tsplice_eV[1] = self.NearEdgeData[-1, 0]\n\t\telse:\n\t\t\tsplice_eV[1] = float(self.SpliceText2.GetValue())\n\t\tif self.raw_file is not None and self.ASF_Data is None:\n\t\t\tself.Full_E, self.Imaginary_Spectrum, self.NearEdgeData, self.splice_ind = data.merge_spectra(self.NearEdgeData, self.ASF_E, self.ASF_Data, merge_points=splice_eV, add_background=self.AddBackgroundCheckBox.GetValue(), plotting_extras=True)\n\n\t\telif self.raw_file is None and self.ASF_Data is not None:\n\t\t\tself.Full_E = self.ASF_E\n\t\t\tself.Imaginary_Spectrum = self.ASF_Data\n\n\t\telif self.raw_file is not None and self.ASF_Data is not None:\n\t\t\t\n\t\t\tself.Full_E, self.Imaginary_Spectrum, self.NearEdgeData, self.splice_ind = data.merge_spectra(self.NearEdgeData, self.ASF_E, self.ASF_Data, merge_points=splice_eV, add_background=self.AddBackgroundCheckBox.GetValue(), fix_distortions=self.FixDistortionsCheckBox.GetValue(), plotting_extras=True)\n\n\t\t\t### get start and end Y values from nexafs and asf data\n\t\t\t##splice_nexafs_Im = numpy.interp(splice_eV, raw_Im[:, 0], raw_Im[:, 1])\n\t\t\t###splice_asf_Im = numpy.interp(splice_eV, self.total_asf[:, 0], self.total_asf[:, 2])\n\t\t\t##splice_asf_Im = (data.coeffs_to_ASF(splice_eV[0],self.total_Im_coeffs[numpy.where(self.total_E<splice_eV[0])[0][-1]]),data.coeffs_to_ASF(splice_eV[1],self.total_Im_coeffs[numpy.where(self.total_E<splice_eV[1])[0][-1]]))\n\t\t\t##cut_boolean = (splice_eV[0]<raw_Im[:, 0]) == (raw_Im[:, 0]<splice_eV[1])\n\t\t\t### Merge Y values\n\t\t\t##if not self.AddBackgroundCheckBox.GetValue():\n\t\t\t\t##logger.info(\"Merge data sets\")\n\t\t\t\t##scale = (splice_asf_Im[1]-splice_asf_Im[0])/(splice_nexafs_Im[1]-splice_nexafs_Im[0])\n\t\t\t\t##scaled_nexafs_Im = ((raw_Im[:, 1]-splice_nexafs_Im[0])*scale)+splice_asf_Im[0]\n\t\t\t\t##self.asf_bg = None # We won't be using this variable this time\n\t\t\t##else:\n\t\t\t\t##logger.info(\"Add data sets (this will currently only work at energies below 30 keV)\")\n\t\t\t\t### Set up background function\n\t\t\t\t### We trust this point to be just before the absorption edge\n\t\t\t\t##trusted_ind = max(0, numpy.where(self.total_asf[:, 0]>splice_eV[0])[0][0]-1)\n\t\t\t\t##Log_total_asf = numpy.log(self.total_asf[:, 2])\n\t\t\t\t### Lets trust the 5 points before our trusted point and make an initial guess at the background function\n\t\t\t\t##p = numpy.polyfit(self.total_asf[(trusted_ind-5):trusted_ind, 0], Log_total_asf[(trusted_ind-5):trusted_ind], 1)\n\t\t\t\t### Now lets look for the points up util the absorption edge\n\t\t\t\t##p_vals = numpy.exp(numpy.polyval(p, self.total_asf[(trusted_ind-5):-1, 0]))\n\t\t\t\t##p_err = max(p_vals[0:5]-self.total_asf[(trusted_ind-5):trusted_ind, 2])\n\t\t\t\t##edge_ind = numpy.where(self.total_asf[trusted_ind:-1, 2]-p_vals[4:-1]>p_err*10)\n\t\t\t\t##if len(edge_ind[0])!=0:\n\t\t\t\t\t##edge_ind = edge_ind[0][0]\n\t\t\t\t##else:\n\t\t\t\t\t##edge_ind = trusted_ind\n\t\t\t\t### Redo background using the 5 points before the background point\n\t\t\t\t##p = numpy.polyfit(self.total_asf[(trusted_ind+edge_ind-5):trusted_ind+edge_ind, 0], Log_total_asf[(trusted_ind+edge_ind-5):trusted_ind+edge_ind], 1)\n\t\t\t\t##asf_bg = numpy.exp(numpy.polyval(p, raw_Im[:, 0]))\n\t\t\t\t##logger.info(\"Background defined as: y=exp(%(p1)ex %(p0)+e)\" % {\"p1\":p[1], \"p0\":p[0]})\n\t\t\t\t### Apply background function\n\t\t\t\t##scale = (splice_asf_Im[1]-numpy.exp(numpy.polyval(p, splice_eV[1])))/splice_nexafs_Im[1]\n\t\t\t\t##scaled_nexafs_Im = raw_Im[:, 1]*scale+asf_bg\n\t\t\t\t### store background data for plotting\n\t\t\t\t##cut_boolean_wide = numpy.roll(cut_boolean, -1) + numpy.roll(cut_boolean, 1)\n\t\t\t\t##self.asf_bg = [[trusted_ind+edge_ind-5, trusted_ind+edge_ind], numpy.vstack((raw_Im[cut_boolean_wide, 0], asf_bg[cut_boolean_wide])).T]\n\t\t\t\n\t\t\t##nexafs_cut = numpy.vstack((raw_Im[cut_boolean, 0], scaled_nexafs_Im[cut_boolean])).T\n\t\t\t####Merge point-wise data sets together\n\t\t\t##asf_cut_high = self.total_asf[self.total_asf[:, 0]>splice_eV[1], :]\n\t\t\t##asf_cut_low = self.total_asf[self.total_asf[:, 0]<splice_eV[0], :]\n\t\t\t##self.merged_Im = numpy.vstack((asf_cut_low[:, [0, 2]], (splice_eV[0], splice_asf_Im[0]), nexafs_cut, (splice_eV[1], splice_asf_Im[1]), asf_cut_high[:, [0, 2]]))\n\t\t\t\n\t\t\t####Merge coeff data together\n\t\t\t##coeffs_cut_high = self.total_Im_coeffs[self.total_E[:-1]>splice_eV[1],:]\n\t\t\t##coeffs_cut_low = self.total_Im_coeffs[self.total_E[:-1]<splice_eV[0],:]\n\t\t\t###convert points to coeffs\n\t\t\t##nexafs_coeffs_cut = numpy.zeros((len(nexafs_cut)+1,5))\n\t\t\t##Y = numpy.append(numpy.insert(nexafs_cut[:,1],0,splice_asf_Im[0]),splice_asf_Im[1])\n\t\t\t##nexafs_E = numpy.append(numpy.insert(nexafs_cut[:,0],0,splice_eV[0]),splice_eV[1])\n\t\t\t##M = (Y[1:]-Y[:-1])/(nexafs_E[1:]-nexafs_E[:-1])\n\t\t\t##nexafs_coeffs_cut[:,0] = M\n\t\t\t##nexafs_coeffs_cut[:,1] = Y[:-1]-M*nexafs_E[:-1]\n\t\t\t###assemble merged coeffs and energy values\n\t\t\t##self.merged_Im_coeffs = numpy.vstack((coeffs_cut_low, nexafs_coeffs_cut, self.total_Im_coeffs[-coeffs_cut_high.shape[0]-2,:], coeffs_cut_high))\n\t\t\t##self.merged_E = numpy.concatenate((self.total_E[self.total_E<splice_eV[0]], nexafs_E, self.total_E[self.total_E>splice_eV[1]]))\n\t\t\t### Extras for plotting\n\t\t\t##self.splice_ind = (len(asf_cut_low[:, 0]), -len(asf_cut_high[:, 0]))\n\t\t\t##cut_boolean = (splice_eV[0]<=raw_Im[:, 0]) != (raw_Im[:, 0]<=splice_eV[1])\n\t\t\t##self.nexafs_CutOut = numpy.vstack((raw_Im[cut_boolean, 0], scaled_nexafs_Im[cut_boolean])).T\n\t\t### Previous calculation of f_1 is no longer matching displayed f_2 data\n\t\t##self.KK_Real_Spectrum = None","def mergeLumis(inputdata, lumimask):\n mergedlumis = LumiList()\n doublelumis = LumiList()\n for report in inputdata:\n doublelumis = mergedlumis & LumiList(runsAndLumis=report)\n mergedlumis = mergedlumis | LumiList(runsAndLumis=report)\n return mergedlumis.getCompactList(), (LumiList(compactList=lumimask) - mergedlumis).getCompactList(), doublelumis.getCompactList()","def toSeasonAggFormat(self):\n df_a = self.toHomeAwayFormat().copy()\n df_b = df_a.copy()\n\n for col in list(df_a.columns).copy():\n if col[0] == \"H\":\n df_a = df_a.rename(columns={col:(col[1:])})\n\n if col[0] == \"A\":\n df_a = df_a.rename(columns={col:('Opp'+col[1:])})\n\n #df_a['Win'] = 1\n\n for col in list(df_b.columns).copy():\n if col[0] == \"H\":\n df_b = df_b.rename(columns={col:('Opp'+col[1:])})\n\n if col[0] == \"A\":\n df_b = df_b.rename(columns={col:(col[1:])})\n\n df_c = df_a.append(df_b, sort=True)\n\n return df_c","def transformation(self):\n for key in self.combination_dict.keys():\n if self.combination_dict[key]['column_count'] == 2:\n if self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'tem' or self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'tem':\n self.temporal_transformation(self.combination_dict[key])\n elif self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'cat' or self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'cat':\n self.categorical_transformation(self.combination_dict[key])\n elif self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'num' and self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'num':\n self.numerical_transformation(self.combination_dict[key])\n\n elif self.combination_dict[key]['column_count'] == 3:\n num_count = 0\n num_column = []\n if self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'num':\n num_count += 1\n num_column.append(0)\n elif self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'num':\n num_count += 1\n num_column.append(1)\n elif self.data_dict[self.combination_dict[key]['column3']]['data_type'] == 'num':\n num_count += 1\n num_column.append(2)\n\n if num_count == 1:\n self.three_column_groupby_logic(self.combination_dict[key], num_column)\n\n m_score_pie = []\n m_score_bar = []\n m_score_line = []\n m_score_scatter = []\n # for key in self.scenario_dict:\n # if self.scenario_dict\n for key in self.scenario_dict:\n if math.isnan(self.scenario_dict[key][\"Scatter_chart_score\"]):\n m_score_scatter.append(0)\n else:\n m_score_scatter.append(self.scenario_dict[key][\"Scatter_chart_score\"])\n m_score_pie.append(self.scenario_dict[key][\"Pie_chart_score\"])\n m_score_bar.append(self.scenario_dict[key][\"Bar_chart_score\"])\n m_score_line.append(self.scenario_dict[key][\"Line_chart_score\"])\n\n m_score_pie /= np.max(m_score_pie)\n m_score_bar /= np.max(m_score_bar)\n m_score_line /= np.max(m_score_line)\n m_score_scatter /= np.max(m_score_scatter)\n m_score = [m_score_pie, m_score_bar, m_score_line, m_score_scatter]\n match_index = np.argmax(m_score, axis = 0)\n i = 0\n for key in self.scenario_dict:\n if match_index[i] == 0:\n self.scenario_dict[key][\"Chart_Type\"] = \"pie\"\n if match_index[i] == 1:\n self.scenario_dict[key][\"Chart_Type\"] = \"bar\"\n if match_index[i] == 2:\n self.scenario_dict[key][\"Chart_Type\"] = \"line\"\n if match_index[i] == 3:\n self.scenario_dict[key][\"Chart_Type\"] = \"scatter\"\n self.scenario_dict[key][\"m_score\"] = m_score[match_index[i]][i]\n i += 1\n\n return self.scenario_dict","def transform(self, dataset: NumpyOrPandas) -> NumpyDataset:\n # checks here\n super().transform(dataset)\n # convert to accepted dtype and get attributes\n dataset = dataset.to_pandas()\n df = dataset.data\n\n # transform\n roles = NumericRole()\n outputs = []\n for n, i in enumerate(df.columns):\n new_arr = self.dicts[i][\"fg\"].transform(df[i].values)\n output = dataset.empty().to_numpy()\n output.set_data(new_arr, self.dicts[i][\"feats\"], roles)\n outputs.append(output)\n # create resulted\n return dataset.empty().to_numpy().concat(outputs)","def fix_fields(self):\n males = self.df[\"Sex\"] == \"M\"\n self.df[\"Sex\"] = np.array(males, dtype=int)\n\n logger.debug(\"Fixing bounded values...\")\n self.fix_bounded_values()\n logger.debug(\"Fixing range values...\")\n self.fix_range_fields()\n logger.debug(\"Fixing keyworded fields...\")\n self.fix_keyword_fields()\n logger.debug(\"Fixing temperature fields...\")\n self.fix_temperature_fields()\n logger.debug(\"Fixing nationality fields...\")\n self.fix_nationality_field()\n logger.debug(\"Fixing percentage fields...\")\n self.fix_percentage_fields()\n logger.debug(\"Combining fields...\")\n self.combine_fields()","def process_data(data):\n nghds = find_expensive_neighborhoods(data, n=3, summary_statistic=np.mean)\n data = ( data.pipe(add_total_bathrooms)\n .pipe(add_power,'Gr_Liv_Area', 2)\n .pipe(add_power,'Garage_Area', 2)\n .pipe(add_expensive_neighborhood, nghds)\n .pipe(select_columns, ['SalePrice',\n 'Gr_Liv_Area',\n 'Garage_Area',\n 'Gr_Liv_Area2',\n 'Garage_Area2',\n 'TotalBathrooms',\n 'in_expensive_neighborhood']) )\n\n data.dropna(inplace = True)\n X = data.drop(['SalePrice'], axis = 1)\n X = standardize(X)\n y = data.loc[:, 'SalePrice']\n y = standardize(y)\n return X, y","def MergeTR(data):\n for i in range(1, data.shape[1], 2):\n data.iloc[:, i] = data.iloc[:, i: i + 2].mean(axis=1)\n\n return data.drop(data.columns[[i + 1 for i in range(1, data.shape[1], 2)]], axis=\"columns\")","def normalize(data):\n row = np.size(data, 0) # number of data points\n col = np.size(data, 1) # dimensionality of data points\n for j in range(col):\n # find the average for each column\n col_sum = 0\n for i in range(row):\n col_sum = col_sum + data[i][j]\n col_sum = col_sum / row\n # subtract the average from each value in the column\n for i in range(row):\n data[i][j] = data[i][j] - col_sum\n return data","def normalize(data):\n row = np.size(data, 0) # number of data points\n col = np.size(data, 1) # dimensionality of data points\n for j in range(col):\n # find the average for each column\n col_sum = 0\n for i in range(row):\n col_sum = col_sum + data[i][j]\n col_sum = col_sum / row\n # subtract the average from each value in the column\n for i in range(row):\n data[i][j] = data[i][j] - col_sum\n return data","def umls_cui_annotator(self, data: pd.DataFrame, key: str, code_level: str) -> pd.DataFrame:\n\n # reduce data to only those columns needed for merging\n clinical_ids = data[[key, code_level]].drop_duplicates()\n\n # merge reduced clinical concepts with umls concepts\n if self.umls_double_merge is True:\n # merge 1 - align omop source codes to umls sabs\n umls_cui_1 = clinical_ids.merge(self.umls_cui_data, how='inner', left_on=code_level, right_on='CODE')\n # merge 2 - align umls cuis from merge 1 to cuis in full umls (this adds additional sabs not found in omop)\n umls_cui_2 = umls_cui_1[[key, code_level, 'CUI']].merge(self.umls_cui_data, how='left', on='CUI')\n umls_cui = pd.concat([umls_cui_1, umls_cui_2])\n else:\n umls_cui = clinical_ids.merge(self.umls_cui_data, how='inner', left_on=code_level, right_on='CODE')\n\n umls_cui_semtype = umls_cui.merge(self.umls_tui_data, how='left', on='CUI').drop_duplicates()\n\n # update column names\n umls_cui_semtype.columns = [key, code_level, 'UMLS_CUI', 'UMLS_SAB', 'UMLS_CODE', 'UMLS_SEM_TYPE']\n\n return umls_cui_semtype","def preprocess_data(df, min_vote_count=1000):\n # note that order matters!\n df = remove_rows_without_feature(df, 'budget')\n df = remove_rows_without_feature(df, 'runtime')\n df = remove_rows_with_non_english_movies(df)\n df = binarize_homepage(df)\n df = add_producers_feature(df)\n df = add_executive_producers_feature(df)\n df = get_movie_scores(df, min_vote_count)\n df = binarize_english(df)\n df = bin_ratings(df)\n df = binarize_genres(df)\n df = binarize_belongs_to_collection(df)\n df = binarize_production_countries(df)\n df = drop_unnecessary_columns(df)\n\n # Export to CSV\n y = df[['rating']]\n x = df.drop(['rating'], 1)\n\n y.to_csv(r'../dataset/Y.csv', index=False)\n x.to_csv(r'../dataset/X.csv', index=False)","def compute(self, df_1, df_2, col, catcol):\n\n assert col in df_1 and catcol in df_1\n assert col in df_2 and catcol in df_2\n\n np_df_col_1 = np.asarray(df_1[col])\n np_df_col_2 = np.asarray(df_2[col])\n\n mean = np.mean([np_df_col_1, np_df_col_2], axis=0)\n diff = np_df_col_1 - np_df_col_2\n md = np.mean(diff)\n sd = np.std(diff, axis=0)\n\n categories = np.concatenate(np.unique(df_1[catcol]), np.unique(df_2[catcol]))\n colors = cm.rainbow(np.linspace(0, 1, len(categories)))\n colordict = list(dict(zip(categories, colors)))\n\n return {\n \"axis\": catcol,\n \"mean\": mean,\n \"diff\": diff,\n \"color\": colordict,\n \"md\": md,\n \"sd\": sd,\n }","def preprocess_mul(bd, var1, var2):\n filepath_mv = f\"team67-ptp/data/{var1}_{var2}.csv\"\n filepath = bd\n data = feather.read_dataframe(filepath)\n df = data.copy()\n df2 = df[[var1, var2]]\n if df2[var1].dtype is \"category\" and df2[var2].dtype is \"category\":\n df2[var1] = df2[var1].astype(\"category\").cat.codes\n df2[var2] = df2[var2].astype(\"category\").cat.codes\n filename = filepath_mv\n df2.to_csv(filename, index=False, encoding=\"utf-16\")\n print(\"Succesfully exported to csv\")\n elif df2[var1].dtype is \"category\" and df2[var2].dtype is not \"category\":\n df2[var1] = df2[var1].astype(\"category\").cat.codes\n filename = filepath_mv\n df2.to_csv(filename, index=False, encoding=\"utf-16\")\n print(\"Succesfully exported to csv\")\n elif df2[var1].dtype is not \"category\" and df2[var2].dtype is \"category\":\n df2[var2] = df2[var2].astype(\"category\").cat.codes\n filename = filepath_mv\n df2.to_csv(filename, index=False, encoding=\"utf-16\")\n print(\"Succesfully exported to csv\")\n else:\n filename = filepath_mv\n df2.to_csv(filename, index=False, encoding=\"utf-16\")\n print(\"Succesfully exported to csv\")","def normalize_data(self):\r\n # quantify data for each column except classification column for noise reduction\r\n for column_header in self.classification_training_data.columns:\r\n if column_header == \"Class\":\r\n continue\r\n if column_header == \"Age\":\r\n bin_size = 2\r\n elif column_header == \"Ht\":\r\n bin_size = 5\r\n else:\r\n bin_size = 1\r\n for idx in self.classification_training_data.index:\r\n self.classification_training_data.at[idx, column_header] = math.floor(\r\n self.classification_training_data[column_header][idx] / bin_size) * bin_size","def data_column_conversion(data:pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:\n data = data.assign(W = (data.label == 'W') + 0,D = (data.label == 'D') + 0,L = (data.label == 'L') + 0)\n data = data.drop(\"label\",axis=1)\n return data","def my_featurize(apartment):\n col =np.array([1, 2, 0, 0, 0, 0, 0, 0 ])\n a= pd.DataFrame(apartment[col])\n if(apartment.get('condition')== 'good'):\n col[1] =1\n else:\n if(apartment.get('condition')== 'zero condition'):\n col[1] = 0\n col[2] =apartment.get('num_rooms')\n col[3] =apartment.get('area')\n col[4] =apartment.get('num_bathrooms')\n col[5] =apartment.get('floor')\n col[6] =apartment.get('ceiling_height')\n col[7] =apartment.get('max_floor')\n\n return col, apartment['price']","def test_combine(self):\n fc1 = self.read_feature()\n fc2 = self.read_feature('Aegean_Sea')\n fc1.merge(fc2)\n name = 'Weird Disjoint Regions'\n combined = fc1.combine(name)\n assert len(combined.features) == 1\n self.check_feature(combined.features[0], expected_name=name,\n expected_type='MultiPolygon')","def _convert_column_uncertainty(self, col):\n col_new = []\n for x, dx in zip(self[col], self[col + \" Unc.\"]):\n x2 = _parse_float_uncertainty(x, dx)\n col_new.append(x2)\n self.df[col] = col_new\n del self.df[col + \" Unc.\"]","def format_data(self, data):\n # data or full_score is a list of tuples, here return just NPClassScore\n formatted_data = None # default when there is no score (missing class)\n if data:\n # there is a score\n formatted_data = f'{data[0][0]:.3f}'\n return formatted_data","def _rewrite_concat(self, node: saldag.Concat):\n\n # Copy over columns from existing relation\n out_rel_cols = node.out_rel.columns\n\n # Combine per-column collusion sets\n for idx, col in enumerate(out_rel_cols):\n columns_at_idx = [in_rel.columns[idx] for in_rel in node.get_in_rels()]\n col.coll_sets = utils.coll_sets_from_columns(columns_at_idx)","def prep_tree_data(self, number: int):\n filename = \"data-before-normalization-{}-out-of-7.csv\".format(number)\n path = str(DATA_PATH.joinpath(\"data-splitted\", filename))\n df = pandas.read_csv(path)\n\n df.drop(df.columns[0], axis=1, inplace=True)\n assessments = [x for x in df.columns.values if x.split(\"_\")[0] == \"assessment\"]\n df['average_score'] = df[assessments].mean(skipna=True, axis=1)\n for assessment in assessments: # somehow he doesn't want to fillna in a batch?\n df[assessment].fillna(df['average_score'], inplace=True)\n clicks = [x for x in df.columns.values if x.split(\"_\")[0] == \"vle\"]\n df['vle_click_average'] = df[clicks].mean(skipna=True, axis=1)\n for click in clicks: # somehow he doesn't want to fillna in a batch?\n df[click].fillna(df['vle_click_average'], inplace=True)\n df.dropna()\n\n self.change_oh_cat(\"gender\", df)\n self.change_oh_cat(\"highest_education\", df)\n self.change_oh_cat(\"imd_band\", df)\n self.change_oh_cat(\"age_band\", df)\n self.change_oh_cat(\"disability\", df)\n result_order = {'final_result__Fail': 0, 'final_result__Withdrawn': 2,\n 'final_result__Pass': 1, 'final_result__Distinction': 3}\n self.change_oh_cat(\"final_result\", df, result_order)\n df[\"final_result\"].replace(2, 0, inplace=True)\n df[\"final_result\"].replace(3, 1, inplace=True)\n\n target = df[\"final_result\"]\n df.drop([\"final_result\"], axis=1, inplace=True)\n\n x_train, x_test, y_train, y_test = train_test_split(df, target, test_size=0.1,\n random_state=32, shuffle=True,\n stratify=target)\n\n return x_train, x_test, y_train, y_test","def merge_sort(self, data):\n print \"Splitting\", data\n if len(data) > 1:\n mid = len(data)// 2\n left_half = data[:mid]\n right_half = data[mid:]\n self.merge_sort(left_half)\n self.merge_sort(right_half)\n i,j,k = (0,0,0)\n while i< len(left_half) and j<len(right_half):\n if left_half[i] < right_half[j]:\n data[k] = left_half[i]\n i += 1\n else:\n data[k] = right_half[j]\n j += 1\n k += 1\n #take care of the unbalance\n while i < len(left_half):\n data[k] = left_half[i]\n i += 1\n k += 1\n while j < len(right_half):\n data[k] = right_half[j]\n j += 1\n k += 1\n print \"merging\", data","def print_rouge_scores(scores):\n rouge_1 = scores['rouge-1']\n rouge_2 = scores['rouge-2']\n rouge_l = scores['rouge-l']\n\n print(\"┌─────────┬────────┬────────┬────────┐\")\n print(\"│ Metric │ Pre │ Rec │ F │\")\n print(\"├─────────┼────────┼────────┼────────┤\")\n print(\"│ ROUGE-1 │ %.4f │ %.4f │ %.4f │\" % (rouge_1['p'], rouge_1['r'], rouge_1['f']))\n print(\"│ ROUGE-2 │ %.4f │ %.4f │ %.4f │\" % (rouge_2['p'], rouge_2['r'], rouge_2['f']))\n print(\"│ ROUGE-L │ %.4f │ %.4f │ %.4f │\" % (rouge_l['p'], rouge_l['r'], rouge_l['f']))\n print(\"└─────────┴────────┴────────┴────────┘\")","def grade_boro(boroughts,data):\n boro_df=data.iloc[np.where(data.BORO==boroughts)]\n return total_grade(boro_df)","def normalized_rankings(df, columns=['Lust', 'Envy', 'Greed', 'Sloth', 'Wrath',\n 'Pride', 'Gluttony']):\n df[columns] = (df[columns] - 4) / 2 # hard coding n=7 case for now","def test_degrade_map_recarray(self):\n random.seed(seed=12345)\n\n nside_coverage = 32\n nside_map = 1024\n nside_new = 256\n\n dtype = [('col1', 'f8'), ('col2', 'f8'), ('col3', 'i4')]\n sparse_map = healsparse.HealSparseMap.make_empty(nside_coverage, nside_map, dtype, primary='col1')\n pixel = np.arange(20000)\n values = np.zeros_like(pixel, dtype=dtype)\n values['col1'] = random.random(size=pixel.size)\n values['col2'] = random.random(size=pixel.size)\n values['col3'] = random.poisson(size=pixel.size, lam=2)\n sparse_map.update_values_pix(pixel, values)\n\n ra, dec = hpg.pixel_to_angle(nside_map, pixel)\n\n # Make the test values\n hpmap_col1 = np.zeros(hpg.nside_to_npixel(nside_map)) + hpg.UNSEEN\n hpmap_col2 = np.zeros(hpg.nside_to_npixel(nside_map)) + hpg.UNSEEN\n hpmap_col3 = np.zeros(hpg.nside_to_npixel(nside_map)) + hpg.UNSEEN\n hpmap_col1[pixel] = values['col1']\n hpmap_col2[pixel] = values['col2']\n hpmap_col3[pixel] = values['col3']\n\n # Degrade healpix maps\n hpmap_col1 = hp.ud_grade(hpmap_col1, nside_out=nside_new, order_in='NESTED', order_out='NESTED')\n hpmap_col2 = hp.ud_grade(hpmap_col2, nside_out=nside_new, order_in='NESTED', order_out='NESTED')\n hpmap_col3 = hp.ud_grade(hpmap_col3, nside_out=nside_new, order_in='NESTED', order_out='NESTED')\n ipnest_test = hpg.angle_to_pixel(nside_new, ra, dec)\n\n # Degrade the old map\n new_map = sparse_map.degrade(nside_out=nside_new)\n testing.assert_almost_equal(new_map.get_values_pos(ra, dec, lonlat=True)['col1'],\n hpmap_col1[ipnest_test])\n testing.assert_almost_equal(new_map.get_values_pos(ra, dec, lonlat=True)['col2'],\n hpmap_col2[ipnest_test])\n testing.assert_almost_equal(new_map.get_values_pos(ra, dec, lonlat=True)['col3'],\n hpmap_col3[ipnest_test])\n\n # Test degrade-on-read\n self.test_dir = tempfile.mkdtemp(dir='./', prefix='TestHealSparse-')\n\n fname = os.path.join(self.test_dir, 'test_recarray_degrade.hs')\n sparse_map.write(fname)\n\n new_map2 = healsparse.HealSparseMap.read(fname, degrade_nside=nside_new)\n\n testing.assert_almost_equal(new_map2.get_values_pos(ra, dec, lonlat=True)['col1'],\n hpmap_col1[ipnest_test])\n testing.assert_almost_equal(new_map2.get_values_pos(ra, dec, lonlat=True)['col2'],\n hpmap_col2[ipnest_test])\n testing.assert_almost_equal(new_map2.get_values_pos(ra, dec, lonlat=True)['col3'],\n hpmap_col3[ipnest_test])","def test_load_then_featurize_data_multiple_columns():\n feat = ImageFeaturizer(auto_sample=True)\n feat.load_data(**LOAD_DATA_ARGS_MULT)\n feat.featurize_preloaded_data(save_features=True)\n check_array = np.load(CHECK_ARRAY_MULT.format('squeezenet'))\n\n try:\n compare_featurizer_class(feat, (227, 227), check_array, featurized=True,\n check_csv=CHECK_CSV_MULT.format('squeezenet'), **COMPARE_ARGS_MULT)\n\n finally:\n # Remove path to the generated csv at the end of the test\n remove_generated_paths()\n del feat"],"string":"[\n \"def organize_data(scores, stds):\\n\\n scores = pd.Series(list(scores.values()),\\n index=map(str, scores.keys()),\\n name=\\\"SCORE MEAN\\\")\\n scores.index.name = \\\"AUXILIARY LOSS WEIGHTS\\\"\\n stds = pd.Series(list(stds.values()),\\n index=map(str, stds.keys()),\\n name=\\\"SCORE STD\\\")\\n stds.index.name = \\\"AUXILIARY LOSS WEIGHTS\\\"\\n return scores, stds\",\n \"def merge_data(agg_cases, lk_info, geolocation_data):\\n merged_df = pd.merge(agg_cases, lk_info, left_on='IdLandkreis', right_on = 'Key')\\n merged_df[\\\"RelativFall\\\"] = merged_df[\\\"AnzahlFall\\\"] / merged_df[\\\"Bev Insgesamt\\\"]\\n merged_df[\\\"RelativTodesfall\\\"] = merged_df[\\\"AnzahlTodesfall\\\"] / merged_df[\\\"Bev Insgesamt\\\"]\\n merged_df = pd.merge(merged_df, geolocation_data, left_on=\\\"Key\\\", right_on=\\\"cca_2\\\")\\n return merged_df\",\n \"def combine_arb_rows(df_gimmes):\\n\\n df_gimmes = df_gimmes.merge(df_gimmes, how='inner',\\n left_on='Underdog_A',\\n right_on='Favorite_B')\\n\\n df_gimmes.drop(['Favorite_B_x', 'Favorite_B_y', 'ML_B_x',\\n 'ML_B_y', 'Date_y', 'ML_A_x', 'ML_A_y'],\\n axis=1,\\n inplace=True)\\n\\n return df_gimmes.rename({'Source_x': 'Source_A',\\n 'Source_y': 'Source_B',\\n 'best_ML_A_x': 'best_ML_A',\\n 'best_ML_A_y': 'best_ML_B',\\n 'Underdog_A_x': 'Underdog_A',\\n 'Underdog_A_y': 'Underdog_B'},\\n axis=1)\",\n \"def convert_attribute(df):\\n\\n df['Popularity'] = ['Oversold' if x == '(1.0, 2.0]' else 'Unpopular' if x == '(-1.0, 0.0]' or x == '(0.0, 0.5]' else 'Popular' for x in df['percent_of_cap_binRange1']]\\n\\n return df\",\n \"def merge_rowdicts(\\n list_of_rowdicts, psm_colnames_to_merge_multiple_values, joinchar=\\\"<|>\\\"\\n):\\n merged_d = {}\\n fieldnames = []\\n for rowdict in list_of_rowdicts:\\n for k in rowdict.keys():\\n if k not in fieldnames:\\n fieldnames.append(k)\\n for fieldname in fieldnames:\\n values = []\\n for d in list_of_rowdicts:\\n if fieldname in d.keys():\\n values.append(d[fieldname])\\n if fieldname in psm_colnames_to_merge_multiple_values.keys():\\n no_empty_values = [v for v in values if v != \\\"\\\"]\\n values_as_floats = [float(value) for value in no_empty_values]\\n\\n if psm_colnames_to_merge_multiple_values[fieldname] == \\\"max_value\\\":\\n merged_d[fieldname] = max(values_as_floats)\\n\\n elif psm_colnames_to_merge_multiple_values[fieldname] == \\\"min_value\\\":\\n merged_d[fieldname] = min(values_as_floats)\\n\\n elif psm_colnames_to_merge_multiple_values[fieldname] == \\\"avg_value\\\":\\n merged_d[fieldname] = sum(values_as_floats) / len(values_as_floats)\\n\\n elif psm_colnames_to_merge_multiple_values[fieldname] == \\\"most_frequent\\\":\\n value_occurences = Counter(no_empty_values)\\n most_common_value, most_occurences = value_occurences.most_common(1)[0]\\n value_occurences_dict = dict(value_occurences)\\n final_values = []\\n for value in no_empty_values:\\n if value in final_values:\\n continue\\n if value_occurences_dict[value] == most_occurences:\\n final_values.append(value)\\n merged_d[fieldname] = joinchar.join(final_values)\\n\\n else:\\n if len(set(values)) == 1:\\n merged_d[fieldname] = values[0]\\n else:\\n no_empty_values = [v for v in values if v != \\\"\\\"]\\n if len(set(no_empty_values)) == 1:\\n merged_d[fieldname] = no_empty_values[0]\\n else:\\n merged_d[fieldname] = joinchar.join(values)\\n return merged_d\",\n \"def merge_race_loops(self,race_data,loop_data,race_merge_cols = ['Driver','race_name'],\\n loop_merge_cols = ['Driver','race_name'],loop_cols = ['Fastest Lap']):\\n return race_data.merge(loop_data[loop_merge_cols+loop_cols],left_on = race_merge_cols,\\n right_on = loop_merge_cols,suffixes = ('','_l'))\",\n \"def standardize(train_data_jets, test_data_jets):\\n \\n nbr_jets = train_data_jets.shape[0]\\n \\n for jet in range(nbr_jets):\\n # extract features for standardization\\n train_data_features = train_data_jets[jet][:,2:] \\n test_data_features = test_data_jets[jet][:,2:] \\n # store train mean and std without considering nan values\\n train_mean = np.nanmean(train_data_features, axis=0)\\n train_std = np.nanstd(train_data_features, axis=0)\\n # standardize train and test data with train mean and std\\n train_data_features = (train_data_features - train_mean) / train_std\\n test_data_features = (test_data_features - train_mean) / train_std\\n # insert standardized features into original dataset with predictions\\n train_data_jets[jet][:,2:] = train_data_features\\n test_data_jets[jet][:,2:] = test_data_features\\n \\n return train_data_jets, test_data_jets\",\n \"def rescaleData(data,column_names):\\n for column_name in column_names:\\n min_value = np.min(data[column_name])\\n max_value = np.max(data[column_name])\\n data[column_name] = (data[column_name] - min_value) / (max_value - min_value)\",\n \"def get_grades(self, force=False):\\n df = self._fetch_status_data(force)\\n df = df[['Name', 'Grade', 'Final grade']].copy()\\n\\n gradere = re.compile(r'Grade(\\\\d+(.\\\\d+)?).*')\\n\\n # raw grade column is \\\"GradeXX / YY\\\"\\n df['Grade'] = pandas.to_numeric(\\n df.Grade.str.replace(gradere, lambda m: m.group(1)),\\n errors='coerce',\\n )\\n\\n return df\",\n \"def normalizeData(meanAndStd, dataset):\\n\\n for i in range(len(dataset)):\\n for j in range(len(dataset[i])-1):\\n mean = meanAndStd[j][\\\"mean\\\"]\\n std = meanAndStd[j][\\\"std\\\"]\\n dataset[i][j] = (dataset[i][j] - mean)/std\",\n \"def _split_flattened(data, split_ratio, seed=default.DEFAULT_CV_RANDOM_SEED):\\n\\n check.argument_numeric(split_ratio, 0, 1)\\n\\n pc = np.sum(data.values != 0)\\n gs_count = int(split_ratio * pc)\\n idx = _make_shuffled_index(pc, seed=seed)\\n\\n pr_idx = data.values[data.values != 0].copy()\\n gs_idx = data.values[data.values != 0].copy()\\n\\n pr_idx[idx[0:gs_count]] = 0\\n gs_idx[idx[gs_count:]] = 0\\n\\n gs = data.values.copy()\\n pr = data.values.copy()\\n\\n gs[gs != 0] = gs_idx\\n pr[pr != 0] = pr_idx\\n\\n priors_data = pd.DataFrame(pr, index=data.index, columns=data.columns)\\n gold_standard = pd.DataFrame(gs, index=data.index, columns=data.columns)\\n\\n return priors_data, gold_standard\",\n \"def normalize_dataset(self):\",\n \"def processData(twcrDat, era20cDat, eraintDat, merraDat, erafiveDat):\\n #merge everything\\n twcr_era20c = pd.merge(twcrDat, era20cDat, on='tg', how='left')\\n twcr_era20c_eraint = pd.merge(twcr_era20c, eraintDat, on='tg', how='left')\\n twcr_era20c_eraint_merra = pd.merge(twcr_era20c_eraint, merraDat, on='tg', how='left')\\n twcr_era20c_eraint_merra_erafive = pd.merge(twcr_era20c_eraint_merra, erafiveDat, on='tg', how='left')\\n\\n \\n\\n allCorr = twcr_era20c_eraint_merra_erafive[['tg', 'lon', 'lat', 'corrTwcr', 'corrEra20c', \\\\\\n 'corrEraint', 'corrMerra', 'corrErafive']]\\n allCorr.columns = ['tg', 'lon', 'lat', '20CR', 'ERA-20C', 'ERA-Interim', 'MERRA', 'ERA-FIVE']\\n allRMSE = twcr_era20c_eraint_merra_erafive[['tg', 'lon', 'lat', 'rmseTwcr', 'rmseEra20c',\\\\\\n 'rmseEraint', 'rmseMerra', 'rmseErafive']]\\n allRMSE.columns = ['tg', 'lon', 'lat', '20CR', 'ERA-20C', 'ERA-Interim', 'MERRA', 'ERA-FIVE']\\n allNSE = twcr_era20c_eraint_merra_erafive[['tg', 'lon', 'lat', 'nseTwcr', 'nseEra20c',\\\\\\n 'nseEraint', 'nseMerra', 'nseErafive']]\\n allNSE.columns = ['tg', 'lon', 'lat', '20CR', 'ERA-20C', 'ERA-Interim', 'MERRA', 'ERA-FIVE']\\n \\n \\n #get max corr values \\n allCorr['Correlation'] = allCorr.iloc[:,3:8].max(axis = 1)\\n allCorr['Reanalysis'] = allCorr.iloc[:, 3:8].idxmax(axis = 1)\\n\\n #get min rmse values - change to cms for visibility\\n allRMSE['RMSE(cm)'] = allRMSE.iloc[:,3:8].min(axis = 1)*100\\n allRMSE['Reanalysis'] = allRMSE.iloc[:, 3:8].idxmin(axis = 1)\\n\\n #get max nse values \\n# <<<<<<< HEAD\\n# allNSE['NSE(%)'] = allNSE.iloc[:,3:8].max(axis = 1)*100\\n# =======\\n ##normalize NSE values\\n allNSE['NNSE'] = 1/(2 - allNSE.iloc[:,3:8].max(axis = 1))\\n# >>>>>>> kiko-stuff\\n allNSE['Reanalysis'] = allNSE.iloc[:, 3:8].idxmax(axis = 1)\\n \\n # #save metrics results \\n allCorr.to_csv(\\\"allCorr.csv\\\")\\n allRMSE.to_csv(\\\"allRMSE.csv\\\")\\n allNSE.to_csv(\\\"allNSE.csv\\\")\\n \\n return allCorr, allRMSE, allNSE\",\n \"def normalize (a_data,a_column,b_method='MinMax') :\\n if b_method == 'MinMax' :\\n loc_scaler = __minmax()\\n a_data[a_column] = loc_scaler.fit_transform(a_data[a_column])\\n elif b_method == 'Standard' :\\n loc_scaler = __standard()\\n a_data[a_column] = loc_scaler.fit_transform(a_data[a_column])\",\n \"def bilateralize(ds):\\n ds_ROIs = ds.copy('deep')\\n ds_ROIs.sa['bilat_ROIs'] = [label.split(' ')[-1] for label in ds_ROIs.sa.all_ROIs]\\n mv.h5save(results_dir + 'ds_ROIs.hdf5', ds_ROIs)\\n print('Combined lateralized ROIs for the provided dataset and saved the dataset.')\\n return ds_ROIs\",\n \"def process_data_p2(data, documentation, geodata):\\r\\n documentation_state = documentation.get_column(\\\"ST_FIPS\\\")\\r\\n mean_per_state = data.groupby(\\\"ST_FIPS\\\", as_index=False).mean()\\r\\n mean_per_state[\\\"ST_FIPS\\\"] = mean_per_state[\\\"ST_FIPS\\\"] \\\\\\r\\n .map(documentation_state.values_mapping)\\r\\n return geodata.merge(mean_per_state, how=\\\"left\\\",\\r\\n left_on=\\\"name\\\", right_on=\\\"ST_FIPS\\\")\",\n \"def icdconvert(df,col_icd='icd',icd_version=9):\\n if icd_version==9:\\n source='icd9'\\n target='icd10'\\n df_gem=load_icd9to10()\\n elif icd_version==10:\\n source='icd10'\\n target='icd9'\\n df_gem=load_icd10to9()\\n else:\\n print('invalid icd version {}, please set curicd to 9 or 10'.format(icd_version))\\n return None\\n \\n print('Comment: because of the discrepancy between icd9 and icd10, diagnosis codes may be mapped to many codes or no codes')\\n \\n output=df.merge(df_gem,how='left',left_on=col_icd,right_on=source)\\n print('{:.2f}% mapped'.format(output[target].notnull().mean()*100))\\n if 'flag' in output.columns:\\n output.drop('flag',axis=1,inplace=True)\\n if col_icd!=source in output.columns:\\n output.drop(source,axis=1,inplace=True)\\n \\n return output\",\n \"def divide_dataset_by_jet(data):\\n \\n # Mask for 23rd column PRI_jet_num (categorical feature) \\n cols = list(range(0,24)) + list(range(25,32))\\n \\n data_0jet = np.copy(data[data[:,24] == '0'])\\n data_0jet = np.asarray(data_0jet[:, cols], dtype=float)\\n \\n data_1jet = np.copy(data[data[:,24] == '1'])\\n data_1jet = np.asarray(data_1jet[:, cols], dtype=float)\\n \\n data_2jet = np.copy(data[data[:,24] == '2'])\\n data_2jet = np.asarray(data_2jet[:, cols], dtype=float)\\n \\n data_3jet = np.copy(data[data[:,24] == '3'])\\n data_3jet = np.asarray(data_3jet[:, cols], dtype=float)\\n \\n return np.array([data_0jet, data_1jet, data_2jet, data_3jet], dtype=object)\",\n \"def merge_dataframe(df, column_name='population'):\\n df['snp'] = df.chrom.astype(str) + '.' + df.position.astype(str)\\n print('Getting open and closed combined data, this might take a while.')\\n # Takes about 20 minutes\\n open_closed = get_open_closed_counts(df)\\n keep_cols = [\\n 'snp', 'chrom', 'position', 'rsid', 'pre_freq', 'post_freq',\\n 'pre_variance', 'post_variance', 'beta', 'p_value', 'z_value', 'ref',\\n 'alt', 'depth', 'ref_depth', 'alt_depth', 'snp_postfreq', 'snp_prefreq',\\n 'population'\\n ]\\n comb_functions = {\\n 'chrom': first, 'position': first, 'rsid': first,\\n 'ref': first, 'alt': first,\\n 'pre_freq': np.median, 'post_freq': np.median,\\n 'pre_variance': np.median, 'post_variance': np.median,\\n 'beta': np.median, 'z_value': np.median, 'depth': np.median,\\n 'ref_depth': np.median, 'alt_depth': np.median,\\n 'snp_postfreq': np.median, 'snp_prefreq': np.median,\\n column_name: ','.join, 'p_value': fishers\\n }\\n print('Collapsing dataframe, this also takes a while.')\\n # Takes about 10 minutes\\n df = df[keep_cols].groupby('snp').agg(comb_functions)\\n # Split the fishers statistics\\n df['fishers_value'], df['fishers_p'] = df.p_value.str\\n print('Merging in open/closed data')\\n df = pd.merge(df, open_closed, left_index=True, right_index=True)\\n print('Organizing')\\n final_cols = [\\n 'chrom', 'position', 'rsid', 'fishers_p', 'ref', 'alt', 'open_best',\\n 'closed_best', 'pop_count', column_name, 'pops_agree',\\n 'most_pops_agree', 'all_open_closed', 'open_closed_freqs',\\n 'fishers_value', 'pre_freq', 'post_freq', 'pre_variance',\\n 'post_variance', 'beta', 'z_value', 'depth', 'ref_depth', 'alt_depth',\\n 'snp_postfreq', 'snp_prefreq'\\n ]\\n final_names = [\\n 'chrom', 'position', 'rsid', 'fishers_p', 'ref', 'alt', 'open_best',\\n 'closed_best', column_name + '_count', column_name + 's', column_name +\\n 's_agree', 'most_{}s_agree'.format(column_name), 'all_open_closed',\\n 'open_closed_freqs', 'fishers_value', 'pre_freq_median',\\n 'post_freq_median', 'pre_variance_median', 'post_variance_median',\\n 'beta_median', 'z_value_median', 'depth_median', 'ref_depth_median',\\n 'alt_depth_median', 'snp_postfreq_median', 'snp_prefreq_median'\\n ]\\n df = df[final_cols]\\n df.columns = final_names\\n print('Done')\\n return df\",\n \"def calculate_questions_grade():\\n import numpy as np\\n with open('qqq.csv') as fo:\\n headers_list = fo.readline().strip().split(',')\\n headers_list.append('questions_grade')\\n data_lines = []\\n scores = []\\n \\n for line in fo:\\n splits = [f.strip() for f in line.split(',')]\\n # splits = [int(s) if get_type(s) is int else s for s in splits]\\n scores.append(int(splits[-1]))\\n data_lines.append(splits)\\n\\n std_dev = np.std(scores)\\n grades = [score*1.0/std_dev for score in scores]\\n max_grade = max(grades)\\n grades = [g*10/max_grade for g in grades]\\n\\n graded_lines = [headers_list]\\n for key, split_line in enumerate(data_lines):\\n split_line.append(round(grades[key], 2))\\n graded_lines.append(split_line)\\n\\n with open('qqq2.csv', 'w') as fp:\\n a = csv.writer(fp, delimiter=',')\\n a.writerows(graded_lines)\",\n \"def _map_raw_data_to_standard(self):\\n data_df = self._raw_data.copy()\\n\\n # if only 1 RT col, split into 2\\n if self._map_cols['goRT'] == self._map_cols['stopRT']:\\n data_df[self._standards['columns']['goRT']] = np.where(\\n data_df[self._map_cols['condition']] == self._map_codes['go'],\\n data_df[self._map_cols['goRT']],\\n None)\\n data_df[self._standards['columns']['stopRT']] = np.where(\\n data_df[self._map_cols['condition']] ==\\n self._map_codes['stop'],\\n data_df[self._map_cols['stopRT']],\\n None)\\n del data_df[self._map_cols['goRT']]\\n else:\\n data_df.loc[\\n data_df[self._map_cols['condition']] !=\\n self._map_codes['go'],\\n self._map_cols['goRT']] = None\\n data_df.loc[\\n data_df[self._map_cols['condition']] !=\\n self._map_codes['stop'],\\n self._map_cols['stopRT']] = None\\n\\n # drop SSDs of non-stop Trials\\n data_df.loc[\\n data_df[self._map_cols['condition']] != self._map_codes['stop'],\\n self._map_cols['SSD']] = None\\n\\n # add block column if not present\\n if self._map_cols['block'] not in data_df.columns:\\n data_df[self._map_cols['block']] = 1\\n\\n # recompute choice accuracy if missing / flagged\\n if (self._map_cols['choice_accuracy'] not in self._raw_data.columns) |\\\\\\n self._compute_acc_col:\\n corr_code = self._map_codes['correct']\\n incorr_code = self._map_codes['incorrect']\\n data_df[self._map_cols['choice_accuracy']] = np.where(\\n data_df[self._map_cols['response']] == data_df[\\n self._map_cols['correct_response']],\\n corr_code,\\n incorr_code)\\n\\n # map columns, key codes to standard\\n rename_column_dict = {self._map_cols[col]: self._standards['columns']\\n [col] for col in self._map_cols.keys()}\\n data_df = data_df.rename(columns=rename_column_dict)\\n\\n # map key codes to various columns\\n condition_map = {\\n self._map_codes['go']: self._standards['key_codes']['go'],\\n self._map_codes['stop']: self._standards['key_codes']['stop'],\\n }\\n acc_map = {\\n self._map_codes['correct']: self._standards['key_codes']['correct'],\\n self._map_codes['incorrect']: self._standards['key_codes']['incorrect'],\\n }\\n no_response_map = {\\n self._map_codes['noResponse']: self._standards['key_codes']['noResponse']\\n }\\n cols_n_maps = [(self._standards['columns']['condition'], condition_map),\\n (self._standards['columns']['choice_accuracy'], acc_map),\\n (self._standards['columns']['goRT'], no_response_map),\\n (self._standards['columns']['stopRT'], no_response_map)]\\n for col, map_dict in cols_n_maps:\\n data_df[col] = data_df[col].map(lambda x: map_dict.get(x,x))\\n\\n assert self._is_preprocessed(data_df)\\n self._transformed_data = data_df\",\n \"def merge(self):\\n for i in range(len(self.main_grid_values)):\\n for j in range(len(self.main_grid_values) - 1):\\n if self.main_grid_values[i][j] == self.main_grid_values[i][j+1]:\\n self.score_value.set(str(int(self.score_value.get()) + self.main_grid_values[i][j]*2))\\n self.main_grid_values[i][j] *= 2\\n self.main_grid_values[i][j+1] = 0\",\n \"def preprocess(table):\\n # drop the column Zeitraum\\n table = table.drop('Zeitraum', axis=1)\\n # drop the rows containing the true results of the elections\\n Idx = np.where(table.Befragte=='Bundestagswahl')[0]\\n Idx = np.append(Idx, np.where(table['CDU/CSU'].str.contains('Umfrage'))[0])\\n table = table.drop(Idx)\\n table.index = np.arange(table.shape[0])\\n # replace the strings %,-\\n table = table.replace('%', '', regex=True)\\n table = table.replace(',', '.', regex=True)\\n table = table.replace('[–?]', '', regex=True)\\n # fix the column Befragte !!!!!!!!!!!!!!\\n table.Befragte = table.Befragte.replace('[T • ?≈O • .]', '', regex=True)\\n # replace all empty entries with NaN\\n table = table.replace('', 'NaN', regex=True)\\n\\n # if the colomn Sonstige contains entries with more than one number\\n try: \\n table.Sonstige = table.Sonstige.astype(float)\\n except ValueError:\\n for i, n in enumerate(table.Sonstige):\\n if len(n) > 2:\\n digits = np.array([digit for digit in np.arange(10).astype(str) if digit in n])\\n table.Sonstige[i] = digits.astype(int).sum()\\n table.Sonstige = table.Sonstige.astype(float)\\n\\n # convert all numbers to float\\n table[table.keys()[1:]] = table[table.keys()[1:]].astype(float)\\n # convert the date to type date\\n table.Datum = pd.to_datetime(table.Datum, format='%d.%m.%Y').dt.date\\n return table\",\n \"def get_student_grade_summary_data(request, course, get_grades=True, get_raw_scores=False, use_offline=False):\\r\\n course_key = course.id\\r\\n enrolled_students = User.objects.filter(\\r\\n courseenrollment__course_id=course_key,\\r\\n courseenrollment__is_active=1,\\r\\n ).prefetch_related(\\\"groups\\\").order_by('username')\\r\\n\\r\\n header = [_('ID'), _('Username'), _('Full Name'), _('edX email'), _('External email')]\\r\\n\\r\\n datatable = {'header': header, 'students': enrolled_students}\\r\\n data = []\\r\\n\\r\\n gtab = GradeTable()\\r\\n\\r\\n for student in enrolled_students:\\r\\n datarow = [student.id, student.username, student.profile.name, student.email]\\r\\n try:\\r\\n datarow.append(student.externalauthmap.external_email)\\r\\n except: # ExternalAuthMap.DoesNotExist\\r\\n datarow.append('')\\r\\n\\r\\n if get_grades:\\r\\n gradeset = student_grades(student, request, course, keep_raw_scores=get_raw_scores, use_offline=use_offline)\\r\\n log.debug('student={0}, gradeset={1}'.format(student, gradeset))\\r\\n with gtab.add_row(student.id) as add_grade:\\r\\n if get_raw_scores:\\r\\n # TODO (ichuang) encode Score as dict instead of as list, so score[0] -> score['earned']\\r\\n for score in gradeset['raw_scores']:\\r\\n add_grade(score.section, getattr(score, 'earned', score[0]))\\r\\n else:\\r\\n for grade_item in gradeset['section_breakdown']:\\r\\n add_grade(grade_item['label'], grade_item['percent'])\\r\\n student.grades = gtab.get_grade(student.id)\\r\\n\\r\\n data.append(datarow)\\r\\n\\r\\n # if getting grades, need to do a second pass, and add grades to each datarow;\\r\\n # on the first pass we don't know all the graded components\\r\\n if get_grades:\\r\\n for datarow in data:\\r\\n # get grades for student\\r\\n sgrades = gtab.get_grade(datarow[0])\\r\\n datarow += sgrades\\r\\n\\r\\n # get graded components and add to table header\\r\\n assignments = gtab.get_graded_components()\\r\\n header += assignments\\r\\n datatable['assignments'] = assignments\\r\\n\\r\\n datatable['data'] = data\\r\\n return datatable\",\n \"def normalize_columns_together(headers, data):\\n\\tcolumn_matrix=data.get_data(headers)\\n\\tmax=column_matrix.max()\\n\\tprint \\\"The maximum:\\t \\\", max\\n\\tmin=column_matrix.min()\\n\\tprint \\\"The minimum:\\t \\\", min\\n\\trange=max-min\\n\\tprint \\\"range: \\\", range\\n\\tcolumn_matrix=column_matrix-min\\n\\tnormalized=column_matrix/range\\n\\treturn normalized\",\n \"def preprocess_lab_data(self):\\n print(\\\"Processing dataset from the labs ...\\\")\\n self.full_dataset[\\\"score\\\"] = self.full_dataset[\\\"score\\\"].apply(lambda x: -1 if x==0 else 1)\",\n \"def right_to_left(data, key, col_tuple, col_tuples, re_tuples,\\n re_triple, cnt):\\n first_col = col_tuples[0]\\n second_col = col_tuples[1]\\n if col_tuple == first_col or col_tuple == second_col:\\n if col_tuple == first_col:\\n data[\\\"tmp_0\\\"] = data[col_tuple[1]].astype(str).str.extract(re_triple[cnt][0].format(key),\\n expand=True)\\n else:\\n data[\\\"tmp_0\\\"] = data[col_tuple[1]].astype(str).str.extract(re_triple[cnt][0],\\n expand=True)\\n data[\\\"tmp_1\\\"] = data[\\\"tmp_0\\\"].str.extract(re_triple[cnt][1],\\n expand=True)\\n if col_tuple == first_col:\\n regex = re_triple[cnt][2].format(key)\\n data[\\\"tmp_0\\\"] = data[\\\"tmp_0\\\"].str.extract(regex, expand=True)\\n else:\\n regex = re_triple[cnt][2]\\n data[\\\"tmp_0\\\"] = data[\\\"tmp_0\\\"].str.extract(regex, expand=True)\\n tmp_0_clean = data[\\\"tmp_0\\\"].str.strip()\\n data[col_tuple[0]] = data[col_tuple[0]].fillna(tmp_0_clean)\\n if col_tuple == first_col:\\n data[col_tuple[1]] = data[col_tuple[1]].replace(re_triple[cnt][0].format(key),\\n np.NaN, regex=True)\\n else:\\n data[col_tuple[1]] = data[col_tuple[1]].replace(re_triple[cnt][0][:-15]+\\\")\\\",\\n np.NaN, regex=True)\\n data[col_tuple[1]] = data[col_tuple[1]].replace(re_triple[cnt][0],\\n np.NaN, regex=True)\\n tmp_1_clean = data[\\\"tmp_1\\\"].str.strip()\\n data[col_tuple[1]] = data[col_tuple[1]].fillna(tmp_1_clean)\\n data = data.drop(columns=\\\"tmp_0\\\")\\n data = data.drop(columns=\\\"tmp_1\\\")\\n return data\",\n \"def groupby_grade(master_dataframe):\\n\\tno_name_df = master_dataframe.drop(['last_name', 'first_name', 'email'], axis=1)\\n\\tgrouped = list(no_name_df.groupby(['grade']))\\n\\tnine, ten, eleven, twelve = grouped[0][1], grouped[1][1], grouped[2][1], grouped[3][1]\\n\\n\\treturn nine, ten, eleven, twelve\",\n \"def normalise(raw_data, normalise_by_column=False):\\n data = raw_data\\n if normalise_by_column:\\n #normaliza valores usando o maximo de cada coluna\\n col_maxes = raw_data.max(axis=0)\\n #divide cada valor pelo maximo correspondente de cada coluna\\n data = raw_data / col_maxes[np.newaxis,:] \\n else:\\n #divide todos os valores pelo maximo do dataset (tudo na mesma escala)\\n data = raw_data / raw_data.max()\\n\\n return data\",\n \"def format_dataset(dataset):\\n X = []\\n y = []\\n\\n for user in dataset:\\n X.append(''.join(dataset[user][0]))\\n y.append(dataset[user][1])\\n\\n return X, y\",\n \"def merge_adjustment_factor(*dataframes, with_self=True):\\n\\n # TODO : Maybe it would be better to select the better of the two\\n # adjustement factors. For students rated low by their team, their rating\\n # would improve the score and for students that rated themselves lower than\\n # their team would get a boost too.\\n\\n if with_self:\\n col = 'Adj Factor (w/ Self)'\\n else:\\n col = 'Adj Factor (w/o Self)'\\n\\n data = {}\\n\\n for i, df in enumerate(dataframes):\\n data['P{}'.format(i + 1)] = df[col]\\n\\n data['Student Name'] = df['Student Name']\\n\\n data = pd.DataFrame(data)\\n\\n data = data.dropna() # if student drops and is deleted after peer eval\\n\\n # calculate a slope value, improvement metric, that characterizes whether\\n # the students' ranking improved over time or didn't, positive values are\\n # improvements and negative means the got rated worse over time\\n\\n x_vals = list(range(len(dataframes)))\\n slopes = []\\n means = []\\n stds = []\\n adjusted_scores = []\\n\\n # I weight the later evals more than the prior ones because the later ones\\n # tend to be more serious because the stakes become more real as the class\\n # progresses.\\n eval_names = ['P1', 'P2', 'P3', 'P4']\\n weights = [0.85, 0.90, 0.95, 1.0]\\n\\n for idx, row in data.iterrows():\\n y_vals = row[eval_names[:len(dataframes)]].values.astype(float)\\n\\n # Weight the latter reviews more than the earlier reviews.\\n mean = np.average(y_vals, weights=weights[:len(dataframes)])\\n\\n # Calculate a \\\"slope\\\" val that indicates how little or how much\\n # improvement there was.\\n opt, _ = curve_fit(lambda x, slope, intercept: slope * x + intercept,\\n x_vals, y_vals)\\n improvement = opt[0]\\n\\n # If the student was rated low but improved over time, bump their\\n # factor up based on the improvement. Also, don't allow any factor's\\n # lower than 0.75. A factor of 0.75 can drop the grade two letter\\n # grades (based on 85).\\n if mean < 0.95 and improvement > 0.0:\\n adjusted_score = mean + 1.5 * improvement\\n else:\\n adjusted_score = max([0.75, mean])\\n\\n adjusted_score = mean\\n\\n means.append(mean)\\n stds.append(y_vals.std())\\n slopes.append(improvement)\\n adjusted_scores.append(adjusted_score)\\n\\n data['Improvement'] = slopes\\n data['Mean Adj Factor'] = means\\n data['STD Adj Factor'] = stds\\n data['Final Adj Factor'] = adjusted_scores\\n\\n return data\",\n \"def test_Smoother_multicol(self):\\n enames = [self.ename, \\\"SID79\\\"]\\n bnames = [self.bname, \\\"BIR79\\\"]\\n out_df = sm.Excess_Risk.by_col(self.df, enames, bnames)\\n outcols = [\\\"{}-{}_excess_risk\\\".format(e, b) for e, b in zip(enames, bnames)]\\n er79 = np.array([0.000000, 2.796607, 0.8383863, 1.217479, 0.943811])\\n answers = [self.er, er79]\\n for col, answer in zip(outcols, answer):\\n self.assertIn(out_df.columns, col)\\n np.testing.assert_allclose(\\n out_df[col].values[:5], answer, rtol=RTOL, atol=ATOL\\n )\",\n \"def _run_alg(data, agg_col, cat_cols, model, null_responses):\\n agg_units = sorted(set(data[agg_col]), key=lambda x: (str(type(x)), x))\\n outlier_scores = collections.defaultdict(dict)\\n agg_to_data = {}\\n agg_col_to_data = {}\\n for agg_unit in agg_units:\\n # TODO: could this be smarter and remove data each time? maybe no savings.\\n # TODO: support numpy only again\\n agg_to_data[agg_unit] = data[data[agg_col] == agg_unit]\\n agg_col_to_data[agg_unit] = {}\\n \\n for col in cat_cols:\\n col_vals = sorted(set(data[col]), key=lambda x: (str(type(x)), x))\\n col_vals = [c for c in col_vals if c not in null_responses]\\n frequencies = {}\\n for agg_unit in agg_units:\\n frequencies[agg_unit],grouped = _get_frequencies(data, col, col_vals, agg_col, agg_unit, agg_to_data)\\n agg_col_to_data[agg_unit][col] = grouped\\n outlier_scores_for_col, expected_frequencies_for_col, p_values_for_col = model.compute_outlier_scores(frequencies)\\n for agg_unit in agg_units:\\n outlier_scores[agg_unit][col] = {'score': outlier_scores_for_col[agg_unit],\\n 'observed_freq': frequencies[agg_unit],\\n 'expected_freq': expected_frequencies_for_col[agg_unit],\\n 'p_value': p_values_for_col[agg_unit]}\\n return outlier_scores, agg_col_to_data\",\n \"def fillgaps(column1,column2,train,test):\\n ddict={}\\n d1=test[[column1,column2]].dropna().values\\n d2=train[[column1,column2]].dropna().values\\n c1=np.array(d1[:,0].tolist()+d2[:,0].tolist())\\n c2=np.array(d1[:,1].tolist()+d2[:,1].tolist())\\n for ic1 in np.unique(c1):\\n ddict[ic1]=(c2[c1==ic1].mean(),c2[c1==ic1].std())\\n full_data = [train, test]\\n for dataset in full_data:\\n for missing in np.where(np.isnan(dataset[column2]))[0]:\\n m,s=ddict[dataset[column1][missing]]\\n if s<=0:\\n dataset[column2][missing]=m\\n else:\\n dataset[column2][missing]=np.random.normal(loc=m,scale=s,size=1)\\n return (train,test)\",\n \"def collate_molgraphs(data):\\n assert len(data[0]) in [3, 4], \\\\\\n 'Expect the tuple to be of length 3 or 4, got {:d}'.format(len(data[0]))\\n if len(data[0]) == 3:\\n smiles, graphs, labels = map(list, zip(*data))\\n masks = None\\n else:\\n smiles, graphs, labels, masks = map(list, zip(*data))\\n \\n bg = dgl.batch(graphs)\\n bg.set_n_initializer(dgl.init.zero_initializer)\\n bg.set_e_initializer(dgl.init.zero_initializer)\\n labels = torch.stack(labels, dim=0)\\n \\n if masks is None:\\n masks = torch.ones(labels.shape)\\n else:\\n masks = torch.stack(masks, dim=0)\\n return smiles, bg, labels, masks\",\n \"def import_current_grades(filename):\\n\\t# import relevant csv file columns into pandas dataframe\\n\\tcg_df = pd.read_csv(filename, usecols=['Student ID', 'Grade Level', 'Student Name',\\n\\t\\t'Period', 'Course Name', 'CAvg'])\\n\\t\\n\\t# create dictionary that maps Student ID to Grade Level\\n\\td = {}\\n\\tfor row in cg_df.iterrows():\\n\\t\\t# get the student ID number\\n\\t\\tid_num = row[1][0]\\n\\t\\t# get the student's grade level\\n\\t\\tgrade = row[1][2]\\n\\t\\tif id_num not in d:\\n\\t\\t\\td[id_num] = grade\\n\\n\\t# collapse dataframe based on student ID, identify cols by Period, \\n\\t# and fill in grade averages\\n\\tcg_df=cg_df.pivot(index='Student ID', columns='Period', values='CAvg').reset_index().set_index('Student ID')\\n\\t\\n\\t# rename the columns\\n\\tcg_df.rename(index=int, columns={'Student ID':'ID', '01 Per':'p1', \\n\\t\\t'02 Per':'p2', '03 Per':'p3', '04 Per':'p4', '05 Per':'p5',\\n\\t\\t'06 Per':'p6', '07 Per':'p7', '08 Per':'p8', 'Grade Level': 'grade'}, inplace=True)\\n\\n\\t# add the grade level column back in\\n\\tcg_df['grade_level'] = cg_df.index.to_series().map(d)\\n\\n\\t# obtain sub-dataframes of each grade\\n\\tnine = cg_df['grade_level'] == 9\\n\\tten = cg_df['grade_level'] == 10\\n\\televen = cg_df['grade_level'] == 11\\n\\ttwelve = cg_df['grade_level'] == 12\\n\\n\\t# fill NAs with appropirate Lunch Periods\\n\\tcg_df[nine].p3.fillna(value='Lunch', inplace=True)\\n\\tcg_df[ten].p4.fillna(value='Lunch', inplace=True)\\n\\tcg_df[eleven].p5.fillna(value='Lunch', inplace=True)\\n\\n\\t# concatenate back into a single DataFrame\\n\\t#cg_df = pd.concat([df9, df10, df11, df12])\\n\\n\\t# fill in Periods 3, 4, 7, 8 based on 3/4 and 7/8 grades\\n\\tfor row in cg_df.iterrows():\\n\\t\\tif row[1][3]:\\n\\t\\t\\tper_3_4_grade = row[1][3]\\n\\t\\t\\trow[1][2] = per_3_4_grade\\n\\t\\t\\trow[1][4] = per_3_4_grade\\n\\t\\telif row[1][8]:\\n\\t\\t\\tper_7_8_grade = row[1][8]\\n\\t\\t\\trow[1][7] = per_7_8_grade\\n\\t\\n\\t# drop cols for periods 3/4 and 7/8\\n\\tcg_df.drop(['03/04 Per', '07/08 Per', '09 Per'], axis=1, inplace=True)\\t\\n\\t\\n\\t# fill the NaNs with -1.0\\n\\tcg_df.fillna(-1.0, inplace=True)\\n\\t\\n\\t# # round the grades down to nearest ten\\n\\tcg_df['p1_r'] = cg_df.p1.apply(round_grade)\\n\\tcg_df['p2_r'] = cg_df.p2.apply(round_grade)\\n\\tcg_df['p3_r'] = cg_df.p3.apply(round_grade)\\n\\tcg_df['p4_r'] = cg_df.p4.apply(round_grade)\\n\\tcg_df['p5_r'] = cg_df.p5.apply(round_grade)\\n\\tcg_df['p6_r'] = cg_df.p6.apply(round_grade)\\n\\tcg_df['p7_r'] = cg_df.p7.apply(round_grade)\\n\\tcg_df['p8_r'] = cg_df.p8.apply(round_grade)\\n\\t\\n\\t# add the letter grade columns\\n\\tcg_df['p1_letter'] = cg_df.p1_r.apply(letter_grade)\\n\\tcg_df['p2_letter'] = cg_df.p2_r.apply(letter_grade)\\n\\tcg_df['p3_letter'] = cg_df.p3_r.apply(letter_grade)\\n\\tcg_df['p4_letter'] = cg_df.p4_r.apply(letter_grade)\\n\\tcg_df['p5_letter'] = cg_df.p5_r.apply(letter_grade)\\n\\tcg_df['p6_letter'] = cg_df.p6_r.apply(letter_grade)\\n\\tcg_df['p7_letter'] = cg_df.p7_r.apply(letter_grade)\\n\\tcg_df['p8_letter'] = cg_df.p8_r.apply(letter_grade)\\n\\t\\n\\t# add percentage signs\\n\\tcg_df['p1'] = cg_df.p1.apply(add_percentage)\\n\\tcg_df['p2'] = cg_df.p2.apply(add_percentage)\\n\\tcg_df['p3'] = cg_df.p3.apply(add_percentage)\\n\\tcg_df['p4'] = cg_df.p4.apply(add_percentage)\\n\\tcg_df['p5'] = cg_df.p5.apply(add_percentage)\\n\\tcg_df['p6'] = cg_df.p6.apply(add_percentage)\\n\\tcg_df['p7'] = cg_df.p7.apply(add_percentage)\\n\\tcg_df['p8'] = cg_df.p8.apply(add_percentage)\\n\\t\\n\\t# drop cols for periods 3/4 and 7/8\\n\\tcg_df.drop(['p1_r', 'p2_r', 'p3_r', 'p4_r', 'p5_r', 'p6_r', 'p7_r', 'p8_r'], axis=1, inplace=True)\\t\\n\\t\\n\\t# replace values\\n\\tcg_df.replace('-1.0%', '-', inplace=True)\\n\\t\\n\\treturn cg_df\",\n \"def _process_data(self):\\r\\n # Rename columns to match final feature class\\r\\n self._rename_columns()\\r\\n # Add point ID column\\r\\n self._add_pointid()\\r\\n # Sort rows by transect id and timestamp\\r\\n self._sort_rows()\\r\\n # Fill Null records with a value\\r\\n self._fill_nulls()\\r\\n # Set site_code to lower case\\r\\n self._lower_site_code()\\r\\n # Create survey_id\\r\\n self._calc_survey_id()\\r\\n # Calculate nativesg column if at least one of the veg columns is a Native seagrass type\\r\\n if set(self.veg_columns).intersection(set(NATIVESG_CODES)) > 0:\\r\\n self.nativesg_columns = list(set(self.veg_columns).intersection(set(NATIVESG_CODES)))\\r\\n self._calc_nativesg()\\r\\n #\\r\",\n \"def comparison_stats(df, els=None):\\n if els is None:\\n els = ['Li', 'Mg', 'Al', 'P', 'Ti', 'Y', 'La', 'Ce', 'Pr', 'Nd', 'Sm',\\n 'Eu', 'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Hf', 'Pb', 'Th',\\n 'U']\\n \\n yl_stats = []\\n \\n for i, e in enumerate(els):\\n x = df.loc[:, e + '_rd'].values\\n yl = df.loc[:, e + '_la'].values\\n \\n yl_stats.append(summary_stats(x, yl, e))\\n \\n yl_stats = pd.concat(yl_stats).T\\n \\n return yl_stats.T\",\n \"def disagg(vec:gpd.GeoDataFrame):\\n\\t\\t# Split GeometryCollections\\n\\t\\tno_coll = []\\n\\t\\tfor i, row in vec.iterrows():\\n\\t\\t\\tgeom = row.geometry\\n\\t\\t\\tif geom.type == 'GeometryCollection':\\n\\t\\t\\t\\tfor part in geom:\\n\\t\\t\\t\\t\\trow2 = row.copy()\\n\\t\\t\\t\\t\\trow2.geometry = part\\n\\t\\t\\t\\t\\tno_coll.append(row2)\\n\\n\\t\\t\\telse:\\n\\t\\t\\t\\t\\tno_coll.append(row) \\n\\n\\t\\t# Split Multi geomries\\n\\t\\tres = []\\n\\t\\tfor row in no_coll:\\n\\t\\t\\tgeom = row.geometry\\n\\t\\t\\tif geom.type.startswith('Multi'):\\n\\t\\t\\t\\tfor part in geom:\\n\\t\\t\\t\\t\\trow2 = row.copy()\\n\\t\\t\\t\\t\\trow2.geometry = part\\n\\t\\t\\t\\t\\tres.append(row2)\\n\\t\\t\\telse:\\n\\t\\t\\t\\t\\tres.append(row)\\n\\n\\t\\treturn gpd.GeoDataFrame(res, crs=vec.crs).reset_index(drop=True)\",\n \"def dataset_1():\\n csv = pd.read_csv('resources/dataset_1.csv') # Pandas loads the CSV file as a DataFrame object\\n csv.fillna('', inplace=True) # Pandas fills empty celles with NaN. We replace every Nan value with an emtpy string.\\n csv.num_rue = csv.num_rue.apply(str) # Cast street numbers to strings\\n # Create a new column named 'address' which concatenates the columns ['num_rue', 'cpltnum_ru', 'type_rue', 'article_ru', 'nom_rue']\\n # csv[['num_rue', 'cpltnum_ru', 'type_rue', 'article_ru', 'nom_rue']] select a subset of the table 'csv'.\\n # .agg(' '.join, axis=1) is equivalent to merge the selected cells of every lines as 'num_rue' + ' ' + 'cpltnum_ru' + ' ' + 'type_rue' + ' ' + 'article_ru' + ' ' + 'nom_rue'\\n csv['address'] = csv[['num_rue', 'cpltnum_ru', 'type_rue', 'article_ru', 'nom_rue']].agg(' '.join, axis=1)\\n return csv\",\n \"def standardize_relations(dset_df, db='DTC'):\\n relation_cols = dict(ChEMBL='Standard Relation', DTC='standard_relation')\\n rel_col = relation_cols[db]\\n\\n dset_df[rel_col].fillna('=', inplace=True)\\n ops = dset_df[rel_col].values\\n if db == 'ChEMBL':\\n # Remove annoying quotes around operators\\n ops = [op.lstrip(\\\"'\\\").rstrip(\\\"'\\\") for op in ops]\\n op_dict = {\\n \\\">\\\": \\\">\\\",\\n \\\">=\\\": \\\">\\\",\\n \\\"<\\\": \\\"<\\\",\\n \\\"<=\\\": \\\"<\\\",\\n \\\"=\\\": \\\"=\\\"\\n }\\n ops = np.array([op_dict.get(op, \\\"@\\\") for op in ops])\\n dset_df[rel_col] = ops\\n dset_df = dset_df[dset_df[rel_col] != \\\"@\\\"]\\n return dset_df\",\n \"def preprocess_data(data):\\n def deal_line(line):\\n pos, rating = line.split(',')\\n row, col = pos.split(\\\"_\\\")\\n row = row.replace(\\\"r\\\", \\\"\\\")\\n col = col.replace(\\\"c\\\", \\\"\\\")\\n return int(row), int(col), float(rating)\\n\\n def statistics(data):\\n row = set([line[0] for line in data])\\n col = set([line[1] for line in data])\\n return min(row), max(row), min(col), max(col)\\n\\n # parse each line\\n data = [deal_line(line) for line in data]\\n\\n # do statistics on the dataset.\\n min_row, max_row, min_col, max_col = statistics(data)\\n print(\\\"number of items: {}, number of users: {}\\\".format(max_row, max_col))\\n\\n # build rating matrix.\\n ratings = sp.lil_matrix((max_row, max_col))\\n for row, col, rating in data:\\n ratings[row - 1, col - 1] = rating\\n return ratings\",\n \"def _normalize(self, data):\\n norm_data = []\\n\\n for row in data:\\n norm_row = []\\n\\n for column in row:\\n # custom format strings for specific objects\\n if isinstance(column, float):\\n format_str = '{{:.{}f}}'.format(2)\\n item = format_str.format(column)\\n\\n elif isinstance(column, datetime):\\n item = column.strftime('%Y-%m-%d %H:%M')\\n\\n else:\\n item = str(column)\\n\\n norm_row.append(item)\\n\\n norm_data.append(norm_row)\\n\\n return norm_data\",\n \"def RGBratios(dataset,fil):\\n dom_fil = get_dominant_color(fil)\\n dataset['R_B_ratio'] = dataset.R/dataset.B\\n dataset['R_G_ratio'] = dataset.R/dataset.G\\n dataset['G_B_ratio'] = dataset.G/dataset.B\\n filter_RB = dom_fil[0]/dom_fil[2]\\n filter_RG = dom_fil[0]/dom_fil[1]\\n filter_GB = dom_fil[1]/dom_fil[2]\\n norm = []\\n for array in list(dataset['DominatColour']):\\n norm.append( np.linalg.norm(array))\\n dataset['Dominant_norm'] = norm\\n\\n norm = []\\n for array in list(dataset['AverageColour']):\\n norm.append( np.linalg.norm(array))\\n\\n\\n dataset['DiffRatio_R_B'] = dataset.R_B_ratio-filter_RB\\n dataset['DiffRatio_R_G'] = dataset.R_G_ratio-filter_RG\\n dataset['DiffRatio_RG_B'] = dataset.G_B_ratio-filter_GB\\n dataset['Average_norm'] = norm\\n return dataset\",\n \"def get_score_summary(fname):\\n gradedata = {}\\n fhandler = open(fname, 'r')\\n rest_data = csv.reader(fhandler)\\n for row in rest_data:\\n if row[10] not in ['P', '', 'GRADE']:\\n gradedata[row[0]] = [row[1], row[10]]\\n gradedata.update(gradedata)\\n fhandler.close()\\n\\n gradereview = {}\\n for value in gradedata.itervalues():\\n if value[0] not in gradereview.iterkeys():\\n count1 = 1\\n count2 = GRADESCL[value[1]]\\n else:\\n count1 = gradereview[value[0]][0] + 1\\n count2 = gradereview[value[0]][1] + GRADESCL[value[1]]\\n gradereview[value[0]] = (count1, count2)\\n gradereview.update(gradereview)\\n grade = {}\\n for key in gradereview.iterkeys():\\n count1 = gradereview[key][0]\\n count2 = gradereview[key][1]/gradereview[key][0]\\n grade[key] = (count1, count2)\\n return grade\",\n \"def updateTags(data):\\n df = data\\n df['Tag'] = np.where(df['godScore'] == 4, 'Keep', df['Tag'])\\n df['Tag'] = np.where(df['godScore'] == 0, 'Junk', df['Tag'])\\n\\n return df\",\n \"def transform_data(data, cols, standardize = True, log_trans = True):\\r\\n data = data[cols]\\r\\n if log_trans:\\r\\n y = np.log(data)\\r\\n y[y == -np.inf] = 0\\r\\n y[y == np.inf] = 0\\r\\n data = (y- data.mean(axis = 0)) / data.std(axis = 0)\\r\\n\\r\\n if standardize:\\r\\n data = (data- data.mean(axis = 0)) / data.std(axis = 0)\\r\\n\\r\\n return data\",\n \"def merge_on_fip(df, directory):\\n pol_fip_data = pd.read_csv(directory, index_col=0)\\n df = df.merge(pol_fip_data, left_on='GESTFIPS', right_on='fips' ,how='left')\\n df.drop(columns=['fips'], inplace=True)\\n \\n return df\",\n \"def join_survey_data(survey, deezer):\\n\\n\\n df = survey.rename(columns={'Age': 'user_age', 'Gender': 'user_gender',\\n 'deezer_id': 'media_id'})\\n\\n for index, row in df.iterrows():\\n if pd.isnull(row['time']):\\n continue\\n time = row['time'].split(',')\\n if row['user_gender'] == 'Male':\\n user_gender = 1\\n else:\\n user_gender = 0\\n if time == None:\\n if row['rating'] == 0:\\n for i in [1480513129, 1479067262, 1478675619]:\\n new = pd.DataFrame(np.array([[999999, i, row['media_id'],\\n 999999, 0, 20001010, 1, 0,\\n 999, 1, user_gender,\\n row['user_id'], None,\\n row['user_age'], 0]]),\\n columns=['genre_id', 'ts_listen',\\n 'media_id', 'album_id',\\n 'context_type',\\n 'release_date',\\n 'platform_name',\\n 'platform_family',\\n 'media_duration',\\n 'listen_type', 'user_gender',\\n 'user_id', 'artist_id',\\n 'user_age',\\n 'is_listened'])\\n deezer = deezer.append(new)\\n elif 'Anytime' in time:\\n for i in [1480513129, 1479067262, 1478675619]:\\n new = pd.DataFrame(np.array([[999999, i, row['media_id'],\\n 999999, 0, 20001010, 1, 0, 999,\\n 1, user_gender,\\n row['user_id'], None,\\n row['user_age'], 0]]),\\n columns=['genre_id', 'ts_listen',\\n 'media_id', 'album_id',\\n 'context_type',\\n 'release_date', 'platform_name',\\n 'platform_family',\\n 'media_duration',\\n 'listen_type', 'user_gender',\\n 'user_id', 'artist_id', 'user_age',\\n 'is_listened'])\\n deezer = deezer.append(new)\\n else:\\n t_dict = {'Morning': 0, 'Afternoon': 0, 'Evening': 0}\\n for t in time:\\n t_dict[t] = 1\\n for i in [('Morning', 1480513129), ('Afternoon', 1479067262),\\n ('Evening', 1478675619)]:\\n new = pd.DataFrame(np.array([[999999, i[1], row['media_id'],\\n 999999, 0, 20001010, 1, 0, 999,\\n 1, user_gender,\\n row['user_id'], None,\\n row['user_age'], t_dict[i[0]]]]),\\n columns=['genre_id',\\n 'ts_listen',\\n 'media_id',\\n 'album_id',\\n 'context_type',\\n 'release_date', 'platform_name',\\n 'platform_family',\\n 'media_duration',\\n 'listen_type', 'user_gender',\\n 'user_id', 'artist_id', 'user_age',\\n 'is_listened'])\\n deezer = deezer.append(new)\\n\\n return deezer\",\n \"def fix_bad_column_blue():\\r\\n\\r\\n science = iraf.hselect('blue????.fits', '$I', 'TURRET == \\\"APERTURE\\\" & LAMPS == \\\"0000000\\\"', Stdout=1)\\r\\n if len(science) > 0:\\r\\n f = open('science_dump', 'w')\\r\\n for fn in science:\\r\\n f.write('%s\\\\n' % fn)\\r\\n f.close()\\r\\n science = iraf.hselect('@science_dump', '$I', 'TURRET == \\\"APERTURE\\\" & LAMPS == \\\"0000000\\\" & AIRMASS > 1.01', Stdout=1)\\r\\n if len(science)>0:\\r\\n os.unlink('science_dump')\\r\\n f = pyfits.open(science[0])\\r\\n bad_column = f[0].data[1608,:].argmin() + 1\\r\\n f.close()\\r\\n f = open('bluebpm', 'w')\\r\\n f.write('%d %d 1 2835\\\\n' % (bad_column, bad_column))\\r\\n f.close()\\r\\n iraf.fixpix('blue????.fits', \\\"bluebpm\\\")\",\n \"def regroup(data):\\n rating = df.groupby(\\\"Rating\\\").max()\\n rating.dtypes\\n rating.shape\\n rating.columns\\n\\n return rating\",\n \"def test_restaurant_grades(dataset,camis_id):\\n #get grades and date for a specified camis_id as g\\n g=dataset.GRADE.iloc[np.where(dataset.CAMIS==camis_id)] \\n date=dataset['GRADE DATE'].iloc[np.where(dataset.CAMIS==camis_id)]\\n date=pd.Series([datetime.datetime.strptime(d,\\\"%m/%d/%Y\\\") for d in date],index=g.index,name='GRADE DATE')\\n g_date=pd.concat([g,date],axis=1)\\n #sort grade according to date\\n g_date=g_date.sort(columns='GRADE DATE',axis=0) \\n \\n return test_grade(list(g_date.GRADE))\",\n \"def _collect_foll(all_detailed_cells, targeted_gids: pd.Series):\\n all_foll_gids = {}\\n\\n for sim_gid, cells in pbar(all_detailed_cells.items(), total=len(all_detailed_cells), desc='sim'):\\n targeted_gid = targeted_gids.loc[sim_gid]\\n\\n sb.CAT.add_cats_cells(cells)\\n cells = cells.drop(targeted_gid)\\n\\n foll_ei_types = cells.loc[cells['frm_cat'] == 'foll', 'ei_type']\\n\\n all_foll_gids[sim_gid] = {\\n f'{ei_type}_foll_gids': tuple(gids)\\n for ei_type, gids in foll_ei_types.groupby(foll_ei_types).groups.items()}\\n\\n all_foll_gids = pd.DataFrame.from_dict(all_foll_gids, orient='index')\\n\\n # fillna doesn't like taking empty tuples\\n for col, values in all_foll_gids.items():\\n all_foll_gids.loc[all_foll_gids[col].isna(), col] = tuple()\\n\\n all_foll_gids = all_foll_gids.rename_axis(index='sim_gid')\\n\\n foll_counts = all_foll_gids.applymap(len)\\n foll_counts.columns = [f'{col[0]}_foll_count'for col in foll_counts]\\n\\n all_foll_gids = pd.concat([all_foll_gids, foll_counts], axis=1)\\n\\n return all_foll_gids\",\n \"def normalize_ratings_per_archetype(ratings):\\n archetype_cols = [c for c in ratings.columns if c != \\\"name\\\"]\\n n_cards = len(ratings[\\\"monored\\\"])\\n for arch_col in archetype_cols:\\n ratings[arch_col] = ratings[arch_col] / (ratings[arch_col].sum() / n_cards)\\n return ratings\",\n \"def g_date(data): \\n #pull out data from the most original dataset and modified the date by year\\n g=data.GRADE\\n date=data['GRADE DATE'] \\n date=[datetime.datetime.strptime(d, \\\"%m/%d/%Y\\\") for d in date]\\n\\n d_by_year=[] \\n for i in range(len(date)): \\n d_by_year.append(datetime.datetime(date[i].year,1,1))\\n\\n d_by_year=pd.Series(d_by_year,index=g.index,name=\\\"GRADE DATE\\\")\\n return pd.concat([g,d_by_year],axis=1)\",\n \"def grouped_colormaps():\\n mlab = ['autumn','winter','spring','summer',\\n 'gray','bone','copper','pink',\\n 'cool','hot',\\n 'hsv','jet','spectral',\\n #'binary', # Seems to be a reverse of gray\\n 'prism','flag']\\n mlab_r = [m+'_r' for m in mlab]\\n brewer = ['Accent','Dark2',\\n 'Spectral',\\n 'Paired',\\n 'Blues','Greens','Greys','Oranges','Purples','Reds',\\n 'Pastel1','Pastel2',\\n 'Set1','Set2','Set3',\\n 'BrBG','BuGn','BuPu','GnBu',\\n 'OrRd',\\n 'PiYG','PRGn','PuBu','PuBuGn',\\n 'PuOr','PuRd',\\n 'RdBu','RdGy','RdPu',\\n 'RdYlBu','RdYlGn',\\n 'YlGn','YlGnBu','YlOrBr','YlOrRd',\\n ]\\n brewer_r = [m+'_r' for m in brewer]\\n gist = ['gist_ncar','gist_rainbow',\\n 'gist_stern','gist_earth',\\n 'gist_gray','gist_heat',\\n #'gist_yarg', # Seems to be a reverse of gray\\n ]\\n gist_r = [m+'_r' for m in gist]\\n\\n return gist + [None] + mlab + [None] + brewer\",\n \"def get_col2id( self, ratios_standardized, db ):\\n\\t\\tcol_info_collection = db.col_info\\n\\t\\tcol_name = []\\n\\t\\tcol_id = []\\n\\t\\tfor i in col_info_collection.find():\\n\\t\\t\\tcol_name.append(i[\\\"egrin2_col_name\\\"])\\n\\t\\t\\tcol_id.append(i[\\\"col_id\\\"])\\n\\t\\tfor i in ratios_standardized.columns.values:\\n\\t \\t\\tif i not in col_name:\\n\\t \\t\\t\\tcol_name.append( i )\\n\\t \\t\\t\\tif len(col_id) > 0:\\n\\t\\t\\t\\t\\tcol_id.append(max(col_id)+1)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tcol_id.append(0)\\n\\t \\tcol_info = pd.DataFrame( zip( col_id, col_name ), index = col_name, columns = [ \\\"col_id\\\", \\\"egrin2_col_name\\\"] )\\n\\t \\treturn col_info\",\n \"def merge_ranges():\",\n \"def _convert_catstrength(self, df, col_name, med_val, low_val):\\n\\n val_cts = df[col_name].value_counts()\\n val_dict = {}\\n\\n for name in val_cts[val_cts > med_val].index:\\n val_dict[name] = 'High' + col_name\\n\\n for name in val_cts[(val_cts <= med_val) & (val_cts > low_val)].index:\\n val_dict[name] = 'Med' + col_name\\n\\n for name in val_cts[val_cts <= low_val].index:\\n val_dict[name] = 'Low' + col_name\\n\\n new_col_name = 'Mapped{}'.format(col_name)\\n\\n df[new_col_name] = df[col_name].map(val_dict)\",\n \"def normalize(data, name):\\n cols = list(data.columns)\\n vals = data.values\\n for i in range(len(vals)):\\n v = vals[i]\\n l = np.sum(v[0:len(v)-1])\\n if l != 0:\\n t = v[0:len(v)-1]/l\\n v = np.append(t, v[-1])\\n vals[i] = v\\n write_data(vals, cols, name)\",\n \"def _prepare_data_to_aug(\\n self, col: pd.Series, freq=0.2\\n ) -> Tuple[pd.Series, pd.Series]:\",\n \"def fix_ranks(df, ranks='Current Ranks', programs='Programs'):\\n\\n # Create columns based on unique program values\\n if programs in df:\\n unique_programs = set(df[programs].unique())\\n if np.nan in unique_programs:\\n unique_programs.remove(np.nan)\\n for value in unique_programs.copy():\\n val_list = value.split(', ')\\n if len(val_list) > 1:\\n for comma_split_value in value.split(', '):\\n if comma_split_value not in unique_programs:\\n unique_programs.add(comma_split_value)\\n unique_programs.remove(value)\\n\\n for x in unique_programs:\\n if x not in df.columns.values:\\n df[x] = \\\"\\\"\\n\\n\\n # Assign Ranks to respective columns\\n for index, x in df[ranks].iteritems():\\n\\n new_col = str(df.iloc[index][programs])\\n x = str(x)\\n\\n # If there are commas\\n\\n if ',' in new_col or x:\\n new_col = new_col.split(', ')\\n x = x.split(', ')\\n\\n for rank, col in zip(x, new_col):\\n df.at[index, col] = rank\\n\\n # No commas\\n\\n else:\\n if df.at[index, col].notnull():\\n print('value already exists: ' + df.loc[index, col])\\n else:\\n df.set_value(index, new_col, x)\\n\\n\\n # Get rid of 'nan' in cells\\n df[df == 'nan'] = np.nan\\n return df\",\n \"def aggregate_results(scores, stds):\\n\\n scores = pd.DataFrame(scores.values(),\\n index=scores.keys(),\\n columns=[\\\"SCORE MEAN\\\", ])\\n stds = pd.DataFrame(stds.values(),\\n index=stds.keys(),\\n columns=[\\\"SCORE STD\\\", ])\\n scores.index.name = param_names\\n stds.index.name = param_names\\n data = []\\n for param_gropby_levels in ((0,), (1,), (2,), (3, 4), (5,)):\\n aggregate_scores = scores.groupby(level=param_gropby_levels).mean()\\n aggregate_stds = scores.groupby(level=param_gropby_levels).std()\\n data.append((aggregate_scores, aggregate_stds))\\n return data\",\n \"def _process_aux_data(self, right_dataframe) -> List[Tuple[int, WeldType]]:\\n import willump.evaluation.willump_executor as wexec\\n\\n join_col_field_types = []\\n join_col_type_map = {}\\n join_col_lookup_string = \\\"\\\"\\n for column, col_type in zip(self.left_df_type.column_names, self.left_df_type.field_types):\\n if column in self.join_col_names:\\n assert(isinstance(col_type, WeldVec))\\n col_type = col_type.elemType\\n join_col_field_types.append(col_type)\\n join_col_type_map[column] = col_type\\n col_right_df_index = list(right_dataframe.columns).index(column)\\n join_col_lookup_string += \\\"%s(lookup(_inp%d, i)),\\\" % (str(join_col_type_map[column]), col_right_df_index)\\n if len(self.join_col_names) > 1:\\n join_col_weld_type = WeldStruct(join_col_field_types)\\n join_col_lookup_string = \\\"{\\\" + join_col_lookup_string + \\\"}\\\"\\n else:\\n join_col_weld_type = join_col_field_types[0]\\n join_col_lookup_string = join_col_lookup_string[:-1]\\n\\n types_string = \\\"{\\\"\\n row_types_list = []\\n types_list = []\\n right_df_column_names = []\\n for i, column in enumerate(right_dataframe):\\n col_weld_type: WeldType = numpy_type_to_weld_type(right_dataframe[column].values.dtype)\\n if column not in self.join_col_names:\\n types_string += str(col_weld_type) + \\\",\\\"\\n row_types_list.append(col_weld_type)\\n types_list.append(WeldVec(col_weld_type))\\n right_df_column_names.append(column)\\n\\n types_string = types_string[:-1] + \\\"}\\\"\\n\\n values_string = \\\"{\\\"\\n for i, column in enumerate(right_dataframe):\\n if column not in self.join_col_names:\\n values_string += \\\"lookup(_inp%d, i),\\\" % i\\n values_string = values_string[:-1] + \\\"}\\\"\\n\\n weld_program = \\\\\\n \\\"\\\"\\\"\\n result(for(_inp%d,\\n dictmerger[JOIN_COL_TYPE, %s, +],\\n | bs: dictmerger[JOIN_COL_TYPE, %s, +], i: i64, x |\\n merge(bs, {JOIN_COL_LOOKUP, %s})\\n ))\\n \\\"\\\"\\\" % (0, types_string, types_string, values_string)\\n weld_program = weld_program.replace(\\\"JOIN_COL_TYPE\\\", str(join_col_weld_type))\\n weld_program = weld_program.replace(\\\"JOIN_COL_LOOKUP\\\", join_col_lookup_string)\\n\\n input_arg_types = {}\\n input_arg_list = []\\n for i, column in enumerate(right_dataframe):\\n input_name = \\\"input%d\\\" % i\\n input_arg_types[input_name] = \\\\\\n WeldVec(numpy_type_to_weld_type(right_dataframe[column].values.dtype))\\n input_arg_list.append(input_name)\\n\\n module_name = wexec.compile_weld_program(weld_program,\\n input_arg_types,\\n input_names=input_arg_list,\\n output_names=[],\\n base_filename=\\\"hash_join_dataframe_indexer\\\")\\n\\n hash_join_dataframe_indexer = importlib.import_module(module_name)\\n input_args = []\\n for column in right_dataframe:\\n input_args.append(right_dataframe[column].values)\\n input_args = tuple(input_args)\\n\\n indexed_right_dataframe = hash_join_dataframe_indexer.caller_func(*input_args)\\n self.right_df_row_type = WeldPandas(row_types_list, right_df_column_names)\\n self.right_df_type = WeldPandas(types_list, right_df_column_names)\\n return [(indexed_right_dataframe, WeldDict(join_col_weld_type, self.right_df_row_type))]\",\n \"def enrich_dataframe(df, name):\\n if(name == 'taux_incidence'):\\n df['taux_incidence'] = df['P']*100000/df['pop']\\n if(name == 'taux_positivite'):\\n df['taux_positivite'] = df['P']/df['T'] * 100\\n if(name == 'taux_occupation'):\\n df['TO'] = df['TO']*100\\n if(name == 'vaccins_vaccines_couv_majeurs'):\\n df['couv_complet'] = 100 * df['n_cum_complet'] / df['pop']\\n if(name == 'vaccins_vaccines_couv_ado_majeurs'):\\n df['couv_complet'] = 100 * df['n_cum_complet'] / df['pop']\\n if(name == 'taux_classes_fermees'):\\n df['taux_classes'] = 100* df['nombre_classes_fermees'] / df['nombre_total_classes']\\n if(name == 'taux_structures_fermees'):\\n df['taux_structures'] = 100* df['nombre_structures_fermees'] / df['nombre_total_structures']\\n\\n \\n \\n return df\",\n \"def clean_unit_errors(merged_df):\\n merged_df[\\\"postprocess_height_cat\\\"] = merged_df[\\\"height_cat\\\"]\\n merged_df[\\\"postprocess_height_cat\\\"] = merged_df[\\n \\\"postprocess_height_cat\\\"\\n ].cat.add_categories([\\\"Include-UH\\\", \\\"Include-UL\\\"])\\n merged_df[\\\"postprocess_weight_cat\\\"] = merged_df[\\\"weight_cat\\\"]\\n merged_df[\\\"postprocess_weight_cat\\\"] = merged_df[\\n \\\"postprocess_weight_cat\\\"\\n ].cat.add_categories([\\\"Include-UH\\\", \\\"Include-UL\\\"])\\n merged_df.loc[merged_df[\\\"height_cat\\\"] == \\\"Unit-Error-Low\\\", \\\"height\\\"] = (\\n merged_df.loc[merged_df[\\\"height_cat\\\"] == \\\"Unit-Error-Low\\\", \\\"height\\\"] * 2.54\\n )\\n merged_df.loc[merged_df[\\\"height_cat\\\"] == \\\"Unit-Error-High\\\", \\\"height\\\"] = (\\n merged_df.loc[merged_df[\\\"height_cat\\\"] == \\\"Unit-Error-High\\\", \\\"height\\\"] / 2.54\\n )\\n merged_df.loc[merged_df[\\\"weight_cat\\\"] == \\\"Unit-Error-Low\\\", \\\"weight\\\"] = (\\n merged_df.loc[merged_df[\\\"weight_cat\\\"] == \\\"Unit-Error-Low\\\", \\\"weight\\\"] * 2.2046\\n )\\n merged_df.loc[merged_df[\\\"weight_cat\\\"] == \\\"Unit-Error-High\\\", \\\"weight\\\"] = (\\n merged_df.loc[merged_df[\\\"weight_cat\\\"] == \\\"Unit-Error-High\\\", \\\"weight\\\"] / 2.2046\\n )\\n merged_df.loc[\\n merged_df[\\\"height_cat\\\"] == \\\"Unit-Error-Low\\\", \\\"postprocess_height_cat\\\"\\n ] = \\\"Include-UL\\\"\\n merged_df.loc[\\n merged_df[\\\"height_cat\\\"] == \\\"Unit-Error-High\\\", \\\"postprocess_height_cat\\\"\\n ] = \\\"Include-UH\\\"\\n merged_df.loc[\\n merged_df[\\\"weight_cat\\\"] == \\\"Unit-Error-Low\\\", \\\"postprocess_weight_cat\\\"\\n ] = \\\"Include-UL\\\"\\n merged_df.loc[\\n merged_df[\\\"weight_cat\\\"] == \\\"Unit-Error-High\\\", \\\"postprocess_weight_cat\\\"\\n ] = \\\"Include-UH\\\"\\n merged_df[\\\"bmi\\\"] = merged_df[\\\"weight\\\"] / ((merged_df[\\\"height\\\"] / 100) ** 2)\\n return merged_df\",\n \"def merge_breached():\\n # read breach and CMS data\\n breachdf = pd.read_csv(updated_breach_file_name, encoding='latin1')\\n breachdf.rename(columns=lambda x: x.strip(), inplace=True)\\n print(breachdf.isnull().sum().sum(), \\\"null columns\\\")\\n\\n df = pd.read_csv(CMS_file_name, encoding='latin1')\\n df.rename(columns=lambda x: x.strip(), inplace=True)\\n\\n print(\\\"dataframes read\\\")\\n\\n # merge data\\n new_df = df.merge(breachdf, left_on='FAC_NAME', right_on='FAC_NAME', how='outer')\\n print(\\\"merged\\\", new_df)\\n\\n new_df.to_csv(merged_file_name, index=False)\\n print(\\\"Written to\\\", merged_file_name)\",\n \"def combine_measurements(row, columns_to_combine):\\n return sum(row[column_index] * percentage for column_index,\\n percentage in columns_to_combine)\",\n \"def aggregate_statistics(self, stat_col, stat_agg):\\n precision_sums = stat_col[self.key_precision]\\n recall_sums = stat_col[self.key_recall]\\n f1score_sums = stat_col[self.key_f1score]\\n supports = stat_col[self.key_f1score+'_support']\\n\\n # Special case - no samples!\\n if sum(supports) == 0:\\n stat_agg[self.key_precision] = 0\\n stat_agg[self.key_precision+'_std'] = 0\\n stat_agg[self.key_recall] = 0\\n stat_agg[self.key_recall+'_std'] = 0\\n stat_agg[self.key_f1score] = 0\\n stat_agg[self.key_f1score+'_std'] = 0\\n\\n else: \\n # Else: calculate weighted precision.\\n precisions_avg = np.average(precision_sums, weights=supports)\\n precisions_var = np.average((precision_sums-precisions_avg)**2, weights=supports)\\n \\n stat_agg[self.key_precision] = precisions_avg\\n stat_agg[self.key_precision+'_std'] = math.sqrt(precisions_var)\\n\\n # Calculate weighted recall.\\n recalls_avg = np.average(recall_sums, weights=supports)\\n recalls_var = np.average((recall_sums-recalls_avg)**2, weights=supports)\\n\\n stat_agg[self.key_recall] = recalls_avg\\n stat_agg[self.key_recall+'_std'] = math.sqrt(recalls_var)\\n\\n # Calculate weighted f1 score.\\n f1scores_avg = np.average(f1score_sums, weights=supports)\\n f1scores_var = np.average((f1score_sums-f1scores_avg)**2, weights=supports)\\n\\n stat_agg[self.key_f1score] = f1scores_avg\\n stat_agg[self.key_f1score+'_std'] = math.sqrt(f1scores_var)\",\n \"def merge_specimen_data(\\n self, mouse_df: DataFrame, embryo_df: DataFrame\\n ) -> DataFrame:\\n specimen_cols = [\\n \\\"_centreID\\\",\\n \\\"_specimenID\\\",\\n \\\"_colonyID\\\",\\n \\\"_isBaseline\\\",\\n \\\"_productionCentre\\\",\\n \\\"_phenotypingCentre\\\",\\n \\\"phenotyping_consortium\\\",\\n ]\\n\\n # Select the relevant columns for specimen data\\n mouse_specimen_df = mouse_df.select(*specimen_cols)\\n if embryo_df is not None:\\n embryo_specimen_df = embryo_df.select(*specimen_cols)\\n\\n # Merge both the mouse specimen data and the embryo specimen data in one DataFrame\\n specimen_df = mouse_specimen_df.union(embryo_specimen_df)\\n else:\\n specimen_df = mouse_specimen_df\\n return specimen_df\",\n \"def FeatureValueFormat(singlegene):\\n comp_exon = np.zeros((len(singlegene['exons']),), dtype=np.object)\\n for i in range(len(singlegene['exons'])):\\n comp_exon[i]= np.array(singlegene['exons'][i])\\n singlegene['exons'] = comp_exon\\n comp_cds = np.zeros((len(singlegene['cds_exons']),), dtype=np.object)\\n for i in range(len(singlegene['cds_exons'])):\\n comp_cds[i]= np.array(singlegene['cds_exons'][i])\\n singlegene['cds_exons'] = comp_cds\\n comp_utr3 = np.zeros((len(singlegene['utr3_exons']),), dtype=np.object)\\n for i in range(len(singlegene['utr3_exons'])):\\n comp_utr3[i]= np.array(singlegene['utr3_exons'][i])\\n singlegene['utr3_exons'] = comp_utr3\\n comp_utr5 = np.zeros((len(singlegene['utr5_exons']),), dtype=np.object)\\n for i in range(len(singlegene['utr5_exons'])):\\n comp_utr5[i]= np.array(singlegene['utr5_exons'][i])\\n singlegene['utr5_exons'] = comp_utr5\\n comp_transcripts = np.zeros((len(singlegene['transcripts']),), dtype=np.object)\\n for i in range(len(singlegene['transcripts'])):\\n comp_transcripts[i]= np.array(singlegene['transcripts'][i])\\n singlegene['transcripts'] = comp_transcripts\\n comp_tss = np.zeros((len(singlegene['tss']),), dtype=np.object)\\n for i in range(len(singlegene['tss'])):\\n comp_tss[i]= np.array(singlegene['tss'][i])\\n singlegene['tss'] = comp_tss\\n comp_tis = np.zeros((len(singlegene['tis']),), dtype=np.object)\\n for i in range(len(singlegene['tis'])):\\n comp_tis[i]= np.array(singlegene['tis'][i])\\n singlegene['tis'] = comp_tis\\n comp_cleave = np.zeros((len(singlegene['cleave']),), dtype=np.object)\\n for i in range(len(singlegene['cleave'])):\\n comp_cleave[i]= np.array(singlegene['cleave'][i])\\n singlegene['cleave'] = comp_cleave\\n comp_cdsStop = np.zeros((len(singlegene['cdsStop']),), dtype=np.object)\\n for i in range(len(singlegene['cdsStop'])):\\n comp_cdsStop[i]= np.array(singlegene['cdsStop'][i])\\n singlegene['cdsStop'] = comp_cdsStop\\n\\n return singlegene\",\n \"def mergeAggregatedCsvData(self, contexts, obj, aggData1, aggData2):\\n return aggData1 + aggData2\",\n \"def combine_data(self):\\n\\t\\tself.Full_E = None\\n\\t\\tself.Imaginary_Spectrum = None\\n\\t\\tif self.raw_file is not None:\\n\\t\\t\\tlogger.info(\\\"Convert to scattering factors\\\")\\n\\t\\t\\tself.NearEdgeData = data.convert_data(self.raw_file,self.DataTypeCombo.GetValue(),'ASF')\\n#\\t\\t\\tif self.InvertDataCheckBox.GetValue():\\n#\\t\\t\\t\\tself.NearEdgeData[:,1] = numpy.abs(self.NearEdgeData[:,1] - 2*numpy.mean(self.NearEdgeData[:,1]))\\n\\t\\tlogger.info(\\\"Combine Data\\\")\\n\\t\\t# Get splice points\\n\\t\\tsplice_eV = numpy.array([10.0, 30000.0]) # Henke limits\\n\\t\\tif self.SpliceText1.GetValue() == \\\"Start\\\":\\n\\t\\t\\tif self.raw_file is not None:\\n\\t\\t\\t\\tsplice_eV[0] = self.NearEdgeData[0, 0]\\n\\t\\telse:\\n\\t\\t\\tsplice_eV[0] = float(self.SpliceText1.GetValue())\\n\\t\\tif self.SpliceText2.GetValue() == \\\"End\\\":\\n\\t\\t\\tif self.raw_file is not None:\\n\\t\\t\\t\\tsplice_eV[1] = self.NearEdgeData[-1, 0]\\n\\t\\telse:\\n\\t\\t\\tsplice_eV[1] = float(self.SpliceText2.GetValue())\\n\\t\\tif self.raw_file is not None and self.ASF_Data is None:\\n\\t\\t\\tself.Full_E, self.Imaginary_Spectrum, self.NearEdgeData, self.splice_ind = data.merge_spectra(self.NearEdgeData, self.ASF_E, self.ASF_Data, merge_points=splice_eV, add_background=self.AddBackgroundCheckBox.GetValue(), plotting_extras=True)\\n\\n\\t\\telif self.raw_file is None and self.ASF_Data is not None:\\n\\t\\t\\tself.Full_E = self.ASF_E\\n\\t\\t\\tself.Imaginary_Spectrum = self.ASF_Data\\n\\n\\t\\telif self.raw_file is not None and self.ASF_Data is not None:\\n\\t\\t\\t\\n\\t\\t\\tself.Full_E, self.Imaginary_Spectrum, self.NearEdgeData, self.splice_ind = data.merge_spectra(self.NearEdgeData, self.ASF_E, self.ASF_Data, merge_points=splice_eV, add_background=self.AddBackgroundCheckBox.GetValue(), fix_distortions=self.FixDistortionsCheckBox.GetValue(), plotting_extras=True)\\n\\n\\t\\t\\t### get start and end Y values from nexafs and asf data\\n\\t\\t\\t##splice_nexafs_Im = numpy.interp(splice_eV, raw_Im[:, 0], raw_Im[:, 1])\\n\\t\\t\\t###splice_asf_Im = numpy.interp(splice_eV, self.total_asf[:, 0], self.total_asf[:, 2])\\n\\t\\t\\t##splice_asf_Im = (data.coeffs_to_ASF(splice_eV[0],self.total_Im_coeffs[numpy.where(self.total_E<splice_eV[0])[0][-1]]),data.coeffs_to_ASF(splice_eV[1],self.total_Im_coeffs[numpy.where(self.total_E<splice_eV[1])[0][-1]]))\\n\\t\\t\\t##cut_boolean = (splice_eV[0]<raw_Im[:, 0]) == (raw_Im[:, 0]<splice_eV[1])\\n\\t\\t\\t### Merge Y values\\n\\t\\t\\t##if not self.AddBackgroundCheckBox.GetValue():\\n\\t\\t\\t\\t##logger.info(\\\"Merge data sets\\\")\\n\\t\\t\\t\\t##scale = (splice_asf_Im[1]-splice_asf_Im[0])/(splice_nexafs_Im[1]-splice_nexafs_Im[0])\\n\\t\\t\\t\\t##scaled_nexafs_Im = ((raw_Im[:, 1]-splice_nexafs_Im[0])*scale)+splice_asf_Im[0]\\n\\t\\t\\t\\t##self.asf_bg = None # We won't be using this variable this time\\n\\t\\t\\t##else:\\n\\t\\t\\t\\t##logger.info(\\\"Add data sets (this will currently only work at energies below 30 keV)\\\")\\n\\t\\t\\t\\t### Set up background function\\n\\t\\t\\t\\t### We trust this point to be just before the absorption edge\\n\\t\\t\\t\\t##trusted_ind = max(0, numpy.where(self.total_asf[:, 0]>splice_eV[0])[0][0]-1)\\n\\t\\t\\t\\t##Log_total_asf = numpy.log(self.total_asf[:, 2])\\n\\t\\t\\t\\t### Lets trust the 5 points before our trusted point and make an initial guess at the background function\\n\\t\\t\\t\\t##p = numpy.polyfit(self.total_asf[(trusted_ind-5):trusted_ind, 0], Log_total_asf[(trusted_ind-5):trusted_ind], 1)\\n\\t\\t\\t\\t### Now lets look for the points up util the absorption edge\\n\\t\\t\\t\\t##p_vals = numpy.exp(numpy.polyval(p, self.total_asf[(trusted_ind-5):-1, 0]))\\n\\t\\t\\t\\t##p_err = max(p_vals[0:5]-self.total_asf[(trusted_ind-5):trusted_ind, 2])\\n\\t\\t\\t\\t##edge_ind = numpy.where(self.total_asf[trusted_ind:-1, 2]-p_vals[4:-1]>p_err*10)\\n\\t\\t\\t\\t##if len(edge_ind[0])!=0:\\n\\t\\t\\t\\t\\t##edge_ind = edge_ind[0][0]\\n\\t\\t\\t\\t##else:\\n\\t\\t\\t\\t\\t##edge_ind = trusted_ind\\n\\t\\t\\t\\t### Redo background using the 5 points before the background point\\n\\t\\t\\t\\t##p = numpy.polyfit(self.total_asf[(trusted_ind+edge_ind-5):trusted_ind+edge_ind, 0], Log_total_asf[(trusted_ind+edge_ind-5):trusted_ind+edge_ind], 1)\\n\\t\\t\\t\\t##asf_bg = numpy.exp(numpy.polyval(p, raw_Im[:, 0]))\\n\\t\\t\\t\\t##logger.info(\\\"Background defined as: y=exp(%(p1)ex %(p0)+e)\\\" % {\\\"p1\\\":p[1], \\\"p0\\\":p[0]})\\n\\t\\t\\t\\t### Apply background function\\n\\t\\t\\t\\t##scale = (splice_asf_Im[1]-numpy.exp(numpy.polyval(p, splice_eV[1])))/splice_nexafs_Im[1]\\n\\t\\t\\t\\t##scaled_nexafs_Im = raw_Im[:, 1]*scale+asf_bg\\n\\t\\t\\t\\t### store background data for plotting\\n\\t\\t\\t\\t##cut_boolean_wide = numpy.roll(cut_boolean, -1) + numpy.roll(cut_boolean, 1)\\n\\t\\t\\t\\t##self.asf_bg = [[trusted_ind+edge_ind-5, trusted_ind+edge_ind], numpy.vstack((raw_Im[cut_boolean_wide, 0], asf_bg[cut_boolean_wide])).T]\\n\\t\\t\\t\\n\\t\\t\\t##nexafs_cut = numpy.vstack((raw_Im[cut_boolean, 0], scaled_nexafs_Im[cut_boolean])).T\\n\\t\\t\\t####Merge point-wise data sets together\\n\\t\\t\\t##asf_cut_high = self.total_asf[self.total_asf[:, 0]>splice_eV[1], :]\\n\\t\\t\\t##asf_cut_low = self.total_asf[self.total_asf[:, 0]<splice_eV[0], :]\\n\\t\\t\\t##self.merged_Im = numpy.vstack((asf_cut_low[:, [0, 2]], (splice_eV[0], splice_asf_Im[0]), nexafs_cut, (splice_eV[1], splice_asf_Im[1]), asf_cut_high[:, [0, 2]]))\\n\\t\\t\\t\\n\\t\\t\\t####Merge coeff data together\\n\\t\\t\\t##coeffs_cut_high = self.total_Im_coeffs[self.total_E[:-1]>splice_eV[1],:]\\n\\t\\t\\t##coeffs_cut_low = self.total_Im_coeffs[self.total_E[:-1]<splice_eV[0],:]\\n\\t\\t\\t###convert points to coeffs\\n\\t\\t\\t##nexafs_coeffs_cut = numpy.zeros((len(nexafs_cut)+1,5))\\n\\t\\t\\t##Y = numpy.append(numpy.insert(nexafs_cut[:,1],0,splice_asf_Im[0]),splice_asf_Im[1])\\n\\t\\t\\t##nexafs_E = numpy.append(numpy.insert(nexafs_cut[:,0],0,splice_eV[0]),splice_eV[1])\\n\\t\\t\\t##M = (Y[1:]-Y[:-1])/(nexafs_E[1:]-nexafs_E[:-1])\\n\\t\\t\\t##nexafs_coeffs_cut[:,0] = M\\n\\t\\t\\t##nexafs_coeffs_cut[:,1] = Y[:-1]-M*nexafs_E[:-1]\\n\\t\\t\\t###assemble merged coeffs and energy values\\n\\t\\t\\t##self.merged_Im_coeffs = numpy.vstack((coeffs_cut_low, nexafs_coeffs_cut, self.total_Im_coeffs[-coeffs_cut_high.shape[0]-2,:], coeffs_cut_high))\\n\\t\\t\\t##self.merged_E = numpy.concatenate((self.total_E[self.total_E<splice_eV[0]], nexafs_E, self.total_E[self.total_E>splice_eV[1]]))\\n\\t\\t\\t### Extras for plotting\\n\\t\\t\\t##self.splice_ind = (len(asf_cut_low[:, 0]), -len(asf_cut_high[:, 0]))\\n\\t\\t\\t##cut_boolean = (splice_eV[0]<=raw_Im[:, 0]) != (raw_Im[:, 0]<=splice_eV[1])\\n\\t\\t\\t##self.nexafs_CutOut = numpy.vstack((raw_Im[cut_boolean, 0], scaled_nexafs_Im[cut_boolean])).T\\n\\t\\t### Previous calculation of f_1 is no longer matching displayed f_2 data\\n\\t\\t##self.KK_Real_Spectrum = None\",\n \"def mergeLumis(inputdata, lumimask):\\n mergedlumis = LumiList()\\n doublelumis = LumiList()\\n for report in inputdata:\\n doublelumis = mergedlumis & LumiList(runsAndLumis=report)\\n mergedlumis = mergedlumis | LumiList(runsAndLumis=report)\\n return mergedlumis.getCompactList(), (LumiList(compactList=lumimask) - mergedlumis).getCompactList(), doublelumis.getCompactList()\",\n \"def toSeasonAggFormat(self):\\n df_a = self.toHomeAwayFormat().copy()\\n df_b = df_a.copy()\\n\\n for col in list(df_a.columns).copy():\\n if col[0] == \\\"H\\\":\\n df_a = df_a.rename(columns={col:(col[1:])})\\n\\n if col[0] == \\\"A\\\":\\n df_a = df_a.rename(columns={col:('Opp'+col[1:])})\\n\\n #df_a['Win'] = 1\\n\\n for col in list(df_b.columns).copy():\\n if col[0] == \\\"H\\\":\\n df_b = df_b.rename(columns={col:('Opp'+col[1:])})\\n\\n if col[0] == \\\"A\\\":\\n df_b = df_b.rename(columns={col:(col[1:])})\\n\\n df_c = df_a.append(df_b, sort=True)\\n\\n return df_c\",\n \"def transformation(self):\\n for key in self.combination_dict.keys():\\n if self.combination_dict[key]['column_count'] == 2:\\n if self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'tem' or self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'tem':\\n self.temporal_transformation(self.combination_dict[key])\\n elif self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'cat' or self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'cat':\\n self.categorical_transformation(self.combination_dict[key])\\n elif self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'num' and self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'num':\\n self.numerical_transformation(self.combination_dict[key])\\n\\n elif self.combination_dict[key]['column_count'] == 3:\\n num_count = 0\\n num_column = []\\n if self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'num':\\n num_count += 1\\n num_column.append(0)\\n elif self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'num':\\n num_count += 1\\n num_column.append(1)\\n elif self.data_dict[self.combination_dict[key]['column3']]['data_type'] == 'num':\\n num_count += 1\\n num_column.append(2)\\n\\n if num_count == 1:\\n self.three_column_groupby_logic(self.combination_dict[key], num_column)\\n\\n m_score_pie = []\\n m_score_bar = []\\n m_score_line = []\\n m_score_scatter = []\\n # for key in self.scenario_dict:\\n # if self.scenario_dict\\n for key in self.scenario_dict:\\n if math.isnan(self.scenario_dict[key][\\\"Scatter_chart_score\\\"]):\\n m_score_scatter.append(0)\\n else:\\n m_score_scatter.append(self.scenario_dict[key][\\\"Scatter_chart_score\\\"])\\n m_score_pie.append(self.scenario_dict[key][\\\"Pie_chart_score\\\"])\\n m_score_bar.append(self.scenario_dict[key][\\\"Bar_chart_score\\\"])\\n m_score_line.append(self.scenario_dict[key][\\\"Line_chart_score\\\"])\\n\\n m_score_pie /= np.max(m_score_pie)\\n m_score_bar /= np.max(m_score_bar)\\n m_score_line /= np.max(m_score_line)\\n m_score_scatter /= np.max(m_score_scatter)\\n m_score = [m_score_pie, m_score_bar, m_score_line, m_score_scatter]\\n match_index = np.argmax(m_score, axis = 0)\\n i = 0\\n for key in self.scenario_dict:\\n if match_index[i] == 0:\\n self.scenario_dict[key][\\\"Chart_Type\\\"] = \\\"pie\\\"\\n if match_index[i] == 1:\\n self.scenario_dict[key][\\\"Chart_Type\\\"] = \\\"bar\\\"\\n if match_index[i] == 2:\\n self.scenario_dict[key][\\\"Chart_Type\\\"] = \\\"line\\\"\\n if match_index[i] == 3:\\n self.scenario_dict[key][\\\"Chart_Type\\\"] = \\\"scatter\\\"\\n self.scenario_dict[key][\\\"m_score\\\"] = m_score[match_index[i]][i]\\n i += 1\\n\\n return self.scenario_dict\",\n \"def transform(self, dataset: NumpyOrPandas) -> NumpyDataset:\\n # checks here\\n super().transform(dataset)\\n # convert to accepted dtype and get attributes\\n dataset = dataset.to_pandas()\\n df = dataset.data\\n\\n # transform\\n roles = NumericRole()\\n outputs = []\\n for n, i in enumerate(df.columns):\\n new_arr = self.dicts[i][\\\"fg\\\"].transform(df[i].values)\\n output = dataset.empty().to_numpy()\\n output.set_data(new_arr, self.dicts[i][\\\"feats\\\"], roles)\\n outputs.append(output)\\n # create resulted\\n return dataset.empty().to_numpy().concat(outputs)\",\n \"def fix_fields(self):\\n males = self.df[\\\"Sex\\\"] == \\\"M\\\"\\n self.df[\\\"Sex\\\"] = np.array(males, dtype=int)\\n\\n logger.debug(\\\"Fixing bounded values...\\\")\\n self.fix_bounded_values()\\n logger.debug(\\\"Fixing range values...\\\")\\n self.fix_range_fields()\\n logger.debug(\\\"Fixing keyworded fields...\\\")\\n self.fix_keyword_fields()\\n logger.debug(\\\"Fixing temperature fields...\\\")\\n self.fix_temperature_fields()\\n logger.debug(\\\"Fixing nationality fields...\\\")\\n self.fix_nationality_field()\\n logger.debug(\\\"Fixing percentage fields...\\\")\\n self.fix_percentage_fields()\\n logger.debug(\\\"Combining fields...\\\")\\n self.combine_fields()\",\n \"def process_data(data):\\n nghds = find_expensive_neighborhoods(data, n=3, summary_statistic=np.mean)\\n data = ( data.pipe(add_total_bathrooms)\\n .pipe(add_power,'Gr_Liv_Area', 2)\\n .pipe(add_power,'Garage_Area', 2)\\n .pipe(add_expensive_neighborhood, nghds)\\n .pipe(select_columns, ['SalePrice',\\n 'Gr_Liv_Area',\\n 'Garage_Area',\\n 'Gr_Liv_Area2',\\n 'Garage_Area2',\\n 'TotalBathrooms',\\n 'in_expensive_neighborhood']) )\\n\\n data.dropna(inplace = True)\\n X = data.drop(['SalePrice'], axis = 1)\\n X = standardize(X)\\n y = data.loc[:, 'SalePrice']\\n y = standardize(y)\\n return X, y\",\n \"def MergeTR(data):\\n for i in range(1, data.shape[1], 2):\\n data.iloc[:, i] = data.iloc[:, i: i + 2].mean(axis=1)\\n\\n return data.drop(data.columns[[i + 1 for i in range(1, data.shape[1], 2)]], axis=\\\"columns\\\")\",\n \"def normalize(data):\\n row = np.size(data, 0) # number of data points\\n col = np.size(data, 1) # dimensionality of data points\\n for j in range(col):\\n # find the average for each column\\n col_sum = 0\\n for i in range(row):\\n col_sum = col_sum + data[i][j]\\n col_sum = col_sum / row\\n # subtract the average from each value in the column\\n for i in range(row):\\n data[i][j] = data[i][j] - col_sum\\n return data\",\n \"def normalize(data):\\n row = np.size(data, 0) # number of data points\\n col = np.size(data, 1) # dimensionality of data points\\n for j in range(col):\\n # find the average for each column\\n col_sum = 0\\n for i in range(row):\\n col_sum = col_sum + data[i][j]\\n col_sum = col_sum / row\\n # subtract the average from each value in the column\\n for i in range(row):\\n data[i][j] = data[i][j] - col_sum\\n return data\",\n \"def umls_cui_annotator(self, data: pd.DataFrame, key: str, code_level: str) -> pd.DataFrame:\\n\\n # reduce data to only those columns needed for merging\\n clinical_ids = data[[key, code_level]].drop_duplicates()\\n\\n # merge reduced clinical concepts with umls concepts\\n if self.umls_double_merge is True:\\n # merge 1 - align omop source codes to umls sabs\\n umls_cui_1 = clinical_ids.merge(self.umls_cui_data, how='inner', left_on=code_level, right_on='CODE')\\n # merge 2 - align umls cuis from merge 1 to cuis in full umls (this adds additional sabs not found in omop)\\n umls_cui_2 = umls_cui_1[[key, code_level, 'CUI']].merge(self.umls_cui_data, how='left', on='CUI')\\n umls_cui = pd.concat([umls_cui_1, umls_cui_2])\\n else:\\n umls_cui = clinical_ids.merge(self.umls_cui_data, how='inner', left_on=code_level, right_on='CODE')\\n\\n umls_cui_semtype = umls_cui.merge(self.umls_tui_data, how='left', on='CUI').drop_duplicates()\\n\\n # update column names\\n umls_cui_semtype.columns = [key, code_level, 'UMLS_CUI', 'UMLS_SAB', 'UMLS_CODE', 'UMLS_SEM_TYPE']\\n\\n return umls_cui_semtype\",\n \"def preprocess_data(df, min_vote_count=1000):\\n # note that order matters!\\n df = remove_rows_without_feature(df, 'budget')\\n df = remove_rows_without_feature(df, 'runtime')\\n df = remove_rows_with_non_english_movies(df)\\n df = binarize_homepage(df)\\n df = add_producers_feature(df)\\n df = add_executive_producers_feature(df)\\n df = get_movie_scores(df, min_vote_count)\\n df = binarize_english(df)\\n df = bin_ratings(df)\\n df = binarize_genres(df)\\n df = binarize_belongs_to_collection(df)\\n df = binarize_production_countries(df)\\n df = drop_unnecessary_columns(df)\\n\\n # Export to CSV\\n y = df[['rating']]\\n x = df.drop(['rating'], 1)\\n\\n y.to_csv(r'../dataset/Y.csv', index=False)\\n x.to_csv(r'../dataset/X.csv', index=False)\",\n \"def compute(self, df_1, df_2, col, catcol):\\n\\n assert col in df_1 and catcol in df_1\\n assert col in df_2 and catcol in df_2\\n\\n np_df_col_1 = np.asarray(df_1[col])\\n np_df_col_2 = np.asarray(df_2[col])\\n\\n mean = np.mean([np_df_col_1, np_df_col_2], axis=0)\\n diff = np_df_col_1 - np_df_col_2\\n md = np.mean(diff)\\n sd = np.std(diff, axis=0)\\n\\n categories = np.concatenate(np.unique(df_1[catcol]), np.unique(df_2[catcol]))\\n colors = cm.rainbow(np.linspace(0, 1, len(categories)))\\n colordict = list(dict(zip(categories, colors)))\\n\\n return {\\n \\\"axis\\\": catcol,\\n \\\"mean\\\": mean,\\n \\\"diff\\\": diff,\\n \\\"color\\\": colordict,\\n \\\"md\\\": md,\\n \\\"sd\\\": sd,\\n }\",\n \"def preprocess_mul(bd, var1, var2):\\n filepath_mv = f\\\"team67-ptp/data/{var1}_{var2}.csv\\\"\\n filepath = bd\\n data = feather.read_dataframe(filepath)\\n df = data.copy()\\n df2 = df[[var1, var2]]\\n if df2[var1].dtype is \\\"category\\\" and df2[var2].dtype is \\\"category\\\":\\n df2[var1] = df2[var1].astype(\\\"category\\\").cat.codes\\n df2[var2] = df2[var2].astype(\\\"category\\\").cat.codes\\n filename = filepath_mv\\n df2.to_csv(filename, index=False, encoding=\\\"utf-16\\\")\\n print(\\\"Succesfully exported to csv\\\")\\n elif df2[var1].dtype is \\\"category\\\" and df2[var2].dtype is not \\\"category\\\":\\n df2[var1] = df2[var1].astype(\\\"category\\\").cat.codes\\n filename = filepath_mv\\n df2.to_csv(filename, index=False, encoding=\\\"utf-16\\\")\\n print(\\\"Succesfully exported to csv\\\")\\n elif df2[var1].dtype is not \\\"category\\\" and df2[var2].dtype is \\\"category\\\":\\n df2[var2] = df2[var2].astype(\\\"category\\\").cat.codes\\n filename = filepath_mv\\n df2.to_csv(filename, index=False, encoding=\\\"utf-16\\\")\\n print(\\\"Succesfully exported to csv\\\")\\n else:\\n filename = filepath_mv\\n df2.to_csv(filename, index=False, encoding=\\\"utf-16\\\")\\n print(\\\"Succesfully exported to csv\\\")\",\n \"def normalize_data(self):\\r\\n # quantify data for each column except classification column for noise reduction\\r\\n for column_header in self.classification_training_data.columns:\\r\\n if column_header == \\\"Class\\\":\\r\\n continue\\r\\n if column_header == \\\"Age\\\":\\r\\n bin_size = 2\\r\\n elif column_header == \\\"Ht\\\":\\r\\n bin_size = 5\\r\\n else:\\r\\n bin_size = 1\\r\\n for idx in self.classification_training_data.index:\\r\\n self.classification_training_data.at[idx, column_header] = math.floor(\\r\\n self.classification_training_data[column_header][idx] / bin_size) * bin_size\",\n \"def data_column_conversion(data:pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame:\\n data = data.assign(W = (data.label == 'W') + 0,D = (data.label == 'D') + 0,L = (data.label == 'L') + 0)\\n data = data.drop(\\\"label\\\",axis=1)\\n return data\",\n \"def my_featurize(apartment):\\n col =np.array([1, 2, 0, 0, 0, 0, 0, 0 ])\\n a= pd.DataFrame(apartment[col])\\n if(apartment.get('condition')== 'good'):\\n col[1] =1\\n else:\\n if(apartment.get('condition')== 'zero condition'):\\n col[1] = 0\\n col[2] =apartment.get('num_rooms')\\n col[3] =apartment.get('area')\\n col[4] =apartment.get('num_bathrooms')\\n col[5] =apartment.get('floor')\\n col[6] =apartment.get('ceiling_height')\\n col[7] =apartment.get('max_floor')\\n\\n return col, apartment['price']\",\n \"def test_combine(self):\\n fc1 = self.read_feature()\\n fc2 = self.read_feature('Aegean_Sea')\\n fc1.merge(fc2)\\n name = 'Weird Disjoint Regions'\\n combined = fc1.combine(name)\\n assert len(combined.features) == 1\\n self.check_feature(combined.features[0], expected_name=name,\\n expected_type='MultiPolygon')\",\n \"def _convert_column_uncertainty(self, col):\\n col_new = []\\n for x, dx in zip(self[col], self[col + \\\" Unc.\\\"]):\\n x2 = _parse_float_uncertainty(x, dx)\\n col_new.append(x2)\\n self.df[col] = col_new\\n del self.df[col + \\\" Unc.\\\"]\",\n \"def format_data(self, data):\\n # data or full_score is a list of tuples, here return just NPClassScore\\n formatted_data = None # default when there is no score (missing class)\\n if data:\\n # there is a score\\n formatted_data = f'{data[0][0]:.3f}'\\n return formatted_data\",\n \"def _rewrite_concat(self, node: saldag.Concat):\\n\\n # Copy over columns from existing relation\\n out_rel_cols = node.out_rel.columns\\n\\n # Combine per-column collusion sets\\n for idx, col in enumerate(out_rel_cols):\\n columns_at_idx = [in_rel.columns[idx] for in_rel in node.get_in_rels()]\\n col.coll_sets = utils.coll_sets_from_columns(columns_at_idx)\",\n \"def prep_tree_data(self, number: int):\\n filename = \\\"data-before-normalization-{}-out-of-7.csv\\\".format(number)\\n path = str(DATA_PATH.joinpath(\\\"data-splitted\\\", filename))\\n df = pandas.read_csv(path)\\n\\n df.drop(df.columns[0], axis=1, inplace=True)\\n assessments = [x for x in df.columns.values if x.split(\\\"_\\\")[0] == \\\"assessment\\\"]\\n df['average_score'] = df[assessments].mean(skipna=True, axis=1)\\n for assessment in assessments: # somehow he doesn't want to fillna in a batch?\\n df[assessment].fillna(df['average_score'], inplace=True)\\n clicks = [x for x in df.columns.values if x.split(\\\"_\\\")[0] == \\\"vle\\\"]\\n df['vle_click_average'] = df[clicks].mean(skipna=True, axis=1)\\n for click in clicks: # somehow he doesn't want to fillna in a batch?\\n df[click].fillna(df['vle_click_average'], inplace=True)\\n df.dropna()\\n\\n self.change_oh_cat(\\\"gender\\\", df)\\n self.change_oh_cat(\\\"highest_education\\\", df)\\n self.change_oh_cat(\\\"imd_band\\\", df)\\n self.change_oh_cat(\\\"age_band\\\", df)\\n self.change_oh_cat(\\\"disability\\\", df)\\n result_order = {'final_result__Fail': 0, 'final_result__Withdrawn': 2,\\n 'final_result__Pass': 1, 'final_result__Distinction': 3}\\n self.change_oh_cat(\\\"final_result\\\", df, result_order)\\n df[\\\"final_result\\\"].replace(2, 0, inplace=True)\\n df[\\\"final_result\\\"].replace(3, 1, inplace=True)\\n\\n target = df[\\\"final_result\\\"]\\n df.drop([\\\"final_result\\\"], axis=1, inplace=True)\\n\\n x_train, x_test, y_train, y_test = train_test_split(df, target, test_size=0.1,\\n random_state=32, shuffle=True,\\n stratify=target)\\n\\n return x_train, x_test, y_train, y_test\",\n \"def merge_sort(self, data):\\n print \\\"Splitting\\\", data\\n if len(data) > 1:\\n mid = len(data)// 2\\n left_half = data[:mid]\\n right_half = data[mid:]\\n self.merge_sort(left_half)\\n self.merge_sort(right_half)\\n i,j,k = (0,0,0)\\n while i< len(left_half) and j<len(right_half):\\n if left_half[i] < right_half[j]:\\n data[k] = left_half[i]\\n i += 1\\n else:\\n data[k] = right_half[j]\\n j += 1\\n k += 1\\n #take care of the unbalance\\n while i < len(left_half):\\n data[k] = left_half[i]\\n i += 1\\n k += 1\\n while j < len(right_half):\\n data[k] = right_half[j]\\n j += 1\\n k += 1\\n print \\\"merging\\\", data\",\n \"def print_rouge_scores(scores):\\n rouge_1 = scores['rouge-1']\\n rouge_2 = scores['rouge-2']\\n rouge_l = scores['rouge-l']\\n\\n print(\\\"┌─────────┬────────┬────────┬────────┐\\\")\\n print(\\\"│ Metric │ Pre │ Rec │ F │\\\")\\n print(\\\"├─────────┼────────┼────────┼────────┤\\\")\\n print(\\\"│ ROUGE-1 │ %.4f │ %.4f │ %.4f │\\\" % (rouge_1['p'], rouge_1['r'], rouge_1['f']))\\n print(\\\"│ ROUGE-2 │ %.4f │ %.4f │ %.4f │\\\" % (rouge_2['p'], rouge_2['r'], rouge_2['f']))\\n print(\\\"│ ROUGE-L │ %.4f │ %.4f │ %.4f │\\\" % (rouge_l['p'], rouge_l['r'], rouge_l['f']))\\n print(\\\"└─────────┴────────┴────────┴────────┘\\\")\",\n \"def grade_boro(boroughts,data):\\n boro_df=data.iloc[np.where(data.BORO==boroughts)]\\n return total_grade(boro_df)\",\n \"def normalized_rankings(df, columns=['Lust', 'Envy', 'Greed', 'Sloth', 'Wrath',\\n 'Pride', 'Gluttony']):\\n df[columns] = (df[columns] - 4) / 2 # hard coding n=7 case for now\",\n \"def test_degrade_map_recarray(self):\\n random.seed(seed=12345)\\n\\n nside_coverage = 32\\n nside_map = 1024\\n nside_new = 256\\n\\n dtype = [('col1', 'f8'), ('col2', 'f8'), ('col3', 'i4')]\\n sparse_map = healsparse.HealSparseMap.make_empty(nside_coverage, nside_map, dtype, primary='col1')\\n pixel = np.arange(20000)\\n values = np.zeros_like(pixel, dtype=dtype)\\n values['col1'] = random.random(size=pixel.size)\\n values['col2'] = random.random(size=pixel.size)\\n values['col3'] = random.poisson(size=pixel.size, lam=2)\\n sparse_map.update_values_pix(pixel, values)\\n\\n ra, dec = hpg.pixel_to_angle(nside_map, pixel)\\n\\n # Make the test values\\n hpmap_col1 = np.zeros(hpg.nside_to_npixel(nside_map)) + hpg.UNSEEN\\n hpmap_col2 = np.zeros(hpg.nside_to_npixel(nside_map)) + hpg.UNSEEN\\n hpmap_col3 = np.zeros(hpg.nside_to_npixel(nside_map)) + hpg.UNSEEN\\n hpmap_col1[pixel] = values['col1']\\n hpmap_col2[pixel] = values['col2']\\n hpmap_col3[pixel] = values['col3']\\n\\n # Degrade healpix maps\\n hpmap_col1 = hp.ud_grade(hpmap_col1, nside_out=nside_new, order_in='NESTED', order_out='NESTED')\\n hpmap_col2 = hp.ud_grade(hpmap_col2, nside_out=nside_new, order_in='NESTED', order_out='NESTED')\\n hpmap_col3 = hp.ud_grade(hpmap_col3, nside_out=nside_new, order_in='NESTED', order_out='NESTED')\\n ipnest_test = hpg.angle_to_pixel(nside_new, ra, dec)\\n\\n # Degrade the old map\\n new_map = sparse_map.degrade(nside_out=nside_new)\\n testing.assert_almost_equal(new_map.get_values_pos(ra, dec, lonlat=True)['col1'],\\n hpmap_col1[ipnest_test])\\n testing.assert_almost_equal(new_map.get_values_pos(ra, dec, lonlat=True)['col2'],\\n hpmap_col2[ipnest_test])\\n testing.assert_almost_equal(new_map.get_values_pos(ra, dec, lonlat=True)['col3'],\\n hpmap_col3[ipnest_test])\\n\\n # Test degrade-on-read\\n self.test_dir = tempfile.mkdtemp(dir='./', prefix='TestHealSparse-')\\n\\n fname = os.path.join(self.test_dir, 'test_recarray_degrade.hs')\\n sparse_map.write(fname)\\n\\n new_map2 = healsparse.HealSparseMap.read(fname, degrade_nside=nside_new)\\n\\n testing.assert_almost_equal(new_map2.get_values_pos(ra, dec, lonlat=True)['col1'],\\n hpmap_col1[ipnest_test])\\n testing.assert_almost_equal(new_map2.get_values_pos(ra, dec, lonlat=True)['col2'],\\n hpmap_col2[ipnest_test])\\n testing.assert_almost_equal(new_map2.get_values_pos(ra, dec, lonlat=True)['col3'],\\n hpmap_col3[ipnest_test])\",\n \"def test_load_then_featurize_data_multiple_columns():\\n feat = ImageFeaturizer(auto_sample=True)\\n feat.load_data(**LOAD_DATA_ARGS_MULT)\\n feat.featurize_preloaded_data(save_features=True)\\n check_array = np.load(CHECK_ARRAY_MULT.format('squeezenet'))\\n\\n try:\\n compare_featurizer_class(feat, (227, 227), check_array, featurized=True,\\n check_csv=CHECK_CSV_MULT.format('squeezenet'), **COMPARE_ARGS_MULT)\\n\\n finally:\\n # Remove path to the generated csv at the end of the test\\n remove_generated_paths()\\n del feat\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5142071","0.5063618","0.49147916","0.48610595","0.4855685","0.48401833","0.48022565","0.47576785","0.47571915","0.47113907","0.4704346","0.46972775","0.46791285","0.465144","0.46333814","0.46222302","0.46004865","0.45951855","0.45889464","0.45674625","0.45648956","0.45494893","0.45424545","0.45346707","0.4530931","0.44993272","0.4498462","0.44894275","0.44876686","0.4485726","0.44843847","0.44771388","0.4466288","0.4457639","0.44543684","0.44521955","0.4446785","0.44390705","0.44308317","0.4429627","0.44203517","0.44150096","0.44087097","0.4401935","0.43969595","0.43960965","0.43959293","0.4393969","0.43874493","0.43866557","0.4383595","0.4380758","0.43799904","0.43723387","0.43605143","0.43599916","0.43582442","0.43561023","0.4346355","0.43425828","0.43417913","0.4340543","0.43381438","0.4336629","0.43356612","0.43284717","0.43262595","0.43233806","0.43207708","0.4318993","0.43144888","0.43128714","0.43097746","0.43083248","0.43060476","0.43014276","0.42995933","0.42989543","0.4297828","0.42972773","0.42964306","0.42964306","0.42936268","0.42917746","0.42866197","0.42778215","0.42772937","0.4276389","0.42731047","0.4270744","0.42694047","0.426796","0.42677337","0.42620045","0.42620045","0.4259294","0.425835","0.4258019","0.42497256","0.42461112"],"string":"[\n \"0.5142071\",\n \"0.5063618\",\n \"0.49147916\",\n \"0.48610595\",\n \"0.4855685\",\n \"0.48401833\",\n \"0.48022565\",\n \"0.47576785\",\n \"0.47571915\",\n \"0.47113907\",\n \"0.4704346\",\n \"0.46972775\",\n \"0.46791285\",\n \"0.465144\",\n \"0.46333814\",\n \"0.46222302\",\n \"0.46004865\",\n \"0.45951855\",\n \"0.45889464\",\n \"0.45674625\",\n \"0.45648956\",\n \"0.45494893\",\n \"0.45424545\",\n \"0.45346707\",\n \"0.4530931\",\n \"0.44993272\",\n \"0.4498462\",\n \"0.44894275\",\n \"0.44876686\",\n \"0.4485726\",\n \"0.44843847\",\n \"0.44771388\",\n \"0.4466288\",\n \"0.4457639\",\n \"0.44543684\",\n \"0.44521955\",\n \"0.4446785\",\n \"0.44390705\",\n \"0.44308317\",\n \"0.4429627\",\n \"0.44203517\",\n \"0.44150096\",\n \"0.44087097\",\n \"0.4401935\",\n \"0.43969595\",\n \"0.43960965\",\n \"0.43959293\",\n \"0.4393969\",\n \"0.43874493\",\n \"0.43866557\",\n \"0.4383595\",\n \"0.4380758\",\n \"0.43799904\",\n \"0.43723387\",\n \"0.43605143\",\n \"0.43599916\",\n \"0.43582442\",\n \"0.43561023\",\n \"0.4346355\",\n \"0.43425828\",\n \"0.43417913\",\n \"0.4340543\",\n \"0.43381438\",\n \"0.4336629\",\n \"0.43356612\",\n \"0.43284717\",\n \"0.43262595\",\n \"0.43233806\",\n \"0.43207708\",\n \"0.4318993\",\n \"0.43144888\",\n \"0.43128714\",\n \"0.43097746\",\n \"0.43083248\",\n \"0.43060476\",\n \"0.43014276\",\n \"0.42995933\",\n \"0.42989543\",\n \"0.4297828\",\n \"0.42972773\",\n \"0.42964306\",\n \"0.42964306\",\n \"0.42936268\",\n \"0.42917746\",\n \"0.42866197\",\n \"0.42778215\",\n \"0.42772937\",\n \"0.4276389\",\n \"0.42731047\",\n \"0.4270744\",\n \"0.42694047\",\n \"0.426796\",\n \"0.42677337\",\n \"0.42620045\",\n \"0.42620045\",\n \"0.4259294\",\n \"0.425835\",\n \"0.4258019\",\n \"0.42497256\",\n \"0.42461112\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7066124"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":766,"numItemsPerPage":100,"numTotalItems":95772,"offset":76600,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjQ4MjY2MSwianRpIjoiOTEyNDYzYWItMjRiMi00YzNkLWJkZjItYjI2ZjUwNzRiNmM5Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODY0ODYyNjEsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.mhW6wa9XPHz2K0tlvGSBLsOCPijlVoE-3oZxLzQwHmWsyg8Z0ZGQPXFE7xhm6KEJKWsMShXXUZSgXghtZP0rDA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Search the shallowest nodes in the search tree first. | def breadthFirstSearch(problem):
# Initialization
startState = problem.getStartState()
#print "Start:", startState
if problem.isGoalState(startState):
return [] # No action needed
route = util.Stack()
closed = set([startState])
queue = util.Queue() # BFS use queue
#print probl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def depth_first_search(self):\r\n queue = [self.root]\r\n ordered = []\r\n while queue:\r\n node = queue.pop()\r\n ordered.append(node)\r\n queue.extend(node.children)\r\n \r\n while ordered:\r\n yield ordered.pop()",
"def depth_first... | [
"0.69212437",
"0.6735283",
"0.65919083",
"0.6531888",
"0.65225047",
"0.6516331",
"0.64351237",
"0.63715476",
"0.6349844",
"0.63109314",
"0.6268638",
"0.6257352",
"0.6188826",
"0.61808306",
"0.61757225",
"0.60988986",
"0.6075095",
"0.60709614",
"0.6029654",
"0.60286295",
"0.60... | 0.0 | -1 |
Search the node of least total cost first. | def uniformCostSearch(problem):
# Initialization
startState = problem.getStartState()
if problem.isGoalState(startState):
return [] # No action needed
closedSet = set()
queue = util.PriorityQueue()
queue.push((startState, None, 0), 0)
cameFrom = dict() # Stores most efficient previ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _find_lowest_cost_node(self) -> str:\n lowest_cost = float(\"inf\")\n lowest_cost_node = None\n for node in self.costs:\n cost = self.costs[node]\n if cost < lowest_cost and node not in self.closed_nodes:\n lowest_cost = cost\n lowest_cos... | [
"0.7481386",
"0.6694703",
"0.6617916",
"0.66044676",
"0.65461653",
"0.6504626",
"0.64884573",
"0.647909",
"0.6447552",
"0.6389625",
"0.6388501",
"0.63831747",
"0.63667625",
"0.63632995",
"0.6247147",
"0.62458867",
"0.6236465",
"0.62307185",
"0.6224366",
"0.6224366",
"0.620009... | 0.0 | -1 |
A heuristic function estimates the cost from the current state to the nearest goal in the provided SearchProblem. This heuristic is trivial. | def nullHeuristic(state, problem=None):
return 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def uniformCostSearch(problem):\n # Initialization\n startState = problem.getStartState()\n\n if problem.isGoalState(startState):\n return [] # No action needed\n\n closedSet = set()\n queue = util.PriorityQueue()\n queue.push((startState, None, 0), 0)\n cameFrom = dict() # Stores most ... | [
"0.7426833",
"0.74019325",
"0.7289642",
"0.7283483",
"0.725888",
"0.7187531",
"0.71825695",
"0.71577257",
"0.7100921",
"0.7079827",
"0.70684016",
"0.70438904",
"0.7041257",
"0.70360833",
"0.70249933",
"0.70163226",
"0.7000325",
"0.6999366",
"0.6981361",
"0.696439",
"0.6958896... | 0.0 | -1 |
Search the node that has the lowest combined cost and heuristic first. | def aStarSearch(problem, heuristic=nullHeuristic):
# Initialization
startState = problem.getStartState()
if problem.isGoalState(startState):
return [] # No action needed
closedSet = set()
queue = util.PriorityQueue()
queue.push((startState, None, 0), heuristic(startState, problem))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _find_lowest_cost_node(self) -> str:\n lowest_cost = float(\"inf\")\n lowest_cost_node = None\n for node in self.costs:\n cost = self.costs[node]\n if cost < lowest_cost and node not in self.closed_nodes:\n lowest_cost = cost\n lowest_cos... | [
"0.74892557",
"0.73000836",
"0.71319556",
"0.6932297",
"0.69006664",
"0.68994766",
"0.6878126",
"0.6841432",
"0.68067044",
"0.6788856",
"0.6784744",
"0.6753299",
"0.6739961",
"0.6634752",
"0.66336495",
"0.66227347",
"0.6616595",
"0.65899295",
"0.65704364",
"0.65478617",
"0.65... | 0.59718484 | 98 |
If the lowered text is 'true' or 'false' the appropriate boolean is returned | def load(text: str, options: Dict[str, str]) -> bool:
text = text.strip().lower()
if text in {"true", "yes", "on"}:
return True
if text in {"false", "no", "off"}:
return False
raise LoadingError("Can't determine boolean value") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def booleanize(text):\n ltext = text.lower()\n if ltext == 'true':\n booleanized = True\n elif ltext == 'false':\n booleanized = False\n else:\n raise ValueError('A monk asked: Is \"{}\" true or false.'.format(text))\n return booleanized",
"def convertStringToBool(nodeText):\n... | [
"0.7524646",
"0.6901851",
"0.66677165",
"0.6506218",
"0.6444906",
"0.64205945",
"0.63552976",
"0.6349662",
"0.63489693",
"0.6329074",
"0.6318621",
"0.63013846",
"0.6268736",
"0.62516916",
"0.62137383",
"0.6187557",
"0.61653936",
"0.61290747",
"0.6118757",
"0.6102744",
"0.6065... | 0.59432244 | 26 |
Helper function to print a summary of a classifier performance | def print_summary(accuracies, group, df):
p_ids = np.unique(group)
print("Accuracies: ")
for accuracy, p_id in zip(accuracies, p_ids):
print(f"Participant {p_id}: accuracy = {accuracy}")
num_window_baseline = len(df[(df['id'] == p_id) & (df['is_hot'] == 0)].to_numpy())
num_window_pa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_brief_summary(self):\n print (\"Model {}\".format(self.modelName))\n print (\"Precision {}\".format(self.precision))\n print (\"Recall {}\".format(self.recall))\n print (\"f1 score {}\".format(self.f1))\n \n # work here\n print (\"\\nGold NER label... | [
"0.7270726",
"0.70429623",
"0.69756776",
"0.69746333",
"0.69292647",
"0.68630636",
"0.68172073",
"0.67152035",
"0.6708259",
"0.6703403",
"0.66761726",
"0.666146",
"0.6644447",
"0.6610819",
"0.65767056",
"0.6573381",
"0.6570995",
"0.65342104",
"0.65081817",
"0.64916724",
"0.64... | 0.61948866 | 51 |
Takes a text file of filenames and makes a list of filenames | def files_to_list(filename):
with open(filename, encoding='utf-8') as f:
files = f.readlines()
files = [f.rstrip() for f in files]
return files | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def files_to_list(filename):\n curdir = Path(filename).parent\n outs = []\n with open(filename, encoding=\"utf-8\") as fin:\n for line in fin:\n if line.strip():\n fname = Path(line.strip().split(\"\\t\")[0])\n if fname.exists():\n outs.ap... | [
"0.7637841",
"0.7474673",
"0.7343156",
"0.7303888",
"0.7272115",
"0.7217177",
"0.72108996",
"0.720633",
"0.7137104",
"0.70621216",
"0.7039949",
"0.7039732",
"0.70373243",
"0.69536304",
"0.69348955",
"0.6909082",
"0.68814677",
"0.6870362",
"0.6851389",
"0.68349504",
"0.6805668... | 0.7483208 | 1 |
Loads wavdata into torch array | def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
return torch.from_numpy(data).float(), sampling_rate | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_wav_to_torch(self, full_path):\n data, sampling_rate = load(full_path, sr=self.sampling_rate)\n data = 0.95 * normalize(data)\n\n if self.augment:\n amplitude = np.random.uniform(low=0.3, high=1.0)\n data = data * amplitude\n\n return torch.from_numpy(data... | [
"0.71620315",
"0.66123015",
"0.63704735",
"0.6138623",
"0.61151135",
"0.6111932",
"0.6079597",
"0.6057111",
"0.6023101",
"0.6000103",
"0.5971272",
"0.594401",
"0.59429103",
"0.5886555",
"0.58604324",
"0.5857195",
"0.58190787",
"0.58040625",
"0.5787966",
"0.57829833",
"0.57731... | 0.7503719 | 0 |
Clears all of the tables that will be populated. | def clear_tables(cursor):
cursor.execute("delete from Review_Votes")
cursor.execute("delete from Review") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clear_tables(self):\n for table in self.modified_tables:\n self.api.do_table_clear(table)\n self.modified_tables = []",
"def empty_tables(self):\n for table in TABLES_TO_EMPTY:\n self.empty_table(table)",
"def empty_tables():\n Wordform.objects.all().delete()\n... | [
"0.860994",
"0.8198014",
"0.8091217",
"0.80763143",
"0.8029768",
"0.79402643",
"0.78967285",
"0.78723335",
"0.78344613",
"0.7790497",
"0.7782716",
"0.77736187",
"0.7606922",
"0.75869834",
"0.74377674",
"0.73800296",
"0.73114467",
"0.729961",
"0.7238571",
"0.7226146",
"0.71941... | 0.76389 | 12 |
Drops indexes from all of the tables that will be populated | def drop_indexes(cursor):
try:
cursor.execute("DROP INDEX Review_Business_id_index ON review")
cursor.execute("DROP INDEX Review_date_index ON review")
cursor.execute("DROP INDEX Review_stars_index ON review")
cursor.execute("DROP INDEX Review_user_id_index ON review")
cursor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def drop_non_unique_indexes(self):\n for idx in self.droppable_indexes:\n log.info(\"Dropping index '{}' on intermediate table\".format(idx.name))\n self.ddl_guard()\n self.execute_sql(sql.drop_index(idx.name, self.new_table_name))",
"def setup():\n with get_session() a... | [
"0.75803274",
"0.7347539",
"0.72600216",
"0.72363985",
"0.7221425",
"0.7056768",
"0.70466244",
"0.68622136",
"0.68118054",
"0.6661401",
"0.6569707",
"0.656742",
"0.6566741",
"0.6531738",
"0.6496841",
"0.64940965",
"0.6480109",
"0.6465053",
"0.64434534",
"0.6427655",
"0.639116... | 0.74130267 | 1 |
Creates indexes on all of the tables that will be (have been) populated | def create_indexes(cursor):
try:
cursor.execute("CREATE INDEX Review_Business_id_index ON review (business_id)")
cursor.execute("CREATE INDEX Review_date_index ON review (review_date)")
cursor.execute("CREATE INDEX Review_stars_index ON review (stars)")
cursor.execute("CREATE INDEX R... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_index_tables(self):\n # List of urls that have been indexed\n self.con.execute('create table urllist(url)')\n # List of words\n self.con.execute('create table wordlist(word)')\n # What doc the word is and where it is in the doc\n self.con.execute('create table w... | [
"0.7511958",
"0.74734473",
"0.74729383",
"0.74468094",
"0.7099334",
"0.70934683",
"0.703564",
"0.697659",
"0.69103444",
"0.686805",
"0.6862599",
"0.68624216",
"0.68158984",
"0.67738",
"0.6735264",
"0.67322457",
"0.67276037",
"0.6716077",
"0.66908944",
"0.6667861",
"0.6622081"... | 0.6693698 | 18 |
Read in the json data set file and load into database | def parse_file(file_path, batch_size=100, how_many=-1):
db = MySQLdb.connect(**login_info)
# From http://stackoverflow.com/questions/3942888/unicodeencodeerror-latin-1-codec-cant-encode-character
db.set_character_set('utf8')
cursor = db.cursor()
# From http://stackoverflow.com/questions/3942888/u... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_db(self):\n with open(self.filename, 'r') as database:\n data = json.load(database)\n self.data = data",
"def load_from_json(path_to_db):\r\n with open(path_to_db, 'r') as fproc:\r\n data_ = json.load(fproc)\r\n \r\n return data_",
"def import_local_jso... | [
"0.77956593",
"0.7332696",
"0.73022217",
"0.6952608",
"0.6949045",
"0.6850796",
"0.66739786",
"0.6601182",
"0.65837497",
"0.6536796",
"0.6525358",
"0.65125436",
"0.64937377",
"0.64803547",
"0.6470408",
"0.6457074",
"0.6454217",
"0.6452688",
"0.63827",
"0.63395876",
"0.6338714... | 0.0 | -1 |
This function persists a list of YelpReview objects. Original implementations persisted each one individually, which performed slowly. This one accepts a collection and takes advantage of parameterized queries to persist in batches | def persist_list_o_review_objects(list_o_yros, cursor):
review_data = []
review_set_count = 0
review_votes_data = []
review_votes_set_count = 0
for yro in list_o_yros:
review_data += [yro.review_id, yro.business_id, yro.user_id, yro.stars, yro.review_text, yro.review_date]
review_se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_all(self, obj_list):\n\n for obj in obj_list:\n self.save(obj)",
"def save_many(self, values, expires_in=None):\n raise NotImplementedError()",
"def save_all(self):\r\n for index in range(self.count()):\r\n self.save(index)",
"def save_all(objs: List[ModelB... | [
"0.6075593",
"0.584283",
"0.5832359",
"0.58084935",
"0.5804483",
"0.57376075",
"0.5726918",
"0.5712181",
"0.56696975",
"0.55989873",
"0.55971265",
"0.557263",
"0.54924774",
"0.5456378",
"0.5439148",
"0.5396648",
"0.5394186",
"0.53573656",
"0.53533447",
"0.53365165",
"0.528779... | 0.69471145 | 0 |
Saves a single YelpReview object to database. This is the original implementation but it turns out that this was poorly performing, so I scrapped it in favor of persist_list_o_business_objects | def persist_review_object(yro, cursor):
try:
# Review
sql = " INSERT INTO Review " \
" (review_id, business_id, user_id, stars, review_text, review_date) " \
" VALUES " \
" (%s, %s, %s, %s, %s, %s) "
cursor.execute(sql, [yro.review_id, yro.business_i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def persist_list_o_review_objects(list_o_yros, cursor):\n\n review_data = []\n review_set_count = 0\n review_votes_data = []\n review_votes_set_count = 0\n for yro in list_o_yros:\n review_data += [yro.review_id, yro.business_id, yro.user_id, yro.stars, yro.review_text, yro.review_date]\n ... | [
"0.62934196",
"0.57796127",
"0.5716861",
"0.553972",
"0.54539156",
"0.5425099",
"0.5403932",
"0.5344349",
"0.52043295",
"0.52019465",
"0.5195939",
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"0.5138815",
"0.5138815",
"0.5138815",
"0.5138815",
"0.5138815",
"0.5138815",
"0.5138815"... | 0.674485 | 0 |
Draw heatmaps of GT or prediction. | def draw_instance_xy_heatmap(self,
heatmap: torch.Tensor,
overlaid_image: Optional[np.ndarray],
n: int = 20,
mix: bool = True,
weight: float = 0.5):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_heatmap(self, y_pred: np.ndarray, output_dir: Path) -> None:\n df = pd.crosstab(\n pd.Series(self.y_test),\n pd.Series(y_pred),\n rownames=[\"True:\"],\n colnames=[\"Predicted:\"],\n margins=True,\n )\n plt.figure()\n sns.h... | [
"0.63661927",
"0.6329287",
"0.62715816",
"0.62563485",
"0.62451196",
"0.6241104",
"0.6234506",
"0.6222076",
"0.62010497",
"0.61973405",
"0.61956346",
"0.61879754",
"0.6177805",
"0.6177805",
"0.6175374",
"0.6162437",
"0.60854924",
"0.6061174",
"0.6035436",
"0.6029675",
"0.5990... | 0.0 | -1 |
Extract onedimensional heatmap from twodimensional heatmap and calculate the number of keypoint. | def split_simcc_xy(self, heatmap: Union[np.ndarray, torch.Tensor]):
size = heatmap.size()
k = size[0] if size[0] <= 20 else 20
maps = []
for _ in range(k):
xy_dict = {}
single_heatmap = heatmap[_]
xy_dict['x'], xy_dict['y'] = self.merge_maps(single_hea... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_nb_vals(i, pnts, dem, top_left_cor, cellsize, rows, cols):\n nb_x = np.zeros((5,5)) # this 5 by 5 max would contain the x coordinate of 16 neighbor pixels of a sample point\n nb_y = np.zeros((5,5)) # this 5 by 5 matrix would contain the y coordinate of 16 neighbor pixels of a sample point\n nb_z =... | [
"0.5875513",
"0.57561564",
"0.5722097",
"0.56651795",
"0.56617665",
"0.5555297",
"0.55204695",
"0.55097175",
"0.54291666",
"0.5423284",
"0.5420427",
"0.54138064",
"0.5410358",
"0.5395294",
"0.53594035",
"0.5349509",
"0.5343586",
"0.5334859",
"0.53226465",
"0.53174984",
"0.530... | 0.5741452 | 2 |
Synthesis of onedimensional heatmap. | def merge_maps(self, map_2d):
x = map_2d.data.max(0, keepdim=True)[0]
y = map_2d.data.max(1, keepdim=True)[0]
return x, y | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def manipulate_heat_data(self): \n self.exh.T_array = ( 0.5 * (self.exh.T_inlet_array +\n self.exh.T_outlet_array) + 273.15)\n self.exh.delta_T_array = ( self.exh.T_inlet_array -\n self.exh.T_outlet_array )\n \n self.cool.delta_T_array = ( self.cool.T_inlet_array -... | [
"0.6389852",
"0.62525254",
"0.6004237",
"0.59933597",
"0.58478814",
"0.5792686",
"0.5781802",
"0.5777574",
"0.574419",
"0.57376945",
"0.57200783",
"0.5710218",
"0.56996936",
"0.56954104",
"0.566794",
"0.5607255",
"0.5584501",
"0.5583896",
"0.5573592",
"0.5570283",
"0.55618614... | 0.0 | -1 |
Draw a twodimensional heatmap fused with the original image. | def draw_2d_heatmaps(self, heatmap_2d):
np_heatmap = ToPILImage()(heatmap_2d).convert('RGB')
cv_img = cv.cvtColor(np.asarray(np_heatmap), cv.COLOR_RGB2BGR)
map_2d = cv.applyColorMap(cv_img, cv.COLORMAP_JET)
return map_2d | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_heat_map(image, heat_map):\n return image",
"def heat_map(path):\r\n x, y= np.loadtxt(fname=path, delimiter='\\t',dtype=int,\r\n usecols = (1,2), skiprows=100, unpack = True)\r\n\r\n fig, (ax,ax2) = plt.subplots(nrows=2, sharex=True, figsize=(20,10))\r\n\r\n extent = [x[0... | [
"0.74944293",
"0.6625564",
"0.6494976",
"0.6417634",
"0.6353444",
"0.60608757",
"0.6025617",
"0.59894353",
"0.59773195",
"0.59464633",
"0.5894591",
"0.58209354",
"0.581542",
"0.58102053",
"0.580214",
"0.580186",
"0.5799282",
"0.57984924",
"0.5793625",
"0.57743084",
"0.5765347... | 0.7041537 | 1 |
Paste the foreground on the background. | def image_cover(self, background: np.ndarray, foreground: np.ndarray,
x: int, y: int):
fore_size = foreground.shape
background[y:y + fore_size[0], x:x + fore_size[1]] = foreground
return background | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_foreground(self):\n \n # crop, resize, color quantize\n self.crop_to_gridline()\n self.crop_title()\n self.set_size()\n self.color_quantization()\n self.display()\n img_and_pix = self.separate_colors()\n colors, images, pixels = zip(*img_and_pi... | [
"0.65060604",
"0.6462951",
"0.619184",
"0.6119588",
"0.600456",
"0.5852422",
"0.5847824",
"0.5837442",
"0.5816989",
"0.5773358",
"0.57702744",
"0.57267535",
"0.57213074",
"0.56553453",
"0.559615",
"0.5578791",
"0.5566179",
"0.5562031",
"0.5551797",
"0.5535956",
"0.54598844",
... | 0.5635058 | 14 |
Paste onedimensional heatmaps onto the background in turn. | def add_1d_heatmaps(self,
maps: dict,
background: np.ndarray,
map2d_size: Union[tuple, list],
K: int,
interval: int = 10):
y_startpoint, x_startpoint = [int(1.1*map2d_size[1]),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _cmd_heatmap(args):\n cnarrs = []\n for fname in args.filenames:\n cnarr = read_cna(fname)\n if args.adjust_xy:\n is_sample_female = verify_sample_sex(\n cnarr, args.sample_sex, args.male_reference, args.diploid_parx_genome\n )\n cnarr = cnarr... | [
"0.6045487",
"0.59562194",
"0.58167315",
"0.57513165",
"0.56921065",
"0.5689646",
"0.5628119",
"0.5594635",
"0.5566843",
"0.5559819",
"0.5533765",
"0.55296916",
"0.5498906",
"0.5494224",
"0.5464383",
"0.5461347",
"0.54449046",
"0.5444866",
"0.54397726",
"0.54291356",
"0.54224... | 0.68941486 | 0 |
Wrapper of the command line tool sed to change variable values. Please note that some characters like / must be escaped to work. | def changeConfVar(varName, varValue, file="config/scipion.conf", escapeSlash=False):
if escapeSlash:
varValue = varValue.replace('/', '\/')
command = ['bash', '-c', 'sed -i -e '
'"s/%s = .*/%s = %s/" '
'%s' % (varName, varName, varValue, file)]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sed_like_thing(pattern, repl, path):\n\n with codecs.open(path, 'rb', 'utf8') as inf:\n data = inf.read()\n\n data = re.sub(pattern, repl, data)\n\n with codecs.open(path, 'wb+', 'utf8') as outf:\n outf.write(data)",
"def bs_set(self, cmd, arg):\n\t\tif arg:\n\t\t\tfor x in self.split(... | [
"0.56367666",
"0.56140023",
"0.56025064",
"0.55010325",
"0.5443878",
"0.5441514",
"0.5424399",
"0.52970827",
"0.5294301",
"0.52636224",
"0.52600396",
"0.5246227",
"0.52333087",
"0.52145934",
"0.52100545",
"0.51816446",
"0.5165895",
"0.5081385",
"0.50499505",
"0.5047105",
"0.5... | 0.6695324 | 0 |
Applies one linear downprojection layer, then softmax. | def build_graph(self, inputs, masks):
with vs.variable_scope("SimpleSoftmaxLayer"):
# Linear downprojection layer
logits = tf.contrib.layers.fully_connected(inputs, num_outputs=1, activation_fn=None) # shape (batch_size, seq_len, 1)
logits = tf.squeeze(logits, axis=[2]) # sh... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def forward(self, inputs):\n x_wrd = self.lookup(inputs)\n\n # bilinear attention\n x_avg = x_wrd.mean(dim=1)\n x = x_wrd.matmul(self.M)\n x = x.matmul(x_avg.unsqueeze(1).transpose(1, 2))\n if self.b is not None:\n x += self.b\n\n x = F.tanh(x) \n ... | [
"0.6428857",
"0.6281456",
"0.62317896",
"0.61771274",
"0.61514837",
"0.61489147",
"0.6069403",
"0.6053344",
"0.6052496",
"0.6032014",
"0.60229355",
"0.6014218",
"0.600776",
"0.60007215",
"0.5996227",
"0.5970958",
"0.59685767",
"0.5956204",
"0.59428155",
"0.5934878",
"0.593083... | 0.0 | -1 |
Keys attend to values. For each key, return an attention distribution and an attention output vector. | def build_graph(self, values, values_mask, keys):
with vs.variable_scope("BasicAttn"):
# Calculate attention distribution
values_t = tf.transpose(values, perm=[0, 2, 1]) # (batch_size, value_vec_size, num_values)
attn_logits = tf.matmul(keys, values_t) # shape (batch_size, n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _compute_attention(\n self, query_tensor, key_tensor, value_tensor, attention_mask=None\n ):\n # Take the dot product between \"query\" and \"key\" to get the raw\n # attention scores.\n attention_scores = tf.einsum( # pragma: no cover\n self._dot_product_equation, ke... | [
"0.64202654",
"0.5826104",
"0.5797294",
"0.5704355",
"0.57004243",
"0.56263703",
"0.5615481",
"0.5609999",
"0.5568032",
"0.55614036",
"0.555588",
"0.55433214",
"0.55367774",
"0.55298233",
"0.54890084",
"0.5437827",
"0.5414771",
"0.539568",
"0.5339974",
"0.5238919",
"0.5231451... | 0.5771328 | 3 |
Keys attend to values. For each key, return an attention distribution and an attention output vector. | def build_graph(self, values, values_mask, keys, keys_mask):
with vs.variable_scope("CrossAttn"):
# Calculate attention distribution
values_t = tf.transpose(values, perm=[0, 2, 1]) # (batch_size, value_vec_size, num_values)
attn_matrix = tf.matmul(keys, values_t) # shape (ba... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _compute_attention(\n self, query_tensor, key_tensor, value_tensor, attention_mask=None\n ):\n # Take the dot product between \"query\" and \"key\" to get the raw\n # attention scores.\n attention_scores = tf.einsum( # pragma: no cover\n self._dot_product_equation, ke... | [
"0.642035",
"0.5826609",
"0.57973295",
"0.577184",
"0.5703602",
"0.5700191",
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"0.5615878",
"0.5609656",
"0.55687195",
"0.5560532",
"0.5555562",
"0.5543729",
"0.5535787",
"0.55293334",
"0.54884267",
"0.5436761",
"0.54138774",
"0.5395364",
"0.5339128",
"0.5238084",
... | 0.51390535 | 27 |
Keys attend to values. For each key, return an attention distribution and an attention output vector. | def build_graph(self, values, values_mask, keys, keys_mask):
with vs.variable_scope("BidirectionalAttn"):
# Divide the weight matrix in 3 parts
weights_sim1 = tf.get_variable(name = "weights_sim1", shape = [self.key_vec_size, 1], dtype = tf.float32, initializer = tf.random_normal_initia... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _compute_attention(\n self, query_tensor, key_tensor, value_tensor, attention_mask=None\n ):\n # Take the dot product between \"query\" and \"key\" to get the raw\n # attention scores.\n attention_scores = tf.einsum( # pragma: no cover\n self._dot_product_equation, ke... | [
"0.6419863",
"0.58255297",
"0.5794626",
"0.57707596",
"0.5703138",
"0.5698814",
"0.5624967",
"0.56137186",
"0.560936",
"0.55668586",
"0.5561049",
"0.5554973",
"0.5542148",
"0.5534193",
"0.55285573",
"0.54866225",
"0.54355294",
"0.54146504",
"0.53954834",
"0.5339041",
"0.52375... | 0.0 | -1 |
Keys attend to values. For each key, return an attention distribution and an attention output vector. | def build_graph(self, values, values_mask, keys):
with vs.variable_scope("CoAttn"):
#########################################################################
# Introduce a non-linear projection layer on top of the question encoding
# to allow f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _compute_attention(\n self, query_tensor, key_tensor, value_tensor, attention_mask=None\n ):\n # Take the dot product between \"query\" and \"key\" to get the raw\n # attention scores.\n attention_scores = tf.einsum( # pragma: no cover\n self._dot_product_equation, ke... | [
"0.642035",
"0.5826609",
"0.57973295",
"0.577184",
"0.5703602",
"0.5700191",
"0.5626382",
"0.5615878",
"0.5609656",
"0.55687195",
"0.5560532",
"0.5555562",
"0.5543729",
"0.5535787",
"0.55293334",
"0.54884267",
"0.5436761",
"0.54138774",
"0.5395364",
"0.5339128",
"0.5238084",
... | 0.49701777 | 48 |
Applies one linear downprojection layer, then softmax. | def build_graph(self, char_embeddings):
with vs.variable_scope("CharLevelCNN"):
batch_size = tf.shape(char_embeddings)[0]
phrase_len = tf.shape(char_embeddings)[1]
word_len = tf.shape(char_embeddings)[2]
char_embedding_size = tf.shape(char_embeddings)[3]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def forward(self, inputs):\n x_wrd = self.lookup(inputs)\n\n # bilinear attention\n x_avg = x_wrd.mean(dim=1)\n x = x_wrd.matmul(self.M)\n x = x.matmul(x_avg.unsqueeze(1).transpose(1, 2))\n if self.b is not None:\n x += self.b\n\n x = F.tanh(x) \n ... | [
"0.6429836",
"0.62832594",
"0.6231738",
"0.61789125",
"0.6153917",
"0.61492455",
"0.6071372",
"0.6055186",
"0.6054922",
"0.6033591",
"0.60238487",
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"0.5972523",
"0.59712166",
"0.59581536",
"0.594444",
"0.5935604",
"0.59327... | 0.0 | -1 |
Takes masked softmax over given dimension of logits. | def masked_softmax(logits, mask, dim):
exp_mask = (1 - tf.cast(mask, 'float')) * (-1e30) # -large where there's padding, 0 elsewhere
masked_logits = tf.add(logits, exp_mask) # where there's padding, set logits to -large
prob_dist = tf.nn.softmax(masked_logits, dim)
return masked_logits, prob_dist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def masked_softmax(logits, mask, dim=-1, log_softmax=False):\n mask = mask.type(torch.float32)\n masked_logits = mask * logits + (1 - mask) * -1e30\n softmax_fn = F.log_softmax if log_softmax else F.softmax\n probs = softmax_fn(masked_logits, dim)\n\n return probs",
"def masked_softmax(tensor, mas... | [
"0.80401665",
"0.7654991",
"0.761089",
"0.75716364",
"0.7517807",
"0.74387515",
"0.7438501",
"0.7347286",
"0.7323559",
"0.73165834",
"0.7309688",
"0.73022497",
"0.7278672",
"0.72193605",
"0.7214303",
"0.71653897",
"0.7160705",
"0.7139164",
"0.70996326",
"0.70656717",
"0.70488... | 0.8118963 | 1 |
create a kdtree on data X. | def create(self, X, dimensions=None):
n_samples, n_features = X.shape
self.X = X
if not dimensions:
dimensions = n_features
self.root = KdNode(depth=0,
splitting_feature=0,
splitting_value=np.median(X[:, 0]),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_kdtree(self):\n if self.method==2:\n coordinates = self.unassigned_data[0:3,:]\n else:\n coordinates = self.unassigned_data[0:2,:]\n tree = cKDTree(coordinates.T)\n\n return tree",
"def build_kdtree(points, depth=0):\n n = len(points) - 1\n\n if n <= 0:\n retur... | [
"0.76762074",
"0.66742635",
"0.66742635",
"0.665417",
"0.65665555",
"0.6538967",
"0.64309573",
"0.63635075",
"0.6318616",
"0.6176345",
"0.6175313",
"0.60938823",
"0.6080377",
"0.60334706",
"0.59839034",
"0.5976341",
"0.59736675",
"0.59532875",
"0.5938413",
"0.58962005",
"0.58... | 0.694763 | 1 |
el input es la cuenta, se scrapea las urls de los 25 ultimos posteos | def recent_25_posts(username):
url = "https://www.instagram.com/" + username + "/"
#la ubicacion del chromedriver.exe
browser = webdriver.Chrome(executable_path=r"C:\Users\pablo\Downloads\chromedriver.exe")
#browser = Chrome()
browser.get(url)
post = 'https://www.instagram.com/p/'
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_urls(*params: str, num_of_links: int = 1) -> list:\n urls = []\n try:\n for i in range(num_of_links):\n url = \"https://finviz.com/screener.ashx?v=111\"\n codes = ','.join(rts_codes[len(rts_codes)*(num_of_links - i - 1)//num_of_links:(len(rts_codes)*(num_of_links - i)//nu... | [
"0.62636787",
"0.625272",
"0.6234339",
"0.6171161",
"0.6116975",
"0.6103526",
"0.60449106",
"0.60208243",
"0.598695",
"0.59798187",
"0.596747",
"0.5944079",
"0.5834039",
"0.5832724",
"0.58214533",
"0.5794802",
"0.57335097",
"0.5716157",
"0.5706557",
"0.5690079",
"0.5687363",
... | 0.58384776 | 12 |
para cada url extraemos los detalles | def insta_details(urls):
#la ubicacion del chromedriver.exe
#browser = Chrome()
browser = webdriver.Chrome(executable_path=r"C:\Users\pablo\Downloads\chromedriver.exe")
post_details = []
for link in urls:
browser.get(link)
try:
# extraemos los likes, si tu navegado... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getURLs():",
"def getVotacion(self, url):",
"def data_collector(self, n, url, ret):\n try:\n html = urllib2.urlopen(url).read()\n soup = BeautifulSoup(html)\n ret[n] = [soup.title.string, url, html[0:100]]\n except:\n ret[n] = [\"Error\", url, \"Err... | [
"0.6954483",
"0.69486177",
"0.6624388",
"0.66198707",
"0.65335274",
"0.62732834",
"0.6233779",
"0.61924917",
"0.6190488",
"0.61417556",
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"0.6059383",
"0.6056347",
"0.601275",
"0.59806997",
"0.59611386",
"0.59448546",
"0.59428... | 0.5743172 | 44 |
This function should return a list of two agents that will form the team, initialized using firstIndex and secondIndex as their agent index numbers. isRed is True if the red team is being created, and will be False if the blue team is being created. As a potentially helpful development aid, this function can take addit... | def createTeam(firstIndex, secondIndex, isRed,
first = 'danielAgent2', second = 'danielAgent2'):
return [eval(first)(firstIndex), eval(second)(secondIndex)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createTeam(firstIndex, secondIndex, isRed,\n first = 'ReflexCaptureAgent', second = 'DefensiveReflexAgent'):\n return [eval(first)(firstIndex), eval(second)(secondIndex)]",
"def createTeam(firstIndex, secondIndex, isRed,\n first = 'OffensiveReflexAgent', second = 'DefensiveReflex... | [
"0.8381065",
"0.81612027",
"0.81612027",
"0.8137621",
"0.81249464",
"0.8116331",
"0.81150866",
"0.81116426",
"0.80987436",
"0.8088097",
"0.8085381",
"0.8085381",
"0.8085381",
"0.8033649",
"0.79911155",
"0.7974004",
"0.79596937",
"0.79452187",
"0.79083234",
"0.79005665",
"0.78... | 0.7831062 | 20 |
Get distance between two points going only through the defense land | def getMazeDistanceDefense(self, p1, p2):
try:
return self.distancerDefense.getDistance(p1, p2)
except Exception:
return self.getMazeDistance(p1, p2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def distance(self, other_pt, is_lla=True):\n return 0.0",
"def get_distance(first: Point, second: Point) -> Float:\n\n return sqrt(\n (second.x - first.x) ** 2\n +\n (second.y - first.y) ** 2\n )",
"def __get_distance(point1: np.ndarray, point2: np.ndarray) -> float:\n ... | [
"0.744277",
"0.71968544",
"0.7164096",
"0.7129097",
"0.707927",
"0.7068014",
"0.7024101",
"0.7008225",
"0.698951",
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"0.693927",
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"0.68569434",
"0.68446594",
"0.6841914",
"0.68375",
"0.68300796",
"0.6805769",
"0.... | 0.72054726 | 1 |
Examine map and create doors and rooms structure | def examineMaze(self, gameState):
w = self.walls.width
h = self.walls.height
walls = self.walls.deepCopy()
food1 = self.getFoodYouAreDefending(gameState)
food2 = self.getFood(gameState)
# Save map as 0, 1, 2 and 3 (0:walls, 1:spaces, 2:babies, 3:food)
for x in range(w):
for y in range... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_map(json_game_map):\n room_hash = {}\n\n for room in constants.ROOMS:\n # Set name, description, and neighbors\n room_hash[room] = Room.Room()\n room_hash[room].set_name(room)\n room_hash[room].set_short_description(constants.ROOMS[room]['short_description'])\n r... | [
"0.68635213",
"0.6726352",
"0.65176284",
"0.63991296",
"0.6356377",
"0.6259297",
"0.61154157",
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"0.56676257",
"0.56442124",
"0.56310874",
"0.5589932",
"0.55891556",
"0.55738705",
"0.55... | 0.56462735 | 15 |
Get centroid (average point) of beliefs for an opponent | def getBeliefsCentroid(self, idx):
x = 0.0
y = 0.0
total = 0.0
for p in self.beliefs[idx]:
x += p[0]
y += p[1]
total += 1.0
return (round(x / total), round(y / total)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calc_centroid(self):\n num = 0\n centroid = numpy.zeros(3, float)\n for atm in self:\n if atm.position is not None:\n centroid += atm.position\n num += 1\n return centroid / num",
"def centroid(self) -> Point:\n points = self.no... | [
"0.7214463",
"0.6820861",
"0.67982733",
"0.67874646",
"0.67413944",
"0.6737949",
"0.6652046",
"0.6643116",
"0.6635537",
"0.6628157",
"0.65908074",
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"0.6527422",
"0.6517385",
"0.6476208",
"0.6454607",
"0.64542395",
"0.6439064",
"0.64369... | 0.78195196 | 0 |
Get closest possible position of the opponent according to beliefs (if more than one possible, picks one randomly) | def getClosestPositionOpponent(self, idx, pos, defense=False):
minD = 10000
opponentPos = [pos]
for p in self.beliefs[idx]:
if defense:
d = self.getMazeDistanceDefense(pos, p)
else:
d = self.getMazeDistance(pos, p)
if minD > d:
minD = d
opponentPos = [p]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _closest_front_opponent(self, raw_obs, o, target):\n delta = target - o\n min_d = None\n closest = None\n for p in raw_obs['right_team']:\n delta_opp = p - o\n if np.dot(delta, delta_opp) <= 0:\n continue\n d = self._object_distance(o,... | [
"0.7216672",
"0.7017943",
"0.68256783",
"0.68243515",
"0.67419386",
"0.665059",
"0.66476333",
"0.6628106",
"0.6597398",
"0.6581693",
"0.6525909",
"0.6463664",
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"0.64473104",
"0.64380753",
"0.6420585",
"0.6400714",
"0.63967943",
"0.63782734",
"0.6370442",
"0.63704... | 0.7711962 | 0 |
Get minimum possible distance to opponent | def getMinimumDistanceOpponent(self, idx, pos, defense=False):
minD = 10000
if defense:
for p in self.beliefs[idx]:
minD = min(minD, self.getMazeDistanceDefense(pos, p))
else:
for p in self.beliefs[idx]:
minD = min(minD, self.getMazeDistance(pos, p))
return minD | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_min_distance(self):\n return round(min(self.combined_euclidian_distance))",
"def min_distance(self, target):\n difference = self.pivot - target\n return max(math.sqrt(np.dot(difference, difference)) - self.radius, 0)",
"def _kings_distance(self, piece):\n min_distance = cons... | [
"0.72254676",
"0.68654174",
"0.686012",
"0.6761133",
"0.65992033",
"0.65834254",
"0.65676284",
"0.65481323",
"0.64017946",
"0.6369925",
"0.6305183",
"0.62864286",
"0.62798434",
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"0.62629676",
"0.62517095",
"0.6235636",
"0.6219681",
"0.6214266",
"0.6207137",
"0.62... | 0.75307536 | 0 |
Get minimum distance to middle of the board (my pacmanLand, opponents ghostLand) | def getMinimumDistancePacmanLand(self, pos):
minD = 10000
for p in self.ghostLandPositions:
minD = min(minD, self.getMazeDistance(pos, p))
return minD | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _kings_distance(self, piece):\n min_distance = constant.BOARD_DIMENSION - 1\n opponent_pieces = self.get_all_pieces(piece.get_player().other)\n for opp_piece in opponent_pieces:\n distance = abs(piece.row - opp_piece.row) + abs(piece.col - opp_piece.col) / 2\n if dist... | [
"0.68152446",
"0.6605605",
"0.650739",
"0.64382017",
"0.6427428",
"0.63290226",
"0.6286821",
"0.6265279",
"0.62342113",
"0.61676383",
"0.6067976",
"0.6067287",
"0.6066152",
"0.60641265",
"0.6040633",
"0.6036753",
"0.6031766",
"0.600847",
"0.5970721",
"0.5944521",
"0.593572",
... | 0.72644186 | 0 |
Split food in top and bottom food, separated by median | def getSplitFoodList(self, gameState, margin=1):
foodList = self.getFood(gameState).asList()
foodListY = [p[1] for p in foodList]
med = median(foodListY)
splitList = [[], []]
for i, p in enumerate(foodList):
if p[1] <= med - margin:
splitList[0].append(p)
elif p[1] > med + margin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calc_median(numbers):\n middle_index = len(numbers) // 2\n return sorted(numbers[middle_index]) # sorted returns the numbers sorted without changing",
"def _split_medians( medians, cutoff ):\n five_prime = []\n three_prime = []\n for key, median in medians.iteritems():\n if median <... | [
"0.6657142",
"0.6612814",
"0.6509927",
"0.64185447",
"0.63596123",
"0.623155",
"0.62060475",
"0.61511374",
"0.61496574",
"0.61215526",
"0.608347",
"0.60135335",
"0.6012894",
"0.6006112",
"0.59933764",
"0.5985323",
"0.59740883",
"0.5955984",
"0.5941399",
"0.5927735",
"0.590135... | 0.67823243 | 0 |
The opponent died, update its belief to its starting position | def setOpponentToZeroPos(self, idx):
self.beliefs[idx] = util.Counter()
for pos in self.opponentZeroPos:
self.beliefs[idx][pos] = 1.0 / len(self.opponentZeroPos) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enter_knock_back_state(self):\n if self.name == 'player':\n self.x_vel = 4\n else:\n self.x_vel = -4\n\n self.state = c.KNOCK_BACK\n self.origin_pos = self.rect.topleft",
"def set_eaten(self):\n self.state['return'] = True\n self.state['blue'] =... | [
"0.6459408",
"0.6303483",
"0.62613565",
"0.61890364",
"0.61888254",
"0.6181082",
"0.6180529",
"0.61471343",
"0.6126692",
"0.6061175",
"0.60561824",
"0.605579",
"0.6021284",
"0.598978",
"0.59865355",
"0.59644526",
"0.5958022",
"0.5945965",
"0.59203374",
"0.5912217",
"0.591127"... | 0.0 | -1 |
Set all legal positions with equal probability | def initializeBeliefsUniformly(self, gameState, idx):
self.beliefs[idx] = util.Counter()
for p in self.legalPositions:
self.beliefs[idx][p] = 1.0
self.beliefs[idx].normalize() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def advance_generation(self):\n # Todo: implement\n for particle in self.particles:\n if particle.value > particle.best_value:\n particle.best_position = particle.x\n particle.best_value = particle.value\n rp = random.uniform(0.0, 1.0)\n ... | [
"0.6410945",
"0.6389048",
"0.6353788",
"0.62320787",
"0.6200577",
"0.61826175",
"0.6122533",
"0.6099427",
"0.60831887",
"0.60590976",
"0.60477996",
"0.59618634",
"0.5942169",
"0.59254664",
"0.59145194",
"0.5910171",
"0.585056",
"0.5843743",
"0.5838484",
"0.58381385",
"0.58333... | 0.6138709 | 6 |
Set beliefs of one opponent to one single position | def setBeliefs(self, position, idx):
self.beliefs[idx] = util.Counter()
self.beliefs[idx][position] = 1.0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setOpponentToZeroPos(self, idx):\n self.beliefs[idx] = util.Counter()\n for pos in self.opponentZeroPos:\n self.beliefs[idx][pos] = 1.0 / len(self.opponentZeroPos)",
"def updateEatenOpponents2(self, gameState, chosenAction):\n myNewPos = self.getSuccessor(gameState, chosenAction).getAgentState(... | [
"0.6645985",
"0.62803483",
"0.62631357",
"0.60880804",
"0.59455585",
"0.58821756",
"0.5861061",
"0.575506",
"0.5748615",
"0.5737099",
"0.5726178",
"0.5698866",
"0.56927747",
"0.5685124",
"0.5672717",
"0.56487983",
"0.5603451",
"0.5589098",
"0.5574924",
"0.55408394",
"0.552870... | 0.6230174 | 3 |
Observe noisy distance for opponent and update beliefs according to them | def observe(self, observation, gameState, myPosition, idx):
noisyDistance = observation
noZero = False
for p in self.legalPositions:
if self.beliefs[idx][p] <= 0:
self.beliefs[idx].pop(p, None)
continue
trueDistance = util.manhattanDistance(p, myPosition)
prob = gameState.g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def observe(self, observation, gameState):\n noisyDistance = observation\n pacmanPosition = gameState.getPacmanPosition()\n\n \"*** YOUR CODE HERE ***\"\n\n # Replace this code with a correct observation update\n # Be sure to handle the \"jail\" edge case where the ghost is eaten... | [
"0.7071808",
"0.65330017",
"0.6131267",
"0.6055183",
"0.59546125",
"0.58331645",
"0.5822836",
"0.57872796",
"0.57719946",
"0.57357174",
"0.5674904",
"0.5614523",
"0.56074476",
"0.5586609",
"0.5580293",
"0.55711126",
"0.55707365",
"0.5560386",
"0.5550653",
"0.5536806",
"0.5532... | 0.6566999 | 1 |
Update beliefs knowing that the opponent has mad a move | def elapseTime(self, idx):
newBeliefs = util.Counter()
for oldPos in self.legalPositions:
if self.beliefs[idx][oldPos] <= 0:
continue
newPosDist = self.getPositionDistribution(oldPos)
for newPos, prob in newPosDist.items():
newBeliefs[newPos] += prob * self.beliefs[idx][oldPos]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self, is_my_turn, clue_word, clue_num_guesses, guesses):\r\n pass",
"def updateEatenOpponents1(self, gameState, idx):\n teammatePos = gameState.getAgentState((self.index + 2) % 4).getPosition()\n pos = gameState.getAgentState(idx).getPosition()\n if pos is None and len(self.beliefs[idx... | [
"0.6580274",
"0.65605146",
"0.63730043",
"0.63291717",
"0.6262298",
"0.6249394",
"0.62343293",
"0.61137736",
"0.6058033",
"0.60558474",
"0.6041198",
"0.5981814",
"0.5868395",
"0.582787",
"0.58258116",
"0.5741464",
"0.57372504",
"0.57355183",
"0.5731516",
"0.57227194",
"0.5681... | 0.54553366 | 65 |
From a position, return all the possible new positions after a move of the opponent with an equal probability distribution | def getPositionDistribution(self, position):
dist = util.Counter()
(x, y) = position
total = 1.0
dist[position] = 1.0
if not self.walls[x + 1][y]:
dist[(x + 1, y)] = 1.0
total += 1.0
if not self.walls[x - 1][y]:
dist[(x - 1, y)] = 1.0
total += 1.0
if not self.walls[x... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GenerateMoves(position):\n return [move for move in POSSIBLE_MOVES if move <= position]",
"def evaluate_position(num_items):\n comp_wins = 0\n player_wins = 0\n\n initial_move = random.randrange(MAX_REMOVE + 1)\n num_items -= initial_move\n next_move = random.randrange(MAX_REMOVE + 1)\n ... | [
"0.65959424",
"0.6590027",
"0.65190655",
"0.64605963",
"0.63720465",
"0.626335",
"0.62120914",
"0.619868",
"0.61953133",
"0.6146514",
"0.6142938",
"0.6122146",
"0.6102404",
"0.60923314",
"0.6071721",
"0.59913236",
"0.59494513",
"0.5934421",
"0.59281343",
"0.5920294",
"0.59020... | 0.5599773 | 44 |
Get the positions of the babies (the food we are protecting) that are gone (aka have been eaten by the opponent) and save them in eaten | def getEatenBabies(self, gameState):
eaten = []
newFood = self.getFoodYouAreDefending(gameState)
for pos in self.babies:
if not newFood[pos[0]][pos[1]]:
eaten.append(pos)
return eaten | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def trackGhosts(self, gameState):\n\n # Get some values that we will use later\n myState = gameState.getAgentState(self.index)\n myPos = myState.getPosition()\n noisyDistances = gameState.getAgentDistances()\n eatenBabies = self.getEatenBabies(gameState)\n\n # Track each opponent\n opponentFo... | [
"0.6153549",
"0.6146357",
"0.6052339",
"0.5816061",
"0.5764447",
"0.57265055",
"0.5720799",
"0.57053065",
"0.5586472",
"0.5576027",
"0.5562706",
"0.553079",
"0.5517852",
"0.55137324",
"0.54935753",
"0.54551",
"0.5438007",
"0.5432379",
"0.5397186",
"0.53894365",
"0.5360449",
... | 0.7700618 | 0 |
Detect if teammate has eaten an opponent, and update beliefs accordingly | def updateEatenOpponents1(self, gameState, idx):
teammatePos = gameState.getAgentState((self.index + 2) % 4).getPosition()
pos = gameState.getAgentState(idx).getPosition()
if pos is None and len(self.beliefs[idx]) == 1 and self.beliefs[idx].keys()[0] == teammatePos:
self.setOpponentToZeroPos(idx)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def updateEatenOpponents2(self, gameState, chosenAction):\n myNewPos = self.getSuccessor(gameState, chosenAction).getAgentState(self.index).getPosition()\n for idx in self.getOpponents(gameState):\n pos = gameState.getAgentState(idx).getPosition()\n if pos is not None and pos == myNewPos:\n ... | [
"0.66890574",
"0.6368508",
"0.61436534",
"0.61104083",
"0.6092847",
"0.592126",
"0.59061474",
"0.5894191",
"0.5868768",
"0.58523476",
"0.5842589",
"0.5804285",
"0.58025306",
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"0.5759558",
"0.57070696",
"0.5706069",
"0.56675106",
"0.5637546",
"0.5619569",
"0.561879... | 0.72929925 | 0 |
Detect if the agent has eaten an opponent after taking choseAction, and update beliefs accordingly | def updateEatenOpponents2(self, gameState, chosenAction):
myNewPos = self.getSuccessor(gameState, chosenAction).getAgentState(self.index).getPosition()
for idx in self.getOpponents(gameState):
pos = gameState.getAgentState(idx).getPosition()
if pos is not None and pos == myNewPos:
self.setOp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def chooseAction(self, gameState):\n actions = gameState.getLegalActions(self.index)\n\n # You can profile your evaluation time by uncommenting these lines\n # start = time.time()\n opIndices = self.getOpponents(gameState)\n opStates = [gameState.getAgentState(i) for i in opIndic... | [
"0.6729112",
"0.6670614",
"0.66492426",
"0.6632579",
"0.660503",
"0.6600719",
"0.6590416",
"0.65770644",
"0.6573669",
"0.65158534",
"0.6472443",
"0.646066",
"0.6408163",
"0.64000434",
"0.6376658",
"0.6342673",
"0.6341901",
"0.6238633",
"0.6162677",
"0.6136566",
"0.6121612",
... | 0.6999971 | 0 |
Takes care of the beliefs of the opponents (updates our beliefs for every opponent) | def trackGhosts(self, gameState):
# Get some values that we will use later
myState = gameState.getAgentState(self.index)
myPos = myState.getPosition()
noisyDistances = gameState.getAgentDistances()
eatenBabies = self.getEatenBabies(gameState)
# Track each opponent
opponentFound = [False] ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_beliefs(self, features,\n beliefs):\n raise NotImplementedError",
"def updateEatenOpponents1(self, gameState, idx):\n teammatePos = gameState.getAgentState((self.index + 2) % 4).getPosition()\n pos = gameState.getAgentState(idx).getPosition()\n if pos is None and len(... | [
"0.64550847",
"0.619645",
"0.61560863",
"0.5892248",
"0.5866587",
"0.5861313",
"0.5817298",
"0.5783986",
"0.57212657",
"0.5699778",
"0.5691566",
"0.5670923",
"0.5643945",
"0.560926",
"0.5593857",
"0.55895865",
"0.55704576",
"0.55554897",
"0.55401003",
"0.55354905",
"0.5532957... | 0.5608274 | 14 |
Picks among the actions with the highest Q(s,a). | def chooseAction(self, gameState):
# Track opponents position
self.trackGhosts(gameState)
actions = gameState.getLegalActions(self.index)
# actions.remove(Directions.STOP)
# You can profile your evaluation time by uncommenting these lines
values = [self.evaluate(gameState, a) for a in actions... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def maxQ(self,state):\r\n \r\n maxQ = float('-inf')\r\n maxA = 0\r\n \r\n for a in self.actions:\r\n q = self.Q(state,a)\r\n #print(q,a)\r\n if q > maxQ:\r\n maxQ = q\r\n maxA = a\r\n return(maxQ,maxA)",
"def... | [
"0.7763381",
"0.77043587",
"0.76056725",
"0.755616",
"0.73318",
"0.7316181",
"0.71344316",
"0.71120584",
"0.6966468",
"0.6956663",
"0.6948445",
"0.69366306",
"0.6920287",
"0.6893597",
"0.68934804",
"0.68932414",
"0.6878565",
"0.6877915",
"0.68764764",
"0.6848047",
"0.6837404"... | 0.0 | -1 |
Finds the next successor which is a grid position (location tuple). | def getSuccessor(self, gameState, action):
successor = gameState.generateSuccessor(self.index, action)
pos = successor.getAgentState(self.index).getPosition()
if pos != nearestPoint(pos):
# Only half a grid position was covered
return successor.generateSuccessor(self.index, action)
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_next_empty_cell(grid):\n for i, row in enumerate(grid):\n for j, col in enumerate(row):\n if col == 0:\n return (i, j)\n return None",
"def findNextMove(curHVal):\n minHCalc = curHVal; #Initializing to curHVal\n minNewPosition = (0,0); #Initializing it\n\n ... | [
"0.6865463",
"0.6577259",
"0.65367836",
"0.63662726",
"0.63535637",
"0.6322907",
"0.6322907",
"0.6322907",
"0.6322907",
"0.6313924",
"0.6313924",
"0.6313924",
"0.6308633",
"0.6286739",
"0.6271493",
"0.6265178",
"0.6261508",
"0.6254691",
"0.6224646",
"0.6223371",
"0.6201753",
... | 0.625926 | 23 |
Computes a linear combination of features and feature weights | def evaluate(self, gameState, action):
self.updateCurrentBehavior(gameState, action)
features = self.getFeatures(gameState, action)
weights = self.getWeights(gameState, action)
# print "Ghost: ", self.index
# print "Behavior:", self.behavior
# print "Action: ", action
# for f in features:... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def apply_weights(self):\n return self.X.dot(self.get_weights())",
"def test_feature_computation(self):\n k = [2, 3, 4, 5, 6]\n mn = self.create_chain_model(k)\n d = 4\n\n for i in range(len(k)):\n mn.set_unary_weights(i, np.random.randn(k[i], d))",
"def _update_sa... | [
"0.6412511",
"0.63885987",
"0.6331432",
"0.6307596",
"0.62957096",
"0.6278508",
"0.62640095",
"0.6231558",
"0.6230448",
"0.6221143",
"0.61972445",
"0.61786664",
"0.61722106",
"0.6153286",
"0.61511993",
"0.6127169",
"0.6098815",
"0.6063193",
"0.6059293",
"0.60585326",
"0.60457... | 0.0 | -1 |
Chooses the bahavior of the agent depending on the game state | def updateCurrentBehavior(self, gameState, action):
self.behavior = "attack" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def random_agent(self, state):\n\t\trndint = random.randint\n\t\treturn self.state[state][rndint(0, len(self.state[state]))]",
"def choose(self):\n # pick agent A\n keys = list(self._agents.keys())\n keyA = random.choice(keys)\n agentA = self.model.schedule.agents[keyA]\n\n # p... | [
"0.64281553",
"0.61350286",
"0.59930456",
"0.59657705",
"0.5951671",
"0.5935685",
"0.5917523",
"0.58895385",
"0.5866359",
"0.58634853",
"0.5859631",
"0.58510923",
"0.5799187",
"0.5787101",
"0.5779855",
"0.5767384",
"0.5765602",
"0.5759769",
"0.57395715",
"0.573207",
"0.571422... | 0.0 | -1 |
Returns a counter of features for the state | def getFeatures(self, gameState, action):
features = util.Counter()
successor = self.getSuccessor(gameState, action)
features['successorScore'] = self.getScore(successor)
return features | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getFeatures(self, gameState, action):\n features = util.Counter()\n successor = self.getSuccessor(gameState)\n features['successorScore'] = self.getScore(successor)\n return features",
"def getFeatures(self, gameState, action):\n features = util.Counter()\n successor = self.getSuccessor(gam... | [
"0.75101334",
"0.74563867",
"0.7398609",
"0.7374521",
"0.7282197",
"0.6932936",
"0.68492305",
"0.68008125",
"0.6752222",
"0.6714029",
"0.6711698",
"0.67100084",
"0.66294867",
"0.6587385",
"0.6538587",
"0.6513832",
"0.64583915",
"0.6438229",
"0.6429208",
"0.641838",
"0.6405082... | 0.7463647 | 4 |
Normally, weights do not depend on the gamestate. They can be either a counter or a dictionary. | def getWeights(self, gameState, action):
return {'successorScore': 1.0} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getWeights(self, gameState, actton):\n\t\treturn {'successorScore': 1.0}",
"def weight(self):",
"def getWeights(self, gameState, action):\n return {'successorScore': 1.0}",
"def getWeights(self, gameState, action):\n # return {'successorScore': 1.0}\n if self.isOffensive:\n ... | [
"0.71871144",
"0.70065325",
"0.689758",
"0.68833196",
"0.68198305",
"0.68198305",
"0.6802522",
"0.6791841",
"0.6781377",
"0.6764985",
"0.6690768",
"0.6690768",
"0.6690768",
"0.6690768",
"0.6690768",
"0.6690768",
"0.6690768",
"0.6690768",
"0.6655643",
"0.6637496",
"0.65772384"... | 0.7246131 | 5 |
The idea is to avoid dead ends (paths that can make you get trapped) when we are being chased | def getFeaturesEscape(self, gameState, action):
features = util.Counter()
successor = self.getSuccessor(gameState, action)
features['successorScore'] = self.getScore(successor)
myState = successor.getAgentState(self.index)
myPos = myState.getPosition()
teammateState = successor.getAgentState((... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _perturbInPlaceHard(self):\n die",
"def solve(self):\n while self.character.path[-1] != 88:\n n = self.next_move()\n if n is None:\n self.character.path += ['Error: Could not find full path (budget does not suffice or unreachable).']\n break\n... | [
"0.5883941",
"0.57382286",
"0.5662084",
"0.5626856",
"0.560686",
"0.5590391",
"0.5552893",
"0.55336696",
"0.549596",
"0.5457089",
"0.5429637",
"0.5426103",
"0.5414072",
"0.5394398",
"0.53816",
"0.5381497",
"0.5381497",
"0.53670853",
"0.53554404",
"0.53365237",
"0.53141695",
... | 0.0 | -1 |
Serialize compatible dictionary to bytes. Copies entire dictionary in the process. | def encode(cls, dictionary: Dict[str, Any]) -> bytes:
if not isinstance(dictionary, dict):
raise TypeError( # pragma: nocover
"dictionary must be of dict type, got type {}".format(type(dictionary))
)
patched_dict = copy.deepcopy(dictionary)
cls._patch_dic... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_bytes(_dict, enc='utf-8'):\n return bytes(json.dumps(_dict), enc)",
"def _encode_dict(source: dict) -> bytes:\n result_data = b\"d\"\n\n for key, value in source.items():\n result_data += encode(key) + encode(value)\n\n return result_data + b\"e\"",
"def recursive_force_bytes(d):\r\n ... | [
"0.7369727",
"0.67605",
"0.66103524",
"0.6496112",
"0.6161774",
"0.6155406",
"0.6106692",
"0.6106692",
"0.6106692",
"0.60654813",
"0.60654813",
"0.60654813",
"0.5975645",
"0.5975645",
"0.59424126",
"0.59424126",
"0.59202325",
"0.5919495",
"0.59118193",
"0.5903205",
"0.5845758... | 0.7491539 | 0 |
Deserialize a compatible dictionary | def decode(cls, buffer: bytes) -> Dict[str, Any]:
pstruct = Struct()
pstruct.ParseFromString(buffer)
dictionary = dict(pstruct)
cls._patch_dict_restore(dictionary)
return dictionary | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_dict(cls, dikt) -> 'Data':\n return util.deserialize_model(dikt, cls)",
"def from_dict(cls, dikt) -> 'BundleData':\n return util.deserialize_model(dikt, cls)",
"def from_dict(cls, dikt):\n return deserialize_model(dikt, cls)",
"def from_dict(cls, dikt):\n return deseriali... | [
"0.69509226",
"0.6912338",
"0.6808362",
"0.6808362",
"0.67847705",
"0.67847705",
"0.67847705",
"0.67847705",
"0.67847705",
"0.67847705",
"0.67701757",
"0.6746996",
"0.6732208",
"0.6703498",
"0.66005915",
"0.6502228",
"0.64887947",
"0.6456437",
"0.64193887",
"0.6401303",
"0.63... | 0.60870665 | 69 |
Configura la ventana base de la aplicacion | def widgetSetup(self):
self.master.resizable(0, 0)
self.master.iconbitmap('logo.ico')
self.master.title("Ejercicio POO")
self.master.bind("<Return>", lambda e: self.create())
self.master.bind("<Delete>", lambda e: self.delete()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def config():\n config_django()\n config_svisor()",
"def config():",
"def config():",
"def configure(self):",
"def configure(self):",
"def configure(self):",
"def configure(self):",
"def configuration():",
"def config(self):\n pass",
"def config(self):\n pass",
"def configur... | [
"0.7150946",
"0.69282407",
"0.69282407",
"0.6809258",
"0.6809258",
"0.6809258",
"0.6809258",
"0.67006826",
"0.66206926",
"0.66206926",
"0.65607136",
"0.6530743",
"0.6530743",
"0.6480236",
"0.6359279",
"0.63244313",
"0.6270004",
"0.612241",
"0.61017054",
"0.604313",
"0.5991318... | 0.0 | -1 |
Metodo para crear las etiquetas principales | def crearEtiqueta(self, texto, fuente, fila, columna, color):
etiqueta = Label(self.master, text=texto, font=fuente)
etiqueta.grid(row=fila, column=columna,sticky=W, padx=10)
etiqueta.configure(bg=color)
return etiqueta | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create(self):",
"def create():",
"def create():",
"def create(self):\n ...",
"def _create_td(self):\n raise NotImplementedError",
"def __init__(self, altura, peso, edad):\n\t\tself.altura = altura # OJO TODAS LAS VARIABLES SON PUBLICAS \n\t\tself.peso = peso \n\t\tself.edad = edad\n\t\t... | [
"0.65900284",
"0.6451826",
"0.6451826",
"0.62090474",
"0.6074923",
"0.6064894",
"0.60394955",
"0.59705216",
"0.59705216",
"0.59705216",
"0.5963673",
"0.5912172",
"0.588354",
"0.5746422",
"0.5710383",
"0.5701193",
"0.5692463",
"0.5659934",
"0.5643292",
"0.56388277",
"0.5593089... | 0.0 | -1 |
Inicializa las etiquetas llamando al metodo crearEtiquetas usando los parametros correspondientes | def iniciarEtiquetas(self):
self.ingrese = Label(self.master, text="Ingrese sus datos", font="Arial 12", width=45)
self.ingrese.grid(row=0, column=0, sticky=N, columnspan=5, pady=10)
self.ingrese.configure(bg="#9a32cd")
self.tituloLabel = self.crearEtiqueta("Título", "Arial 12", 1, 0, "#... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init(self, parameters):\n pass",
"def __init__(self, periodo, reunion, sesion, tipo_sesion, fecha):\n self.periodo = periodo\n self.reunion = reunion\n self.sesion = sesion\n self.tipo_sesion = tipo_sesion\n self.fecha = fecha\n\n self.html_version_taquigrafic... | [
"0.609267",
"0.5903246",
"0.58946306",
"0.5880777",
"0.5836138",
"0.5729836",
"0.5700392",
"0.56584805",
"0.56492573",
"0.56484693",
"0.5626518",
"0.5626206",
"0.5621491",
"0.5573241",
"0.55474275",
"0.5540638",
"0.55209315",
"0.55170137",
"0.55021054",
"0.5498434",
"0.549363... | 0.5844404 | 4 |
Crea los cheks del final para elegir temas | def crearChecks(self):
check1 = Checkbutton(self.master, text="Tema 1", variable=self.checkStatus1, command= self.updateCheck)
check1.grid(row=7, column=1)
check2 = Checkbutton(self.master, text="Tema 2", variable=self.checkStatus2, command= self.updateCheck)
check2.grid(row=8, column=1)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def strt_now():\r\n \r\n tbdstryed = [a, ps, asso, web, welframe,creator]\r\n for e in range(len(tbdstryed)):\r\n tbdstryed[e].destroy()\r\n \r\n root.geometry('590x700')\r\n root.resizable(False,False)\r\n \r\n Generator(root).place()",
"def _build_em_dirs(self):\n for ... | [
"0.5500215",
"0.5353049",
"0.53220683",
"0.5288207",
"0.5216976",
"0.52123725",
"0.517569",
"0.51338184",
"0.50983876",
"0.5093755",
"0.5079386",
"0.5031075",
"0.5024734",
"0.502358",
"0.5019019",
"0.5013016",
"0.5008696",
"0.50014335",
"0.4996971",
"0.49944517",
"0.4922818",... | 0.0 | -1 |
Actualiza los checks segun el clic que haga el usuario para cambiar el tema | def updateCheck(self):
if (self.checkStatus1.get() == True):
self.master.configure(background='#f5f5f0')
self.checkStatus2.set(False)
self.checkStatus3.set(False)
elif (self.checkStatus2.get() == True):
self.master.configure(background='#ff99ff')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_inputs(self):\n\n self._check_resident_prefs()\n self._check_hospital_prefs()",
"def set_user_inputs(self):\n com_util.send_to(self.driver, element['clickOnEmail'], self.email)\n com_util.send_to(self.driver, element['clickOnPassword'], self.password)\n com_util.tap_... | [
"0.58495677",
"0.5720982",
"0.5639392",
"0.563029",
"0.5596402",
"0.5578114",
"0.5577288",
"0.5559616",
"0.5500324",
"0.5477476",
"0.5410121",
"0.53707904",
"0.53696257",
"0.536456",
"0.5351729",
"0.5326439",
"0.53194314",
"0.5306327",
"0.53050387",
"0.5297544",
"0.52928156",... | 0.0 | -1 |
Metodo para crear los entrys necesarios | def crearEntrada(self, master, valueForm, ancho, fila, columna):
return Entry(self.master, width=ancho, textvariable=valueForm).grid(row=fila, column=columna, pady=10) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_entry(entry):\n Entry.create(**entry)\n return entry",
"def create_entry(number, name, type_1, type_2, health_points, attack, defense, special_attack, special_defense, speed,\n generation, is_legendary):\n battle_stats = {'HP': health_points, 'Attack': attack, 'Defense': defen... | [
"0.7037715",
"0.668965",
"0.65381885",
"0.6488124",
"0.64373475",
"0.63334",
"0.61468595",
"0.60944563",
"0.6050023",
"0.60440207",
"0.60156155",
"0.59940416",
"0.59843445",
"0.59843445",
"0.5930455",
"0.5886901",
"0.5883002",
"0.5862357",
"0.5852909",
"0.5843249",
"0.5841745... | 0.5515448 | 40 |
Inicializa las entradas principales de carga del formulario | def iniciarEntradas(self):
tituloEntry = self.crearEntrada(self.master, self.tituloString, 30, 1, 1)
descripcionEntry = self.crearEntrada(self.master, self.descripcionString, 30, 2, 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUp(self):\n self.user = User.objects.get(username='Aslan')\n self.user.save()\n self.setUpFormData()\n self.form = CompoundForm(self.user, self.formData)",
"def __init__(self, corp_app, field_objs, *args, **kwargs):\n self.corp_app = corp_app\n self.field_objs = f... | [
"0.60940665",
"0.5666006",
"0.55598676",
"0.5470787",
"0.5469469",
"0.5435855",
"0.5424151",
"0.5399974",
"0.53901106",
"0.53865784",
"0.5367861",
"0.5339532",
"0.5334651",
"0.53280663",
"0.53130263",
"0.53030586",
"0.5268036",
"0.525582",
"0.52500373",
"0.52249557",
"0.52124... | 0.5676018 | 1 |
Muestra el estado de la base de datos | def iniciarTreeView(self):
self.verDatos.configure(height=10, columns=3)
self.verDatos["columns"] = ("idbase","titulo", "descripcion")
self.verDatos.column("#0", width=80, minwidth=20, anchor=E)
self.verDatos.column("idbase", width=60, minwidth=20, anchor=W)
self.verDatos.column(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def retrieve_from_db(self):\n pass",
"def fetch_data(self):",
"def _database(self):\n ...",
"def insert_data(self):\n # Make a connexion with a mock database\n self.generate_data_collection()",
"def _get_db_data(self) -> None:\n if self._db_data:\n return\n ... | [
"0.6352631",
"0.61261106",
"0.6058513",
"0.58711433",
"0.58188546",
"0.5707736",
"0.56725967",
"0.56422293",
"0.5641166",
"0.56061065",
"0.56004846",
"0.558248",
"0.5577022",
"0.5545722",
"0.5523512",
"0.5523512",
"0.55162567",
"0.55161726",
"0.55157053",
"0.55157053",
"0.549... | 0.0 | -1 |
Inicializa los botones para ABM tambien crear base y tabla en caso de no existir | def iniciarBotones(self):
alta = Button(self.master, text="Alta", font="Arial 10", command= self.create)
alta.grid(row=6, column=0, pady=15)
modificar = Button(self.master,text="Modificar", font="Arial 10",command= self.update, width="8")
modificar.grid(row=1, column=2, rowspan=1)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def setup(bot):\r\n await bot.add_cog(Tables(bot))",
"def _bots_initialization(self) -> None:\n\n assert isinstance(self.bots_configs, list), f\"Incorrect bot_farm config file. bot_farm_config['bots'] \" \\\n f\"must be dict, but now: {type(self.... | [
"0.67972714",
"0.64946496",
"0.6441267",
"0.6308263",
"0.61106575",
"0.60673463",
"0.6015159",
"0.59536266",
"0.59439516",
"0.5892423",
"0.58683693",
"0.5814385",
"0.5802463",
"0.57986337",
"0.5793498",
"0.57770497",
"0.5773846",
"0.5769958",
"0.57306194",
"0.5730383",
"0.572... | 0.6335093 | 3 |
Borra el formulario cuando sea necesario para una nueva carga o para evitar repeticiones | def reset(self):
self.descripcionString.set("")
self.tituloString.set("") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def buscarFactura(self):\n\n if not self.lineNumero.isEnabled() and self.facturaSeleccionada != None:\n QtGui.QMessageBox.information(self,\"Aviso\",\"Ya se ha seleccionado una factura\")\n elif not self.lineNumero.isEnabled():\n self.lineNumero.setEnabled(True)\n sel... | [
"0.6147121",
"0.6094942",
"0.6081782",
"0.6065424",
"0.605813",
"0.605813",
"0.60315925",
"0.5945697",
"0.58566546",
"0.576516",
"0.57575446",
"0.5739197",
"0.56512237",
"0.56397307",
"0.56377494",
"0.5607466",
"0.5599807",
"0.55913407",
"0.55809695",
"0.55348605",
"0.5534168... | 0.0 | -1 |
Borra el arbol completo para actualizar las entradas | def resetTree(self):
for fila in self.verDatos.get_children():
self.verDatos.delete(fila) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def commit(self):",
"def actualizar_velocidad(self):\r\n pass",
"def actualizar_puntaje(self):\r\n pass",
"def save():",
"def updateAll(self):\n \tself.idToUpdate=''\n \tself.newState=''\n \tself.save()",
"def actualizar_valores(self,valores): \n self.__valores.update(valore... | [
"0.6122221",
"0.6036619",
"0.6020295",
"0.59620243",
"0.58875334",
"0.5807845",
"0.57788527",
"0.5673862",
"0.5673862",
"0.56692386",
"0.5656522",
"0.5656522",
"0.5656522",
"0.5656522",
"0.5656522",
"0.5628181",
"0.56140774",
"0.56119204",
"0.55775714",
"0.55766964",
"0.55766... | 0.0 | -1 |
Metodo para actualizar el arbol junto al formulario y carga de nuevo con los datos actualizados | def updateTree(self):
self.reset()
self.resetTree()
self.read() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def actualizar(self):\n if self.obraSocialSeleccionada!=None:\n self.cargar_productos(self.obraSocialSeleccionada)\n else:\n self.cargarProductosSinObra()",
"def actualizar_puntaje(self):\r\n pass",
"def actualizar_tabla(self):\n self.ajustar_tabla()\n s... | [
"0.63073975",
"0.62418467",
"0.59441423",
"0.5581971",
"0.5581573",
"0.55443275",
"0.550863",
"0.5449518",
"0.5426941",
"0.5420971",
"0.53737736",
"0.5362197",
"0.5340136",
"0.5321031",
"0.5316212",
"0.5314762",
"0.5310911",
"0.52641654",
"0.5248064",
"0.5235256",
"0.52185756... | 0.0 | -1 |
Permite elegir un dato de la base para cambiar o borrar | def selectTree(self, event):
item = self.verDatos.selection()
self.idInteger.set(self.verDatos.item(item)['values'][0])
self.tituloString.set(self.verDatos.item(item)['values'][1])
self.descripcionString.set(self.verDatos.item(item)['values'][2]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def SaveData(self, conn):\n t = time.process_time()\n log = Logger()\n cursor = conn.cursor()\n sqlQuery = \"insert into old_data \" \\\n \"select * from new_data\"\n cursor.execute(sqlQuery)\n conn.commit()\n conn.close()\n elapsed_t = time... | [
"0.57277966",
"0.5657513",
"0.5653637",
"0.56170994",
"0.54800206",
"0.5394856",
"0.5384301",
"0.53081447",
"0.5278276",
"0.5271337",
"0.52682054",
"0.52480567",
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"0.5181496",
"0.5179283",
"0.5169548",
"0.5168879",
"0.51631016",
"0.51624215",
"0.514839",
"0.512611... | 0.0 | -1 |
Lee los registros existentes en la base abierta y en la tabla productos | def read(self):
try:
datos = self.base.readData()
for i in range(len(datos)):
self.verDatos.insert('', i+1, text = i+1, values = (datos[i][0], datos[i][1], datos[i][2]))
except:
showerror("Error", exc_info()[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def buscarProd(self):\n medicamento = str(self.lineMedicamento.text())\n monodroga = str(self.lineMonodroga.text())\n data = self.getAllTabla(self.tableProductos)\n\n if medicamento != \"\":\n dataMedic = filter(lambda x: x[1].upper() == medicamento.upper(), data.values())\n ... | [
"0.7017241",
"0.68887484",
"0.64854866",
"0.6218559",
"0.61975086",
"0.6082025",
"0.60445845",
"0.59041625",
"0.58877075",
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"0.5607758",
"0.5550904",
"0.5536961",
"0.548066",
"0.5475638",
"0.5454109... | 0.0 | -1 |
Crea la tabla si no existe | def crearTabla(self):
mensaje = self.base.createTable()
showinfo('Resultado', mensaje) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_table_if_not_exists(self) -> bigquery.Table:\n table = self.client.create_table(\n table=bigquery.Table(table_ref=self._table_ref, schema=Schema),\n exists_ok=True,\n )\n logging.info(\"table %s already exists.\", table.full_table_id)\n return table",
... | [
"0.7952568",
"0.7628907",
"0.75765896",
"0.7453806",
"0.7421533",
"0.7417688",
"0.73947656",
"0.7391235",
"0.73709404",
"0.7370457",
"0.73420954",
"0.7339442",
"0.7294078",
"0.72117263",
"0.7173514",
"0.71690124",
"0.7127797",
"0.71181244",
"0.710121",
"0.70358473",
"0.702753... | 0.71380335 | 16 |
Crea la base si no existe | def crearBD(self):
if self.base.isConnected():
mensaje = "Usted ya se encuentra conectado a la base " + self.base.getDbName() + ", ¿Desea Crear una nueva?"
if askyesno("Atención", mensaje):
nombre = tkinter.simpledialog.askstring("Elija el Nombre de la Base", prompt="Nomb... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_base(self):\r\n self.mycursor.execute(\r\n 'CREATE DATABASE IF NOT EXISTS purbeurre CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci')\r\n self.mycursor.execute('USE purbeurre')",
"def create():\n\tcreate_db()",
"def create_db(self, path: str) -> None:\n if os.path.is... | [
"0.7140915",
"0.6911187",
"0.67524874",
"0.6660668",
"0.6562868",
"0.6482472",
"0.64741087",
"0.64741087",
"0.6414927",
"0.6377228",
"0.6242515",
"0.6120511",
"0.61106586",
"0.6092203",
"0.6079177",
"0.6060785",
"0.604896",
"0.6047035",
"0.603988",
"0.6019423",
"0.60109794",
... | 0.62442166 | 10 |
Valida cada registro segun los criterios | def validarRE(self, datoAValidar):
return validarTitulo(datoAValidar) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate():",
"def validadores(self):\n\n camposRequeridos = [getattr(self,\"lineRazon\")]\n ValidarDatos.setValidador(camposRequeridos)\n\n camposRequeridos = [getattr(self,\"lineCuit\")]\n ValidarDatos.setValidador(camposRequeridos)\n\n camposRequeridos = [getattr(self,\"... | [
"0.6289605",
"0.62867755",
"0.6256649",
"0.6026372",
"0.60167295",
"0.6007827",
"0.5972027",
"0.59138054",
"0.59136087",
"0.5894613",
"0.5884084",
"0.5839058",
"0.58008385",
"0.5777286",
"0.5777286",
"0.5755494",
"0.57548887",
"0.57548887",
"0.57548887",
"0.5723866",
"0.57150... | 0.5682032 | 23 |
Main hook entry point | def execute(self, operation, file_path, context, parent_action, file_version, read_only, **kwargs):
# AARDMAN ADDITION
# adds short cuts to file browser
aaNukeUtils.aaSetupShortCuts()
if file_path:
file_path = file_path.replace("/", os.path.sep)
if operation == "cu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def entry_point():",
"def entry_point():",
"def entry_point():",
"def _hook(self):",
"def on_hook(self) -> None:",
"def setup_hooks(self):\n pass",
"def main():\n pass",
"def main(self):\r\n pass",
"def _post_hooks(self):",
"def main():\n PLUGIN_ENTRY().run(\"\")",
"def ... | [
"0.7423908",
"0.7423908",
"0.7423908",
"0.73854935",
"0.7256732",
"0.7225447",
"0.7175162",
"0.71340764",
"0.7110802",
"0.7008554",
"0.7006415",
"0.6989436",
"0.6963864",
"0.6871478",
"0.6857729",
"0.67905015",
"0.67806673",
"0.6760804",
"0.6760804",
"0.6760804",
"0.6760804",... | 0.0 | -1 |
Use the tknukewritenode app interface to find and reset the render path of any Tank write nodes in the current script | def _reset_write_node_render_paths(self):
write_node_app = self.parent.engine.apps.get("tk-nuke-writenode")
if not write_node_app:
return False
# only need to forceably reset the write node render paths if the app version
# is less than or equal to v0.1.11
from distu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def updateTree(self):\n self.reset()\n self.resetTree() \n self.read()",
"def changeNodeLib(ned, createNodeWin):\n pass",
"def updatetree(self):\n if self.node:\n self.node.update()\n self.draw()",
"def fRenderTargetBackupTab():\n node = nuke.thisNode()\n # create t... | [
"0.5345771",
"0.5233489",
"0.5084201",
"0.49955806",
"0.49239224",
"0.4833652",
"0.48127708",
"0.4807053",
"0.48013398",
"0.4722902",
"0.47158727",
"0.47130924",
"0.47050682",
"0.4693489",
"0.46925712",
"0.4680197",
"0.46643654",
"0.46602747",
"0.46585917",
"0.46569446",
"0.4... | 0.65005773 | 0 |
Initialisation, where df is a pandas DataFrame and var is the name of the column to study and init_pars is a dictionary with initial values | def __init__(self,df, init_pars, var='dep_var', var_name='Volume of Nile'):
self.df = df
self.var = var
self.var_name = var_name
self.y = np.array(df[var].values.flatten())
self.times = df.index
self.pardict = init_pars
self.options = {'eps':1e-09,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init(param):\n MODULE_HELPER.check_parameter(param, key='featureCount_exec', dtype=str)\n MODULE_HELPER.check_parameter(param, key='featureCount_t', dtype=str)\n MODULE_HELPER.check_parameter(param, key='featureCount_id', dtype=str)\n MODULE_HELPER.check_parameter(param, key='featureCount_by_meta',... | [
"0.5726554",
"0.55129045",
"0.5489595",
"0.5355379",
"0.52944136",
"0.5286923",
"0.5284127",
"0.5245464",
"0.52217746",
"0.52201474",
"0.52157134",
"0.52084327",
"0.5173756",
"0.5162606",
"0.5160928",
"0.51536846",
"0.513594",
"0.51033354",
"0.5099015",
"0.5089083",
"0.508381... | 0.7033673 | 0 |
Iterate over the observations and update the filtered values after each iteration | def iterate(self, plot=True, estimate=False, init_params=None):
# Create empty arrays to store values
F = np.zeros(len(self.y))
a = np.zeros(len(self.y))
v = np.zeros(len(self.y))
P = np.zeros(len(self.y))
# Initialize at the initial values parsed to the class
P[0... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self, other):\n for filter, value in other.items():\n self.__setitem__(filter, value)",
"def update(self):\n for filter in self.filters:\n filter.update(self.learning_rate)",
"def filter(self):\n self.filter_means = [self.m_0]\n self.filter_covs = [self.P_0]\n... | [
"0.6360926",
"0.6218109",
"0.6081106",
"0.6078264",
"0.6032189",
"0.5884993",
"0.5883774",
"0.58139133",
"0.5798917",
"0.57884675",
"0.5541844",
"0.5534231",
"0.55329645",
"0.55150574",
"0.5511411",
"0.55061525",
"0.54159504",
"0.54150975",
"0.54017776",
"0.539216",
"0.538981... | 0.0 | -1 |
Iterate over the observations and update the filtered values after each iteration | def missing_data(self, plot=True):
# Set some of the observations to missing
self.remove_data()
# Create empty arrays to store values
F = np.zeros(len(self.y))
a = np.zeros(len(self.y))
v = np.zeros(len(self.y))
P = np.zeros(len(self.y))
# Initialize at th... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self, other):\n for filter, value in other.items():\n self.__setitem__(filter, value)",
"def update(self):\n for filter in self.filters:\n filter.update(self.learning_rate)",
"def update(self, updates, predicate):\n for row in self.rows:\n if predicate(row... | [
"0.63613224",
"0.62198365",
"0.6080035",
"0.60798997",
"0.60306054",
"0.58865666",
"0.5883915",
"0.58141124",
"0.5800074",
"0.5787996",
"0.5541149",
"0.5536493",
"0.55307084",
"0.5513393",
"0.55123764",
"0.5507698",
"0.54158753",
"0.5414475",
"0.5403888",
"0.53936756",
"0.538... | 0.0 | -1 |
Execute the resize_data.sh package script. | def AddUnifiedUserDataAssertion(self, input_zip):
subprocess.call([os.path.join(DEVICE_RELEASE_TOOLS, 'resize_userdata.sh'), str(TARGET_PRODUCT)])
"""Include the required binaries in the output zip."""
self.output_zip.write(os.path.join(OUT_DIR, 'resize_userdata.zip'), os.path.join(INSTALL_UNIFY_USERDATA_PATH, 'r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def script(arch, version, variant, packages, mirror, disk_size, swap_size, image_format, root_password, hostname, no_confirm, output):\n\n # Checking validity of the command-line arguments.\n check_arguments(locals())\n\n # Checking if dependencies for this script are installed.\n check_dependencies(ar... | [
"0.58786595",
"0.58316845",
"0.5807729",
"0.5498334",
"0.5434105",
"0.54233754",
"0.54066753",
"0.5386028",
"0.5220113",
"0.52190685",
"0.5190704",
"0.51890326",
"0.51768124",
"0.5113704",
"0.5107601",
"0.5094947",
"0.5084976",
"0.50797784",
"0.50737107",
"0.50622976",
"0.501... | 0.0 | -1 |
Overskriver metoden i en subklasse kun en dummyversjon | def encode(self, text): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mezclar_bolsa(self):",
"def regular(self):",
"def subectIsSelf():",
"def lassh():",
"def setup_class(self):\n\n class SubFLRW(FLRW):\n def w(self, z):\n return super().w(z)\n\n self.cls = SubFLRW\n # H0, Om0, Ode0\n self.cls_args = (70 * u.km / u.s ... | [
"0.6570687",
"0.63065004",
"0.60361695",
"0.6011499",
"0.59461576",
"0.5945843",
"0.5942525",
"0.5942525",
"0.5942525",
"0.5942525",
"0.5942525",
"0.5942525",
"0.5942525",
"0.5942525",
"0.5942525",
"0.5942525",
"0.5895928",
"0.5890295",
"0.5882326",
"0.58123356",
"0.58123356"... | 0.0 | -1 |
Overskriver metoden i en subklasse kun en dummyversjon | def decode(self, crypto): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mezclar_bolsa(self):",
"def regular(self):",
"def subectIsSelf():",
"def lassh():",
"def __init__(self):\n super(Hardswish, self).__init__()",
"def setup_class(self):\n\n class SubFLRW(FLRW):\n def w(self, z):\n return super().w(z)\n\n self.cls = SubFLRW... | [
"0.6569991",
"0.63043004",
"0.6036007",
"0.6010795",
"0.59454274",
"0.594468",
"0.5941208",
"0.5941208",
"0.5941208",
"0.5941208",
"0.5941208",
"0.5941208",
"0.5941208",
"0.5941208",
"0.5941208",
"0.5941208",
"0.5896316",
"0.588907",
"0.58811665",
"0.581134",
"0.581134",
"0... | 0.0 | -1 |
klartekst > koder denne > dekoder cipherteksten og sjekker at de er lik | def verify(self):
# tekstlig testing om koden fungerer
text = self.klar_tekst_start + " ble sendt til mottaker som krypteringen " + \
self.crypto + ".\nMottaker dekrypterte dette til " + self.klar_tekst_slutt
return text | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def operate_cipher(self):",
"def cipher_feedback(self):",
"def encrypt():\n\tnull = 0",
"def decrypt_vigenere(ciphertext: str, keyword: str) -> str:\n plaintext = \"\"\n # PUT YOUR CODE HERE\n key_lenght = len(keyword)\n text_lenght = len(ciphertext)\n\n while key_lenght != text_lenght:\n ... | [
"0.74144083",
"0.7249195",
"0.6612421",
"0.65690863",
"0.65647596",
"0.6559704",
"0.6540644",
"0.65369374",
"0.651486",
"0.6376439",
"0.62908095",
"0.6285373",
"0.6259204",
"0.6240055",
"0.62363446",
"0.6201683",
"0.6181846",
"0.6151049",
"0.6124505",
"0.60732675",
"0.6069720... | 0.5520573 | 82 |
multiplying a number by 5 function | def five_mult(x):
return 5 * x | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def multiply_numbers(first_number, second_number):",
"def multiply(value, multiplier):\n return value*multiplier",
"def helper(num):\r\n \r\n return lambda x: num * product(x)",
"def mod_5(x):\r\n return x%5",
"def multiplier(self) -> global___Expression:",
"def multiply_by_4(x):\n\t... | [
"0.7047754",
"0.6995963",
"0.69738007",
"0.69666034",
"0.6825088",
"0.6717752",
"0.66833895",
"0.66504025",
"0.66504025",
"0.657937",
"0.6422854",
"0.6368382",
"0.6347893",
"0.63396317",
"0.63196534",
"0.62954944",
"0.6254306",
"0.6249496",
"0.62182474",
"0.62080914",
"0.6205... | 0.8823891 | 0 |
recursion of a value | def tri_recursion(k):
if(k>0):
result = k + tri_recursion(k-1)
# print(result)
else:
result = 0
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recur(number):\n if number == 1 or number == 0:\n return number\n else:\n check = recur(max(number - 1, 0))+recur(max(number - 2, 0))\n return check",
"def recursive(input):\n\n # Base Case: Argument input greater than 0.\n if input <= 0:\n return 0\n else:\n ... | [
"0.6645191",
"0.6535834",
"0.6381994",
"0.63048947",
"0.6251828",
"0.6248429",
"0.61911374",
"0.6188937",
"0.6067438",
"0.60306674",
"0.5995112",
"0.59597176",
"0.594877",
"0.59247464",
"0.5921001",
"0.5912498",
"0.5909545",
"0.58961886",
"0.5894752",
"0.5893231",
"0.58901757... | 0.686639 | 0 |
returns a list of all lines encountered in start, stop, start, stop form distances are from self.center_of_mass along angle | def find_points(self, angle):
angle_rads = angle * 1.0 / 360 * 2 * pi
points = []
self.pic.setPenColor(255, 0, 0)
cur_pos_x = self.xcenter
cur_pos_y = self.ycenter
in_line = False
while self.in_bounds(cur_pos_x, cur_pos_y):
if in_line:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vary_distance_lines(self, n_lines,\n start_theta=-180.0, end_theta=+180.0):\n delta_theta = end_theta - start_theta\n thetas = [start_theta + ((float(i) / (n_lines - 1)) * delta_theta)\n for i in range(n_lines)]\n thetas = list(map(np.radians, th... | [
"0.6329989",
"0.6249319",
"0.62350285",
"0.5960659",
"0.582981",
"0.5782247",
"0.57550585",
"0.5716322",
"0.5686487",
"0.56017864",
"0.5553188",
"0.5502591",
"0.5473933",
"0.54706573",
"0.5465637",
"0.54402316",
"0.54249376",
"0.5414995",
"0.5412383",
"0.54076225",
"0.5404326... | 0.56110704 | 9 |
Perform a simple key validation. In case key is invalid raises exception. | def validate_key(key):
try:
secret.Secret(key)
except secret.Secret.InvalidSecret as e:
raise KeyIsInvalid(e.message) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isValidKey(key):\n return True",
"def validate_key_throw(*args):\n validation_result = validate_key(*args)\n if not validation_result:\n raise ValueError(str(validation_result))\n return validation_result",
"def _check_key(self, key):\n raise NotImplementedError",
"def validate_ke... | [
"0.7987643",
"0.7463676",
"0.73541176",
"0.71353656",
"0.69899154",
"0.69897455",
"0.69897455",
"0.69758594",
"0.6815677",
"0.6798602",
"0.6623998",
"0.65970945",
"0.65419054",
"0.6538826",
"0.6535715",
"0.65314674",
"0.64951634",
"0.64704937",
"0.64693004",
"0.6450726",
"0.6... | 0.7446105 | 2 |
Allows to set key only once | def key(self, key_val):
if self.key_exists():
raise EncryptedField.KeyAlreadyExists()
validate_key(key_val)
if self.model_class not in self._keys:
EncryptedField._keys[self.model_class] = {}
EncryptedField._keys[self.model_class][id(self)] = key_val | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def key(self, key):\n return self.__key.set(key)",
"def set_key(self, key):\n self.key = key",
"def _newKey(self, key):\n pass",
"def set_key(self, key):\n\t\tif key in self.control_map:\n\t\t\tindex = self.control_map.index(key)\n\t\t\tcurrent_key = self.control_map[self.option_index]\n... | [
"0.7539311",
"0.74440265",
"0.74026906",
"0.7250793",
"0.723817",
"0.723817",
"0.70751673",
"0.70751673",
"0.7062565",
"0.7028014",
"0.7028014",
"0.6877834",
"0.67624867",
"0.67489475",
"0.66592693",
"0.6637476",
"0.6608595",
"0.6566029",
"0.65246844",
"0.6480407",
"0.6471509... | 0.0 | -1 |
create a qos policy bandwidth limit rules client | def get_client(client_mgr,
set_property=False,
with_name="qos_dscp_marking_rules_client"):
manager = getattr(client_mgr, 'manager', client_mgr)
net_client = getattr(manager, 'networks_client')
try:
_params = manager.default_params_with_timeout_values.copy()
except E... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bandwidth_limit_rule_create(request, policy_id, **kwargs):\n body = {'bandwidth_limit_rule': kwargs}\n if 'tenant_id' not in kwargs:\n kwargs['tenant_id'] = request.user.project_id\n body = {'bandwidth_limit_rule': kwargs}\n rule = 'bandwidth_limit_rule'\n bandwidth_limit_rule = neutroncl... | [
"0.6386853",
"0.63850254",
"0.6247085",
"0.6161535",
"0.6106285",
"0.60777605",
"0.5950055",
"0.5935536",
"0.590857",
"0.5905467",
"0.5810064",
"0.5765103",
"0.56232476",
"0.5616136",
"0.558902",
"0.5584673",
"0.5562813",
"0.55526304",
"0.5522369",
"0.5482483",
"0.54449725",
... | 0.0 | -1 |
generates XML I/O mapping for package ACCO, adding it to globalMap | def makeMapping(globalMap):
from memops.xml.Implementation import bool2str, str2bool
# Set up top level dictionaries
loadMaps = globalMap.get('loadMaps')
mapsByGuid = globalMap.get('mapsByGuid')
abstractTypes = globalMap.get('ACCO').get('abstractTypes')
exolinks = globalMap.get('ACCO').get('exolinks')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeMapping(globalMap):\n \n from memops.xml.Implementation import bool2str, str2bool\n\n # Set up top level dictionaries\n loadMaps = globalMap.get('loadMaps')\n mapsByGuid = globalMap.get('mapsByGuid')\n\n abstractTypes = globalMap.get('ANAP').get('abstractTypes')\n exolinks = globalMap.get('ANAP').ge... | [
"0.64070976",
"0.6367374",
"0.62000525",
"0.6076145",
"0.56269336",
"0.540561",
"0.54037416",
"0.5370882",
"0.53047323",
"0.51571155",
"0.5128653",
"0.5097843",
"0.50681853",
"0.5067503",
"0.5059321",
"0.50549144",
"0.5013462",
"0.50095636",
"0.50081456",
"0.49879944",
"0.498... | 0.65873575 | 0 |
Performs Encoding and Decoding at once | def img_recolor(self, args, input_image_path):
ec = encoder.Encoder(output_path=args.intermediate_representation, method=args.method,
size=args.size, p=args.p, grid_size=args.grid_size, plot=args.plot, quantize=args.quantize)
dc = decoder.Decoder(output_path=args.ou... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decode(self, encoded):",
"def encode(self, decoded):",
"def handle_encode(self, results):\n \n config.COD_PROMPT = config.ENC_PROMPT\n print config.ENC_PROMPT + \" encoding results...\"\n \n # while there is another decoder, run each item through the next decoder\n ... | [
"0.70250314",
"0.692117",
"0.6498695",
"0.636545",
"0.61599547",
"0.6076334",
"0.60151535",
"0.60063004",
"0.5958467",
"0.5958467",
"0.5940094",
"0.59077394",
"0.58827055",
"0.58827055",
"0.58725035",
"0.58725035",
"0.5870088",
"0.583431",
"0.58002776",
"0.5792148",
"0.576668... | 0.0 | -1 |
Generates a sample transition probability matrix given the count matrix C using dirichlet distributions | def sample_nonrev(C, nsample=1):
# copy C
C = np.array(C)
if nsample==1:
return _sample_nonrev_single(C)
elif nsample > 1:
res = np.empty((nsample), dtype=object)
for i in range(nsample):
res[i] = _sample_nonrev_single(C)
return res
else:
raise Val... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def entropy_permutation_test(ordered_pitch_types, single_pitch_pdf, conditional_joint_probabilities, total_transitions,\n n=1000):\n pitch_types, pitch_probabilities = zip(*single_pitch_pdf.items())\n permutation_entropies = []\n progress = progressbar.ProgressBar()\n\n for ... | [
"0.60476565",
"0.59997904",
"0.58673024",
"0.58671576",
"0.5793469",
"0.5774345",
"0.5758262",
"0.5752579",
"0.57467324",
"0.5681701",
"0.5671376",
"0.5654487",
"0.56216985",
"0.5615002",
"0.56125927",
"0.56113505",
"0.56080693",
"0.55988497",
"0.55925566",
"0.5580419",
"0.55... | 0.0 | -1 |
Return true if this term and other are strictly syntactically equivalent. This would tipically be in the magic method `__eq__`, but we use `__eq__` for a different purpose, namely, to be able to use Python expressions such as "loc(b1)==table" in order to construct a FOL atom. | def is_syntactically_equal(self, other):
raise NotImplementedError() # To be subclassed | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, other):\n if other is self:\n return True\n if isinstance(other, CoordFunctionSymb):\n if other.parent() != self.parent():\n return False\n else:\n return bool(other._express == self._express)\n else:\n ... | [
"0.73922414",
"0.72952706",
"0.7224389",
"0.7203774",
"0.6997577",
"0.6990958",
"0.6936724",
"0.6910764",
"0.6866715",
"0.68585664",
"0.6852991",
"0.6825722",
"0.68223184",
"0.67959416",
"0.6793662",
"0.67728335",
"0.67344457",
"0.673216",
"0.667139",
"0.66642874",
"0.6662836... | 0.7148483 | 4 |
Return a hash of the current object. Meant to be used by TermReference objects to wrap Terms appropriately for use within associative containers. | def hash(self):
raise NotImplementedError() # To be subclassed | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __Hash(self):\n return self._Hash()",
"def hash(self):\n return self.__hash__()",
"def hash(self):\n return self.hash_by_id(self.id)",
"def current_hash(self):",
"def hash(self):\n return hashlib.sha1(str(self._dict))",
"def get_hash(self):\n return self.__hash",
"def... | [
"0.7544915",
"0.745015",
"0.74020696",
"0.7351971",
"0.73146653",
"0.7294813",
"0.7268655",
"0.7268655",
"0.7268655",
"0.7268655",
"0.726741",
"0.7251138",
"0.7248584",
"0.72445667",
"0.7236863",
"0.7205611",
"0.7174264",
"0.7149143",
"0.7121638",
"0.7120802",
"0.71172434",
... | 0.70241517 | 32 |
Creates a folder if it does not exists | def create_folder(folder_name):
if not os.path.exists(folder_name):
os.makedirs(folder_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_folder_if_needed(path):\n if os.path.exists(path):\n print(\"{} dir exists\".format(path))\n else:\n print(\"{} dir does not exist. Creating dir.\".format(path))\n os.mkdir(path)",
"def create_folder(path):\n if not exists(path):\n os.makedirs(path)",
"def create... | [
"0.85329485",
"0.85087234",
"0.842846",
"0.8422511",
"0.83173305",
"0.8302552",
"0.82762134",
"0.82187194",
"0.8175441",
"0.8163295",
"0.814333",
"0.814166",
"0.8140945",
"0.8131764",
"0.8117364",
"0.81125724",
"0.80800235",
"0.80615175",
"0.8053969",
"0.80054647",
"0.8000349... | 0.8088543 | 17 |
Given a file returns the number of lines where a certain pattern appears. | def count_patterns(pattern, file):
count = 0
with open(file, 'r') as f:
for line in f:
if re.search(pattern, line):
count += 1
print("The pattern '{}' appears {} times.".format(pattern, count)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def line_count(file):\n with open(file, \"r\") as f:\n return sum(1 for line in f)",
"def count_lines(filename):\n with open(filename, 'rb') as f:\n return sum(1 for line in f)",
"def count_lines(filename):\r\n with open(filename, 'rb') as f:\r\n return sum(1 for line in f)",
"d... | [
"0.79906595",
"0.7601465",
"0.7589926",
"0.7400159",
"0.7400159",
"0.7367526",
"0.7361509",
"0.7314271",
"0.7307912",
"0.7246522",
"0.72217196",
"0.71740115",
"0.7155738",
"0.7092855",
"0.7072881",
"0.7064458",
"0.70398235",
"0.7038543",
"0.7035276",
"0.7028555",
"0.7019521",... | 0.79991657 | 0 |
Function to clean the datasets following the given instructions | def clean_dataset(dataset, pollsters, output_name):
# No banned pollsters
no_banned = pollsters[pollsters['Banned by 538'] == 'no'].Pollster
# Interviews with no banned pollster
dataset = dataset[dataset.pollster.isin(no_banned)]
# Non tracked interviews
dataset = dataset[dataset.tracking == Fal... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean(args):\n with_dataset(args, Dataset._clean)",
"def cleaning (data):",
"def _clean_data(self, dataset):\n dataset.dropna(inplace=True)\n # Problem: handle missing data (in a different way), noisy data, inconsistent data",
"def clean():\n filter_phase_data()\n combine_phase_dat... | [
"0.7972246",
"0.78305405",
"0.770086",
"0.7358835",
"0.7004315",
"0.6960603",
"0.6881473",
"0.68532133",
"0.67468274",
"0.6705054",
"0.6648155",
"0.66471887",
"0.66403866",
"0.6639197",
"0.6567839",
"0.6521795",
"0.64767987",
"0.6475485",
"0.64592075",
"0.63912",
"0.63877296"... | 0.63390654 | 25 |
Given a dataset with a string column , filter rows containing a certain pattern. | def pattern_search(pattern, dataset, column):
# Filter
dataset = dataset[dataset[column].str.contains(pattern, regex=True)]
# Reset index
dataset = dataset.reset_index(drop=True)
# Return
return dataset | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filter(self, filter_strings):\n if filter_strings is None:\n filter_strings = []\n\n result = RowSet()\n for row in self.rows:\n match = True\n for s in filter_strings:\n if not row.filter(s):\n match = False\n ... | [
"0.6799201",
"0.59878045",
"0.5795762",
"0.5779332",
"0.5757394",
"0.574811",
"0.5722822",
"0.57140815",
"0.56623626",
"0.56492823",
"0.5607389",
"0.5594936",
"0.5588069",
"0.5576866",
"0.5554488",
"0.5542753",
"0.55349904",
"0.5508957",
"0.54967576",
"0.546392",
"0.5438134",... | 0.7813946 | 0 |
Given a dataset with pollsters column and the pollsters grade in traditional format, it merges the columns and floors the grade. | def grade_cleaning(dataset, pollsters):
# Merge grades
dataset_grades = pd.merge(dataset, pollsters[["Pollster", "538 Grade", "Predictive Plus-Minus"]],
how='inner', left_on='pollster', right_on='Pollster')
dataset_grades = dataset_grades.drop(['Pollster'], axis=1)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def organize_data(scores, stds):\n\n scores = pd.Series(list(scores.values()),\n index=map(str, scores.keys()),\n name=\"SCORE MEAN\")\n scores.index.name = \"AUXILIARY LOSS WEIGHTS\"\n stds = pd.Series(list(stds.values()),\n ... | [
"0.5142071",
"0.5063618",
"0.49147916",
"0.48610595",
"0.4855685",
"0.48401833",
"0.48022565",
"0.47576785",
"0.47571915",
"0.47113907",
"0.4704346",
"0.46972775",
"0.46791285",
"0.465144",
"0.46333814",
"0.46222302",
"0.46004865",
"0.45951855",
"0.45889464",
"0.45674625",
"0... | 0.7066124 | 0 |
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